index int64 0 0 | repo_id stringclasses 596 values | file_path stringlengths 31 168 | content stringlengths 1 6.2M |
|---|---|---|---|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/extraction_examples.ipynb | import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
const SYSTEM_PROMPT_TEMPLATE = `You are an expert extraction algorithm.
Only extract relevant information from the text.
If you do not know the value of an attribute asked to extract, you may omit the attribute's value.`;
// Define a custom prompt to provide instructions and any additional context.
// 1) You can add examples into the prompt template to improve extraction quality
// 2) Introduce additional parameters to take context into account (e.g., include metadata
// about the document from which the text was extracted.)
const prompt = ChatPromptTemplate.fromMessages([
["system", SYSTEM_PROMPT_TEMPLATE],
// ↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓
new MessagesPlaceholder("examples"),
// ↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑
["human", "{text}"]
]);import { HumanMessage } from "@langchain/core/messages";
const promptValue = await prompt.invoke({
text: "this is some text",
examples: [new HumanMessage("testing 1 2 3")],
});
promptValue.toChatMessages();import { z } from "zod";
const personSchema = z.object({
name: z.optional(z.string()).describe("The name of the person"),
hair_color: z.optional(z.string()).describe("The color of the person's hair, if known"),
height_in_meters: z.optional(z.string()).describe("Height measured in meters")
}).describe("Information about a person.");
const peopleSchema = z.object({
people: z.array(personSchema)
});import {
AIMessage,
type BaseMessage,
HumanMessage,
ToolMessage
} from "@langchain/core/messages";
import { v4 as uuid } from "uuid";
type OpenAIToolCall = {
id: string,
type: "function",
function: {
name: string;
arguments: string;
}
};
type Example = {
input: string;
toolCallOutputs: Record<string, any>[];
}
/**
* This function converts an example into a list of messages that can be fed into an LLM.
*
* This code serves as an adapter that transforms our example into a list of messages
* that can be processed by a chat model.
*
* The list of messages for each example includes:
*
* 1) HumanMessage: This contains the content from which information should be extracted.
* 2) AIMessage: This contains the information extracted by the model.
* 3) ToolMessage: This provides confirmation to the model that the tool was requested correctly.
*
* The inclusion of ToolMessage is necessary because some chat models are highly optimized for agents,
* making them less suitable for an extraction use case.
*/
function toolExampleToMessages(example: Example): BaseMessage[] {
const openAIToolCalls: OpenAIToolCall[] = example.toolCallOutputs.map((output) => {
return {
id: uuid(),
type: "function",
function: {
// The name of the function right now corresponds
// to the passed name.
name: "extract",
arguments: JSON.stringify(output),
},
};
});
const messages: BaseMessage[] = [
new HumanMessage(example.input),
new AIMessage({
content: "",
additional_kwargs: { tool_calls: openAIToolCalls }
})
];
const toolMessages = openAIToolCalls.map((toolCall, i) => {
// Return the mocked successful result for a given tool call.
return new ToolMessage({
content: "You have correctly called this tool.",
tool_call_id: toolCall.id
});
});
return messages.concat(toolMessages);
}
const examples: Example[] = [
{
input: "The ocean is vast and blue. It's more than 20,000 feet deep. There are many fish in it.",
toolCallOutputs: [{}]
},
{
input: "Fiona traveled far from France to Spain.",
toolCallOutputs: [{
name: "Fiona",
}]
}
];
const exampleMessages = [];
for (const example of examples) {
exampleMessages.push(...toolExampleToMessages(example));
}const promptValueWithExamples = await prompt.invoke({
text: "this is some text",
examples: exampleMessages
});
promptValueWithExamples.toChatMessages();import { ChatOpenAI } from "@langchain/openai";
// We will be using tool calling mode, which
// requires a tool calling capable model.
const llm = new ChatOpenAI({
// Consider benchmarking with the best model you can to get
// a sense of the best possible quality.
model: "gpt-4-0125-preview",
temperature: 0,
});
// For function/tool calling, we can also supply an name for the schema
// to give the LLM additional context about what it's extracting.
const extractionRunnable = prompt.pipe(llm.withStructuredOutput(peopleSchema, { name: "people" }));const text = "The solar system is large, but earth has only 1 moon.";
for (let i = 0; i < 5; i++) {
const result = await extractionRunnable.invoke({
text,
examples: []
});
console.log(result);
}for (let i = 0; i < 5; i++) {
const result = await extractionRunnable.invoke({
text,
// Example messages from above
examples: exampleMessages
});
console.log(result);
}await extractionRunnable.invoke({
text: "My name is Hair-ison. My hair is black. I am 3 meters tall.",
examples: exampleMessages,
}); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/tool_configure.ipynb | import { z } from "zod";
import { tool } from "@langchain/core/tools";
import type { RunnableConfig } from "@langchain/core/runnables";
const reverseTool = tool(
async (input: { text: string }, config?: RunnableConfig) => {
const originalString = input.text + (config?.configurable?.additional_field ?? "");
return originalString.split("").reverse().join("");
}, {
name: "reverse",
description: "A test tool that combines input text with a configurable parameter.",
schema: z.object({
text: z.string()
}),
}
);await reverseTool.invoke(
{text: "abc"}, {configurable: {additional_field: "123"}}
) |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/routing.mdx | # How to route execution within a chain
:::info Prerequisites
This guide assumes familiarity with the following concepts:
- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)
- [Chaining runnables](/docs/how_to/sequence/)
- [Configuring chain parameters at runtime](/docs/how_to/binding)
- [Prompt templates](/docs/concepts/prompt_templates)
- [Chat Messages](/docs/concepts/messages)
:::
This guide covers how to do routing in the LangChain Expression Language.
Routing allows you to create non-deterministic chains where the output of a previous step defines the next step. Routing helps provide structure and consistency around interactions with LLMs.
There are two ways to perform routing:
1. Conditionally return runnables from a [`RunnableLambda`](/docs/how_to/functions) (recommended)
2. Using a `RunnableBranch` (legacy)
We'll illustrate both methods using a two step sequence where the first step classifies an input question as being about LangChain, Anthropic, or Other, then routes to a corresponding prompt chain.
## Using a custom function
You can use a custom function to route between different outputs. Here's an example:
import CodeBlock from "@theme/CodeBlock";
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/anthropic @langchain/core
```
import FactoryFunctionExample from "@examples/guides/expression_language/how_to_routing_custom_function.ts";
<CodeBlock language="typescript">{FactoryFunctionExample}</CodeBlock>
## Routing by semantic similarity
One especially useful technique is to use embeddings to route a query to the most relevant prompt. Here's an example:
import SemanticSimilarityExample from "@examples/guides/expression_language/how_to_routing_semantic_similarity.ts";
<CodeBlock language="typescript">{SemanticSimilarityExample}</CodeBlock>
## Using a RunnableBranch
A `RunnableBranch` is initialized with a list of (condition, runnable) pairs and a default runnable. It selects which branch by passing each condition the input it's invoked with. It selects the first condition to evaluate to True, and runs the corresponding runnable to that condition with the input.
If no provided conditions match, it runs the default runnable.
Here's an example of what it looks like in action:
import BranchExample from "@examples/guides/expression_language/how_to_routing_runnable_branch.ts";
<CodeBlock language="typescript">{BranchExample}</CodeBlock>
## Next steps
You've now learned how to add routing to your composed LCEL chains.
Next, check out the other [how-to guides on runnables](/docs/how_to/#langchain-expression-language) in this section.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/query_few_shot.ipynb | // @lc-docs-hide-cell
import { ChatOpenAI } from '@langchain/openai';
const llm = new ChatOpenAI({
model: "gpt-4o",
temperature: 0,
})import { z } from "zod";
const subQueriesDescription = `
If the original question contains multiple distinct sub-questions,
or if there are more generic questions that would be helpful to answer in
order to answer the original question, write a list of all relevant sub-questions.
Make sure this list is comprehensive and covers all parts of the original question.
It's ok if there's redundancy in the sub-questions, it's better to cover all the bases than to miss some.
Make sure the sub-questions are as narrowly focused as possible in order to get the most relevant results.`
const searchSchema = z.object({
query: z.string().describe("Primary similarity search query applied to video transcripts."),
subQueries: z.array(z.string()).optional().describe(subQueriesDescription),
publishYear: z.number().optional().describe("Year video was published")
})import { ChatPromptTemplate } from "@langchain/core/prompts"
import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables"
const system = `You are an expert at converting user questions into database queries.
You have access to a database of tutorial videos about a software library for building LLM-powered applications.
Given a question, return a list of database queries optimized to retrieve the most relevant results.
If there are acronyms or words you are not familiar with, do not try to rephrase them.`
const prompt = ChatPromptTemplate.fromMessages(
[
["system", system],
["placeholder", "{examples}"],
["human", "{question}"],
]
)
const llmWithTools = llm.withStructuredOutput(searchSchema, {
name: "Search",
})
const queryAnalyzer = RunnableSequence.from([
{
question: new RunnablePassthrough(),
},
prompt,
llmWithTools
]);await queryAnalyzer.invoke(
"what's the difference between web voyager and reflection agents? do both use langgraph?"
)const examples = []const question = "What's chat langchain, is it a langchain template?"
const query = {
query: "What is chat langchain and is it a langchain template?",
subQueries: ["What is chat langchain", "What is a langchain template"],
}
examples.push({ "input": question, "toolCalls": [query] })const question2 = "How to build multi-agent system and stream intermediate steps from it"
const query2 = {
query: "How to build multi-agent system and stream intermediate steps from it",
subQueries: [
"How to build multi-agent system",
"How to stream intermediate steps from multi-agent system",
"How to stream intermediate steps",
],
}
examples.push({ "input": question2, "toolCalls": [query2] })const question3 = "LangChain agents vs LangGraph?"
const query3 = {
query: "What's the difference between LangChain agents and LangGraph? How do you deploy them?",
subQueries: [
"What are LangChain agents",
"What is LangGraph",
"How do you deploy LangChain agents",
"How do you deploy LangGraph",
],
}
examples.push({ "input": question3, "toolCalls": [query3] });import {
AIMessage,
BaseMessage,
HumanMessage,
SystemMessage,
ToolMessage,
} from "@langchain/core/messages";
import { v4 as uuidV4 } from "uuid";
const toolExampleToMessages = (example: Record<string, any>): Array<BaseMessage> => {
const messages: Array<BaseMessage> = [new HumanMessage({ content: example.input })];
const openaiToolCalls = example.toolCalls.map((toolCall) => {
return {
id: uuidV4(),
type: "function" as const,
function: {
name: "search",
arguments: JSON.stringify(toolCall),
},
};
});
messages.push(new AIMessage({ content: "", additional_kwargs: { tool_calls: openaiToolCalls } }));
const toolOutputs = "toolOutputs" in example ? example.toolOutputs : Array(openaiToolCalls.length).fill("You have correctly called this tool.");
toolOutputs.forEach((output, index) => {
messages.push(new ToolMessage({ content: output, tool_call_id: openaiToolCalls[index].id }));
});
return messages;
}
const exampleMessages = examples.map((ex) => toolExampleToMessages(ex)).flat();import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
import { RunnableSequence } from "@langchain/core/runnables";
const queryAnalyzerWithExamples = RunnableSequence.from([
{
question: new RunnablePassthrough(),
examples: () => exampleMessages,
},
prompt,
llmWithTools
]);await queryAnalyzerWithExamples.invoke(
"what's the difference between web voyager and reflection agents? do both use langgraph?"
) |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/query_multiple_queries.ipynb | import { Chroma } from "@langchain/community/vectorstores/chroma"
import { OpenAIEmbeddings } from "@langchain/openai"
import "chromadb";
const texts = ["Harrison worked at Kensho", "Ankush worked at Facebook"]
const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" })
const vectorstore = await Chroma.fromTexts(
texts,
{},
embeddings,
{
collectionName: "multi_query"
}
)
const retriever = vectorstore.asRetriever(1);import { z } from "zod";
const searchSchema = z.object({
queries: z.array(z.string()).describe("Distinct queries to search for")
}).describe("Search over a database of job records.");// @lc-docs-hide-cell
import { ChatOpenAI } from '@langchain/openai';
const llm = new ChatOpenAI({
model: "gpt-4o",
temperature: 0,
})import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnableSequence, RunnablePassthrough } from "@langchain/core/runnables";
const system = `You have the ability to issue search queries to get information to help answer user information.
If you need to look up two distinct pieces of information, you are allowed to do that!`;
const prompt = ChatPromptTemplate.fromMessages([
["system", system],
["human", "{question}"],
])
const llmWithTools = llm.withStructuredOutput(searchSchema, {
name: "Search"
});
const queryAnalyzer = RunnableSequence.from([
{
question: new RunnablePassthrough(),
},
prompt,
llmWithTools
]);await queryAnalyzer.invoke("where did Harrison Work")await queryAnalyzer.invoke("where did Harrison and ankush Work")import { RunnableConfig, RunnableLambda } from "@langchain/core/runnables";
const chain = async (question: string, config?: RunnableConfig) => {
const response = await queryAnalyzer.invoke(question, config);
const docs = [];
for (const query of response.queries) {
const newDocs = await retriever.invoke(query, config);
docs.push(...newDocs);
}
// You probably want to think about reranking or deduplicating documents here
// But that is a separate topic
return docs;
}
const customChain = new RunnableLambda({ func: chain });await customChain.invoke("where did Harrison Work")await customChain.invoke("where did Harrison and ankush Work") |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/recursive_text_splitter.ipynb | import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
const text = `Hi.\n\nI'm Harrison.\n\nHow? Are? You?\nOkay then f f f f.
This is a weird text to write, but gotta test the splittingggg some how.\n\n
Bye!\n\n-H.`;
const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 10,
chunkOverlap: 1,
});
const output = await splitter.createDocuments([text]);
console.log(output.slice(0, 3));import { Document } from "@langchain/core/documents";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
const text = `Hi.\n\nI'm Harrison.\n\nHow? Are? You?\nOkay then f f f f.
This is a weird text to write, but gotta test the splittingggg some how.\n\n
Bye!\n\n-H.`;
const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 10,
chunkOverlap: 1,
});
const docOutput = await splitter.splitDocuments([
new Document({ pageContent: text }),
]);
console.log(docOutput.slice(0, 3));import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { Document } from "@langchain/core/documents";
const text = `Some other considerations include:
- Do you deploy your backend and frontend together, or separately?
- Do you deploy your backend co-located with your database, or separately?
**Production Support:** As you move your LangChains into production, we'd love to offer more hands-on support.
Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to share more about what you're building, and our team will get in touch.
## Deployment Options
See below for a list of deployment options for your LangChain app. If you don't see your preferred option, please get in touch and we can add it to this list.`;
const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 50,
chunkOverlap: 1,
separators: ["|", "##", ">", "-"],
});
const docOutput = await splitter.splitDocuments([
new Document({ pageContent: text }),
]);
console.log(docOutput.slice(0, 3)); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/sql_large_db.mdx | # How to deal with large databases
:::info Prerequisites
This guide assumes familiarity with the following:
- [Question answering over SQL data](/docs/tutorials/sql_qa)
:::
In order to write valid queries against a database, we need to feed the model the table names, table schemas, and feature values for it to query over.
When there are many tables, columns, and/or high-cardinality columns, it becomes impossible for us to dump the full information about our database in every prompt.
Instead, we must find ways to dynamically insert into the prompt only the most relevant information. Let's take a look at some techniques for doing this.
## Setup
First, install the required packages and set your environment variables. This example will use OpenAI as the LLM.
```bash
npm install langchain @langchain/community @langchain/openai typeorm sqlite3
```
```bash
export OPENAI_API_KEY="your api key"
# Uncomment the below to use LangSmith. Not required.
# export LANGCHAIN_API_KEY="your api key"
# export LANGCHAIN_TRACING_V2=true
# Reduce tracing latency if you are not in a serverless environment
# export LANGCHAIN_CALLBACKS_BACKGROUND=true
```
The below example will use a SQLite connection with Chinook database. Follow these [installation steps](https://database.guide/2-sample-databases-sqlite/) to create `Chinook.db` in the same directory as this notebook:
- Save [this](https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql) file as `Chinook_Sqlite.sql`
- Run sqlite3 `Chinook.db`
- Run `.read Chinook_Sqlite.sql`
- Test `SELECT * FROM Artist LIMIT 10;`
Now, `Chinhook.db` is in our directory and we can interface with it using the Typeorm-driven `SqlDatabase` class:
import CodeBlock from "@theme/CodeBlock";
import DbCheck from "@examples/use_cases/sql/db_check.ts";
<CodeBlock language="typescript">{DbCheck}</CodeBlock>
## Many tables
One of the main pieces of information we need to include in our prompt is the schemas of the relevant tables.
When we have very many tables, we can't fit all of the schemas in a single prompt.
What we can do in such cases is first extract the names of the tables related to the user input, and then include only their schemas.
One easy and reliable way to do this is using OpenAI function-calling and Zod models. LangChain comes with a built-in `createExtractionChainZod` chain that lets us do just this:
import LargeDbExample from "@examples/use_cases/sql/large_db.ts";
<CodeBlock language="typescript">{LargeDbExample}</CodeBlock>
We've seen how to dynamically include a subset of table schemas in a prompt within a chain.
Another possible approach to this problem is to let an Agent decide for itself when to look up tables by giving it a Tool to do so.
## High-cardinality columns
High-cardinality refers to columns in a database that have a vast range of unique values.
These columns are characterized by a high level of uniqueness in their data entries, such as individual names, addresses, or product serial numbers.
High-cardinality data can pose challenges for indexing and querying, as it requires more sophisticated strategies to efficiently filter and retrieve specific entries.
In order to filter columns that contain proper nouns such as addresses, song names or artists, we first need to double-check the spelling in order to filter the data correctly.
One naive strategy it to create a vector store with all the distinct proper nouns that exist in the database.
We can then query that vector store each user input and inject the most relevant proper nouns into the prompt.
First we need the unique values for each entity we want, for which we define a function that parses the result into a list of elements:
import HighCardinalityExample from "@examples/use_cases/sql/large_db_high_cardinality.ts";
<CodeBlock language="typescript">{HighCardinalityExample}</CodeBlock>
We can see that with retrieval we're able to correct the spelling and get back a valid result.
Another possible approach to this problem is to let an Agent decide for itself when to look up proper nouns.
## Next steps
You've now learned about some prompting strategies to improve SQL generation.
Next, check out some of the other guides in this section, like [how to validate queries](/docs/how_to/sql_query_checking).
You might also be interested in the query analysis guide [on handling high cardinality](/docs/how_to/query_high_cardinality).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/document_loader_directory.mdx | # How to load data from a directory
This covers how to load all documents in a directory.
The second argument is a map of file extensions to loader factories. Each file will be passed to the matching loader, and the resulting documents will be concatenated together.
Example folder:
```text
src/document_loaders/example_data/example/
├── example.json
├── example.jsonl
├── example.txt
└── example.csv
```
Example code:
```typescript
import { DirectoryLoader } from "langchain/document_loaders/fs/directory";
import {
JSONLoader,
JSONLinesLoader,
} from "langchain/document_loaders/fs/json";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { CSVLoader } from "@langchain/community/document_loaders/fs/csv";
const loader = new DirectoryLoader(
"src/document_loaders/example_data/example",
{
".json": (path) => new JSONLoader(path, "/texts"),
".jsonl": (path) => new JSONLinesLoader(path, "/html"),
".txt": (path) => new TextLoader(path),
".csv": (path) => new CSVLoader(path, "text"),
}
);
const docs = await loader.load();
console.log({ docs });
```
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/tool_stream_events.ipynb | import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({
model: "claude-3-5-sonnet-20240620",
temperature: 0,
});import { z } from "zod";
import { tool } from "@langchain/core/tools";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
const specialSummarizationTool = tool(async (input) => {
const prompt = ChatPromptTemplate.fromTemplate(
"You are an expert writer. Summarize the following text in 10 words or less:\n\n{long_text}"
);
const reverse = (x: string) => {
return x.split("").reverse().join("");
};
const chain = prompt
.pipe(model)
.pipe(new StringOutputParser())
.pipe(reverse);
const summary = await chain.invoke({ long_text: input.long_text });
return summary;
}, {
name: "special_summarization_tool",
description: "A tool that summarizes input text using advanced techniques.",
schema: z.object({
long_text: z.string(),
}),
});const LONG_TEXT = `
NARRATOR:
(Black screen with text; The sound of buzzing bees can be heard)
According to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway because bees don't care what humans think is impossible.
BARRY BENSON:
(Barry is picking out a shirt)
Yellow, black. Yellow, black. Yellow, black. Yellow, black. Ooh, black and yellow! Let's shake it up a little.
JANET BENSON:
Barry! Breakfast is ready!
BARRY:
Coming! Hang on a second.`;
await specialSummarizationTool.invoke({ long_text: LONG_TEXT });const stream = await specialSummarizationTool.streamEvents(
{ long_text: LONG_TEXT },
{ version: "v2" },
);
for await (const event of stream) {
if (event.event === "on_chat_model_end") {
// Never triggers!
console.log(event);
}
}const specialSummarizationToolWithConfig = tool(async (input, config) => {
const prompt = ChatPromptTemplate.fromTemplate(
"You are an expert writer. Summarize the following text in 10 words or less:\n\n{long_text}"
);
const reverse = (x: string) => {
return x.split("").reverse().join("");
};
const chain = prompt
.pipe(model)
.pipe(new StringOutputParser())
.pipe(reverse);
// Pass the "config" object as an argument to any executed runnables
const summary = await chain.invoke({ long_text: input.long_text }, config);
return summary;
}, {
name: "special_summarization_tool",
description: "A tool that summarizes input text using advanced techniques.",
schema: z.object({
long_text: z.string(),
}),
});const stream = await specialSummarizationToolWithConfig.streamEvents(
{ long_text: LONG_TEXT },
{ version: "v2" },
);
for await (const event of stream) {
if (event.event === "on_chat_model_end") {
// Never triggers!
console.log(event);
}
}const stream = await specialSummarizationToolWithConfig.streamEvents(
{ long_text: LONG_TEXT },
{ version: "v2" },
);
for await (const event of stream) {
if (event.event === "on_chat_model_stream") {
// Never triggers!
console.log(event);
}
}import { AsyncLocalStorageProviderSingleton } from "@langchain/core/singletons";
import { AsyncLocalStorage } from "async_hooks";
AsyncLocalStorageProviderSingleton.initializeGlobalInstance(
new AsyncLocalStorage()
); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/callbacks_serverless.ipynb | import { RunnableLambda } from "@langchain/core/runnables";
import { awaitAllCallbacks } from "@langchain/core/callbacks/promises";
const runnable = RunnableLambda.from(() => "hello!");
const customHandler = {
handleChainEnd: async () => {
await new Promise((resolve) => setTimeout(resolve, 2000));
console.log("Call finished");
},
};
const startTime = new Date().getTime();
await runnable.invoke({ number: "2" }, { callbacks: [customHandler] });
console.log(`Elapsed time: ${new Date().getTime() - startTime}ms`);
await awaitAllCallbacks();
console.log(`Final elapsed time: ${new Date().getTime() - startTime}ms`);process.env.LANGCHAIN_CALLBACKS_BACKGROUND = "false";
const startTimeBlocking = new Date().getTime();
await runnable.invoke({ number: "2" }, { callbacks: [customHandler] });
console.log(`Initial elapsed time: ${new Date().getTime() - startTimeBlocking}ms`); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/functions.ipynb | import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnableLambda } from "@langchain/core/runnables";
import { ChatOpenAI } from "@langchain/openai";
const lengthFunction = (input: { foo: string }): { length: string } => {
return {
length: input.foo.length.toString(),
};
};
const model = new ChatOpenAI({ model: "gpt-4o" });
const prompt = ChatPromptTemplate.fromTemplate("What is {length} squared?");
const chain = RunnableLambda.from(lengthFunction)
.pipe(prompt)
.pipe(model)
.pipe(new StringOutputParser());
await chain.invoke({ "foo": "bar" });import { RunnableSequence } from "@langchain/core/runnables";
const storyPrompt = ChatPromptTemplate.fromTemplate("Tell me a short story about {topic}");
const storyModel = new ChatOpenAI({ model: "gpt-4o" });
const chainWithCoercedFunction = RunnableSequence.from([
storyPrompt,
storyModel,
(input) => input.content.slice(0, 5),
]);
await chainWithCoercedFunction.invoke({ "topic": "bears" });import { type RunnableConfig } from "@langchain/core/runnables";
const echo = (text: string, config: RunnableConfig) => {
const prompt = ChatPromptTemplate.fromTemplate("Reverse the following text: {text}");
const model = new ChatOpenAI({ model: "gpt-4o" });
const chain = prompt.pipe(model).pipe(new StringOutputParser());
return chain.invoke({ text }, config);
};
const output = await RunnableLambda.from(echo).invoke("foo", {
tags: ["my-tag"],
callbacks: [{
handleLLMEnd: (output) => console.log(output),
}],
});const streamingPrompt = ChatPromptTemplate.fromTemplate(
"Write a comma-separated list of 5 animals similar to: {animal}. Do not include numbers"
);
const strChain = streamingPrompt.pipe(model).pipe(new StringOutputParser());
const stream = await strChain.stream({ animal: "bear" });
for await (const chunk of stream) {
console.log(chunk);
}// This is a custom parser that splits an iterator of llm tokens
// into a list of strings separated by commas
async function* splitIntoList(input) {
// hold partial input until we get a comma
let buffer = "";
for await (const chunk of input) {
// add current chunk to buffer
buffer += chunk;
// while there are commas in the buffer
while (buffer.includes(",")) {
// split buffer on comma
const commaIndex = buffer.indexOf(",");
// yield everything before the comma
yield [buffer.slice(0, commaIndex).trim()];
// save the rest for the next iteration
buffer = buffer.slice(commaIndex + 1);
}
}
// yield the last chunk
yield [buffer.trim()];
}
const listChain = strChain.pipe(splitIntoList);
const listChainStream = await listChain.stream({"animal": "bear"});
for await (const chunk of listChainStream) {
console.log(chunk);
}await listChain.invoke({"animal": "bear"}) |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/chat_model_caching.mdx | ---
sidebar_position: 3
---
# How to cache chat model responses
:::info Prerequisites
This guide assumes familiarity with the following concepts:
- [Chat models](/docs/concepts/chat_models)
- [LLMs](/docs/concepts/text_llms)
:::
LangChain provides an optional caching layer for chat models. This is useful for two reasons:
It can save you money by reducing the number of API calls you make to the LLM provider, if you're often requesting the same completion multiple times.
It can speed up your application by reducing the number of API calls you make to the LLM provider.
import CodeBlock from "@theme/CodeBlock";
```typescript
import { ChatOpenAI } from "@langchain/openai";
// To make the caching really obvious, lets use a slower model.
const model = new ChatOpenAI({
model: "gpt-4",
cache: true,
});
```
## In Memory Cache
The default cache is stored in-memory. This means that if you restart your application, the cache will be cleared.
```typescript
console.time();
// The first time, it is not yet in cache, so it should take longer
const res = await model.invoke("Tell me a joke!");
console.log(res);
console.timeEnd();
/*
AIMessage {
lc_serializable: true,
lc_kwargs: {
content: "Why don't scientists trust atoms?\n\nBecause they make up everything!",
additional_kwargs: { function_call: undefined, tool_calls: undefined }
},
lc_namespace: [ 'langchain_core', 'messages' ],
content: "Why don't scientists trust atoms?\n\nBecause they make up everything!",
name: undefined,
additional_kwargs: { function_call: undefined, tool_calls: undefined }
}
default: 2.224s
*/
```
```typescript
console.time();
// The second time it is, so it goes faster
const res2 = await model.invoke("Tell me a joke!");
console.log(res2);
console.timeEnd();
/*
AIMessage {
lc_serializable: true,
lc_kwargs: {
content: "Why don't scientists trust atoms?\n\nBecause they make up everything!",
additional_kwargs: { function_call: undefined, tool_calls: undefined }
},
lc_namespace: [ 'langchain_core', 'messages' ],
content: "Why don't scientists trust atoms?\n\nBecause they make up everything!",
name: undefined,
additional_kwargs: { function_call: undefined, tool_calls: undefined }
}
default: 181.98ms
*/
```
## Caching with Redis
LangChain also provides a Redis-based cache. This is useful if you want to share the cache across multiple processes or servers.
To use it, you'll need to install the `redis` package:
```bash npm2yarn
npm install ioredis @langchain/community @langchain/core
```
Then, you can pass a `cache` option when you instantiate the LLM. For example:
import RedisCacheExample from "@examples/cache/chat_models/redis.ts";
<CodeBlock language="typescript">{RedisCacheExample}</CodeBlock>
## Caching on the File System
:::warning
This cache is not recommended for production use. It is only intended for local development.
:::
LangChain provides a simple file system cache.
By default the cache is stored a temporary directory, but you can specify a custom directory if you want.
```typescript
const cache = await LocalFileCache.create();
```
## Next steps
You've now learned how to cache model responses to save time and money.
Next, check out the other how-to guides on chat models, like [how to get a model to return structured output](/docs/how_to/structured_output) or [how to create your own custom chat model](/docs/how_to/custom_chat).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/structured_output.ipynb | import { z } from "zod";
const joke = z.object({
setup: z.string().describe("The setup of the joke"),
punchline: z.string().describe("The punchline to the joke"),
rating: z.number().optional().describe("How funny the joke is, from 1 to 10"),
});
const structuredLlm = model.withStructuredOutput(joke);
await structuredLlm.invoke("Tell me a joke about cats")const structuredLlm = model.withStructuredOutput(joke, { name: "joke" });
await structuredLlm.invoke("Tell me a joke about cats")const structuredLlm = model.withStructuredOutput(
{
"name": "joke",
"description": "Joke to tell user.",
"parameters": {
"title": "Joke",
"type": "object",
"properties": {
"setup": {"type": "string", "description": "The setup for the joke"},
"punchline": {"type": "string", "description": "The joke's punchline"},
},
"required": ["setup", "punchline"],
},
}
)
await structuredLlm.invoke("Tell me a joke about cats", { name: "joke" })const structuredLlm = model.withStructuredOutput(joke, {
method: "json_mode",
name: "joke",
})
await structuredLlm.invoke(
"Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys"
)const joke = z.object({
setup: z.string().describe("The setup of the joke"),
punchline: z.string().describe("The punchline to the joke"),
rating: z.number().optional().describe("How funny the joke is, from 1 to 10"),
});
const structuredLlm = model.withStructuredOutput(joke, { includeRaw: true, name: "joke" });
await structuredLlm.invoke("Tell me a joke about cats");import { JsonOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
type Person = {
name: string;
height_in_meters: number;
};
type People = {
people: Person[];
};
const formatInstructions = `Respond only in valid JSON. The JSON object you return should match the following schema:
{{ people: [{{ name: "string", height_in_meters: "number" }}] }}
Where people is an array of objects, each with a name and height_in_meters field.
`
// Set up a parser
const parser = new JsonOutputParser<People>();
// Prompt
const prompt = await ChatPromptTemplate.fromMessages(
[
[
"system",
"Answer the user query. Wrap the output in `json` tags\n{format_instructions}",
],
[
"human",
"{query}",
]
]
).partial({
format_instructions: formatInstructions,
})const query = "Anna is 23 years old and she is 6 feet tall"
console.log((await prompt.format({ query })).toString())const chain = prompt.pipe(model).pipe(parser);
await chain.invoke({ query })import { AIMessage } from "@langchain/core/messages";
import { ChatPromptTemplate } from "@langchain/core/prompts";
type Person = {
name: string;
height_in_meters: number;
};
type People = {
people: Person[];
};
const schema = `{{ people: [{{ name: "string", height_in_meters: "number" }}] }}`
// Prompt
const prompt = await ChatPromptTemplate.fromMessages(
[
[
"system",
`Answer the user query. Output your answer as JSON that
matches the given schema: \`\`\`json\n{schema}\n\`\`\`.
Make sure to wrap the answer in \`\`\`json and \`\`\` tags`
],
[
"human",
"{query}",
]
]
).partial({
schema
});
/**
* Custom extractor
*
* Extracts JSON content from a string where
* JSON is embedded between ```json and ``` tags.
*/
const extractJson = (output: AIMessage): Array<People> => {
const text = output.content as string;
// Define the regular expression pattern to match JSON blocks
const pattern = /```json(.*?)```/gs;
// Find all non-overlapping matches of the pattern in the string
const matches = text.match(pattern);
// Process each match, attempting to parse it as JSON
try {
return matches?.map(match => {
// Remove the markdown code block syntax to isolate the JSON string
const jsonStr = match.replace(/```json|```/g, '').trim();
return JSON.parse(jsonStr);
}) ?? [];
} catch (error) {
throw new Error(`Failed to parse: ${output}`);
}
}const query = "Anna is 23 years old and she is 6 feet tall"
console.log((await prompt.format({ query })).toString())import { RunnableLambda } from "@langchain/core/runnables";
const chain = prompt.pipe(model).pipe(new RunnableLambda({ func: extractJson }));
await chain.invoke({ query }) |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/time_weighted_vectorstore.mdx | # How to create a time-weighted retriever
:::info Prerequisites
This guide assumes familiarity with the following concepts:
- [Retrievers](/docs/concepts/retrievers)
- [Vector stores](/docs/concepts/#vectorstores)
- [Retrieval-augmented generation (RAG)](/docs/tutorials/rag)
:::
This guide covers the [`TimeWeightedVectorStoreRetriever`](https://api.js.langchain.com/classes/langchain.retrievers_time_weighted.TimeWeightedVectorStoreRetriever.html),
which uses a combination of semantic similarity and a time decay.
The algorithm for scoring them is:
```
semantic_similarity + (1.0 - decay_rate) ^ hours_passed
```
Notably, `hours_passed` refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain "fresh."
```typescript
let score = (1.0 - this.decayRate) ** hoursPassed + vectorRelevance;
```
`this.decayRate` is a configurable decimal number between 0 and 1. A lower number means that documents will be "remembered" for longer, while a higher number strongly weights more recently accessed documents.
Note that setting a decay rate of exactly 0 or 1 makes `hoursPassed` irrelevant and makes this retriever equivalent to a standard vector lookup.
It is important to note that due to required metadata, all documents must be added to the backing vector store using the `addDocuments` method on the **retriever**, not the vector store itself.
import CodeBlock from "@theme/CodeBlock";
import Example from "@examples/retrievers/time-weighted-retriever.ts";
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @langchain/core
```
<CodeBlock language="typescript">{Example}</CodeBlock>
## Next steps
You've now learned how to use time as a factor when performing retrieval.
Next, check out the [broader tutorial on RAG](/docs/tutorials/rag), or this section to learn how to
[create your own custom retriever over any data source](/docs/how_to/custom_retriever/).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/output_parser_xml.ipynb | import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({
model: "claude-3-sonnet-20240229",
maxTokens: 512,
temperature: 0.1,
});
const query = `Generate the shortened filmograph for Tom Hanks.`;
const result = await model.invoke(query + ` Please enclose the movies in "movie" tags.`);
console.log(result.content);import { XMLOutputParser } from "@langchain/core/output_parsers";
// We will add these instructions to the prompt below
const parser = new XMLOutputParser();
parser.getFormatInstructions();import { ChatPromptTemplate } from "@langchain/core/prompts";
const prompt = ChatPromptTemplate.fromTemplate(`{query}\n{format_instructions}`);
const partialedPrompt = await prompt.partial({
format_instructions: parser.getFormatInstructions(),
});
const chain = partialedPrompt.pipe(model).pipe(parser);
const output = await chain.invoke({
query: "Generate the shortened filmograph for Tom Hanks.",
});
console.log(JSON.stringify(output, null, 2));const parserWithTags = new XMLOutputParser({ tags: ["movies", "actor", "film", "name", "genre"] });
// We will add these instructions to the prompt below
parserWithTags.getFormatInstructions();import { ChatPromptTemplate } from "@langchain/core/prompts";
const promptWithTags = ChatPromptTemplate.fromTemplate(`{query}\n{format_instructions}`);
const partialedPromptWithTags = await promptWithTags.partial({
format_instructions: parserWithTags.getFormatInstructions(),
});
const chainWithTags = partialedPromptWithTags.pipe(model).pipe(parserWithTags);
const outputWithTags = await chainWithTags.invoke({
query: "Generate the shortened filmograph for Tom Hanks.",
});
console.log(JSON.stringify(outputWithTags, null, 2)); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/debugging.mdx | # How to debug your LLM apps
import CodeBlock from "@theme/CodeBlock";
Like building any type of software, at some point you'll need to debug when building with LLMs.
A model call will fail, or model output will be misformatted, or there will be some nested model calls and it won't be clear where along the way an incorrect output was created.
Here are a few different tools and functionalities to aid in debugging.
## Tracing
Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls.
As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent.
The best way to do this is with [LangSmith](https://smith.langchain.com).
After you sign up at the link above, make sure to set your environment variables to start logging traces:
```shell
export LANGCHAIN_TRACING_V2="true"
export LANGCHAIN_API_KEY="..."
# Reduce tracing latency if you are not in a serverless environment
# export LANGCHAIN_CALLBACKS_BACKGROUND=true
```
Let's suppose we have an agent, and want to visualize the actions it takes and tool outputs it receives. Without any debugging, here's what we see:
import SimpleAgent from "@examples/guides/debugging/simple_agent.ts";
<CodeBlock language="typescript">{SimpleAgent}</CodeBlock>
```bash
{
input: 'Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?',
output: 'So Christopher Nolan, the director of the 2023 film Oppenheimer, is 53 years old, which is approximately 19,345 days old (assuming 365 days per year).'
}
```
We don't get much output, but since we set up LangSmith we can easily see what happened under the hood:
https://smith.langchain.com/public/fd3a4aa1-dfea-4d17-9d44-a306e7b230d3/r
## `verbose`
If you're prototyping in Jupyter Notebooks or running Node scripts, it can be helpful to print out the intermediate steps of a chain run.
There are a number of ways to enable printing at varying degrees of verbosity.
### `{ verbose: true }`
Setting the `verbose` parameter will cause any LangChain component with callback support (chains, models, agents, tools, retrievers) to print the inputs they receive and outputs they generate.
This is the most verbose setting and will fully log raw inputs and outputs.
import SimpleAgentVerbose from "@examples/guides/debugging/simple_agent_verbose.ts";
<CodeBlock language="typescript">{SimpleAgentVerbose}</CodeBlock>
<details>
<summary>Console output</summary>
```bash
[chain/start] [1:chain:AgentExecutor] Entering Chain run with input: {
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?"
}
[chain/start] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent] Entering Chain run with input: {
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"steps": []
}
[chain/start] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 3:chain:RunnableAssign] Entering Chain run with input: {
"input": ""
}
[chain/start] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 3:chain:RunnableAssign > 4:chain:RunnableMap] Entering Chain run with input: {
"input": ""
}
[chain/start] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 3:chain:RunnableAssign > 4:chain:RunnableMap > 5:chain:RunnableLambda] Entering Chain run with input: {
"input": ""
}
[chain/end] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 3:chain:RunnableAssign > 4:chain:RunnableMap > 5:chain:RunnableLambda] [0ms] Exiting Chain run with output: {
"output": []
}
[chain/end] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 3:chain:RunnableAssign > 4:chain:RunnableMap] [1ms] Exiting Chain run with output: {
"agent_scratchpad": []
}
[chain/end] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 3:chain:RunnableAssign] [1ms] Exiting Chain run with output: {
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"steps": [],
"agent_scratchpad": []
}
[chain/start] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 6:prompt:ChatPromptTemplate] Entering Chain run with input: {
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"steps": [],
"agent_scratchpad": []
}
[chain/end] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 6:prompt:ChatPromptTemplate] [0ms] Exiting Chain run with output: {
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"prompt_values",
"ChatPromptValue"
],
"kwargs": {
"messages": [
{
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"SystemMessage"
],
"kwargs": {
"content": "You are a helpful assistant",
"additional_kwargs": {},
"response_metadata": {}
}
},
{
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"HumanMessage"
],
"kwargs": {
"content": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"additional_kwargs": {},
"response_metadata": {}
}
}
]
}
}
[llm/start] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 7:llm:ChatAnthropic] Entering LLM run with input: {
"messages": [
[
{
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"SystemMessage"
],
"kwargs": {
"content": "You are a helpful assistant",
"additional_kwargs": {},
"response_metadata": {}
}
},
{
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"HumanMessage"
],
"kwargs": {
"content": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"additional_kwargs": {},
"response_metadata": {}
}
}
]
]
}
[llm/start] [1:llm:ChatAnthropic] Entering LLM run with input: {
"messages": [
[
{
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"SystemMessage"
],
"kwargs": {
"content": "You are a helpful assistant",
"additional_kwargs": {},
"response_metadata": {}
}
},
{
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"HumanMessage"
],
"kwargs": {
"content": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"additional_kwargs": {},
"response_metadata": {}
}
}
]
]
}
[llm/end] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 7:llm:ChatAnthropic] [1.98s] Exiting LLM run with output: {
"generations": [
[
{
"text": "",
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"AIMessageChunk"
],
"kwargs": {
"content": [
{
"type": "tool_use",
"id": "toolu_01NUVejujVo2y8WGVtZ49KAN",
"name": "tavily_search_results_json",
"input": {
"input": "Oppenheimer 2023 film director age"
}
}
],
"additional_kwargs": {
"id": "msg_015MqAHr84dBCAqBgjou41Km",
"type": "message",
"role": "assistant",
"model": "claude-3-sonnet-20240229",
"stop_sequence": null,
"usage": {
"input_tokens": 409,
"output_tokens": 68
},
"stop_reason": "tool_use"
},
"tool_call_chunks": [
{
"name": "tavily_search_results_json",
"args": "{\"input\":\"Oppenheimer 2023 film director age\"}",
"id": "toolu_01NUVejujVo2y8WGVtZ49KAN",
"index": 0
}
],
"tool_calls": [
{
"name": "tavily_search_results_json",
"args": {
"input": "Oppenheimer 2023 film director age"
},
"id": "toolu_01NUVejujVo2y8WGVtZ49KAN"
}
],
"invalid_tool_calls": [],
"response_metadata": {}
}
}
}
]
]
}
[llm/end] [1:llm:ChatAnthropic] [1.98s] Exiting LLM run with output: {
"generations": [
[
{
"text": "",
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"AIMessageChunk"
],
"kwargs": {
"content": [
{
"type": "tool_use",
"id": "toolu_01NUVejujVo2y8WGVtZ49KAN",
"name": "tavily_search_results_json",
"input": {
"input": "Oppenheimer 2023 film director age"
}
}
],
"additional_kwargs": {
"id": "msg_015MqAHr84dBCAqBgjou41Km",
"type": "message",
"role": "assistant",
"model": "claude-3-sonnet-20240229",
"stop_sequence": null,
"usage": {
"input_tokens": 409,
"output_tokens": 68
},
"stop_reason": "tool_use"
},
"tool_call_chunks": [
{
"name": "tavily_search_results_json",
"args": "{\"input\":\"Oppenheimer 2023 film director age\"}",
"id": "toolu_01NUVejujVo2y8WGVtZ49KAN",
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[chain/start] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 8:parser:ToolCallingAgentOutputParser] Entering Chain run with input: {
"lc": 1,
"type": "constructor",
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"kwargs": {
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{
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"model": "claude-3-sonnet-20240229",
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"usage": {
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[chain/end] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent > 8:parser:ToolCallingAgentOutputParser] [0ms] Exiting Chain run with output: {
"output": [
{
"tool": "tavily_search_results_json",
"toolInput": {
"input": "Oppenheimer 2023 film director age"
},
"toolCallId": "toolu_01NUVejujVo2y8WGVtZ49KAN",
"log": "Invoking \"tavily_search_results_json\" with {\"input\":\"Oppenheimer 2023 film director age\"}\n[{\"type\":\"tool_use\",\"id\":\"toolu_01NUVejujVo2y8WGVtZ49KAN\",\"name\":\"tavily_search_results_json\",\"input\":{\"input\":\"Oppenheimer 2023 film director age\"}}]",
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[chain/end] [1:chain:AgentExecutor > 2:chain:ToolCallingAgent] [1.98s] Exiting Chain run with output: {
"output": [
{
"tool": "tavily_search_results_json",
"toolInput": {
"input": "Oppenheimer 2023 film director age"
},
"toolCallId": "toolu_01NUVejujVo2y8WGVtZ49KAN",
"log": "Invoking \"tavily_search_results_json\" with {\"input\":\"Oppenheimer 2023 film director age\"}\n[{\"type\":\"tool_use\",\"id\":\"toolu_01NUVejujVo2y8WGVtZ49KAN\",\"name\":\"tavily_search_results_json\",\"input\":{\"input\":\"Oppenheimer 2023 film director age\"}}]",
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[agent/action] [1:chain:AgentExecutor] Agent selected action: {
"tool": "tavily_search_results_json",
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[tool/start] [1:chain:AgentExecutor > 9:tool:TavilySearchResults] Entering Tool run with input: "Oppenheimer 2023 film director age"
[tool/start] [1:tool:TavilySearchResults] Entering Tool run with input: "Oppenheimer 2023 film director age"
[tool/end] [1:chain:AgentExecutor > 9:tool:TavilySearchResults] [2.20s] Exiting Tool run with output: "[{"title":"Oppenheimer (2023) - IMDb","url":"https://www.imdb.com/title/tt15398776/","content":"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.","score":0.96643,"raw_content":null},{"title":"Christopher Nolan's Oppenheimer - Rotten Tomatoes","url":"https://editorial.rottentomatoes.com/article/everything-we-know-about-christopher-nolans-oppenheimer/","content":"Billboards and movie theater pop-ups across Los Angeles have been ticking down for months now: Christopher Nolan's epic account of J. Robert Oppenheimer, the father of the atomic bomb, is nearing an explosive release on July 21, 2023. Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.","score":0.92804,"raw_content":null},{"title":"Oppenheimer (film) - Wikipedia","url":"https://en.wikipedia.org/wiki/Oppenheimer_(film)","content":"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\nCritical response\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \"more objective view of his story from a different character's point of view\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \"big-atures\", since the special effects team had tried to build the models as physically large as possible. He felt that \"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \"emotional\" and resembling that of a thriller, while also remarking that Nolan had \"Trojan-Horsed a biopic into a thriller\".[72]\nCasting\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\", while also underscoring that it is a \"huge shift in perception about the reality of Oppenheimer's perception\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.","score":0.92404,"raw_content":null},{"title":"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \"I Try to ...","url":"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/","content":"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\nRELATED:\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\nCONNECT FacebookTwitterInstagram\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\n Subscribe\nEverything Zoomer\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.","score":0.92002,"raw_content":null},{"title":"'Oppenheimer' Review: A Man for Our Time - The New York Times","url":"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html","content":"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\n","score":0.91831,"raw_content":null}]"
[tool/end] [1:tool:TavilySearchResults] [2.20s] Exiting Tool run with output: "[{"title":"Oppenheimer (2023) - IMDb","url":"https://www.imdb.com/title/tt15398776/","content":"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.","score":0.96643,"raw_content":null},{"title":"Christopher Nolan's Oppenheimer - Rotten Tomatoes","url":"https://editorial.rottentomatoes.com/article/everything-we-know-about-christopher-nolans-oppenheimer/","content":"Billboards and movie theater pop-ups across Los Angeles have been ticking down for months now: Christopher Nolan's epic account of J. Robert Oppenheimer, the father of the atomic bomb, is nearing an explosive release on July 21, 2023. Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.","score":0.92804,"raw_content":null},{"title":"Oppenheimer (film) - Wikipedia","url":"https://en.wikipedia.org/wiki/Oppenheimer_(film)","content":"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\nCritical response\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \"more objective view of his story from a different character's point of view\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \"big-atures\", since the special effects team had tried to build the models as physically large as possible. He felt that \"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \"emotional\" and resembling that of a thriller, while also remarking that Nolan had \"Trojan-Horsed a biopic into a thriller\".[72]\nCasting\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\", while also underscoring that it is a \"huge shift in perception about the reality of Oppenheimer's perception\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.","score":0.92404,"raw_content":null},{"title":"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \"I Try to ...","url":"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/","content":"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\nRELATED:\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\nCONNECT FacebookTwitterInstagram\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\n Subscribe\nEverything Zoomer\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.","score":0.92002,"raw_content":null},{"title":"'Oppenheimer' Review: A Man for Our Time - The New York Times","url":"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html","content":"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\n","score":0.91831,"raw_content":null}]"
[chain/start] [1:chain:AgentExecutor > 10:chain:ToolCallingAgent] Entering Chain run with input: {
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"steps": [
{
"action": {
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"log": "Invoking \"tavily_search_results_json\" with {\"input\":\"Oppenheimer 2023 film director age\"}\n[{\"type\":\"tool_use\",\"id\":\"toolu_01NUVejujVo2y8WGVtZ49KAN\",\"name\":\"tavily_search_results_json\",\"input\":{\"input\":\"Oppenheimer 2023 film director age\"}}]",
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"observation": "[{\"title\":\"Oppenheimer (2023) - IMDb\",\"url\":\"https://www.imdb.com/title/tt15398776/\",\"content\":\"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.\",\"score\":0.96643,\"raw_content\":null},{\"title\":\"Christopher Nolan's Oppenheimer - Rotten Tomatoes\",\"url\":\"https://editorial.rottentomatoes.com/article/everything-we-know-about-christopher-nolans-oppenheimer/\",\"content\":\"Billboards and movie theater pop-ups across Los Angeles have been ticking down for months now: Christopher Nolan's epic account of J. Robert Oppenheimer, the father of the atomic bomb, is nearing an explosive release on July 21, 2023. Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. He felt that \\\"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\\\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \\\"emotional\\\" and resembling that of a thriller, while also remarking that Nolan had \\\"Trojan-Horsed a biopic into a thriller\\\".[72]\\nCasting\\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\\\", while also underscoring that it is a \\\"huge shift in perception about the reality of Oppenheimer's perception\\\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \\\"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\\\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.\",\"score\":0.92404,\"raw_content\":null},{\"title\":\"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \\\"I Try to ...\",\"url\":\"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/\",\"content\":\"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\\nRELATED:\\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\\nCONNECT FacebookTwitterInstagram\\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\\n Subscribe\\nEverything Zoomer\\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.\",\"score\":0.92002,\"raw_content\":null},{\"title\":\"'Oppenheimer' Review: A Man for Our Time - The New York Times\",\"url\":\"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html\",\"content\":\"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\\n\",\"score\":0.91831,\"raw_content\":null}]"
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[chain/start] [1:chain:AgentExecutor > 10:chain:ToolCallingAgent > 11:chain:RunnableAssign > 12:chain:RunnableMap] Entering Chain run with input: {
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[chain/start] [1:chain:AgentExecutor > 10:chain:ToolCallingAgent > 11:chain:RunnableAssign > 12:chain:RunnableMap > 13:chain:RunnableLambda] Entering Chain run with input: {
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Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. 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[chain/end] [1:chain:AgentExecutor > 10:chain:ToolCallingAgent > 11:chain:RunnableAssign] [3ms] Exiting Chain run with output: {
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Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. He felt that \\\"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\\\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \\\"emotional\\\" and resembling that of a thriller, while also remarking that Nolan had \\\"Trojan-Horsed a biopic into a thriller\\\".[72]\\nCasting\\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\\\", while also underscoring that it is a \\\"huge shift in perception about the reality of Oppenheimer's perception\\\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \\\"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\\\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.\",\"score\":0.92404,\"raw_content\":null},{\"title\":\"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \\\"I Try to ...\",\"url\":\"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/\",\"content\":\"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\\nRELATED:\\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\\nCONNECT FacebookTwitterInstagram\\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\\n Subscribe\\nEverything Zoomer\\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.\",\"score\":0.92002,\"raw_content\":null},{\"title\":\"'Oppenheimer' Review: A Man for Our Time - The New York Times\",\"url\":\"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html\",\"content\":\"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\\n\",\"score\":0.91831,\"raw_content\":null}]"
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Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. 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[chain/start] [1:chain:AgentExecutor > 10:chain:ToolCallingAgent > 14:prompt:ChatPromptTemplate] Entering Chain run with input: {
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Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. 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"content": "[{\"title\":\"Oppenheimer (2023) - IMDb\",\"url\":\"https://www.imdb.com/title/tt15398776/\",\"content\":\"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.\",\"score\":0.96643,\"raw_content\":null},{\"title\":\"Christopher Nolan's Oppenheimer - Rotten Tomatoes\",\"url\":\"https://editorial.rottentomatoes.com/article/everything-we-know-about-christopher-nolans-oppenheimer/\",\"content\":\"Billboards and movie theater pop-ups across Los Angeles have been ticking down for months now: Christopher Nolan's epic account of J. Robert Oppenheimer, the father of the atomic bomb, is nearing an explosive release on July 21, 2023. Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. He felt that \\\"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\\\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \\\"emotional\\\" and resembling that of a thriller, while also remarking that Nolan had \\\"Trojan-Horsed a biopic into a thriller\\\".[72]\\nCasting\\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\\\", while also underscoring that it is a \\\"huge shift in perception about the reality of Oppenheimer's perception\\\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \\\"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\\\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.\",\"score\":0.92404,\"raw_content\":null},{\"title\":\"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \\\"I Try to ...\",\"url\":\"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/\",\"content\":\"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\\nRELATED:\\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\\nCONNECT FacebookTwitterInstagram\\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\\n Subscribe\\nEverything Zoomer\\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.\",\"score\":0.92002,\"raw_content\":null},{\"title\":\"'Oppenheimer' Review: A Man for Our Time - The New York Times\",\"url\":\"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html\",\"content\":\"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\\n\",\"score\":0.91831,\"raw_content\":null}]",
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[llm/start] [1:llm:ChatAnthropic] Entering LLM run with input: {
"messages": [
[
{
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"type": "constructor",
"id": [
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"kwargs": {
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{
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"kwargs": {
"content": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"additional_kwargs": {},
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},
{
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{
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"name": "tavily_search_results_json",
"input": {
"input": "Oppenheimer 2023 film director age"
}
}
],
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"id": "msg_015MqAHr84dBCAqBgjou41Km",
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"usage": {
"input_tokens": 409,
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{
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"content": "[{\"title\":\"Oppenheimer (2023) - IMDb\",\"url\":\"https://www.imdb.com/title/tt15398776/\",\"content\":\"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.\",\"score\":0.96643,\"raw_content\":null},{\"title\":\"Christopher Nolan's Oppenheimer - Rotten Tomatoes\",\"url\":\"https://editorial.rottentomatoes.com/article/everything-we-know-about-christopher-nolans-oppenheimer/\",\"content\":\"Billboards and movie theater pop-ups across Los Angeles have been ticking down for months now: Christopher Nolan's epic account of J. Robert Oppenheimer, the father of the atomic bomb, is nearing an explosive release on July 21, 2023. Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. 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[for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\\\", while also underscoring that it is a \\\"huge shift in perception about the reality of Oppenheimer's perception\\\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \\\"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\\\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.\",\"score\":0.92404,\"raw_content\":null},{\"title\":\"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \\\"I Try to ...\",\"url\":\"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/\",\"content\":\"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\\nRELATED:\\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\\nCONNECT FacebookTwitterInstagram\\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\\n Subscribe\\nEverything Zoomer\\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.\",\"score\":0.92002,\"raw_content\":null},{\"title\":\"'Oppenheimer' Review: A Man for Our Time - The New York Times\",\"url\":\"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html\",\"content\":\"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\\n\",\"score\":0.91831,\"raw_content\":null}]",
"additional_kwargs": {
"name": "tavily_search_results_json"
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"response_metadata": {}
}
}
]
]
}
[llm/end] [1:chain:AgentExecutor > 10:chain:ToolCallingAgent > 15:llm:ChatAnthropic] [3.50s] Exiting LLM run with output: {
"generations": [
[
{
"text": "",
"message": {
"lc": 1,
"type": "constructor",
"id": [
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"kwargs": {
"content": [
{
"type": "text",
"text": "Based on the search results, the 2023 film Oppenheimer was directed by Christopher Nolan. Some key information about Christopher Nolan:\n\n- He is a British-American film director, producer and screenwriter.\n- He was born on July 30, 1970, making him currently 52 years old.\n\nTo calculate his age in days:"
},
{
"type": "tool_use",
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
"name": "calculator",
"input": {
"input": "52 * 365"
}
}
],
"additional_kwargs": {
"id": "msg_01RBDqmJKNXiEjgt5Xrng4mz",
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"usage": {
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"tool_call_chunks": [
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],
"tool_calls": [
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"input": "52 * 365"
},
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE"
}
],
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}
}
}
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]
}
[llm/end] [1:llm:ChatAnthropic] [3.50s] Exiting LLM run with output: {
"generations": [
[
{
"text": "",
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
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"kwargs": {
"content": [
{
"type": "text",
"text": "Based on the search results, the 2023 film Oppenheimer was directed by Christopher Nolan. Some key information about Christopher Nolan:\n\n- He is a British-American film director, producer and screenwriter.\n- He was born on July 30, 1970, making him currently 52 years old.\n\nTo calculate his age in days:"
},
{
"type": "tool_use",
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
"name": "calculator",
"input": {
"input": "52 * 365"
}
}
],
"additional_kwargs": {
"id": "msg_01RBDqmJKNXiEjgt5Xrng4mz",
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"tool_call_chunks": [
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"args": "{\"input\":\"52 * 365\"}",
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
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"tool_calls": [
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[chain/start] [1:chain:AgentExecutor > 10:chain:ToolCallingAgent > 16:parser:ToolCallingAgentOutputParser] Entering Chain run with input: {
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"AIMessageChunk"
],
"kwargs": {
"content": [
{
"type": "text",
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},
{
"type": "tool_use",
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
"name": "calculator",
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"response_metadata": {}
}
}
[chain/end] [1:chain:AgentExecutor > 10:chain:ToolCallingAgent > 16:parser:ToolCallingAgentOutputParser] [1ms] Exiting Chain run with output: {
"output": [
{
"tool": "calculator",
"toolInput": {
"input": "52 * 365"
},
"toolCallId": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
"log": "Invoking \"calculator\" with {\"input\":\"52 * 365\"}\n[{\"type\":\"text\",\"text\":\"Based on the search results, the 2023 film Oppenheimer was directed by Christopher Nolan. Some key information about Christopher Nolan:\\n\\n- He is a British-American film director, producer and screenwriter.\\n- He was born on July 30, 1970, making him currently 52 years old.\\n\\nTo calculate his age in days:\"},{\"type\":\"tool_use\",\"id\":\"toolu_01NVTbm5aNYSm1wGYb6XF7jE\",\"name\":\"calculator\",\"input\":{\"input\":\"52 * 365\"}}]",
"messageLog": [
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"type": "constructor",
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"kwargs": {
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{
"type": "text",
"text": "Based on the search results, the 2023 film Oppenheimer was directed by Christopher Nolan. Some key information about Christopher Nolan:\n\n- He is a British-American film director, producer and screenwriter.\n- He was born on July 30, 1970, making him currently 52 years old.\n\nTo calculate his age in days:"
},
{
"type": "tool_use",
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"name": "calculator",
"input": {
"input": "52 * 365"
}
}
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"id": "msg_01RBDqmJKNXiEjgt5Xrng4mz",
"type": "message",
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"stop_sequence": null,
"usage": {
"input_tokens": 2810,
"output_tokens": 137
},
"stop_reason": "tool_use"
},
"tool_call_chunks": [
{
"name": "calculator",
"args": "{\"input\":\"52 * 365\"}",
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
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}
],
"tool_calls": [
{
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"input": "52 * 365"
},
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE"
}
],
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"response_metadata": {}
}
}
]
}
]
}
[chain/end] [1:chain:AgentExecutor > 10:chain:ToolCallingAgent] [3.51s] Exiting Chain run with output: {
"output": [
{
"tool": "calculator",
"toolInput": {
"input": "52 * 365"
},
"toolCallId": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
"log": "Invoking \"calculator\" with {\"input\":\"52 * 365\"}\n[{\"type\":\"text\",\"text\":\"Based on the search results, the 2023 film Oppenheimer was directed by Christopher Nolan. Some key information about Christopher Nolan:\\n\\n- He is a British-American film director, producer and screenwriter.\\n- He was born on July 30, 1970, making him currently 52 years old.\\n\\nTo calculate his age in days:\"},{\"type\":\"tool_use\",\"id\":\"toolu_01NVTbm5aNYSm1wGYb6XF7jE\",\"name\":\"calculator\",\"input\":{\"input\":\"52 * 365\"}}]",
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},
{
"type": "tool_use",
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[agent/action] [1:chain:AgentExecutor] Agent selected action: {
"tool": "calculator",
"toolInput": {
"input": "52 * 365"
},
"toolCallId": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
"log": "Invoking \"calculator\" with {\"input\":\"52 * 365\"}\n[{\"type\":\"text\",\"text\":\"Based on the search results, the 2023 film Oppenheimer was directed by Christopher Nolan. Some key information about Christopher Nolan:\\n\\n- He is a British-American film director, producer and screenwriter.\\n- He was born on July 30, 1970, making him currently 52 years old.\\n\\nTo calculate his age in days:\"},{\"type\":\"tool_use\",\"id\":\"toolu_01NVTbm5aNYSm1wGYb6XF7jE\",\"name\":\"calculator\",\"input\":{\"input\":\"52 * 365\"}}]",
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{
"type": "tool_use",
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[tool/start] [1:chain:AgentExecutor > 17:tool:Calculator] Entering Tool run with input: "52 * 365"
[tool/start] [1:tool:Calculator] Entering Tool run with input: "52 * 365"
[tool/end] [1:chain:AgentExecutor > 17:tool:Calculator] [3ms] Exiting Tool run with output: "18980"
[tool/end] [1:tool:Calculator] [3ms] Exiting Tool run with output: "18980"
[chain/start] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent] Entering Chain run with input: {
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"steps": [
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"log": "Invoking \"tavily_search_results_json\" with {\"input\":\"Oppenheimer 2023 film director age\"}\n[{\"type\":\"tool_use\",\"id\":\"toolu_01NUVejujVo2y8WGVtZ49KAN\",\"name\":\"tavily_search_results_json\",\"input\":{\"input\":\"Oppenheimer 2023 film director age\"}}]",
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Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. He felt that \\\"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\\\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \\\"emotional\\\" and resembling that of a thriller, while also remarking that Nolan had \\\"Trojan-Horsed a biopic into a thriller\\\".[72]\\nCasting\\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\\\", while also underscoring that it is a \\\"huge shift in perception about the reality of Oppenheimer's perception\\\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \\\"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\\\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.\",\"score\":0.92404,\"raw_content\":null},{\"title\":\"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \\\"I Try to ...\",\"url\":\"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/\",\"content\":\"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\\nRELATED:\\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\\nCONNECT FacebookTwitterInstagram\\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\\n Subscribe\\nEverything Zoomer\\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.\",\"score\":0.92002,\"raw_content\":null},{\"title\":\"'Oppenheimer' Review: A Man for Our Time - The New York Times\",\"url\":\"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html\",\"content\":\"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\\n\",\"score\":0.91831,\"raw_content\":null}]"
},
{
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"log": "Invoking \"calculator\" with {\"input\":\"52 * 365\"}\n[{\"type\":\"text\",\"text\":\"Based on the search results, the 2023 film Oppenheimer was directed by Christopher Nolan. Some key information about Christopher Nolan:\\n\\n- He is a British-American film director, producer and screenwriter.\\n- He was born on July 30, 1970, making him currently 52 years old.\\n\\nTo calculate his age in days:\"},{\"type\":\"tool_use\",\"id\":\"toolu_01NVTbm5aNYSm1wGYb6XF7jE\",\"name\":\"calculator\",\"input\":{\"input\":\"52 * 365\"}}]",
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[chain/start] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 19:chain:RunnableAssign] Entering Chain run with input: {
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[chain/start] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 19:chain:RunnableAssign > 20:chain:RunnableMap] Entering Chain run with input: {
"input": ""
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[chain/start] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 19:chain:RunnableAssign > 20:chain:RunnableMap > 21:chain:RunnableLambda] Entering Chain run with input: {
"input": ""
}
[chain/end] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 19:chain:RunnableAssign > 20:chain:RunnableMap > 21:chain:RunnableLambda] [1ms] Exiting Chain run with output: {
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"content": "[{\"title\":\"Oppenheimer (2023) - IMDb\",\"url\":\"https://www.imdb.com/title/tt15398776/\",\"content\":\"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.\",\"score\":0.96643,\"raw_content\":null},{\"title\":\"Christopher Nolan's Oppenheimer - Rotten Tomatoes\",\"url\":\"https://editorial.rottentomatoes.com/article/everything-we-know-about-christopher-nolans-oppenheimer/\",\"content\":\"Billboards and movie theater pop-ups across Los Angeles have been ticking down for months now: Christopher Nolan's epic account of J. Robert Oppenheimer, the father of the atomic bomb, is nearing an explosive release on July 21, 2023. Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. He felt that \\\"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\\\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \\\"emotional\\\" and resembling that of a thriller, while also remarking that Nolan had \\\"Trojan-Horsed a biopic into a thriller\\\".[72]\\nCasting\\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\\\", while also underscoring that it is a \\\"huge shift in perception about the reality of Oppenheimer's perception\\\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \\\"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\\\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.\",\"score\":0.92404,\"raw_content\":null},{\"title\":\"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \\\"I Try to ...\",\"url\":\"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/\",\"content\":\"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\\nRELATED:\\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\\nCONNECT FacebookTwitterInstagram\\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\\n Subscribe\\nEverything Zoomer\\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.\",\"score\":0.92002,\"raw_content\":null},{\"title\":\"'Oppenheimer' Review: A Man for Our Time - The New York Times\",\"url\":\"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html\",\"content\":\"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. 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[chain/end] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 19:chain:RunnableAssign > 20:chain:RunnableMap] [2ms] Exiting Chain run with output: {
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"content": "[{\"title\":\"Oppenheimer (2023) - IMDb\",\"url\":\"https://www.imdb.com/title/tt15398776/\",\"content\":\"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.\",\"score\":0.96643,\"raw_content\":null},{\"title\":\"Christopher Nolan's Oppenheimer - Rotten Tomatoes\",\"url\":\"https://editorial.rottentomatoes.com/article/everything-we-know-about-christopher-nolans-oppenheimer/\",\"content\":\"Billboards and movie theater pop-ups across Los Angeles have been ticking down for months now: Christopher Nolan's epic account of J. Robert Oppenheimer, the father of the atomic bomb, is nearing an explosive release on July 21, 2023. Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. He felt that \\\"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\\\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \\\"emotional\\\" and resembling that of a thriller, while also remarking that Nolan had \\\"Trojan-Horsed a biopic into a thriller\\\".[72]\\nCasting\\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\\\", while also underscoring that it is a \\\"huge shift in perception about the reality of Oppenheimer's perception\\\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \\\"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\\\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.\",\"score\":0.92404,\"raw_content\":null},{\"title\":\"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \\\"I Try to ...\",\"url\":\"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/\",\"content\":\"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\\nRELATED:\\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\\nCONNECT FacebookTwitterInstagram\\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\\n Subscribe\\nEverything Zoomer\\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.\",\"score\":0.92002,\"raw_content\":null},{\"title\":\"'Oppenheimer' Review: A Man for Our Time - The New York Times\",\"url\":\"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html\",\"content\":\"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. 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[chain/end] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 19:chain:RunnableAssign] [4ms] Exiting Chain run with output: {
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"observation": "[{\"title\":\"Oppenheimer (2023) - IMDb\",\"url\":\"https://www.imdb.com/title/tt15398776/\",\"content\":\"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.\",\"score\":0.96643,\"raw_content\":null},{\"title\":\"Christopher Nolan's Oppenheimer - Rotten Tomatoes\",\"url\":\"https://editorial.rottentomatoes.com/article/everything-we-know-about-christopher-nolans-oppenheimer/\",\"content\":\"Billboards and movie theater pop-ups across Los Angeles have been ticking down for months now: Christopher Nolan's epic account of J. Robert Oppenheimer, the father of the atomic bomb, is nearing an explosive release on July 21, 2023. Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.\",\"score\":0.92804,\"raw_content\":null},{\"title\":\"Oppenheimer (film) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Oppenheimer_(film)\",\"content\":\"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\\nCritical response\\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \\\"more objective view of his story from a different character's point of view\\\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. 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[llm/start] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 23:llm:ChatAnthropic] Entering LLM run with input: {
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The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. 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The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \\\"big-atures\\\", since the special effects team had tried to build the models as physically large as possible. He felt that \\\"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\\\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \\\"emotional\\\" and resembling that of a thriller, while also remarking that Nolan had \\\"Trojan-Horsed a biopic into a thriller\\\".[72]\\nCasting\\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\\\", while also underscoring that it is a \\\"huge shift in perception about the reality of Oppenheimer's perception\\\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \\\"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\\\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.\",\"score\":0.92404,\"raw_content\":null},{\"title\":\"'Oppenheimer' Director Christopher Nolan On Filmmaking at 53: \\\"I Try to ...\",\"url\":\"https://www.everythingzoomer.com/arts-entertainment/2023/11/21/oppenheimer-director-christopher-nolan-on-filmmaking-at-53-i-try-to-challenge-myself-with-every-film/\",\"content\":\"Oppenheimer will be available to own on 4K Ultra HD, Blu-ray and DVD — including more than three hours of bonus features — on November 21.\\nRELATED:\\nVisiting the Trinity Site Featured in ‘Oppenheimer’ Is a Sobering Reminder of the Horror of Nuclear Weapons\\nBarbenheimer: How ‘Barbie’ and ‘Oppenheimer’ Became the Unlikely Movie Marriage of the Summer\\nBlast From the Past: ‘Asteroid City’ & ‘Oppenheimer’ and the Age of Nuclear Anxiety\\nEXPLORE HealthMoneyTravelFoodStyleBook ClubClassifieds#ZoomerDailyPolicy & PerspectiveArts & EntertainmentStars & RoyaltySex & Love\\nCONNECT FacebookTwitterInstagram\\nSUBSCRIBE Terms of Subscription ServiceE-NewslettersSubscribe to Zoomer Magazine\\nBROWSE AboutMastheadContact UsAdvertise with UsPrivacy Policy\\nEverythingZoomer.com is part of the ZoomerMedia Digital Network “I think with experience — and with the experience of watching your films with an audience over the years — you do more and more recognize the human elements that people respond to, and the things that move you and the things that move the audience.”\\n “What’s interesting, as you watch the films over time, is that some of his preoccupations are the same, but then some of them have changed over time with who he is as a person and what’s going on in his own life,” Thomas said.\\n The British-American director’s latest explosive drama, Oppenheimer, which has earned upwards of US$940 million at the global box office, follows theoretical physicist J. Robert Oppenheimer (played by Cillian Murphy) as he leads the team creating the first atomic bomb, as director of the Manhattan Project’s Los Alamos Laboratory.\\n Subscribe\\nEverything Zoomer\\n‘Oppenheimer’ Director Christopher Nolan On Filmmaking at 53: “I Try to Challenge Myself with Every Film”\\nDirector Christopher Nolan poses upon his arrival for the premiere of the movie 'Oppenheimer' in Paris on July 11, 2023.\",\"score\":0.92002,\"raw_content\":null},{\"title\":\"'Oppenheimer' Review: A Man for Our Time - The New York Times\",\"url\":\"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html\",\"content\":\"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\\n\",\"score\":0.91831,\"raw_content\":null}]",
"additional_kwargs": {
"name": "tavily_search_results_json"
},
"response_metadata": {}
}
},
{
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"AIMessageChunk"
],
"kwargs": {
"content": [
{
"type": "text",
"text": "Based on the search results, the 2023 film Oppenheimer was directed by Christopher Nolan. Some key information about Christopher Nolan:\n\n- He is a British-American film director, producer and screenwriter.\n- He was born on July 30, 1970, making him currently 52 years old.\n\nTo calculate his age in days:"
},
{
"type": "tool_use",
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
"name": "calculator",
"input": {
"input": "52 * 365"
}
}
],
"additional_kwargs": {
"id": "msg_01RBDqmJKNXiEjgt5Xrng4mz",
"type": "message",
"role": "assistant",
"model": "claude-3-sonnet-20240229",
"stop_sequence": null,
"usage": {
"input_tokens": 2810,
"output_tokens": 137
},
"stop_reason": "tool_use"
},
"tool_call_chunks": [
{
"name": "calculator",
"args": "{\"input\":\"52 * 365\"}",
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
"index": 0
}
],
"tool_calls": [
{
"name": "calculator",
"args": {
"input": "52 * 365"
},
"id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE"
}
],
"invalid_tool_calls": [],
"response_metadata": {}
}
},
{
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"ToolMessage"
],
"kwargs": {
"tool_call_id": "toolu_01NVTbm5aNYSm1wGYb6XF7jE",
"content": "18980",
"additional_kwargs": {
"name": "calculator"
},
"response_metadata": {}
}
}
]
]
}
[llm/end] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 23:llm:ChatAnthropic] [2.16s] Exiting LLM run with output: {
"generations": [
[
{
"text": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year).",
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"AIMessageChunk"
],
"kwargs": {
"content": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year).",
"additional_kwargs": {
"id": "msg_01TYp6vJRKJQgXXRoqVrDGTR",
"type": "message",
"role": "assistant",
"model": "claude-3-sonnet-20240229",
"stop_sequence": null,
"usage": {
"input_tokens": 2960,
"output_tokens": 51
},
"stop_reason": "end_turn"
},
"tool_call_chunks": [],
"tool_calls": [],
"invalid_tool_calls": [],
"response_metadata": {}
}
}
}
]
]
}
[llm/end] [1:llm:ChatAnthropic] [2.16s] Exiting LLM run with output: {
"generations": [
[
{
"text": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year).",
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"AIMessageChunk"
],
"kwargs": {
"content": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year).",
"additional_kwargs": {
"id": "msg_01TYp6vJRKJQgXXRoqVrDGTR",
"type": "message",
"role": "assistant",
"model": "claude-3-sonnet-20240229",
"stop_sequence": null,
"usage": {
"input_tokens": 2960,
"output_tokens": 51
},
"stop_reason": "end_turn"
},
"tool_call_chunks": [],
"tool_calls": [],
"invalid_tool_calls": [],
"response_metadata": {}
}
}
}
]
]
}
[chain/start] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 24:parser:ToolCallingAgentOutputParser] Entering Chain run with input: {
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"AIMessageChunk"
],
"kwargs": {
"content": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year).",
"additional_kwargs": {
"id": "msg_01TYp6vJRKJQgXXRoqVrDGTR",
"type": "message",
"role": "assistant",
"model": "claude-3-sonnet-20240229",
"stop_sequence": null,
"usage": {
"input_tokens": 2960,
"output_tokens": 51
},
"stop_reason": "end_turn"
},
"tool_call_chunks": [],
"tool_calls": [],
"invalid_tool_calls": [],
"response_metadata": {}
}
}
[chain/end] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent > 24:parser:ToolCallingAgentOutputParser] [2ms] Exiting Chain run with output: {
"returnValues": {
"output": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year)."
},
"log": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year)."
}
[chain/end] [1:chain:AgentExecutor > 18:chain:ToolCallingAgent] [2.20s] Exiting Chain run with output: {
"returnValues": {
"output": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year)."
},
"log": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year)."
}
[chain/end] [1:chain:AgentExecutor] [9.92s] Exiting Chain run with output: {
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"output": "So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is approximately 18,980 days old (assuming 365 days per year)."
}
```
</details>
### `Tool({ ..., verbose: true })`
You can also scope verbosity down to a single object, in which case only the inputs and outputs to that object are printed (along with any additional callbacks calls made specifically by that object).
import SimpleAgentVerboseSome from "@examples/guides/debugging/simple_agent_verbose_some.ts";
<CodeBlock language="typescript">{SimpleAgentVerboseSome}</CodeBlock>
<details>
<summary>Console output</summary>
```bash
[tool/start] [1:tool:TavilySearchResults] Entering Tool run with input: "Oppenheimer 2023 film director age"
[tool/end] [1:tool:TavilySearchResults] [1.95s] Exiting Tool run with output: "[{"title":"'Oppenheimer' Review: A Man for Our Time - The New York Times","url":"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html","content":"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\n","score":0.97519,"raw_content":null},{"title":"Oppenheimer's Grandson Reacts to New Christopher Nolan Film | TIME","url":"https://time.com/6297743/oppenheimer-grandson-movie-interview/","content":"July 25, 2023 3:32 PM EDT. M oviegoers turned out in droves this weekend for writer-director Christopher Nolan's new film Oppenheimer, fueling an expectations-shattering domestic box office debut ...","score":0.95166,"raw_content":null},{"title":"Oppenheimer (2023) - IMDb","url":"https://www.imdb.com/title/tt15398776/","content":"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.","score":0.95127,"raw_content":null},{"title":"Oppenheimer (film) - Wikipedia","url":"https://en.wikipedia.org/wiki/Oppenheimer_(film)","content":"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\nCritical response\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \"more objective view of his story from a different character's point of view\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \"big-atures\", since the special effects team had tried to build the models as physically large as possible. He felt that \"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \"emotional\" and resembling that of a thriller, while also remarking that Nolan had \"Trojan-Horsed a biopic into a thriller\".[72]\nCasting\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\", while also underscoring that it is a \"huge shift in perception about the reality of Oppenheimer's perception\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.","score":0.92204,"raw_content":null},{"title":"Oppenheimer (2023) - Full Cast & Crew - IMDb","url":"https://www.imdb.com/title/tt15398776/fullcredits/","content":"Oppenheimer (2023) cast and crew credits, including actors, actresses, directors, writers and more. Menu. Movies. Release Calendar Top 250 Movies Most Popular Movies Browse Movies by Genre Top Box Office Showtimes & Tickets Movie News India Movie Spotlight. ... Peter Oppenheimer - Age 8 (uncredited) Adam Walker Federman ... MIT Student ...","score":0.92179,"raw_content":null}]"
[tool/start] [1:tool:TavilySearchResults] Entering Tool run with input: "Christopher Nolan age"
[tool/end] [1:tool:TavilySearchResults] [1.15s] Exiting Tool run with output: "[{"title":"Christopher Nolan - IMDb","url":"https://www.imdb.com/name/nm0634240/","content":"Christopher Nolan is a British-American writer-director-producer of acclaimed films such as Inception, The Dark Knight, and Interstellar. He was born on July 30, 1970, in London, England.","score":0.96627,"raw_content":null},{"title":"Christopher Nolan: Biography, Movie Director, Filmmaker","url":"https://www.biography.com/movies-tv/christopher-nolan","content":"To meet the team, visit our About Us page: https://www.biography.com/about/a43602329/about-us\nFilmmakers\nMatt Damon\nGreta Gerwig\nMartin Scorsese\nBradley Cooper\nJodie Foster\nDodi Fayed\nDrew Barrymore\nRyan Gosling Was Reluctant to Play Barbie’s Ken\nThe Actors in the Most Wes Anderson Movies\n“The Idol” Raises Eyesbrows at Cannes\n41 Inspiring Famous Women in History\nBen Affleck and Matt Damon’s Lifelong Friendship\nA Part of Hearst Digital Media\nWe may earn commission from links on this page, but we only recommend products we back.\n The Dark Knight and Inception\nIn July 2008, Nolan’s Batman sequel, The Dark Knight, opened and set the record as having the highest weekend gross in the United States, at $158 million; Knight went on to become one of the top five highest-grossing films in America. In the fall of 2014, Nolan returned to the big screen with Interstellar, a nearly three-hour sci-fi epic that follows the journey of a team of astronauts seeking a new world for the inhabitants of a besieged Earth. The director's career then traveled into the stratosphere, when he agreed to helm the re-launch of the comic book hero Batman with the 2005 film Batman Begins, starring Christian Bale as the titular character. Built around three storylines offering different perspectives on a dramatic turn of events in 1940, Dunkirk earned mostly rave reviews for its portrayals of the tensions and terrors of war, picking up Golden Globe nominations for Best Motion Picture—Drama and Best Director, as well as an Academy Award nod for Best Director.\n","score":0.95669,"raw_content":null},{"title":"Christopher Nolan - Biography - IMDb","url":"https://www.imdb.com/name/nm0634240/bio/","content":"Learn about the life and career of acclaimed writer-director Christopher Nolan, who was born on July 30, 1970, in London, England. Find out his filmography, awards, family, trivia and more on IMDb.","score":0.91217,"raw_content":null},{"title":"Christopher Nolan - Wikipedia","url":"https://en.wikipedia.org/wiki/Christopher_Nolan","content":"In early 2003, Nolan approached Warner Bros. with the idea of making a new Batman film, based on the character's origin story.[58] Nolan was fascinated by the notion of grounding it in a more realistic world than a comic-book fantasy.[59] He relied heavily on traditional stunts and miniature effects during filming, with minimal use of computer-generated imagery (CGI).[60] Batman Begins (2005), the biggest project Nolan had undertaken to that point,[61] was released to critical acclaim and commercial success.[62][63] Starring Christian Bale as Bruce Wayne / Batman—along with Michael Caine, Gary Oldman, Morgan Freeman and Liam Neeson—Batman Begins revived the franchise.[64][65] Batman Begins was 2005's ninth-highest-grossing film and was praised for its psychological depth and contemporary relevance;[63][66] it is cited as one of the most influential films of the 2000s.[67] Film author Ian Nathan wrote that within five years of his career, Nolan \"[went] from unknown to indie darling to gaining creative control over one of the biggest properties in Hollywood, and (perhaps unwittingly) fomenting the genre that would redefine the entire industry\".[68]\nNolan directed, co-wrote and produced The Prestige (2006), an adaptation of the Christopher Priest novel about two rival 19th-century magicians.[69] He directed, wrote and edited the short film Larceny (1996),[19] which was filmed over a weekend in black and white with limited equipment and a small cast and crew.[12][20] Funded by Nolan and shot with the UCL Union Film society's equipment, it appeared at the Cambridge Film Festival in 1996 and is considered one of UCL's best shorts.[21] For unknown reasons, the film has since been removed from public view.[19] Nolan filmed a third short, Doodlebug (1997), about a man seemingly chasing an insect with his shoe, only to discover that it is a miniature of himself.[14][22] Nolan and Thomas first attempted to make a feature in the mid-1990s with Larry Mahoney, which they scrapped.[23] During this period in his career, Nolan had little to no success getting his projects off the ground, facing several rejections; he added, \"[T]here's a very limited pool of finance in the UK. Philosophy professor David Kyle Johnson wrote that \"Inception became a classic almost as soon as it was projected on silver screens\", praising its exploration of philosophical ideas, including leap of faith and allegory of the cave.[97] The film grossed over $836 million worldwide.[98] Nominated for eight Academy Awards—including Best Picture and Best Original Screenplay—it won Best Cinematography, Best Sound Mixing, Best Sound Editing and Best Visual Effects.[99] Nolan was nominated for a BAFTA Award and a Golden Globe Award for Best Director, among other accolades.[40]\nAround the release of The Dark Knight Rises (2012), Nolan's third and final Batman film, Joseph Bevan of the British Film Institute wrote a profile on him: \"In the space of just over a decade, Christopher Nolan has shot from promising British indie director to undisputed master of a new brand of intelligent escapism. He further wrote that Nolan's body of work reflect \"a heterogeneity of conditions of products\" extending from low-budget films to lucrative blockbusters, \"a wide range of genres and settings\" and \"a diversity of styles that trumpet his versatility\".[193]\nDavid Bordwell, a film theorist, wrote that Nolan has been able to blend his \"experimental impulses\" with the demands of mainstream entertainment, describing his oeuvre as \"experiments with cinematic time by means of techniques of subjective viewpoint and crosscutting\".[194] Nolan's use of practical, in-camera effects, miniatures and models, as well as shooting on celluloid film, has been highly influential in early 21st century cinema.[195][196] IndieWire wrote in 2019 that, Nolan \"kept a viable alternate model of big-budget filmmaking alive\", in an era where blockbuster filmmaking has become \"a largely computer-generated art form\".[196] Initially reluctant to make a sequel, he agreed after Warner Bros. repeatedly insisted.[78] Nolan wanted to expand on the noir quality of the first film by broadening the canvas and taking on \"the dynamic of a story of the city, a large crime story ... where you're looking at the police, the justice system, the vigilante, the poor people, the rich people, the criminals\".[79] Continuing to minimalise the use of CGI, Nolan employed high-resolution IMAX cameras, making it the first major motion picture to use this technology.[80][81]","score":0.90288,"raw_content":null},{"title":"Christopher Nolan | Biography, Movies, Batman, Oppenheimer, & Facts ...","url":"https://www.britannica.com/biography/Christopher-Nolan-British-director","content":"The sci-fi drama depicted the efforts of a group of scientists to relocate humanity from an Earth vitiated by war and famine to another planet by way of a wormhole. The film turns on this character’s attempt to move past the boundaries of the technology in order to actually plant an idea in a dreamer’s head. His 2023 film Oppenheimer, depicts J. Robert Oppenheimer’s role in the development of the atomic bomb and the later security hearing over his alleged ties to communism. It used a destabilizing reverse-order story line to mirror the fractured mental state of its protagonist, a man with short-term amnesia who is trying to track down the person who murdered his wife. The Dark Knight (2008) leaned even more heavily on the moral and structural decay of its setting, fictional Gotham City, and it revived such classic Batman villains as the Joker (played by Heath Ledger).","score":0.90219,"raw_content":null}]"
[tool/start] [1:tool:Calculator] Entering Tool run with input: "(2023 - 1970) * 365"
[tool/end] [1:tool:Calculator] [3ms] Exiting Tool run with output: "19345"
{
input: 'Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?',
output: 'So Christopher Nolan, the director of the 2023 film Oppenheimer, is currently 52 years old, which is 19,345 days old (assuming 365 days per year).'
}
MacBook-Pro-4:examples jacoblee$ yarn start examples/src/guides/debugging/simple_agent_verbose_some.ts
(node:78812) ExperimentalWarning: `--experimental-loader` may be removed in the future; instead use `register()`:
--import 'data:text/javascript,import { register } from "node:module"; import { pathToFileURL } from "node:url"; register("file%3A///Users/jacoblee/langchain/langchainjs/node_modules/tsx/dist/loader.js", pathToFileURL("./"));'
(Use `node --trace-warnings ...` to show where the warning was created)
[WARN]: You have enabled LangSmith tracing without backgrounding callbacks.
[WARN]: If you are not using a serverless environment where you must wait for tracing calls to finish,
[WARN]: we suggest setting "process.env.LANGCHAIN_CALLBACKS_BACKGROUND=true" to avoid additional latency.
[tool/start] [1:tool:TavilySearchResults] Entering Tool run with input: "Oppenheimer 2023 film director age"
[tool/end] [1:tool:TavilySearchResults] [1.76s] Exiting Tool run with output: "[{"title":"Oppenheimer (film) - Wikipedia","url":"https://en.wikipedia.org/wiki/Oppenheimer_(film)","content":"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\nCritical response\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \"more objective view of his story from a different character's point of view\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \"big-atures\", since the special effects team had tried to build the models as physically large as possible. He felt that \"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \"emotional\" and resembling that of a thriller, while also remarking that Nolan had \"Trojan-Horsed a biopic into a thriller\".[72]\nCasting\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\", while also underscoring that it is a \"huge shift in perception about the reality of Oppenheimer's perception\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.","score":0.97075,"raw_content":null},{"title":"Christopher Nolan's Oppenheimer - Rotten Tomatoes","url":"https://editorial.rottentomatoes.com/article/everything-we-know-about-christopher-nolans-oppenheimer/","content":"Billboards and movie theater pop-ups across Los Angeles have been ticking down for months now: Christopher Nolan's epic account of J. Robert Oppenheimer, the father of the atomic bomb, is nearing an explosive release on July 21, 2023. Nolan movies are always incredibly secretive, twists locked alongside totems behind safe doors, actors not spilling an ounce of Earl Grey tea.","score":0.9684,"raw_content":null},{"title":"Oppenheimer (2023) - Full Cast & Crew - IMDb","url":"https://www.imdb.com/title/tt15398776/fullcredits/","content":"Oppenheimer (2023) cast and crew credits, including actors, actresses, directors, writers and more. Menu. Movies. Release Calendar Top 250 Movies Most Popular Movies Browse Movies by Genre Top Box Office Showtimes & Tickets Movie News India Movie Spotlight. ... Peter Oppenheimer - Age 8 (uncredited) Adam Walker Federman ... MIT Student ...","score":0.94834,"raw_content":null},{"title":"Oppenheimer (2023) - IMDb","url":"https://www.imdb.com/title/tt15398776/","content":"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.","score":0.92995,"raw_content":null},{"title":"'Oppenheimer' Review: A Man for Our Time - The New York Times","url":"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html","content":"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\n","score":0.92512,"raw_content":null}]"
[tool/start] [1:tool:TavilySearchResults] Entering Tool run with input: "Christopher Nolan age"
[tool/end] [1:tool:TavilySearchResults] [1.69s] Exiting Tool run with output: "[{"title":"Christopher Nolan: Biography, Movie Director, Filmmaker","url":"https://www.biography.com/movies-tv/christopher-nolan","content":"To meet the team, visit our About Us page: https://www.biography.com/about/a43602329/about-us\nFilmmakers\nMatt Damon\nGreta Gerwig\nMartin Scorsese\nBradley Cooper\nJodie Foster\nDodi Fayed\nDrew Barrymore\nRyan Gosling Was Reluctant to Play Barbie’s Ken\nThe Actors in the Most Wes Anderson Movies\n“The Idol” Raises Eyesbrows at Cannes\n41 Inspiring Famous Women in History\nBen Affleck and Matt Damon’s Lifelong Friendship\nA Part of Hearst Digital Media\nWe may earn commission from links on this page, but we only recommend products we back.\n The Dark Knight and Inception\nIn July 2008, Nolan’s Batman sequel, The Dark Knight, opened and set the record as having the highest weekend gross in the United States, at $158 million; Knight went on to become one of the top five highest-grossing films in America. In the fall of 2014, Nolan returned to the big screen with Interstellar, a nearly three-hour sci-fi epic that follows the journey of a team of astronauts seeking a new world for the inhabitants of a besieged Earth. The director's career then traveled into the stratosphere, when he agreed to helm the re-launch of the comic book hero Batman with the 2005 film Batman Begins, starring Christian Bale as the titular character. Built around three storylines offering different perspectives on a dramatic turn of events in 1940, Dunkirk earned mostly rave reviews for its portrayals of the tensions and terrors of war, picking up Golden Globe nominations for Best Motion Picture—Drama and Best Director, as well as an Academy Award nod for Best Director.\n","score":0.96408,"raw_content":null},{"title":"Christopher Nolan - Biography - IMDb","url":"https://www.imdb.com/name/nm0634240/bio/","content":"Learn about the life and career of acclaimed writer-director Christopher Nolan, who was born on July 30, 1970, in London, England. Find out his filmography, awards, family, trivia and more on IMDb.","score":0.95409,"raw_content":null},{"title":"Christopher Nolan - IMDb","url":"https://www.imdb.com/name/nm0634240/","content":"Christopher Nolan is a British-American writer-director-producer of acclaimed films such as Inception, The Dark Knight, and Interstellar. He was born on July 30, 1970, in London, England.","score":0.95401,"raw_content":null},{"title":"Christopher Nolan - Wikipedia","url":"https://en.wikipedia.org/wiki/Christopher_Nolan","content":"In early 2003, Nolan approached Warner Bros. with the idea of making a new Batman film, based on the character's origin story.[58] Nolan was fascinated by the notion of grounding it in a more realistic world than a comic-book fantasy.[59] He relied heavily on traditional stunts and miniature effects during filming, with minimal use of computer-generated imagery (CGI).[60] Batman Begins (2005), the biggest project Nolan had undertaken to that point,[61] was released to critical acclaim and commercial success.[62][63] Starring Christian Bale as Bruce Wayne / Batman—along with Michael Caine, Gary Oldman, Morgan Freeman and Liam Neeson—Batman Begins revived the franchise.[64][65] Batman Begins was 2005's ninth-highest-grossing film and was praised for its psychological depth and contemporary relevance;[63][66] it is cited as one of the most influential films of the 2000s.[67] Film author Ian Nathan wrote that within five years of his career, Nolan \"[went] from unknown to indie darling to gaining creative control over one of the biggest properties in Hollywood, and (perhaps unwittingly) fomenting the genre that would redefine the entire industry\".[68]\nNolan directed, co-wrote and produced The Prestige (2006), an adaptation of the Christopher Priest novel about two rival 19th-century magicians.[69] He directed, wrote and edited the short film Larceny (1996),[19] which was filmed over a weekend in black and white with limited equipment and a small cast and crew.[12][20] Funded by Nolan and shot with the UCL Union Film society's equipment, it appeared at the Cambridge Film Festival in 1996 and is considered one of UCL's best shorts.[21] For unknown reasons, the film has since been removed from public view.[19] Nolan filmed a third short, Doodlebug (1997), about a man seemingly chasing an insect with his shoe, only to discover that it is a miniature of himself.[14][22] Nolan and Thomas first attempted to make a feature in the mid-1990s with Larry Mahoney, which they scrapped.[23] During this period in his career, Nolan had little to no success getting his projects off the ground, facing several rejections; he added, \"[T]here's a very limited pool of finance in the UK. Philosophy professor David Kyle Johnson wrote that \"Inception became a classic almost as soon as it was projected on silver screens\", praising its exploration of philosophical ideas, including leap of faith and allegory of the cave.[97] The film grossed over $836 million worldwide.[98] Nominated for eight Academy Awards—including Best Picture and Best Original Screenplay—it won Best Cinematography, Best Sound Mixing, Best Sound Editing and Best Visual Effects.[99] Nolan was nominated for a BAFTA Award and a Golden Globe Award for Best Director, among other accolades.[40]\nAround the release of The Dark Knight Rises (2012), Nolan's third and final Batman film, Joseph Bevan of the British Film Institute wrote a profile on him: \"In the space of just over a decade, Christopher Nolan has shot from promising British indie director to undisputed master of a new brand of intelligent escapism. He further wrote that Nolan's body of work reflect \"a heterogeneity of conditions of products\" extending from low-budget films to lucrative blockbusters, \"a wide range of genres and settings\" and \"a diversity of styles that trumpet his versatility\".[193]\nDavid Bordwell, a film theorist, wrote that Nolan has been able to blend his \"experimental impulses\" with the demands of mainstream entertainment, describing his oeuvre as \"experiments with cinematic time by means of techniques of subjective viewpoint and crosscutting\".[194] Nolan's use of practical, in-camera effects, miniatures and models, as well as shooting on celluloid film, has been highly influential in early 21st century cinema.[195][196] IndieWire wrote in 2019 that, Nolan \"kept a viable alternate model of big-budget filmmaking alive\", in an era where blockbuster filmmaking has become \"a largely computer-generated art form\".[196] Initially reluctant to make a sequel, he agreed after Warner Bros. repeatedly insisted.[78] Nolan wanted to expand on the noir quality of the first film by broadening the canvas and taking on \"the dynamic of a story of the city, a large crime story ... where you're looking at the police, the justice system, the vigilante, the poor people, the rich people, the criminals\".[79] Continuing to minimalise the use of CGI, Nolan employed high-resolution IMAX cameras, making it the first major motion picture to use this technology.[80][81]","score":0.93205,"raw_content":null},{"title":"Christopher Nolan | Biography, Movies, Batman, Oppenheimer, & Facts ...","url":"https://www.britannica.com/biography/Christopher-Nolan-British-director","content":"The sci-fi drama depicted the efforts of a group of scientists to relocate humanity from an Earth vitiated by war and famine to another planet by way of a wormhole. The film turns on this character’s attempt to move past the boundaries of the technology in order to actually plant an idea in a dreamer’s head. His 2023 film Oppenheimer, depicts J. Robert Oppenheimer’s role in the development of the atomic bomb and the later security hearing over his alleged ties to communism. It used a destabilizing reverse-order story line to mirror the fractured mental state of its protagonist, a man with short-term amnesia who is trying to track down the person who murdered his wife. The Dark Knight (2008) leaned even more heavily on the moral and structural decay of its setting, fictional Gotham City, and it revived such classic Batman villains as the Joker (played by Heath Ledger).","score":0.90859,"raw_content":null}]"
[tool/start] [1:tool:Calculator] Entering Tool run with input: "52 * 365"
[tool/end] [1:tool:Calculator] [2ms] Exiting Tool run with output: "18980"
{
input: 'Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?',
output: '<result>\nTherefore, Christopher Nolan is 18,980 days old.\n</result>'
}
MacBook-Pro-4:examples jacoblee$ yarn start examples/src/guides/debugging/simple_agent_verbose_some.ts
(node:78844) ExperimentalWarning: `--experimental-loader` may be removed in the future; instead use `register()`:
--import 'data:text/javascript,import { register } from "node:module"; import { pathToFileURL } from "node:url"; register("file%3A///Users/jacoblee/langchain/langchainjs/node_modules/tsx/dist/loader.js", pathToFileURL("./"));'
(Use `node --trace-warnings ...` to show where the warning was created)
[WARN]: You have enabled LangSmith tracing without backgrounding callbacks.
[WARN]: If you are not using a serverless environment where you must wait for tracing calls to finish,
[WARN]: we suggest setting "process.env.LANGCHAIN_CALLBACKS_BACKGROUND=true" to avoid additional latency.
[tool/start] [1:tool:TavilySearchResults] Entering Tool run with input: "Oppenheimer 2023 film director age"
[tool/end] [1:tool:TavilySearchResults] [2.63s] Exiting Tool run with output: "[{"title":"Oppenheimer (film) - Wikipedia","url":"https://en.wikipedia.org/wiki/Oppenheimer_(film)","content":"The film continued to hold well in the following weeks, making $32 million and $29.1 million in its fifth and sixth weekends.[174][175] As of September 10, 2023, the highest grossing territories were the United Kingdom ($72 million), Germany ($46.9 million), China ($46.8 million), France ($40.1 million) and Australia ($25.9 million).[176]\nCritical response\nThe film received critical acclaim.[a] Critics praised Oppenheimer primarily for its screenplay, the performances of the cast (particularly Murphy and Downey), and the visuals;[b] it was frequently cited as one of Nolan's best films,[191][192][183] and of 2023, although some criticism was aimed towards the writing of the female characters.[187] Hindustan Times reported that the film was also hailed as one of the best films of the 21st century.[193] He also chose to alternate between scenes in color and black-and-white to convey the story from both subjective and objective perspectives, respectively,[68] with most of Oppenheimer's view shown via the former, while the latter depicts a \"more objective view of his story from a different character's point of view\".[69][67] Wanting to make the film as subjective as possible, the production team decided to include visions of Oppenheimer's conceptions of the quantum world and waves of energy.[70] Nolan noted that Oppenheimer never publicly apologized for his role in the atomic bombings of Hiroshima and Nagasaki, but still desired to portray Oppenheimer as feeling genuine guilt for his actions, believing this to be accurate.[71]\nI think of any character I've dealt with, Oppenheimer is by far the most ambiguous and paradoxical. The production team was able to obtain government permission to film at White Sands Missile Range, but only at highly inconvenient hours, and therefore chose to film the scene elsewhere in the New Mexico desert.[2][95]\nThe production filmed the Trinity test scenes in Belen, New Mexico, with Murphy climbing a 100-foot steel tower, a replica of the original site used in the Manhattan Project, in rough weather.[2][95]\nA special set was built in which gasoline, propane, aluminum powder, and magnesium were used to create the explosive effect.[54] Although they used miniatures for the practical effect, the film's special effects supervisor Scott R. Fisher referred to them as \"big-atures\", since the special effects team had tried to build the models as physically large as possible. He felt that \"while our relationship with that [nuclear] fear has ebbed and flowed with time, the threat itself never actually went away\", and felt the 2022 Russian invasion of Ukraine had caused a resurgence of nuclear anxiety.[54] Nolan had also penned a script for a biopic of Howard Hughes approximately during the time of production of Martin Scorsese's The Aviator (2004), which had given him insight on how to write a script regarding a person's life.[53] Emily Blunt described the Oppenheimer script as \"emotional\" and resembling that of a thriller, while also remarking that Nolan had \"Trojan-Horsed a biopic into a thriller\".[72]\nCasting\nOppenheimer marks the sixth collaboration between Nolan and Murphy, and the first starring Murphy as the lead. [for Oppenheimer] in his approach to trying to deal with the consequences of what he'd been involved with\", while also underscoring that it is a \"huge shift in perception about the reality of Oppenheimer's perception\".[53] He wanted to execute a quick tonal shift after the atomic bombings of Hiroshima and Nagasaki, desiring to go from the \"highest triumphalism, the highest high, to the lowest low in the shortest amount of screen time possible\".[66] For the ending, Nolan chose to make it intentionally vague to be open to interpretation and refrained from being didactic or conveying specific messages in his work.","score":0.95617,"raw_content":null},{"title":"Oppenheimer (2023) - IMDb","url":"https://www.imdb.com/title/tt15398776/","content":"Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb.","score":0.95378,"raw_content":null},{"title":"'Oppenheimer' Review: A Man for Our Time - The New York Times","url":"https://www.nytimes.com/2023/07/19/movies/oppenheimer-review-christopher-nolan.html","content":"Instead, it is here that the film’s complexities and all its many fragments finally converge as Nolan puts the finishing touches on his portrait of a man who contributed to an age of transformational scientific discovery, who personified the intersection of science and politics, including in his role as a Communist boogeyman, who was transformed by his role in the creation of weapons of mass destruction and soon after raised the alarm about the dangers of nuclear war.\n He served as director of a clandestine weapons lab built in a near-desolate stretch of Los Alamos, in New Mexico, where he and many other of the era’s most dazzling scientific minds puzzled through how to harness nuclear reactions for the weapons that killed tens of thousands instantly, ending the war in the Pacific.\n Nolan integrates these black-and-white sections with the color ones, using scenes from the hearing and the confirmation — Strauss’s role in the hearing and his relationship with Oppenheimer directly affected the confirmation’s outcome — to create a dialectical synthesis. To signal his conceit, he stamps the film with the words “fission” (a splitting into parts) and “fusion” (a merging of elements); Nolan being Nolan, he further complicates the film by recurrently kinking up the overarching chronology — it is a lot.\n It’s also at Berkeley that Oppenheimer meets the project’s military head, Leslie Groves (a predictably good Damon), who makes him Los Alamos’s director, despite the leftist causes he supported — among them, the fight against fascism during the Spanish Civil War — and some of his associations, including with Communist Party members like his brother, Frank (Dylan Arnold).\n","score":0.92271,"raw_content":null},{"title":"Oppenheimer (2023) - Full Cast & Crew - IMDb","url":"https://www.imdb.com/title/tt15398776/fullcredits/","content":"Oppenheimer (2023) cast and crew credits, including actors, actresses, directors, writers and more. Menu. Movies. Release Calendar Top 250 Movies Most Popular Movies Browse Movies by Genre Top Box Office Showtimes & Tickets Movie News India Movie Spotlight. ... Peter Oppenheimer - Age 8 (uncredited) Adam Walker Federman ... MIT Student ...","score":0.91904,"raw_content":null},{"title":"Oppenheimer's Grandson Reacts to New Christopher Nolan Film | TIME","url":"https://time.com/6297743/oppenheimer-grandson-movie-interview/","content":"July 25, 2023 3:32 PM EDT. M oviegoers turned out in droves this weekend for writer-director Christopher Nolan's new film Oppenheimer, fueling an expectations-shattering domestic box office debut ...","score":0.91248,"raw_content":null}]"
[tool/start] [1:tool:Calculator] Entering Tool run with input: "(2023 - 1970) * 365"
[tool/end] [1:tool:Calculator] [2ms] Exiting Tool run with output: "19345"
```
</details>
```bash
{
input: 'Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?',
output: "So as of 2023, Christopher Nolan's age is approximately 19,345 days.\n" +
'\n' +
'In summary:\n' +
'- The 2023 film Oppenheimer was directed by Christopher Nolan\n' +
'- Nolan was born on July 30, 1970, making his current age around 53 years old\n' +
'- Converted to days, Nolan is approximately 19,345 days old as of 2023'
}
```
## Other callbacks
`Callbacks` are what we use to execute any functionality within a component outside the primary component logic.
All of the above solutions use `Callbacks` under the hood to log intermediate steps of components.
There are a number of `Callbacks` relevant for debugging that come with LangChain out of the box, like the [`ConsoleCallbackHandler`](https://api.js.langchain.com/classes/langchain_core.tracers_console.ConsoleCallbackHandler.html).
You can also implement your own callbacks to execute custom functionality.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/custom_chat.ipynb | import {
SimpleChatModel,
type BaseChatModelParams,
} from "@langchain/core/language_models/chat_models";
import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
import { AIMessageChunk, type BaseMessage } from "@langchain/core/messages";
import { ChatGenerationChunk } from "@langchain/core/outputs";
interface CustomChatModelInput extends BaseChatModelParams {
n: number;
}
class CustomChatModel extends SimpleChatModel {
n: number;
constructor(fields: CustomChatModelInput) {
super(fields);
this.n = fields.n;
}
_llmType() {
return "custom";
}
async _call(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun
): Promise<string> {
if (!messages.length) {
throw new Error("No messages provided.");
}
// Pass `runManager?.getChild()` when invoking internal runnables to enable tracing
// await subRunnable.invoke(params, runManager?.getChild());
if (typeof messages[0].content !== "string") {
throw new Error("Multimodal messages are not supported.");
}
return messages[0].content.slice(0, this.n);
}
async *_streamResponseChunks(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun
): AsyncGenerator<ChatGenerationChunk> {
if (!messages.length) {
throw new Error("No messages provided.");
}
if (typeof messages[0].content !== "string") {
throw new Error("Multimodal messages are not supported.");
}
// Pass `runManager?.getChild()` when invoking internal runnables to enable tracing
// await subRunnable.invoke(params, runManager?.getChild());
for (const letter of messages[0].content.slice(0, this.n)) {
yield new ChatGenerationChunk({
message: new AIMessageChunk({
content: letter,
}),
text: letter,
});
// Trigger the appropriate callback for new chunks
await runManager?.handleLLMNewToken(letter);
}
}
}const chatModel = new CustomChatModel({ n: 4 });
await chatModel.invoke([["human", "I am an LLM"]]);const stream = await chatModel.stream([["human", "I am an LLM"]]);
for await (const chunk of stream) {
console.log(chunk);
}import { AIMessage, BaseMessage } from "@langchain/core/messages";
import { ChatResult } from "@langchain/core/outputs";
import {
BaseChatModel,
BaseChatModelCallOptions,
BaseChatModelParams,
} from "@langchain/core/language_models/chat_models";
import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
interface AdvancedCustomChatModelOptions
extends BaseChatModelCallOptions {}
interface AdvancedCustomChatModelParams extends BaseChatModelParams {
n: number;
}
class AdvancedCustomChatModel extends BaseChatModel<AdvancedCustomChatModelOptions> {
n: number;
static lc_name(): string {
return "AdvancedCustomChatModel";
}
constructor(fields: AdvancedCustomChatModelParams) {
super(fields);
this.n = fields.n;
}
async _generate(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun
): Promise<ChatResult> {
if (!messages.length) {
throw new Error("No messages provided.");
}
if (typeof messages[0].content !== "string") {
throw new Error("Multimodal messages are not supported.");
}
// Pass `runManager?.getChild()` when invoking internal runnables to enable tracing
// await subRunnable.invoke(params, runManager?.getChild());
const content = messages[0].content.slice(0, this.n);
const tokenUsage = {
usedTokens: this.n,
};
return {
generations: [{ message: new AIMessage({ content }), text: content }],
llmOutput: { tokenUsage },
};
}
_llmType(): string {
return "advanced_custom_chat_model";
}
}const chatModel = new AdvancedCustomChatModel({ n: 4 });
const eventStream = await chatModel.streamEvents([["human", "I am an LLM"]], {
version: "v2",
});
for await (const event of eventStream) {
if (event.event === "on_chat_model_end") {
console.log(JSON.stringify(event, null, 2));
}
}import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
import { BaseChatModel, type BaseChatModelCallOptions, type BaseChatModelParams } from "@langchain/core/language_models/chat_models";
import { BaseMessage } from "@langchain/core/messages";
import { ChatResult } from "@langchain/core/outputs";
interface CustomChatModelOptions extends BaseChatModelCallOptions {
// Some required or optional inner args
tools: Record<string, any>[];
}
interface CustomChatModelParams extends BaseChatModelParams {
temperature: number;
n: number;
}
class CustomChatModel extends BaseChatModel<CustomChatModelOptions> {
temperature: number;
n: number;
static lc_name(): string {
return "CustomChatModel";
}
constructor(fields: CustomChatModelParams) {
super(fields);
this.temperature = fields.temperature;
this.n = fields.n;
}
// Anything returned in this method will be logged as metadata in the trace.
// It is common to pass it any options used to invoke the function.
invocationParams(options?: this["ParsedCallOptions"]) {
return {
tools: options?.tools,
n: this.n,
};
}
async _generate(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun
): Promise<ChatResult> {
if (!messages.length) {
throw new Error("No messages provided.");
}
if (typeof messages[0].content !== "string") {
throw new Error("Multimodal messages are not supported.");
}
const additionalParams = this.invocationParams(options);
const content = await someAPIRequest(messages, additionalParams);
return {
generations: [{ message: new AIMessage({ content }), text: content }],
llmOutput: {},
};
}
_llmType(): string {
return "advanced_custom_chat_model";
}
} |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/document_loader_custom.mdx | # How to write a custom document loader
If you want to implement your own Document Loader, you have a few options.
### Subclassing `BaseDocumentLoader`
You can extend the `BaseDocumentLoader` class directly. The `BaseDocumentLoader` class provides a few convenience methods for loading documents from a variety of sources.
```typescript
abstract class BaseDocumentLoader implements DocumentLoader {
abstract load(): Promise<Document[]>;
}
```
### Subclassing `TextLoader`
If you want to load documents from a text file, you can extend the `TextLoader` class. The `TextLoader` class takes care of reading the file, so all you have to do is implement a parse method.
```typescript
abstract class TextLoader extends BaseDocumentLoader {
abstract parse(raw: string): Promise<string[]>;
}
```
### Subclassing `BufferLoader`
If you want to load documents from a binary file, you can extend the `BufferLoader` class. The `BufferLoader` class takes care of reading the file, so all you have to do is implement a parse method.
```typescript
abstract class BufferLoader extends BaseDocumentLoader {
abstract parse(
raw: Buffer,
metadata: Document["metadata"]
): Promise<Document[]>;
}
```
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/vectorstores.mdx | ---
keywords: [similaritySearchWithScore]
---
# How to create and query vector stores
:::info
Head to [Integrations](/docs/integrations/vectorstores) for documentation on built-in integrations with vectorstore providers.
:::
:::info Prerequisites
This guide assumes familiarity with the following concepts:
- [Vector stores](/docs/concepts/#vectorstores)
- [Embeddings](/docs/concepts/embedding_models)
- [Document loaders](/docs/concepts/document_loaders)
:::
One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding
vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are
'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search
for you.
This walkthrough uses a basic, unoptimized implementation called [`MemoryVectorStore`](https://api.js.langchain.com/classes/langchain.vectorstores_memory.MemoryVectorStore.html) that stores embeddings in-memory and does an exact, linear search for the most similar embeddings.
LangChain contains many built-in integrations - see [this section](/docs/how_to/vectorstores/#which-one-to-pick) for more, or the [full list of integrations](/docs/integrations/vectorstores/).
## Creating a new index
Most of the time, you'll need to load and prepare the data you want to search over. Here's an example that loads a recent speech from a file:
import ExampleLoader from "@examples/indexes/vector_stores/memory_fromdocs.ts";
<CodeBlock language="typescript">{ExampleLoader}</CodeBlock>
Most of the time, you'll need to split the loaded text as a preparation step. See [this section](/docs/concepts/text_splitters) to learn more about text splitters.
## Creating a new index from texts
If you have already prepared the data you want to search over, you can initialize a vector store directly from text chunks:
import CodeBlock from "@theme/CodeBlock";
import ExampleTexts from "@examples/indexes/vector_stores/memory.ts";
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @langchain/core
```
<CodeBlock language="typescript">{ExampleTexts}</CodeBlock>
## Which one to pick?
Here's a quick guide to help you pick the right vector store for your use case:
- If you're after something that can just run inside your Node.js application, in-memory, without any other servers to stand up, then go for [HNSWLib](/docs/integrations/vectorstores/hnswlib), [Faiss](/docs/integrations/vectorstores/faiss), [LanceDB](/docs/integrations/vectorstores/lancedb) or [CloseVector](/docs/integrations/vectorstores/closevector)
- If you're looking for something that can run in-memory in browser-like environments, then go for [MemoryVectorStore](/docs/integrations/vectorstores/memory) or [CloseVector](/docs/integrations/vectorstores/closevector)
- If you come from Python and you were looking for something similar to FAISS, try [HNSWLib](/docs/integrations/vectorstores/hnswlib) or [Faiss](/docs/integrations/vectorstores/faiss)
- If you're looking for an open-source full-featured vector database that you can run locally in a docker container, then go for [Chroma](/docs/integrations/vectorstores/chroma)
- If you're looking for an open-source vector database that offers low-latency, local embedding of documents and supports apps on the edge, then go for [Zep](/docs/integrations/vectorstores/zep)
- If you're looking for an open-source production-ready vector database that you can run locally (in a docker container) or hosted in the cloud, then go for [Weaviate](/docs/integrations/vectorstores/weaviate).
- If you're using Supabase already then look at the [Supabase](/docs/integrations/vectorstores/supabase) vector store to use the same Postgres database for your embeddings too
- If you're looking for a production-ready vector store you don't have to worry about hosting yourself, then go for [Pinecone](/docs/integrations/vectorstores/pinecone)
- If you are already utilizing SingleStore, or if you find yourself in need of a distributed, high-performance database, you might want to consider the [SingleStore](/docs/integrations/vectorstores/singlestore) vector store.
- If you are looking for an online MPP (Massively Parallel Processing) data warehousing service, you might want to consider the [AnalyticDB](/docs/integrations/vectorstores/analyticdb) vector store.
- If you're in search of a cost-effective vector database that allows run vector search with SQL, look no further than [MyScale](/docs/integrations/vectorstores/myscale).
- If you're in search of a vector database that you can load from both the browser and server side, check out [CloseVector](/docs/integrations/vectorstores/closevector). It's a vector database that aims to be cross-platform.
- If you're looking for a scalable, open-source columnar database with excellent performance for analytical queries, then consider [ClickHouse](/docs/integrations/vectorstores/clickhouse).
## Next steps
You've now learned how to load data into a vectorstore.
Next, check out the [full tutorial on retrieval-augmented generation](/docs/tutorials/rag).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/tool_calling.ipynb | import { tool } from "@langchain/core/tools";
import { z } from "zod";
/**
* Note that the descriptions here are crucial, as they will be passed along
* to the model along with the class name.
*/
const calculatorSchema = z.object({
operation: z
.enum(["add", "subtract", "multiply", "divide"])
.describe("The type of operation to execute."),
number1: z.number().describe("The first number to operate on."),
number2: z.number().describe("The second number to operate on."),
});
const calculatorTool = tool(async ({ operation, number1, number2 }) => {
// Functions must return strings
if (operation === "add") {
return `${number1 + number2}`;
} else if (operation === "subtract") {
return `${number1 - number2}`;
} else if (operation === "multiply") {
return `${number1 * number2}`;
} else if (operation === "divide") {
return `${number1 / number2}`;
} else {
throw new Error("Invalid operation.");
}
}, {
name: "calculator",
description: "Can perform mathematical operations.",
schema: calculatorSchema,
});
const llmWithTools = llm.bindTools([calculatorTool]);const res = await llmWithTools.invoke("What is 3 * 12");
console.log(res);const res = await llmWithTools.invoke("What is 3 * 12? Also, what is 11 + 49?");
res.tool_calls;import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({ model: "gpt-4o" });
const modelWithTools = model.bind({
tools: [{
"type": "function",
"function": {
"name": "calculator",
"description": "Can perform mathematical operations.",
"parameters": {
"type": "object",
"properties": {
"operation": {
"type": "string",
"description": "The type of operation to execute.",
"enum": ["add", "subtract", "multiply", "divide"]
},
"number1": {"type": "number", "description": "First integer"},
"number2": {"type": "number", "description": "Second integer"},
},
"required": ["number1", "number2"],
},
},
}],
});
await modelWithTools.invoke(`Whats 119 times 8?`); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/chat_streaming.ipynb | // @lc-docs-hide-cell
import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({
model: "claude-3-5-sonnet-20240620",
});const stream = await model.stream("Write me a 1 verse song about goldfish on the moon")
for await (const chunk of stream) {
console.log(`${chunk.content}\n---`);
}const eventStream = await model.streamEvents(
"Write me a 1 verse song about goldfish on the moon",
{
version: "v2"
},
);
const events = [];
for await (const event of eventStream) {
events.push(event);
}
events.slice(0, 3); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/embed_text.mdx | ---
sidebar_position: 2
---
# How to embed text data
:::info
Head to [Integrations](/docs/integrations/text_embedding) for documentation on built-in integrations with text embedding providers.
:::
:::info Prerequisites
This guide assumes familiarity with the following concepts:
- [Embeddings](/docs/concepts/embedding_models)
:::
Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.
The base Embeddings class in LangChain exposes two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).
## Get started
Below is an example of how to use the OpenAI embeddings. Embeddings occasionally have different embedding methods for queries versus documents, so the embedding class exposes a `embedQuery` and `embedDocuments` method.
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @langchain/core
```
## Get started
```typescript
import { OpenAIEmbeddings } from "@langchain/openai";
const embeddings = new OpenAIEmbeddings();
```
## Embed queries
```typescript
const res = await embeddings.embedQuery("Hello world");
/*
[
-0.004845875, 0.004899438, -0.016358767, -0.024475135, -0.017341806,
0.012571548, -0.019156644, 0.009036391, -0.010227379, -0.026945334,
0.022861943, 0.010321903, -0.023479493, -0.0066544134, 0.007977734,
0.0026371893, 0.025206111, -0.012048521, 0.012943339, 0.013094575,
-0.010580265, -0.003509951, 0.004070787, 0.008639394, -0.020631202,
... 1511 more items
]
*/
```
## Embed documents
```typescript
const documentRes = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
/*
[
[
-0.004845875, 0.004899438, -0.016358767, -0.024475135, -0.017341806,
0.012571548, -0.019156644, 0.009036391, -0.010227379, -0.026945334,
0.022861943, 0.010321903, -0.023479493, -0.0066544134, 0.007977734,
0.0026371893, 0.025206111, -0.012048521, 0.012943339, 0.013094575,
-0.010580265, -0.003509951, 0.004070787, 0.008639394, -0.020631202,
... 1511 more items
]
[
-0.009446913, -0.013253193, 0.013174579, 0.0057552797, -0.038993083,
0.0077763423, -0.0260478, -0.0114384955, -0.0022683728, -0.016509168,
0.041797023, 0.01787183, 0.00552271, -0.0049789557, 0.018146982,
-0.01542166, 0.033752076, 0.006112323, 0.023872782, -0.016535373,
-0.006623321, 0.016116094, -0.0061090477, -0.0044155475, -0.016627092,
... 1511 more items
]
]
*/
```
## Next steps
You've now learned how to use embeddings models with queries and text.
Next, check out how to [avoid excessively recomputing embeddings with caching](/docs/how_to/caching_embeddings), or the [full tutorial on retrieval-augmented generation](/docs/tutorials/rag).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/fallbacks.mdx | import CodeBlock from "@theme/CodeBlock";
# Fallbacks
:::info Prerequisites
This guide assumes familiarity with the following concepts:
- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)
- [Chaining runnables](/docs/how_to/sequence/)
:::
When working with language models, you may encounter issues from the underlying APIs, e.g. rate limits or downtime.
As you move your LLM applications into production it becomes more and more important to have contingencies for errors.
That's why we've introduced the concept of fallbacks.
Crucially, fallbacks can be applied not only on the LLM level but on the whole runnable level.
This is important because often times different models require different prompts. So if your call to OpenAI fails, you don't just want to send the same prompt to Anthropic - you probably want want to use e.g. a different prompt template.
## Handling LLM API errors
This is maybe the most common use case for fallbacks. A request to an LLM API can fail for a variety of reasons - the API could be down,
you could have hit a rate limit, or any number of things.
**IMPORTANT:** By default, many of LangChain's LLM wrappers catch errors and retry.
You will most likely want to turn those off when working with fallbacks. Otherwise the first wrapper will keep on retrying rather than failing.
import ModelExample from "@examples/guides/fallbacks/model.ts";
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/anthropic @langchain/openai @langchain/core
```
<CodeBlock language="typescript">{ModelExample}</CodeBlock>
## Fallbacks for RunnableSequences
We can also create fallbacks for sequences, that are sequences themselves.
Here we do that with two different models: ChatOpenAI and then normal OpenAI (which does not use a chat model).
Because OpenAI is NOT a chat model, you likely want a different prompt.
import ChainExample from "@examples/guides/fallbacks/chain.ts";
<CodeBlock language="typescript">{ChainExample}</CodeBlock>
## Handling long inputs
One of the big limiting factors of LLMs in their context window.
Sometimes you can count and track the length of prompts before sending them to an LLM,
but in situations where that is hard/complicated you can fallback to a model with longer context length.
import LongInputExample from "@examples/guides/fallbacks/long_inputs.ts";
<CodeBlock language="typescript">{LongInputExample}</CodeBlock>
## Fallback to a better model
Often times we ask models to output format in a specific format (like JSON). Models like GPT-3.5 can do this okay, but sometimes struggle.
This naturally points to fallbacks - we can try with a faster and cheaper model, but then if parsing fails we can use GPT-4.
import BetterModelExample from "@examples/guides/fallbacks/better_model.ts";
<CodeBlock language="typescript">{BetterModelExample}</CodeBlock>
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/character_text_splitter.ipynb | import { CharacterTextSplitter } from "@langchain/textsplitters";
import * as fs from "node:fs";
// Load an example document
const rawData = await fs.readFileSync("../../../../examples/state_of_the_union.txt");
const stateOfTheUnion = rawData.toString();
const textSplitter = new CharacterTextSplitter({
separator: "\n\n",
chunkSize: 1000,
chunkOverlap: 200,
});
const texts = await textSplitter.createDocuments([stateOfTheUnion]);
console.log(texts[0])const metadatas = [{ document: 1 }, { document: 2 }];
const documents = await textSplitter.createDocuments(
[stateOfTheUnion, stateOfTheUnion], metadatas
)
console.log(documents[0])const chunks = await textSplitter.splitText(stateOfTheUnion);
chunks[0]; |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/multi_vector.mdx | # How to generate multiple embeddings per document
:::info Prerequisites
This guide assumes familiarity with the following concepts:
- [Retrievers](/docs/concepts/retrievers)
- [Text splitters](/docs/concepts/text_splitters)
- [Retrieval-augmented generation (RAG)](/docs/tutorials/rag)
:::
Embedding different representations of an original document, then returning the original document when any of the representations result in a search hit, can allow you to
tune and improve your retrieval performance. LangChain has a base [`MultiVectorRetriever`](https://api.js.langchain.com/classes/langchain.retrievers_multi_vector.MultiVectorRetriever.html) designed to do just this!
A lot of the complexity lies in how to create the multiple vectors per document.
This guide covers some of the common ways to create those vectors and use the `MultiVectorRetriever`.
Some methods to create multiple vectors per document include:
- smaller chunks: split a document into smaller chunks, and embed those (e.g. the [`ParentDocumentRetriever`](/docs/how_to/parent_document_retriever))
- summary: create a summary for each document, embed that along with (or instead of) the document
- hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document
Note that this also enables another method of adding embeddings - manually. This is great because you can explicitly add questions or queries that should lead to a document being recovered, giving you more control.
## Smaller chunks
Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks.
This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream.
NOTE: this is what the ParentDocumentRetriever does. Here we show what is going on under the hood.
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @langchain/community @langchain/core
```
import CodeBlock from "@theme/CodeBlock";
import SmallChunksExample from "@examples/retrievers/multi_vector_small_chunks.ts";
<CodeBlock language="typescript">{SmallChunksExample}</CodeBlock>
## Summary
Oftentimes a summary may be able to distill more accurately what a chunk is about, leading to better retrieval.
Here we show how to create summaries, and then embed those.
import SummaryExample from "@examples/retrievers/multi_vector_summary.ts";
<CodeBlock language="typescript">{SummaryExample}</CodeBlock>
## Hypothetical queries
An LLM can also be used to generate a list of hypothetical questions that could be asked of a particular document.
These questions can then be embedded and used to retrieve the original document:
import HypotheticalExample from "@examples/retrievers/multi_vector_hypothetical.ts";
<CodeBlock language="typescript">{HypotheticalExample}</CodeBlock>
## Next steps
You've now learned a few ways to generate multiple embeddings per document.
Next, check out the individual sections for deeper dives on specific retrievers, the [broader tutorial on RAG](/docs/tutorials/rag), or this section to learn how to
[create your own custom retriever over any data source](/docs/how_to/custom_retriever/).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/qa_sources.ipynb | import "cheerio";
import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory"
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { pull } from "langchain/hub";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { formatDocumentsAsString } from "langchain/util/document";
import { RunnableSequence, RunnablePassthrough } from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
const loader = new CheerioWebBaseLoader(
"https://lilianweng.github.io/posts/2023-06-23-agent/"
);
const docs = await loader.load();
const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 200 });
const splits = await textSplitter.splitDocuments(docs);
const vectorStore = await MemoryVectorStore.fromDocuments(splits, new OpenAIEmbeddings());
// Retrieve and generate using the relevant snippets of the blog.
const retriever = vectorStore.asRetriever();
const prompt = await pull<ChatPromptTemplate>("rlm/rag-prompt");
const llm = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
const ragChain = RunnableSequence.from([
{
context: retriever.pipe(formatDocumentsAsString),
question: new RunnablePassthrough(),
},
prompt,
llm,
new StringOutputParser()
]);console.log(prompt.promptMessages.map((msg) => msg.prompt.template).join("\n"));await ragChain.invoke("What is task decomposition?")import {
RunnableMap,
RunnablePassthrough,
RunnableSequence
} from "@langchain/core/runnables";
import { formatDocumentsAsString } from "langchain/util/document";
const ragChainWithSources = RunnableMap.from({
// Return raw documents here for now since we want to return them at
// the end - we'll format in the next step of the chain
context: retriever,
question: new RunnablePassthrough(),
}).assign({
answer: RunnableSequence.from([
(input) => {
return {
// Now we format the documents as strings for the prompt
context: formatDocumentsAsString(input.context),
question: input.question
};
},
prompt,
llm,
new StringOutputParser()
]),
})
await ragChainWithSources.invoke("What is Task Decomposition") |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/tool_artifacts.ipynb | import { z } from "zod";
import { tool } from "@langchain/core/tools";
const randomIntToolSchema = z.object({
min: z.number(),
max: z.number(),
size: z.number(),
});
const generateRandomInts = tool(async ({ min, max, size }) => {
const array: number[] = [];
for (let i = 0; i < size; i++) {
array.push(Math.floor(Math.random() * (max - min + 1)) + min);
}
return [
`Successfully generated array of ${size} random ints in [${min}, ${max}].`,
array,
];
}, {
name: "generateRandomInts",
description: "Generate size random ints in the range [min, max].",
schema: randomIntToolSchema,
responseFormat: "content_and_artifact",
});await generateRandomInts.invoke({min: 0, max: 9, size: 10});await generateRandomInts.invoke(
{
name: "generate_random_ints",
args: {min: 0, max: 9, size: 10},
id: "123", // Required
type: "tool_call", // Required
}
);const llmWithTools = llm.bindTools([generateRandomInts])
const aiMessage = await llmWithTools.invoke("generate 6 positive ints less than 25")
aiMessage.tool_callsawait generateRandomInts.invoke(aiMessage.tool_calls[0])await generateRandomInts.invoke(aiMessage.tool_calls[0]["args"])const extractToolCalls = (aiMessage) => aiMessage.tool_calls;
const chain = llmWithTools.pipe(extractToolCalls).pipe(generateRandomInts.map());
await chain.invoke("give me a random number between 1 and 5"); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/query_constructing_filters.ipynb | import { z } from "zod";
const searchSchema = z.object({
query: z.string(),
startYear: z.number().optional(),
author: z.string().optional(),
})
const searchQuery: z.infer<typeof searchSchema> = {
query: "RAG",
startYear: 2022,
author: "LangChain"
}import { Comparison, Comparator } from "langchain/chains/query_constructor/ir";
function constructComparisons(query: z.infer<typeof searchSchema>): Comparison[] {
const comparisons: Comparison[] = [];
if (query.startYear !== undefined) {
comparisons.push(
new Comparison(
"gt" as Comparator,
"start_year",
query.startYear,
)
);
}
if (query.author !== undefined) {
comparisons.push(
new Comparison(
"eq" as Comparator,
"author",
query.author,
)
);
}
return comparisons;
}
const comparisons = constructComparisons(searchQuery);import {
Operation,
Operator,
} from "langchain/chains/query_constructor/ir";
const _filter = new Operation("and" as Operator, comparisons)import { ChromaTranslator } from "@langchain/community/structured_query/chroma";
new ChromaTranslator().visitOperation(_filter) |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/assign.ipynb | import { RunnableParallel, RunnablePassthrough } from "@langchain/core/runnables";
const runnable = RunnableParallel.from({
extra: RunnablePassthrough.assign({
mult: (input: { num: number }) => input.num * 3,
modified: (input: { num: number }) => input.num + 1
})
});
await runnable.invoke({ num: 1 });import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
const vectorstore = await MemoryVectorStore.fromDocuments([
{ pageContent: "harrison worked at kensho", metadata: {} }
], new OpenAIEmbeddings());
const retriever = vectorstore.asRetriever();
const template = `Answer the question based only on the following context:
{context}
Question: {question}
`;
const prompt = ChatPromptTemplate.fromTemplate(template);
const model = new ChatOpenAI({ model: "gpt-4o" });
const generationChain = prompt.pipe(model).pipe(new StringOutputParser());
const retrievalChain = RunnableSequence.from([
{
context: retriever.pipe((docs) => docs[0].pageContent),
question: new RunnablePassthrough()
},
RunnablePassthrough.assign({ output: generationChain }),
]);
const stream = await retrievalChain.stream("where did harrison work?");
for await (const chunk of stream) {
console.log(chunk);
} |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/passthrough.ipynb | import { RunnableParallel, RunnablePassthrough } from "@langchain/core/runnables";
const runnable = RunnableParallel.from({
passed: new RunnablePassthrough<{ num: number }>(),
modified: (input: { num: number }) => input.num + 1,
});
await runnable.invoke({ num: 1 });import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
const vectorstore = await MemoryVectorStore.fromDocuments([
{ pageContent: "harrison worked at kensho", metadata: {} }
], new OpenAIEmbeddings());
const retriever = vectorstore.asRetriever();
const template = `Answer the question based only on the following context:
{context}
Question: {question}
`;
const prompt = ChatPromptTemplate.fromTemplate(template);
const model = new ChatOpenAI({ model: "gpt-4o" });
const retrievalChain = RunnableSequence.from([
{
context: retriever.pipe((docs) => docs[0].pageContent),
question: new RunnablePassthrough()
},
prompt,
model,
new StringOutputParser(),
]);
await retrievalChain.invoke("where did harrison work?"); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/binding.ipynb | import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI } from "@langchain/openai";
const prompt = ChatPromptTemplate.fromMessages(
[
[
"system",
"Write out the following equation using algebraic symbols then solve it. Use the format\n\nEQUATION:...\nSOLUTION:...\n\n",
],
["human", "{equation_statement}"],
]
)
const model = new ChatOpenAI({ temperature: 0 });
const runnable = prompt.pipe(model).pipe(new StringOutputParser());
const res = await runnable.invoke({
equation_statement: "x raised to the third plus seven equals 12"
});
console.log(res);const runnableWithStop = prompt
.pipe(model.bind({ stop: ["SOLUTION"] }))
.pipe(new StringOutputParser());
const shorterResponse = await runnableWithStop.invoke({
equation_statement: "x raised to the third plus seven equals 12"
});
console.log(shorterResponse);const tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
},
}
];
const modelWithTools = new ChatOpenAI({ model: "gpt-4o" }).bind({ tools });
await modelWithTools.invoke("What's the weather in SF, NYC and LA?") |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/extraction_parse.ipynb | import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({
model: "claude-3-sonnet-20240229",
temperature: 0,
})import { z } from "zod";
import { StructuredOutputParser } from "langchain/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
let personSchema = z.object({
name: z.optional(z.string()).describe("The name of the person"),
hair_color: z.optional(z.string()).describe("The color of the person's hair, if known"),
height_in_meters: z.optional(z.string()).describe("Height measured in meters")
}).describe("Information about a person.");
const parser = StructuredOutputParser.fromZodSchema(personSchema);
const prompt = ChatPromptTemplate.fromMessages([
["system", "Answer the user query. Wrap the output in `json` tags\n{format_instructions}"],
["human", "{query}"],
]);
const partialedPrompt = await prompt.partial({
format_instructions: parser.getFormatInstructions(),
});const query = "Anna is 23 years old and she is 6 feet tall";const promptValue = await partialedPrompt.invoke({ query });
console.log(promptValue.toChatMessages());const chain = partialedPrompt.pipe(model).pipe(parser);
await chain.invoke({ query });import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";
personSchema = z.object({
name: z.optional(z.string()).describe("The name of the person"),
hair_color: z.optional(z.string()).describe("The color of the person's hair, if known"),
height_in_meters: z.optional(z.string()).describe("Height measured in meters")
}).describe("Information about a person.");
const peopleSchema = z.object({
people: z.array(personSchema),
});
const SYSTEM_PROMPT_TEMPLATE = [
"Answer the user's query. You must return your answer as JSON that matches the given schema:",
"```json\n{schema}\n```.",
"Make sure to wrap the answer in ```json and ``` tags. Conform to the given schema exactly.",
].join("\n");
const customParsingPrompt = ChatPromptTemplate.fromMessages([
["system", SYSTEM_PROMPT_TEMPLATE],
["human", "{query}"],
]);
const extractJsonFromOutput = (message) => {
const text = message.content;
// Define the regular expression pattern to match JSON blocks
const pattern = /```json\s*((.|\n)*?)\s*```/gs;
// Find all non-overlapping matches of the pattern in the string
const matches = pattern.exec(text);
if (matches && matches[1]) {
try {
return JSON.parse(matches[1].trim());
} catch (error) {
throw new Error(`Failed to parse: ${matches[1]}`);
}
} else {
throw new Error(`No JSON found in: ${message}`);
}
}const customParsingQuery = "Anna is 23 years old and she is 6 feet tall";
const customParsingPromptValue = await customParsingPrompt.invoke({
schema: zodToJsonSchema(peopleSchema),
customParsingQuery
});
customParsingPromptValue.toString();const customParsingChain = prompt.pipe(model).pipe(extractJsonFromOutput);
await customParsingChain.invoke({
schema: zodToJsonSchema(peopleSchema),
customParsingQuery,
}); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/custom_retriever.mdx | # How to write a custom retriever class
:::info Prerequisites
This guide assumes familiarity with the following concepts:
- [Retrievers](/docs/concepts/retrievers)
:::
To create your own retriever, you need to extend the [`BaseRetriever`](https://api.js.langchain.com/classes/langchain_core.retrievers.BaseRetriever.html) class
and implement a `_getRelevantDocuments` method that takes a `string` as its first parameter (and an optional `runManager` for tracing).
This method should return an array of `Document`s fetched from some source. This process can involve calls to a database, to the web using `fetch`, or any other source.
Note the underscore before `_getRelevantDocuments()`. The base class wraps the non-prefixed version in order to automatically handle tracing of the original call.
Here's an example of a custom retriever that returns static documents:
```ts
import {
BaseRetriever,
type BaseRetrieverInput,
} from "@langchain/core/retrievers";
import type { CallbackManagerForRetrieverRun } from "@langchain/core/callbacks/manager";
import { Document } from "@langchain/core/documents";
export interface CustomRetrieverInput extends BaseRetrieverInput {}
export class CustomRetriever extends BaseRetriever {
lc_namespace = ["langchain", "retrievers"];
constructor(fields?: CustomRetrieverInput) {
super(fields);
}
async _getRelevantDocuments(
query: string,
runManager?: CallbackManagerForRetrieverRun
): Promise<Document[]> {
// Pass `runManager?.getChild()` when invoking internal runnables to enable tracing
// const additionalDocs = await someOtherRunnable.invoke(params, runManager?.getChild());
return [
// ...additionalDocs,
new Document({
pageContent: `Some document pertaining to ${query}`,
metadata: {},
}),
new Document({
pageContent: `Some other document pertaining to ${query}`,
metadata: {},
}),
];
}
}
```
Then, you can call `.invoke()` as follows:
```ts
const retriever = new CustomRetriever({});
await retriever.invoke("LangChain docs");
```
```
[
Document {
pageContent: 'Some document pertaining to LangChain docs',
metadata: {}
},
Document {
pageContent: 'Some other document pertaining to LangChain docs',
metadata: {}
}
]
```
## Next steps
You've now seen an example of implementing your own custom retriever.
Next, check out the individual sections for deeper dives on specific retrievers, or the [broader tutorial on RAG](/docs/tutorials/rag).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/custom_tools.ipynb | import { z } from "zod";
import { StructuredToolParams } from "@langchain/core/tools";
const simpleToolSchema: StructuredToolParams = {
name: "get_current_weather",
description: "Get the current weather for a location",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
state: z.string().optional().describe("The state to get the weather for"),
})
}import { z } from "zod";
import { tool } from "@langchain/core/tools";
const adderSchema = z.object({
a: z.number(),
b: z.number(),
});
const adderTool = tool(async (input): Promise<string> => {
const sum = input.a + input.b;
return `The sum of ${input.a} and ${input.b} is ${sum}`;
}, {
name: "adder",
description: "Adds two numbers together",
schema: adderSchema,
});
await adderTool.invoke({ a: 1, b: 2 });import { DynamicStructuredTool } from "@langchain/core/tools";
import { z } from "zod";
const multiplyTool = new DynamicStructuredTool({
name: "multiply",
description: "multiply two numbers together",
schema: z.object({
a: z.number().describe("the first number to multiply"),
b: z.number().describe("the second number to multiply"),
}),
func: async ({ a, b }: { a: number; b: number; }) => {
return (a * b).toString();
},
});
await multiplyTool.invoke({ a: 8, b: 9, });import { DynamicTool } from "@langchain/core/tools";
const searchTool = new DynamicTool({
name: "search",
description: "look things up online",
func: async (_input: string) => {
return "LangChain";
},
});
await searchTool.invoke("foo");import { z } from "zod";
import { tool } from "@langchain/core/tools";
const randomIntToolSchema = z.object({
min: z.number(),
max: z.number(),
size: z.number(),
});
const generateRandomInts = tool(async ({ min, max, size }) => {
const array: number[] = [];
for (let i = 0; i < size; i++) {
array.push(Math.floor(Math.random() * (max - min + 1)) + min);
}
return [
`Successfully generated array of ${size} random ints in [${min}, ${max}].`,
array,
];
}, {
name: "generateRandomInts",
description: "Generate size random ints in the range [min, max].",
schema: randomIntToolSchema,
responseFormat: "content_and_artifact",
});await generateRandomInts.invoke({ min: 0, max: 9, size: 10 });await generateRandomInts.invoke({
name: "generateRandomInts",
args: { min: 0, max: 9, size: 10 },
id: "123", // required
type: "tool_call",
}); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/generative_ui.mdx | # How to build an LLM generated UI
This guide will walk through some high level concepts and code snippets for building generative UI's using LangChain.js. To see the full code for generative UI, [click here to visit our official LangChain Next.js template](https://github.com/langchain-ai/langchain-nextjs-template/blob/main/app/generative_ui/README.md).
The sample implements a tool calling agent, which outputs an interactive UI element when streaming intermediate outputs of tool calls to the client.
We introduce two utilities which wraps the AI SDK to make it easier to yield React elements inside runnables and tool calls: [`createRunnableUI`](https://github.com/langchain-ai/langchain-nextjs-template/blob/7f764d558682214d50b064f4293667123a31e6fe/app/generative_ui/utils/server.tsx#L89)
and [`streamRunnableUI`](https://github.com/langchain-ai/langchain-nextjs-template/blob/7f764d558682214d50b064f4293667123a31e6fe/app/generative_ui/utils/server.tsx#L126).
- The `streamRunnableUI` executes the provided Runnable with `streamEvents` method and sends every `stream` event to the client via the React Server Components stream.
- The `createRunnableUI` wraps the `createStreamableUI` function from AI SDK to properly hook into the Runnable event stream.
The usage is then as follows:
```tsx ai/chain.tsx
"use server";
const tool = tool(
async (input, config) => {
const stream = await createRunnableUI(config);
stream.update(<div>Searching...</div>);
const result = await images(input);
stream.done(
<Images
images={result.images_results
.map((image) => image.thumbnail)
.slice(0, input.limit)}
/>
);
return `[Returned ${result.images_results.length} images]`;
},
{
name: "Images",
description: "A tool to search for images. input should be a search query.",
schema: z.object({
query: z.string().describe("The search query used to search for cats"),
limit: z.number().describe("The number of pictures shown to the user"),
}),
}
);
// add LLM, prompt, etc...
const tools = [tool];
export const agentExecutor = new AgentExecutor({
agent: createToolCallingAgent({ llm, tools, prompt }),
tools,
});
```
:::tip
As of `langchain` version `0.2.8`, the `createToolCallingAgent` function now supports [OpenAI-formatted tools](https://api.js.langchain.com/interfaces/langchain_core.language_models_base.ToolDefinition.html).
:::
```tsx agent.tsx
async function agent(inputs: {
input: string;
chat_history: [role: string, content: string][];
}) {
"use server";
return streamRunnableUI(agentExecutor, {
input: inputs.input,
chat_history: inputs.chat_history.map(
([role, content]) => new ChatMessage(content, role)
),
});
}
export const EndpointsContext = exposeEndpoints({ agent });
```
In order to ensure all of the client components are included in the bundle, we need to wrap all of the Server Actions into `exposeEndpoints` method. These endpoints will be accessible from the client via the Context API, seen in the `useActions` hook.
```tsx
"use client";
import type { EndpointsContext } from "./agent";
export default function Page() {
const actions = useActions<typeof EndpointsContext>();
const [node, setNode] = useState();
return (
<div>
{node}
<button
onClick={async () => {
setNode(await actions.agent({ input: "cats" }));
}}
>
Get images of cats
</button>
</div>
);
}
```
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/how_to/document_loader_pdf.mdx | # How to load PDF files
> [Portable Document Format (PDF)](https://en.wikipedia.org/wiki/PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.
This covers how to load `PDF` documents into the Document format that we use downstream.
By default, one document will be created for each page in the PDF file. You can change this behavior by setting the `splitPages` option to `false`.
## Setup
```bash npm2yarn
npm install pdf-parse
```
## Usage, one document per page
```typescript
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
// Or, in web environments:
// import { WebPDFLoader } from "@langchain/community/document_loaders/web/pdf";
// const blob = new Blob(); // e.g. from a file input
// const loader = new WebPDFLoader(blob);
const loader = new PDFLoader("src/document_loaders/example_data/example.pdf");
const docs = await loader.load();
```
## Usage, one document per file
```typescript
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
const loader = new PDFLoader("src/document_loaders/example_data/example.pdf", {
splitPages: false,
});
const docs = await loader.load();
```
## Usage, custom `pdfjs` build
By default we use the `pdfjs` build bundled with `pdf-parse`, which is compatible with most environments, including Node.js and modern browsers. If you want to use a more recent version of `pdfjs-dist` or if you want to use a custom build of `pdfjs-dist`, you can do so by providing a custom `pdfjs` function that returns a promise that resolves to the `PDFJS` object.
In the following example we use the "legacy" (see [pdfjs docs](https://github.com/mozilla/pdf.js/wiki/Frequently-Asked-Questions#which-browsersenvironments-are-supported)) build of `pdfjs-dist`, which includes several polyfills not included in the default build.
```bash npm2yarn
npm install pdfjs-dist
```
```typescript
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
const loader = new PDFLoader("src/document_loaders/example_data/example.pdf", {
// you may need to add `.then(m => m.default)` to the end of the import
pdfjs: () => import("pdfjs-dist/legacy/build/pdf.js"),
});
```
## Eliminating extra spaces
PDFs come in many varieties, which makes reading them a challenge. The loader parses individual text elements and joins them together with a space by default, but
if you are seeing excessive spaces, this may not be the desired behavior. In that case, you can override the separator with an empty string like this:
```typescript
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
const loader = new PDFLoader("src/document_loaders/example_data/example.pdf", {
parsedItemSeparator: "",
});
const docs = await loader.load();
```
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/key_value_stores.mdx | # Key-value stores
## Overview
LangChain provides a key-value store interface for storing and retrieving data.
LangChain includes a [`BaseStore`](https://api.js.langchain.com/classes/_langchain_core.stores.BaseStore.html) interface,
which allows for storage of arbitrary data. However, LangChain components that require KV-storage accept a
more specific `BaseStore<string, Uint8Array>` instance that stores binary data (referred to as a `ByteStore`), and internally take care of
encoding and decoding data for their specific needs.
This means that as a user, you only need to think about one type of store rather than different ones for different types of data.
## Usage
The key-value store interface in LangChain is used primarily for:
1. Caching [embeddings](/docs/concepts/embedding_models) via [CachedBackedEmbeddings](https://api.js.langchain.com/classes/langchain.embeddings_cache_backed.CacheBackedEmbeddings.html) to avoid recomputing embeddings for repeated queries or when re-indexing content.
2. As a simple [Document](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html) persistence layer in some retrievers.
Please see these how-to guides for more information:
- [How to cache embeddings guide](/docs/how_to/caching_embeddings/).
- [How to retriever using multiple vectors per document](/docs/how_to/custom_retriever/).
## Interface
All [`BaseStore`](https://api.js.langchain.com/classes/_langchain_core.stores.BaseStore.html)s support the following interface. Note that the interface allows for modifying **multiple** key-value pairs at once:
- `mget(keys: string[]): Promise<(Uint8Array | undefined)[]>`: get the contents of multiple keys, returning `undefined` if the key does not exist
- `mset(keyValuePairs: [string, Uint8Array][]): Promise<void>`: set the contents of multiple keys
- `mdelete(keys: string[]): Promise<void>`: delete multiple keys
- `yieldKeys(prefix?: string): AsyncIterator<string>`: yield all keys in the store, optionally filtering by a prefix
## Integrations
Please reference the [stores integration page](/docs/integrations/stores/) for a list of available key-value store integrations.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/chat_models.mdx | # Chat models
## Overview
Large Language Models (LLMs) are advanced machine learning models that excel in a wide range of language-related tasks such as text generation, translation, summarization, question answering, and more, without needing task-specific tuning for every scenario.
Modern LLMs are typically accessed through a chat model interface that takes a list of [messages](/docs/concepts/messages) as input and returns a [message](/docs/concepts/messages) as output.
The newest generation of chat models offer additional capabilities:
- [Tool calling](/docs/concepts/tool_calling): Many popular chat models offer a native [tool calling](/docs/concepts/tool_calling) API. This API allows developers to build rich applications that enable AI to interact with external services, APIs, and databases. Tool calling can also be used to extract structured information from unstructured data and perform various other tasks.
- [Structured output](/docs/concepts/structured_outputs): A technique to make a chat model respond in a structured format, such as JSON that matches a given schema.
- [Multimodality](/docs/concepts/multimodality): The ability to work with data other than text; for example, images, audio, and video.
## Features
LangChain provides a consistent interface for working with chat models from different providers while offering additional features for monitoring, debugging, and optimizing the performance of applications that use LLMs.
- Integrations with many chat model providers (e.g., Anthropic, OpenAI, Ollama, Microsoft Azure, Google Vertex, Amazon Bedrock, Hugging Face, Cohere, Groq). Please see [chat model integrations](/docs/integrations/chat/) for an up-to-date list of supported models.
- Use either LangChain's [messages](/docs/concepts/messages) format or OpenAI format.
- Standard [tool calling API](/docs/concepts/tool_calling): standard interface for binding tools to models, accessing tool call requests made by models, and sending tool results back to the model.
- Standard API for structuring outputs (/docs/concepts/structured_outputs) via the `withStructuredOutput` method.
- Integration with [LangSmith](https://docs.smith.langchain.com) for monitoring and debugging production-grade applications based on LLMs.
- Additional features like standardized [token usage](/docs/concepts/messages#token_usage), [rate limiting](#rate-limiting), [caching](#cache) and more.
## Integrations
LangChain has many chat model integrations that allow you to use a wide variety of models from different providers.
These integrations are one of two types:
1. **Official models**: These are models that are officially supported by LangChain and/or model provider. You can find these models in the `@langchain/<provider>` packages.
2. **Community models**: There are models that are mostly contributed and supported by the community. You can find these models in the `@langchain/community` package.
LangChain chat models are named with a convention that prefixes "Chat" to their class names (e.g., `ChatOllama`, `ChatAnthropic`, `ChatOpenAI`, etc.).
Please review the [chat model integrations](/docs/integrations/chat/) for a list of supported models.
:::note
Models that do **not** include the prefix "Chat" in their name or include "LLM" as a suffix in their name typically refer to older models that do not follow the chat model interface and instead use an interface that takes a string as input and returns a string as output.
:::
## Interface
LangChain chat models implement the [BaseChatModel](https://api.js.langchain.com/classes/_langchain_core.language_models_chat_models.BaseChatModel.html) interface. Because BaseChatModel also implements the [Runnable Interface](/docs/concepts/runnables), chat models support a [standard streaming interface](/docs/concepts/streaming), optimized [batching](/docs/concepts/runnables#batch), and more. Please see the [Runnable Interface](/docs/concepts/runnables) for more details.
Many of the key methods of chat models operate on [messages](/docs/concepts/messages) as input and return messages as output.
Chat models offer a standard set of parameters that can be used to configure the model. These parameters are typically used to control the behavior of the model, such as the temperature of the output, the maximum number of tokens in the response, and the maximum time to wait for a response. Please see the [standard parameters](#standard-parameters) section for more details.
:::note
In documentation, we will often use the terms "LLM" and "Chat Model" interchangeably. This is because most modern LLMs are exposed to users via a chat model interface.
However, LangChain also has implementations of older LLMs that do not follow the chat model interface and instead use an interface that takes a string as input and returns a string as output. These models are typically named without the "Chat" prefix (e.g., `Ollama`, `Anthropic`, `OpenAI`, etc.).
These models implement the [BaseLLM](https://api.js.langchain.com/classes/_langchain_core.language_models_llms.BaseLLM.html) interface and may be named with the "LLM" suffix (e.g., `OpenAILLM`, etc.). Generally, users should not use these models.
:::
### Key methods
The key methods of a chat model are:
1. **invoke**: The primary method for interacting with a chat model. It takes a list of [messages](/docs/concepts/messages) as input and returns a list of messages as output.
2. **stream**: A method that allows you to stream the output of a chat model as it is generated.
3. **batch**: A method that allows you to batch multiple requests to a chat model together for more efficient processing.
4. **bindTools**: A method that allows you to bind a tool to a chat model for use in the model's execution context.
5. **withStructuredOutput**: A wrapper around the `invoke` method for models that natively support [structured output](/docs/concepts/structured_outputs).
Other important methods can be found in the [BaseChatModel API Reference](https://api.js.langchain.com/classes/_langchain_core.language_models_chat_models.BaseChatModel.html).
### Inputs and outputs
Modern LLMs are typically accessed through a chat model interface that takes [messages](/docs/concepts/messages) as input and returns [messages](/docs/concepts/messages) as output. Messages are typically associated with a role (e.g., "system", "human", "assistant") and one or more content blocks that contain text or potentially multimodal data (e.g., images, audio, video).
LangChain supports two message formats to interact with chat models:
1. **LangChain Message Format**: LangChain's own message format, which is used by default and is used internally by LangChain.
2. **OpenAI's Message Format**: OpenAI's message format.
### Standard parameters
Many chat models have standardized parameters that can be used to configure the model:
| Parameter | Description |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | The name or identifier of the specific AI model you want to use (e.g., `"gpt-3.5-turbo"` or `"gpt-4"`). |
| `temperature` | Controls the randomness of the model's output. A higher value (e.g., 1.0) makes responses more creative, while a lower value (e.g., 0.1) makes them more deterministic and focused. |
| `timeout` | The maximum time (in seconds) to wait for a response from the model before canceling the request. Ensures the request doesn’t hang indefinitely. |
| `maxTokens` | Limits the total number of tokens (words and punctuation) in the response. This controls how long the output can be. |
| `stop` | Specifies stop sequences that indicate when the model should stop generating tokens. For example, you might use specific strings to signal the end of a response. |
| `maxRetries` | The maximum number of attempts the system will make to resend a request if it fails due to issues like network timeouts or rate limits. |
| `apiKey` | The API key required for authenticating with the model provider. This is usually issued when you sign up for access to the model. |
| `baseUrl` | The URL of the API endpoint where requests are sent. This is typically provided by the model's provider and is necessary for directing your requests. |
Some important things to note:
- Standard parameters only apply to model providers that expose parameters with the intended functionality. For example, some providers do not expose a configuration for maximum output tokens, so max_tokens can't be supported on these.
- Standard params are currently only enforced on integrations that have their own integration packages (e.g. `@langchain/openai`, `@langchain/anthropic`, etc.), they're not enforced on models in `@langchain/community`.
ChatModels also accept other parameters that are specific to that integration. To find all the parameters supported by a ChatModel head to the [API reference](https://api.js.langchain.com/) for that model.
## Tool calling
Chat models can call [tools](/docs/concepts/tools) to perform tasks such as fetching data from a database, making API requests, or running custom code. Please
see the [tool calling](/docs/concepts/tool_calling) guide for more information.
## Structured outputs
Chat models can be requested to respond in a particular format (e.g., JSON or matching a particular schema). This feature is extremely
useful for information extraction tasks. Please read more about
the technique in the [structured outputs](/docs/concepts/structured_outputs) guide.
## Multimodality
Large Language Models (LLMs) are not limited to processing text. They can also be used to process other types of data, such as images, audio, and video. This is known as [multimodality](/docs/concepts/multimodality).
Currently, only some LLMs support multimodal inputs, and almost none support multimodal outputs. Please consult the specific model documentation for details.
## Context window
A chat model's context window refers to the maximum size of the input sequence the model can process at one time. While the context windows of modern LLMs are quite large, they still present a limitation that developers must keep in mind when working with chat models.
If the input exceeds the context window, the model may not be able to process the entire input and could raise an error. In conversational applications, this is especially important because the context window determines how much information the model can "remember" throughout a conversation. Developers often need to manage the input within the context window to maintain a coherent dialogue without exceeding the limit. For more details on handling memory in conversations, refer to the [memory](https://langchain-ai.github.io/langgraphjs/concepts/memory/).
The size of the input is measured in [tokens](/docs/concepts/tokens) which are the unit of processing that the model uses.
## Advanced topics
### Caching
Chat model APIs can be slow, so a natural question is whether to cache the results of previous conversations. Theoretically, caching can help improve performance by reducing the number of requests made to the model provider. In practice, caching chat model responses is a complex problem and should be approached with caution.
The reason is that getting a cache hit is unlikely after the first or second interaction in a conversation if relying on caching the **exact** inputs into the model. For example, how likely do you think that multiple conversations start with the exact same message? What about the exact same three messages?
An alternative approach is to use semantic caching, where you cache responses based on the meaning of the input rather than the exact input itself. This can be effective in some situations, but not in others.
A semantic cache introduces a dependency on another model on the critical path of your application (e.g., the semantic cache may rely on an [embedding model](/docs/concepts/embedding_models) to convert text to a vector representation), and it's not guaranteed to capture the meaning of the input accurately.
However, there might be situations where caching chat model responses is beneficial. For example, if you have a chat model that is used to answer frequently asked questions, caching responses can help reduce the load on the model provider and improve response times.
Please see the [how to cache chat model responses](/docs/how_to/#chat-model-caching) guide for more details.
## Related resources
- How-to guides on using chat models: [how-to guides](/docs/how_to/#chat-models).
- List of supported chat models: [chat model integrations](/docs/integrations/chat/).
### Conceptual guides
- [Messages](/docs/concepts/messages)
- [Tool calling](/docs/concepts/tool_calling)
- [Multimodality](/docs/concepts/multimodality)
- [Structured outputs](/docs/concepts/structured_outputs)
- [Tokens](/docs/concepts/tokens)
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/chat_history.mdx | # Chat history
:::info Prerequisites
- [Messages](/docs/concepts/messages)
- [Chat models](/docs/concepts/chat_models)
- [Tool calling](/docs/concepts/tool_calling)
:::
Chat history is a record of the conversation between the user and the chat model. It is used to maintain context and state throughout the conversation. The chat history is sequence of [messages](/docs/concepts/messages), each of which is associated with a specific [role](/docs/concepts/messages#role), such as "user", "assistant", "system", or "tool".
## Conversation patterns
Most conversations start with a **system message** that sets the context for the conversation. This is followed by a **user message** containing the user's input, and then an **assistant message** containing the model's response.
The **assistant** may respond directly to the user or if configured with tools request that a [tool](/docs/concepts/tool_calling) be invoked to perform a specific task.
So a full conversation often involves a combination of two patterns of alternating messages:
1. The **user** and the **assistant** representing a back-and-forth conversation.
2. The **assistant** and **tool messages** representing an ["agentic" workflow](/docs/concepts/agents) where the assistant is invoking tools to perform specific tasks.
## Managing chat history
Since chat models have a maximum limit on input size, it's important to manage chat history and trim it as needed to avoid exceeding the [context window](/docs/concepts/chat_models#context_window).
While processing chat history, it's essential to preserve a correct conversation structure.
Key guidelines for managing chat history:
- The conversation should follow one of these structures:
- The first message is either a "user" message or a "system" message, followed by a "user" and then an "assistant" message.
- The last message should be either a "user" message or a "tool" message containing the result of a tool call.
- When using [tool calling](/docs/concepts/tool_calling), a "tool" message should only follow an "assistant" message that requested the tool invocation.
:::tip
Understanding correct conversation structure is essential for being able to properly implement
[memory](https://langchain-ai.github.io/langgraphjs/concepts/memory/) in chat models.
:::
## Related resources
- [How to trim messages](/docs/how_to/trim_messages/)
- [Memory guide](https://langchain-ai.github.io/langgraphjs/concepts/memory/) for information on implementing short-term and long-term memory in chat models using [LangGraph](https://langchain-ai.github.io/langgraphjs/).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/retrievers.mdx | # Retrievers
<span data-heading-keywords="retriever,retrievers"></span>
:::info[Prerequisites]
- [Vector stores](/docs/concepts/vectorstores/)
- [Embeddings](/docs/concepts/embedding_models/)
- [Text splitters](/docs/concepts/text_splitters/)
:::
## Overview
Many different types of retrieval systems exist, including vectorstores, graph databases, and relational databases.
With the rise on popularity of large language models, retrieval systems have become an important component in AI application (e.g., [RAG](/docs/concepts/rag/)).
Because of their importance and variability, LangChain provides a uniform interface for interacting with different types of retrieval systems.
The LangChain [retriever](/docs/concepts/retrievers/) interface is straightforward:
1. Input: A query (string)
2. Output: A list of documents (standardized LangChain [Document](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html) objects)
## Key concept
All retrievers implement a simple interface for retrieving documents using natural language queries.
## Interface
The only requirement for a retriever is the ability to accepts a query and return documents.
In particular, [LangChain's retriever class](https://api.js.langchain.com/classes/_langchain_core.retrievers.BaseRetriever.html) only requires that the `_getRelevantDocuments` method is implemented, which takes a `query: string` and returns a list of [Document](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html) objects that are most relevant to the query.
The underlying logic used to get relevant documents is specified by the retriever and can be whatever is most useful for the application.
A LangChain retriever is a [runnable](/docs/how_to/lcel_cheatsheet/), which is a standard interface is for LangChain components.
This means that it has a few common methods, including `invoke`, that are used to interact with it. A retriever can be invoked with a query:
```typescript
const docs = await retriever.invoke(query);
```
Retrievers return a list of [Document](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html) objects, which have two attributes:
- `pageContent`: The content of this document. Currently is a string.
- `metadata`: Arbitrary metadata associated with this document (e.g., document id, file name, source, etc).
:::info[Further reading]
- See our [how-to guide](/docs/how_to/custom_retriever/) on building your own custom retriever.
:::
## Common types
Despite the flexibility of the retriever interface, a few common types of retrieval systems are frequently used.
### Search apis
It's important to note that retrievers don't need to actually _store_ documents.
For example, we can be built retrievers on top of search APIs that simply return search results!
### Relational or graph database
Retrievers can be built on top of relational or graph databases.
In these cases, [query analysis](/docs/concepts/retrieval/) techniques to construct a structured query from natural language is critical.
For example, you can build a retriever for a SQL database using text-to-SQL conversion. This allows a natural language query (string) retriever to be transformed into a SQL query behind the scenes.
:::info[Further reading]
- See our [tutorial](/docs/tutorials/sql_qa/) for context on how to build a retreiver using a SQL database and text-to-SQL.
- See our [tutorial](/docs/tutorials/graph/) for context on how to build a retreiver using a graph database and text-to-Cypher.
:::
### Lexical search
As discussed in our conceptual review of [retrieval](/docs/concepts/retrieval/), many search engines are based upon matching words in a query to the words in each document.
[BM25](https://en.wikipedia.org/wiki/Okapi_BM25#:~:text=BM25%20is%20a%20bag%2Dof,slightly%20different%20components%20and%20parameters.) and [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) are [two popular lexical search algorithms](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2).
LangChain has retrievers for many popular lexical search algorithms / engines.
:::info[Further reading]
- See the [BM25](/docs/integrations/retrievers/bm25/) retriever integration.
:::
### Vector store
[Vector stores](/docs/concepts/vectorstores/) are a powerful and efficient way to index and retrieve unstructured data.
An vectorstore can be used as a retriever by calling the `asRetriever()` method.
```typescript
const vectorstore = new MyVectorStore();
const retriever = vectorstore.asRetriever();
```
## Advanced retrieval patterns
### Ensemble
Because the retriever interface is so simple, returning a list of `Document` objects given a search query, it is possible to combine multiple retrievers using ensembling.
This is particularly useful when you have multiple retrievers that are good at finding different types of relevant documents.
It is easy to create an [ensemble retriever](/docs/how_to/ensemble_retriever/) that combines multiple retrievers with linear weighted scores:
```typescript
// Initialize the ensemble retriever
const ensembleRetriever = new EnsembleRetriever({
retrievers: [bm25Retriever, vectorStoreRetriever],
weights: [0.5, 0.5],
});
```
When ensembling, how do we combine search results from many retrievers?
This motivates the concept of re-ranking, which takes the output of multiple retrievers and combines them using a more sophisticated algorithm such as [Reciprocal Rank Fusion (RRF)](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf).
### Source document retention
Many retrievers utilize some kind of index to make documents easily searchable.
The process of indexing can include a transformation step (e.g., vectorstores often use document splitting).
Whatever transformation is used, can be very useful to retain a link between the _transformed document_ and the original, giving the retriever the ability to return the _original_ document.
This is particularly useful in AI applications, because it ensures no loss in document context for the model.
For example, you may use small chunk size for indexing documents in a vectorstore.
If you return _only_ the chunks as the retrieval result, then the model will have lost the original document context for the chunks.
LangChain has two different retrievers that can be used to address this challenge.
The [Multi-Vector](/docs/how_to/multi_vector/) retriever allows the user to use any document transformation (e.g., use an LLM to write a summary of the document) for indexing while retaining linkage to the source document.
The [ParentDocument](/docs/how_to/parent_document_retriever/) retriever links document chunks from a text-splitter transformation for indexing while retaining linkage to the source document.
| Name | Index Type | Uses an LLM | When to Use | Description |
| --------------------------------------------------------- | ----------------------------- | ------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [ParentDocument](/docs/how_to/parent_document_retriever/) | Vector store + Document Store | No | If your pages have lots of smaller pieces of distinct information that are best indexed by themselves, but best retrieved all together. | This involves indexing multiple chunks for each document. Then you find the chunks that are most similar in embedding space, but you retrieve the whole parent document and return that (rather than individual chunks). |
| [Multi Vector](/docs/how_to/multi_vector/) | Vector store + Document Store | Sometimes during indexing | If you are able to extract information from documents that you think is more relevant to index than the text itself. | This involves creating multiple vectors for each document. Each vector could be created in a myriad of ways - examples include summaries of the text and hypothetical questions. |
:::info[Further reading]
- See our [how-to guide](/docs/how_to/parent_document_retriever/) on using the ParentDocument retriever.
- See our [how-to guide](/docs/how_to/multi_vector/) on using the MultiVector retriever.
- See our RAG from Scratch video on the [multi vector retriever](https://youtu.be/gTCU9I6QqCE?feature=shared).
:::
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/text_llms.mdx | # String-in, string-out llms
:::tip
You are probably looking for the [Chat Model Concept Guide](/docs/concepts/chat_models) page for more information.
:::
LangChain has implementations for older language models that take a string as input and return a string as output. These models are typically named without the "Chat" prefix (e.g., `Ollama`, `Anthropic`, `OpenAI`, etc.), and may include the "LLM" suffix (e.g., `OpenAILLM`, etc.). These models implement the [`BaseLLM`](https://api.js.langchain.com/classes/_langchain_core.language_models_llms.BaseLLM.html) interface.
Users should be using almost exclusively the newer [Chat Models](/docs/concepts/chat_models) as most
model providers have adopted a chat like interface for interacting with language models.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/tokens.mdx | # Tokens
Modern large language models (LLMs) are typically based on a transformer architecture that processes a sequence of units known as tokens. Tokens are the fundamental elements that models use to break down input and generate output. In this section, we'll discuss what tokens are and how they are used by language models.
## What is a token?
A **token** is the basic unit that a language model reads, processes, and generates. These units can vary based on how the model provider defines them, but in general, they could represent:
- A whole word (e.g., "apple"),
- A part of a word (e.g., "app"),
- Or other linguistic components such as punctuation or spaces.
The way the model tokenizes the input depends on its **tokenizer algorithm**, which converts the input into tokens. Similarly, the model’s output comes as a stream of tokens, which is then decoded back into human-readable text.
## How tokens work in language models
The reason language models use tokens is tied to how they understand and predict language. Rather than processing characters or entire sentences directly, language models focus on **tokens**, which represent meaningful linguistic units. Here's how the process works:
1. **Input Tokenization**: When you provide a model with a prompt (e.g., "LangChain is cool!"), the tokenizer algorithm splits the text into tokens. For example, the sentence could be tokenized into parts like `["Lang", "Chain", " is", " cool", "!"]`. Note that token boundaries don’t always align with word boundaries.
2. **Processing**: The transformer architecture behind these models processes tokens sequentially to predict the next token in a sentence. It does this by analyzing the relationships between tokens, capturing context and meaning from the input.
3. **Output Generation**: The model generates new tokens one by one. These output tokens are then decoded back into human-readable text.
Using tokens instead of raw characters allows the model to focus on linguistically meaningful units, which helps it capture grammar, structure, and context more effectively.
## Tokens don’t have to be text
Although tokens are most commonly used to represent text, they don’t have to be limited to textual data. Tokens can also serve as abstract representations of **multi-modal data**, such as:
- **Images**,
- **Audio**,
- **Video**,
- And other types of data.
At the time of writing, virtually no models support **multi-modal output**, and only a few models can handle **multi-modal inputs** (e.g., text combined with images or audio). However, as advancements in AI continue, we expect **multi-modality** to become much more common. This would allow models to process and generate a broader range of media, significantly expanding the scope of what tokens can represent and how models can interact with diverse types of data.
:::note
In principle, **anything that can be represented as a sequence of tokens** could be modeled in a similar way. For example, **DNA sequences**—which are composed of a series of nucleotides (A, T, C, G)—can be tokenized and modeled to capture patterns, make predictions, or generate sequences. This flexibility allows transformer-based models to handle diverse types of sequential data, further broadening their potential applications across various domains, including bioinformatics, signal processing, and other fields that involve structured or unstructured sequences.
:::
Please see the [multimodality](/docs/concepts/multimodality) section for more information on multi-modal inputs and outputs.
## Why not use characters?
Using tokens instead of individual characters makes models both more efficient and better at understanding context and grammar. Tokens represent meaningful units, like whole words or parts of words, allowing models to capture language structure more effectively than by processing raw characters. Token-level processing also reduces the number of units the model has to handle, leading to faster computation.
In contrast, character-level processing would require handling a much larger sequence of input, making it harder for the model to learn relationships and context. Tokens enable models to focus on linguistic meaning, making them more accurate and efficient in generating responses.
## How tokens correspond to text
Please see this post from [OpenAI](https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them) for more details on how tokens are counted and how they correspond to text.
According to the OpenAI post, the approximate token counts for English text are as follows:
- 1 token ~= 4 chars in English
- 1 token ~= ¾ words
- 100 tokens ~= 75 words
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/why_langchain.mdx | # Why LangChain?
The goal of the `langchain` package and LangChain the company is to make it as easy possible for developers to build applications that reason.
While LangChain originally started as a single open source package, it has evolved into a company and a whole ecosystem.
This page will talk about the LangChain ecosystem as a whole.
Most of the components within in the LangChain ecosystem can be used by themselves - so if you feel particularly drawn to certain components but not others, that is totally fine! Pick and choose whichever components you like best.
## Features
There are several primary needs that LangChain aims to address:
1. **Standardized component interfaces:** The growing number of [models](/docs/integrations/chat/) and [related components](/docs/integrations/vectorstores/) for AI applications has resulted in a wide variety of different APIs that developers need to learn and use.
This diversity can make it challenging for developers to switch between providers or combine components when building applications.
LangChain exposes a standard interface for key components, making it easy to switch between providers.
2. **Orchestration:** As applications become more complex, combining multiple components and models, there's [a growing need to efficiently connect these elements into control flows](https://lilianweng.github.io/posts/2023-06-23-agent/) that can [accomplish diverse tasks](https://www.sequoiacap.com/article/generative-ais-act-o1/).
[Orchestration](<https://en.wikipedia.org/wiki/Orchestration_(computing)>) is crucial for building such applications.
3. **Observability and evaluation:** As applications become more complex, it becomes increasingly difficult to understand what is happening within them.
Furthermore, the pace of development can become rate-limited by the [paradox of choice](https://en.wikipedia.org/wiki/Paradox_of_choice):
for example, developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost.
[Observability](https://en.wikipedia.org/wiki/Observability) and evaluations can help developers monitor their applications and rapidly answer these types of questions with confidence.
## Standardized component interfaces
LangChain provides common interfaces for components that are central to many AI applications.
As an example, all [chat models](/docs/concepts/chat_models/) implement the [BaseChatModel](https://api.js.langchain.com/classes/_langchain_core.language_models_chat_models.BaseChatModel.html) interface.
This provides a standard way to interact with chat models, supporting important but often provider-specific features like [tool calling](/docs/concepts/tool_calling/) and [structured outputs](/docs/concepts/structured_outputs/).
### Example: chat models
Many [model providers](/docs/concepts/chat_models/) support [tool calling](/docs/concepts/tool_calling/), a critical features for many applications (e.g., [agents](https://langchain-ai.github.io/langgraphjs/concepts/agentic_concepts/)), that allows a developer to request model responses that match a particular schema.
The APIs for each provider differ.
LangChain's [chat model](/docs/concepts/chat_models/) interface provides a common way to bind [tools](/docs/concepts/tools) to a model in order to support [tool calling](/docs/concepts/tool_calling/):
```typescript
// Tool creation
const tools = [myTool];
// Tool binding
const modelWithTools = model.bindTools(tools);
```
Similarly, getting models to produce [structured outputs](/docs/concepts/structured_outputs/) is an extremely common use case.
Providers support different approaches for this, including [JSON mode or tool calling](https://platform.openai.com/docs/guides/structured-outputs), with different APIs.
LangChain's [chat model](/docs/concepts/chat_models/) interface provides a common way to produce structured outputs using the `withStructuredOutput()` method:
```typescript
// Define tool as a Zod schema
const schema = z.object({ ... });
// Bind schema to model
const modelWithStructure = model.withStructuredOutput(schema)
```
### Example: retrievers
In the context of [RAG](/docs/concepts/rag/) and LLM application components, LangChain's [retriever](/docs/concepts/retrievers/) interface provides a standard way to connect to many different types of data services or databases (e.g., [vector stores](/docs/concepts/vectorstores) or databases).
The underlying implementation of the retriever depends on the type of data store or database you are connecting to, but all retrievers implement the [runnable interface](/docs/concepts/runnables/), meaning they can be invoked in a common manner.
```typescript
const documents = await myRetriever.invoke("What is the meaning of life?");
```
```text
[
Document({
pageContent: "The meaning of life is 42.",
metadata: { ... },
}),
Document({
pageContent: "The meaning of life is to use LangChain.",
metadata: { ... },
}),
...
]
```
## Orchestration
While standardization for individual components is useful, we've increasingly seen that developers want to _combine_ components into more complex applications.
This motivates the need for [orchestration](<https://en.wikipedia.org/wiki/Orchestration_(computing)>).
There are several common characteristics of LLM applications that this orchestration layer should support:
- **Complex control flow:** The application requires complex patterns such as cycles (e.g., a loop that reiterates until a condition is met).
- **[Persistence](https://langchain-ai.github.io/langgraphjs/concepts/persistence/):** The application needs to maintain [short-term and / or long-term memory](https://langchain-ai.github.io/langgraphjs/concepts/memory/).
- **[Human-in-the-loop](https://langchain-ai.github.io/langgraphjs/concepts/human_in_the_loop/):** The application needs human interaction, e.g., pausing, reviewing, editing, approving certain steps.
The recommended way to do orchestration for these complex applications is [LangGraph](https://langchain-ai.github.io/langgraphjs/concepts/high_level/).
LangGraph is a library that gives developers a high degree of control by expressing the flow of the application as a set of nodes and edges.
LangGraph comes with built-in support for [persistence](https://langchain-ai.github.io/langgraphjs/concepts/persistence/), [human-in-the-loop](https://langchain-ai.github.io/langgraphjs/concepts/human_in_the_loop/), [memory](https://langchain-ai.github.io/langgraphjs/concepts/memory/), and other features.
It's particularly well suited for building [agents](https://langchain-ai.github.io/langgraphjs/concepts/agentic_concepts/) or [multi-agent](https://langchain-ai.github.io/langgraphjs/concepts/multi_agent/) applications.
Importantly, individual LangChain components can be used within LangGraph nodes, but you can also use LangGraph **without** using LangChain components.
:::info[Further reading]
Have a look at our free course, [Introduction to LangGraph](https://academy.langchain.com/courses/intro-to-langgraph), to learn more about how to use LangGraph to build complex applications.
:::
## Observability and evaluation
The pace of AI application development is often rate-limited by high-quality evaluations because there is a paradox of choice.
Developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost.
High quality tracing and evaluations can help you rapidly answer these types of questions with confidence.
[LangSmith](https://docs.smith.langchain.com/) is our platform that supports observability and evaluation for AI applications.
See our conceptual guides on [evaluations](https://docs.smith.langchain.com/concepts/evaluation) and [tracing](https://docs.smith.langchain.com/concepts/tracing) for more details.
:::info[Further reading]
See our video playlist on [LangSmith tracing and evaluations](https://youtube.com/playlist?list=PLfaIDFEXuae0um8Fj0V4dHG37fGFU8Q5S&feature=shared) for more details.
:::
## Conclusion
LangChain offers standard interfaces for components that are central to many AI applications, which offers a few specific advantages:
- **Ease of swapping providers:** It allows you to swap out different component providers without having to change the underlying code.
- **Advanced features:** It provides common methods for more advanced features, such as [streaming](/docs/concepts/runnables/#streaming) and [tool calling](/docs/concepts/tool_calling/).
[LangGraph](https://langchain-ai.github.io/langgraphjs/concepts/high_level/) makes it possible to orchestrate complex applications (e.g., [agents](/docs/concepts/agents/)) and provide features like including [persistence](https://langchain-ai.github.io/langgraphjs/concepts/persistence/), [human-in-the-loop](https://langchain-ai.github.io/langgraphjs/concepts/human_in_the_loop/), or [memory](https://langchain-ai.github.io/langgraphjs/concepts/memory/).
[LangSmith](https://docs.smith.langchain.com/) makes it possible to iterate with confidence on your applications, by providing LLM-specific observability and framework for testing and evaluating your application.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/lcel.mdx | # LangChain Expression Language (LCEL)
:::info Prerequisites
- [Runnable Interface](/docs/concepts/runnables)
:::
The **L**ang**C**hain **E**xpression **L**anguage (LCEL) takes a [declarative](https://en.wikipedia.org/wiki/Declarative_programming) approach to building new [Runnables](/docs/concepts/runnables) from existing Runnables.
This means that you describe what you want to happen, rather than how you want it to happen, allowing LangChain to optimize the run-time execution of the chains.
We often refer to a `Runnable` created using LCEL as a "chain". It's important to remember that a "chain" is `Runnable` and it implements the full [Runnable Interface](/docs/concepts/runnables).
:::note
- The [LCEL cheatsheet](/docs/how_to/lcel_cheatsheet/) shows common patterns that involve the Runnable interface and LCEL expressions.
- Please see the following list of [how-to guides](/docs/how_to/#langchain-expression-language-lcel) that cover common tasks with LCEL.
- A list of built-in `Runnables` can be found in the [LangChain Core API Reference](https://api.js.langchain.com/modules/_langchain_core.runnables.html). Many of these Runnables are useful when composing custom "chains" in LangChain using LCEL.
:::
## Benefits of LCEL
LangChain optimizes the run-time execution of chains built with LCEL in a number of ways:
- **Optimize parallel execution**: Run Runnables in parallel using [RunnableParallel](#RunnableParallel) or run multiple inputs through a given chain in parallel using the [Runnable Batch API](/docs/concepts/runnables#batch). Parallel execution can significantly reduce the latency as processing can be done in parallel instead of sequentially.
- **Simplify streaming**: LCEL chains can be streamed, allowing for incremental output as the chain is executed. LangChain can optimize the streaming of the output to minimize the time-to-first-token(time elapsed until the first chunk of output from a [chat model](/docs/concepts/chat_models) or [llm](/docs/concepts/text_llms) comes out).
Other benefits include:
- [**Seamless LangSmith tracing**](https://docs.smith.langchain.com)
As your chains get more and more complex, it becomes increasingly important to understand what exactly is happening at every step.
With LCEL, **all** steps are automatically logged to [LangSmith](https://docs.smith.langchain.com/) for maximum observability and debuggability.
- **Standard API**: Because all chains are built using the Runnable interface, they can be used in the same way as any other Runnable.
- [**Deployable with LangServe**](/docs/concepts/architecture#langserve): Chains built with LCEL can be deployed using for production use.
## Should I use LCEL?
LCEL is an [orchestration solution](<https://en.wikipedia.org/wiki/Orchestration_(computing)>) -- it allows LangChain to handle run-time execution of chains in an optimized way.
While we have seen users run chains with hundreds of steps in production, we generally recommend using LCEL for simpler orchestration tasks. When the application requires complex state management, branching, cycles or multiple agents, we recommend that users take advantage of [LangGraph](/docs/concepts/architecture#langgraph).
In LangGraph, users define graphs that specify the flow of the application. This allows users to keep using LCEL within individual nodes when LCEL is needed, while making it easy to define complex orchestration logic that is more readable and maintainable.
Here are some guidelines:
- If you are making a single LLM call, you don't need LCEL; instead call the underlying [chat model](/docs/concepts/chat_models) directly.
- If you have a simple chain (e.g., prompt + llm + parser, simple retrieval set up etc.), LCEL is a reasonable fit, if you're taking advantage of the LCEL benefits.
- If you're building a complex chain (e.g., with branching, cycles, multiple agents, etc.) use [LangGraph](/docs/concepts/architecture#langgraph) instead. Remember that you can always use LCEL within individual nodes in LangGraph.
## Composition Primitives
`LCEL` chains are built by composing existing `Runnables` together. The two main composition primitives are [RunnableSequence](https://api.js.langchain.com/classes/_langchain_core.runnables.RunnableSequence.html) and [RunnableParallel](https://api.js.langchain.com/classes/_langchain_core.runnables.RunnableParallel.html).
Many other composition primitives (e.g., [RunnableAssign](https://api.js.langchain.com/classes/_langchain_core.runnables.RunnableAssign.html)) can be thought of as variations of these two primitives.
:::note
You can find a list of all composition primitives in the [LangChain Core API Reference](https://api.js.langchain.com/modules/_langchain_core.runnables.html).
:::
### RunnableSequence
`RunnableSequence` is a composition primitive that allows you "chain" multiple runnables sequentially, with the output of one runnable serving as the input to the next.
```typescript
import { RunnableSequence } from "@langchain/core/runnables";
const chain = new RunnableSequence({
first: runnable1,
// Optional, use if you have more than two runnables
// middle: [...],
last: runnable2,
});
```
Invoking the `chain` with some input:
```typescript
const finalOutput = await chain.invoke(someInput);
```
corresponds to the following:
```typescript
const output1 = await runnable1.invoke(someInput);
const finalOutput = await runnable2.invoke(output1);
```
:::note
`runnable1` and `runnable2` are placeholders for any `Runnable` that you want to chain together.
:::
### RunnableParallel
`RunnableParallel` is a composition primitive that allows you to run multiple runnables concurrently, with the same input provided to each.
```typescript
import { RunnableParallel } from "@langchain/core/runnables";
const chain = new RunnableParallel({
key1: runnable1,
key2: runnable2,
});
```
Invoking the `chain` with some input:
```typescript
const finalOutput = await chain.invoke(someInput);
```
Will yield a `finalOutput` object with the same keys as the input object, but with the values replaced by the output of the corresponding runnable.
```typescript
{
key1: await runnable1.invoke(someInput),
key2: await runnable2.invoke(someInput),
}
```
Recall, that the runnables are executed in parallel, so while the result is the same as
object comprehension shown above, the execution time is much faster.
## Composition Syntax
The usage of `RunnableSequence` and `RunnableParallel` is so common that we created a shorthand syntax for using them. This helps
to make the code more readable and concise.
### The `pipe` method.
You can `pipe` runnables together using the `.pipe(runnable)` method.
```typescript
const chain = runnable1.pipe(runnable2);
```
is Equivalent to:
```typescript
const chain = new RunnableSequence({
first: runnable1,
last: runnable2,
});
```
#### RunnableLambda functions
You can define generic TypeScript functions are runnables through the `RunnableLambda` class.
```typescript
const someFunc = RunnableLambda.from((input) => {
return input;
});
const chain = someFunc.pipe(runnable1);
```
## Legacy chains
LCEL aims to provide consistency around behavior and customization over legacy subclassed chains such as `LLMChain` and
`ConversationalRetrievalChain`. Many of these legacy chains hide important details like prompts, and as a wider variety
of viable models emerge, customization has become more and more important.
For guides on how to do specific tasks with LCEL, check out [the relevant how-to guides](/docs/how_to/#langchain-expression-language-lcel).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/index.mdx | # Conceptual guide
This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly.
We recommend that you go through at least one of the [Tutorials](/docs/tutorials) before diving into the conceptual guide. This will provide practical context that will make it easier to understand the concepts discussed here.
The conceptual guide does not cover step-by-step instructions or specific implementation examples — those are found in the [How-to guides](/docs/how_to/) and [Tutorials](/docs/tutorials). For detailed reference material, please see the [API reference](https://api.js.langchain.com/).
## High level
- **[Why LangChain?](/docs/concepts/why_langchain)**: Overview of the value that LangChain provides.
- **[Architecture](/docs/concepts/architecture)**: How packages are organized in the LangChain ecosystem.
## Concepts
- **[Chat models](/docs/concepts/chat_models)**: LLMs exposed via a chat API that process sequences of messages as input and output a message.
- **[Messages](/docs/concepts/messages)**: The unit of communication in chat models, used to represent model input and output.
- **[Chat history](/docs/concepts/chat_history)**: A conversation represented as a sequence of messages, alternating between user messages and model responses.
- **[Tools](/docs/concepts/tools)**: A function with an associated schema defining the function's name, description, and the arguments it accepts.
- **[Tool calling](/docs/concepts/tool_calling)**: A type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
- **[Structured output](/docs/concepts/structured_outputs)**: A technique to make a chat model respond in a structured format, such as JSON that matches a given schema.
- **[Memory](https://langchain-ai.github.io/langgraphjs/concepts/memory/)**: Information about a conversation that is persisted so that it can be used in future conversations.
- **[Multimodality](/docs/concepts/multimodality)**: The ability to work with data that comes in different forms, such as text, audio, images, and video.
- **[Runnable interface](/docs/concepts/runnables)**: The base abstraction that many LangChain components and the LangChain Expression Language are built on.
- **[Streaming](/docs/concepts/streaming)**: LangChain streaming APIs for surfacing results as they are generated.
- **[LangChain Expression Language (LCEL)](/docs/concepts/lcel)**: A syntax for orchestrating LangChain components. Most useful for simpler applications.
- **[Document loaders](/docs/concepts/document_loaders)**: Load a source as a list of documents.
- **[Retrieval](/docs/concepts/retrieval)**: Information retrieval systems can retrieve structured or unstructured data from a datasource in response to a query.
- **[Text splitters](/docs/concepts/text_splitters)**: Split long text into smaller chunks that can be individually indexed to enable granular retrieval.
- **[Embedding models](/docs/concepts/embedding_models)**: Models that represent data such as text or images in a vector space.
- **[Vector stores](/docs/concepts/vectorstores)**: Storage of and efficient search over vectors and associated metadata.
- **[Retriever](/docs/concepts/retrievers)**: A component that returns relevant documents from a knowledge base in response to a query.
- **[Retrieval Augmented Generation (RAG)](/docs/concepts/rag)**: A technique that enhances language models by combining them with external knowledge bases.
- **[Agents](/docs/concepts/agents)**: Use a [language model](/docs/concepts/chat_models) to choose a sequence of actions to take. Agents can interact with external resources via [tool](/docs/concepts/tools).
- **[Prompt templates](/docs/concepts/prompt_templates)**: Component for factoring out the static parts of a model "prompt" (usually a sequence of messages). Useful for serializing, versioning, and reusing these static parts.
- **[Output parsers](/docs/concepts/output_parsers)**: Responsible for taking the output of a model and transforming it into a more suitable format for downstream tasks. Output parsers were primarily useful prior to the general availability of [tool calling](/docs/concepts/tool_calling) and [structured outputs](/docs/concepts/structured_outputs).
- **[Few-shot prompting](/docs/concepts/few_shot_prompting)**: A technique for improving model performance by providing a few examples of the task to perform in the prompt.
- **[Example selectors](/docs/concepts/example_selectors)**: Used to select the most relevant examples from a dataset based on a given input. Example selectors are used in few-shot prompting to select examples for a prompt.
- **[Callbacks](/docs/concepts/callbacks)**: Callbacks enable the execution of custom auxiliary code in built-in components. Callbacks are used to stream outputs from LLMs in LangChain, trace the intermediate steps of an application, and more.
- **[Tracing](/docs/concepts/tracing)**: The process of recording the steps that an application takes to go from input to output. Tracing is essential for debugging and diagnosing issues in complex applications.
- **[Evaluation](/docs/concepts/evaluation)**: The process of assessing the performance and effectiveness of AI applications. This involves testing the model's responses against a set of predefined criteria or benchmarks to ensure it meets the desired quality standards and fulfills the intended purpose. This process is vital for building reliable applications.
## Glossary
- **[AIMessageChunk](/docs/concepts/messages#aimessagechunk)**: A partial response from an AI message. Used when streaming responses from a chat model.
- **[AIMessage](/docs/concepts/messages#aimessage)**: Represents a complete response from an AI model.
- **[StructuredTool](/docs/concepts/tools#structuredtool)**: The base class for all tools in LangChain.
- **[batch](/docs/concepts/runnables)**: Use to execute a runnable with batch inputs a Runnable.
- **[bindTools](/docs/concepts/chat_models#bind-tools)**: Allows models to interact with tools.
- **[Caching](/docs/concepts/chat_models#caching)**: Storing results to avoid redundant calls to a chat model.
- **[Context window](/docs/concepts/chat_models#context-window)**: The maximum size of input a chat model can process.
- **[Conversation patterns](/docs/concepts/chat_history#conversation-patterns)**: Common patterns in chat interactions.
- **[Document](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html)**: LangChain's representation of a document.
- **[Embedding models](/docs/concepts/multimodality#embedding-models)**: Models that generate vector embeddings for various data types.
- **[HumanMessage](/docs/concepts/messages#humanmessage)**: Represents a message from a human user.
- **[input and output types](/docs/concepts/runnables#input-and-output-types)**: Types used for input and output in Runnables.
- **[Integration packages](/docs/concepts/architecture#partner-packages)**: Third-party packages that integrate with LangChain.
- **[invoke](/docs/concepts/runnables)**: A standard method to invoke a Runnable.
- **[JSON mode](/docs/concepts/structured_outputs#json-mode)**: Returning responses in JSON format.
- **[@langchain/community](/docs/concepts/architecture#langchain-community)**: Community-driven components for LangChain.
- **[@langchain/core](/docs/concepts/architecture#langchain-core)**: Core langchain package. Includes base interfaces and in-memory implementations.
- **[langchain](/docs/concepts/architecture#langchain)**: A package for higher level components (e.g., some pre-built chains).
- **[@langchain/langgraph](/docs/concepts/architecture#langgraph)**: Powerful orchestration layer for LangChain. Use to build complex pipelines and workflows.
- **[Managing chat history](/docs/concepts/chat_history#managing-chat-history)**: Techniques to maintain and manage the chat history.
- **[OpenAI format](/docs/concepts/messages#openai-format)**: OpenAI's message format for chat models.
- **[Propagation of RunnableConfig](/docs/concepts/runnables#propagation-of-runnableconfig)**: Propagating configuration through Runnables.
- **[RemoveMessage](/docs/concepts/messages#removemessage)**: An abstraction used to remove a message from chat history, used primarily in LangGraph.
- **[role](/docs/concepts/messages#role)**: Represents the role (e.g., user, assistant) of a chat message.
- **[RunnableConfig](/docs/concepts/runnables#runnableconfig)**: Use to pass run time information to Runnables (e.g., `runName`, `runId`, `tags`, `metadata`, `maxConcurrency`, `recursionLimit`, `configurable`).
- **[Standard parameters for chat models](/docs/concepts/chat_models#standard-parameters)**: Parameters such as API key, `temperature`, and `maxTokens`,
- **[stream](/docs/concepts/streaming)**: Use to stream output from a Runnable or a graph.
- **[Tokenization](/docs/concepts/tokens)**: The process of converting data into tokens and vice versa.
- **[Tokens](/docs/concepts/tokens)**: The basic unit that a language model reads, processes, and generates under the hood.
- **[Tool artifacts](/docs/concepts/tools#tool-artifacts)**: Add artifacts to the output of a tool that will not be sent to the model, but will be available for downstream processing.
- **[Tool binding](/docs/concepts/tool_calling#tool-binding)**: Binding tools to models.
- **[`tool`](/docs/concepts/tools)**: Function for creating tools in LangChain.
- **[Toolkits](/docs/concepts/tools#toolkits)**: A collection of tools that can be used together.
- **[ToolMessage](/docs/concepts/messages#toolmessage)**: Represents a message that contains the results of a tool execution.
- **[Vector stores](/docs/concepts/vectorstores)**: Datastores specialized for storing and efficiently searching vector embeddings.
- **[withStructuredOutput](/docs/concepts/structured_outputs/#structured-output-method)**: A helper method for chat models that natively support [tool calling](/docs/concepts/tool_calling) to get structured output matching a given schema specified via Zod, JSON schema or a function.
import RedirectAnchors from "@theme/RedirectAnchors";
<RedirectAnchors />
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/architecture.mdx | import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
# Architecture
LangChain is a framework that consists of a number of packages.
<ThemedImage
alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
sources={{
light: useBaseUrl("/svg/langchain_stack_062024.svg"),
dark: useBaseUrl("/svg/langchain_stack_062024_dark.svg"),
}}
title="LangChain Framework Overview"
style={{ width: "100%" }}
/>
## @langchain/core
This package contains base abstractions for different components and ways to compose them together.
The interfaces for core components like chat models, vector stores, tools and more are defined here.
No third-party integrations are defined here.
The dependencies are very lightweight.
## langchain
The main `langchain` package contains chains and retrieval strategies that make up an application's cognitive architecture.
These are NOT third-party integrations.
All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations.
## Integration packages
Popular integrations have their own packages (e.g. `@langchain/openai`, `@langchain/anthropic`, etc) so that they can be properly versioned and appropriately lightweight.
For more information see:
- A list [integrations packages](/docs/integrations/platforms/)
- The [API Reference](https://api.js.langchain.com/) where you can find detailed information about each of the integration package.
## @langchain/community
This package contains third-party integrations that are maintained by the LangChain community.
Key integration packages are separated out (see above).
This contains integrations for various components (chat models, vector stores, tools, etc).
All dependencies in this package are optional to keep the package as lightweight as possible.
## @langchain/langgraph
`@langchain/langgraph` is an orchestration framework aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows.
:::info [Further reading]
- See our LangGraph overview [here](https://langchain-ai.github.io/langgraphjs/concepts/high_level/#core-principles).
- See our LangGraph Academy Course [here](https://academy.langchain.com/courses/intro-to-langgraph).
:::
## LangSmith
A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
For more information, see the [LangSmith documentation](https://docs.smith.langchain.com)
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/embedding_models.mdx | # Embedding models
<span data-heading-keywords="embedding,embeddings"></span>
:::info[Prerequisites]
- [Documents](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html)
:::
:::info[Note]
This conceptual overview focuses on text-based embedding models.
Embedding models can also be [multimodal](/docs/concepts/multimodality) though such models are not currently supported by LangChain.
:::
Imagine being able to capture the essence of any text - a tweet, document, or book - in a single, compact representation.
This is the power of embedding models, which lie at the heart of many retrieval systems.
Embedding models transform human language into a format that machines can understand and compare with speed and accuracy.
These models take text as input and produce a fixed-length array of numbers, a numerical fingerprint of the text's semantic meaning.
Embeddings allow search system to find relevant documents not just based on keyword matches, but on semantic understanding.
## Key concepts
(1) **Embed text as a vector**: Embeddings transform text into a numerical vector representation.
(2) **Measure similarity**: Embedding vectors can be comparing using simple mathematical operations.
## Embedding
### Historical context
The landscape of embedding models has evolved significantly over the years.
A pivotal moment came in 2018 when Google introduced [BERT (Bidirectional Encoder Representations from Transformers)](https://www.nvidia.com/en-us/glossary/bert/).
BERT applied transformer models to embed text as a simple vector representation, which lead to unprecedented performance across various NLP tasks.
However, BERT wasn't optimized for generating sentence embeddings efficiently.
This limitation spurred the creation of [SBERT (Sentence-BERT)](https://www.sbert.net/examples/training/sts/README.html), which adapted the BERT architecture to generate semantically rich sentence embeddings, easily comparable via similarity metrics like cosine similarity, dramatically reduced the computational overhead for tasks like finding similar sentences.
Today, the embedding model ecosystem is diverse, with numerous providers offering their own implementations.
To navigate this variety, researchers and practitioners often turn to benchmarks like the Massive Text Embedding Benchmark (MTEB) [here](https://huggingface.co/blog/mteb) for objective comparisons.
:::info[Further reading]
- See the [seminal BERT paper](https://arxiv.org/abs/1810.04805).
- See Cameron Wolfe's [excellent review](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2) of embedding models.
- See the [Massive Text Embedding Benchmark (MTEB)](https://huggingface.co/blog/mteb) leaderboard for a comprehensive overview of embedding models.
:::
### Interface
LangChain provides a universal interface for working with them, providing standard methods for common operations.
This common interface simplifies interaction with various embedding providers through two central methods:
- `embedDocuments`: For embedding multiple texts (documents)
- `embedQuery`: For embedding a single text (query)
This distinction is important, as some providers employ different embedding strategies for documents (which are to be searched) versus queries (the search input itself).
To illustrate, here's a practical example using LangChain's `.embedDocuments` method to embed a list of strings:
```typescript
import { OpenAIEmbeddings } from "@langchain/openai";
const embeddingsModel = new OpenAIEmbeddings();
const embeddings = await embeddingsModel.embedDocuments([
"Hi there!",
"Oh, hello!",
"What's your name?",
"My friends call me World",
"Hello World!",
]);
console.log(`(${embeddings.length}, ${embeddings[0].length})`);
// (5, 1536)
```
For convenience, you can also use the `embedQuery` method to embed a single text:
```typescript
const queryEmbedding = await embeddingsModel.embedQuery(
"What is the meaning of life?"
);
```
:::info[Further reading]
- See the full list of [LangChain embedding model integrations](/docs/integrations/text_embedding/).
- See these [how-to guides](/docs/how_to/embed_text) for working with embedding models.
:::
### Integrations
LangChain offers many embedding model integrations which you can find [on the embedding models](/docs/integrations/text_embedding/) integrations page.
## Measure similarity
Each embedding is essentially a set of coordinates, often in a high-dimensional space.
In this space, the position of each point (embedding) reflects the meaning of its corresponding text.
Just as similar words might be close to each other in a thesaurus, similar concepts end up close to each other in this embedding space.
This allows for intuitive comparisons between different pieces of text.
By reducing text to these numerical representations, we can use simple mathematical operations to quickly measure how alike two pieces of text are, regardless of their original length or structure.
Some common similarity metrics include:
- **Cosine Similarity**: Measures the cosine of the angle between two vectors.
- **Euclidean Distance**: Measures the straight-line distance between two points.
- **Dot Product**: Measures the projection of one vector onto another.
The choice of similarity metric should be chosen based on the model.
As an example, [OpenAI suggests cosine similarity for their embeddings](https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use), which can be easily implemented:
```typescript
function cosineSimilarity(vec1: number[], vec2: number[]): number {
const dotProduct = vec1.reduce((sum, val, i) => sum + val * vec2[i], 0);
const norm1 = Math.sqrt(vec1.reduce((sum, val) => sum + val * val, 0));
const norm2 = Math.sqrt(vec2.reduce((sum, val) => sum + val * val, 0));
return dotProduct / (norm1 * norm2);
}
const similarity = cosineSimilarity(queryResult, documentResult);
console.log("Cosine Similarity:", similarity);
```
:::info[Further reading]
- See Simon Willison's [nice blog post and video](https://simonwillison.net/2023/Oct/23/embeddings/) on embeddings and similarity metrics.
- See [this documentation](https://developers.google.com/machine-learning/clustering/dnn-clustering/supervised-similarity) from Google on similarity metrics to consider with embeddings.
- See Pinecone's [blog post](https://www.pinecone.io/learn/vector-similarity/) on similarity metrics.
- See OpenAI's [FAQ](https://platform.openai.com/docs/guides/embeddings/faq) on what similarity metric to use with OpenAI embeddings.
:::
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/multimodality.mdx | # Multimodality
## Overview
**Multimodality** refers to the ability to work with data that comes in different forms, such as text, audio, images, and video. Multimodality can appear in various components, allowing models and systems to handle and process a mix of these data types seamlessly.
- **Chat Models**: These could, in theory, accept and generate multimodal inputs and outputs, handling a variety of data types like text, images, audio, and video.
- **Embedding Models**: Embedding Models can represent multimodal content, embedding various forms of data—such as text, images, and audio—into vector spaces.
- **Vector Stores**: Vector stores could search over embeddings that represent multimodal data, enabling retrieval across different types of information.
## Multimodality in chat models
:::info Pre-requisites
- [Chat models](/docs/concepts/chat_models)
- [Messages](/docs/concepts/messages)
:::
Multimodal support is still relatively new and less common, model providers have not yet standardized on the "best" way to define the API. As such, LangChain's multimodal abstractions are lightweight and flexible, designed to accommodate different model providers' APIs and interaction patterns, but are **not** standardized across models.
### How to use multimodal models
- Use the [chat model integration table](/docs/integrations/chat/) to identify which models support multimodality.
- Reference the [relevant how-to guides](/docs/how_to/#multimodal) for specific examples of how to use multimodal models.
### What kind of multimodality is supported?
#### Inputs
Some models can accept multimodal inputs, such as images, audio, video, or files. The types of multimodal inputs supported depend on the model provider. For instance, [Google's Gemini](/docs/integrations/chat/google_generativeai/) supports documents like PDFs as inputs.
Most chat models that support **multimodal inputs** also accept those values in OpenAI's content blocks format. So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.
The gist of passing multimodal inputs to a chat model is to use content blocks that specify a type and corresponding data. For example, to pass an image to a chat model:
```typescript
import { HumanMessage } from "@langchain/core/messages";
const message = new HumanMessage({
content: [
{ type: "text", text: "describe the weather in this image" },
{ type: "image_url", image_url: { url: image_url } },
],
});
const response = await model.invoke([message]);
```
:::caution
The exact format of the content blocks may vary depending on the model provider. Please refer to the chat model's
integration documentation for the correct format. Find the integration in the [chat model integration table](/docs/integrations/chat/).
:::
#### Outputs
Virtually no popular chat models support multimodal outputs at the time of writing (October 2024).
The only exception is OpenAI's chat model ([gpt-4o-audio-preview](/docs/integrations/chat/openai/)), which can generate audio outputs.
Multimodal outputs will appear as part of the [AIMessage](/docs/concepts/messages/#aimessage) response object.
Please see the [ChatOpenAI](/docs/integrations/chat/openai/) for more information on how to use multimodal outputs.
#### Tools
Currently, no chat model is designed to work **directly** with multimodal data in a [tool call request](/docs/concepts/tool_calling) or [ToolMessage](/docs/concepts/tool_calling) result.
However, a chat model can easily interact with multimodal data by invoking tools with references (e.g., a URL) to the multimodal data, rather than the data itself. For example, any model capable of [tool calling](/docs/concepts/tool_calling) can be equipped with tools to download and process images, audio, or video.
## Multimodality in embedding models
:::info Prerequisites
- [Embedding Models](/docs/concepts/embedding_models)
:::
**Embeddings** are vector representations of data used for tasks like similarity search and retrieval.
The current [embedding interface](https://api.js.langchain.com/classes/_langchain_core.embeddings.Embeddings.html) used in LangChain is optimized entirely for text-based data, and will **not** work with multimodal data.
As use cases involving multimodal search and retrieval tasks become more common, we expect to expand the embedding interface to accommodate other data types like images, audio, and video.
## Multimodality in vector stores
:::info Prerequisites
- [Vector stores](/docs/concepts/vectorstores)
:::
Vector stores are databases for storing and retrieving embeddings, which are typically used in search and retrieval tasks. Similar to embeddings, vector stores are currently optimized for text-based data.
As use cases involving multimodal search and retrieval tasks become more common, we expect to expand the vector store interface to accommodate other data types like images, audio, and video.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/structured_outputs.mdx | # Structured outputs
## Overview
For many applications, such as chatbots, models need to respond to users directly in natural language.
However, there are scenarios where we need models to output in a _structured format_.
For example, we might want to store the model output in a database and ensure that the output conforms to the database schema.
This need motivates the concept of structured output, where models can be instructed to respond with a particular output structure.
## Key concepts
**(1) Schema definition:** The output structure is represented as a schema, which can be defined in several ways.
**(2) Returning structured output:** The model is given this schema, and is instructed to return output that conforms to it.
## Recommended usage
This pseudo-code illustrates the recommended workflow when using structured output.
LangChain provides a method, [`withStructuredOutput()`](/docs/how_to/structured_output/#the-.withstructuredoutput-method), that automates the process of binding the schema to the [model](/docs/concepts/chat_models/) and parsing the output.
This helper function is available for all model providers that support structured output.
```typescript
// Define schema
const schema = { foo: "bar" };
// Bind schema to model
const modelWithStructure = model.withStructuredOutput(schema);
// Invoke the model to produce structured output that matches the schema
const structuredOutput = await modelWithStructure.invoke(userInput);
```
## Schema definition
The central concept is that the output structure of model responses needs to be represented in some way.
While types of objects you can use depend on the model you're working with, there are common types of objects that are typically allowed or recommended for structured output in TypeScript.
The simplest and most common format for structured output is a Zod schema definition:
```typescript
import { z } from "zod";
const ResponseFormatter = z.object({
answer: z.string().describe("The answer to the user's question"),
followup_question: z
.string()
.describe("A followup question the user could ask"),
});
```
You can also define a JSONSchema object, which is what Zod schemas are converted to internally before being sent to the model provider:
```json
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "https://example.com/product.schema.json",
"title": "ResponseFormatter",
"type": "object",
"properties": {
"answer": {
"description": "The answer to the user's question",
"type": "string"
},
"followup_question": {
"description": "A followup question the user could ask",
"type": "string"
}
},
"required": ["answer", "followup_question"]
}
```
## Returning structured output
With a schema defined, we need a way to instruct the model to use it.
While one approach is to include this schema in the prompt and _ask nicely_ for the model to use it, this is not recommended.
Several more powerful methods that utilizes native features in the model provider's API are available.
### Using tool calling
Many [model providers support](/docs/integrations/chat/) tool calling, a concept discussed in more detail in our [tool calling guide](/docs/concepts/tool_calling/).
In short, tool calling involves binding a tool to a model and, when appropriate, the model can _decide_ to call this tool and ensure its response conforms to the tool's schema.
With this in mind, the central concept is straightforward: _simply bind our schema to a model as a tool!_
Here is an example using the `ResponseFormatter` schema defined above:
```typescript
import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({
modelName: "gpt-4",
temperature: 0,
});
// Bind ResponseFormatter schema as a tool to the model
const modelWithTools = model.bindTools([ResponseFormatter]);
// Invoke the model
const aiMsg = await modelWithTools.invoke(
"What is the powerhouse of the cell?"
);
```
### JSON mode
In addition to tool calling, some model providers support a feature called `JSON mode`.
This supports JSON schema definition as input and enforces the model to produce a conforming JSON output.
You can find a table of model providers that support JSON mode [here](/docs/integrations/chat/).
Here is an example of how to use JSON mode with OpenAI:
```typescript
import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({
model: "gpt-4",
}).bind({
response_format: { type: "json_object" },
});
const aiMsg = await model.invoke(
"Return a JSON object with key 'random_nums' and a value of 10 random numbers in [0-99]"
);
console.log(aiMsg.content);
// Output: {
// "random_nums": [23, 47, 89, 15, 34, 76, 58, 3, 62, 91]
// }
```
One important point to flag: the model _still_ returns a string, which needs to be parsed into a JSON object.
This can, of course, simply use the `json` library or a JSON output parser if you need more advanced functionality.
See this [how-to guide on the JSON output parser](/docs/how_to/output_parser_json) for more details.
```typescript
import json
const jsonObject = JSON.parse(aiMsg.content)
// {'random_ints': [23, 47, 89, 15, 34, 76, 58, 3, 62, 91]}
```
## Structured output method
There are a few challenges when producing structured output with the above methods:
(1) If using tool calling, tool call arguments needs to be parsed from an object back to the original schema.
(2) In addition, the model needs to be instructed to _always_ use the tool when we want to enforce structured output, which is a provider specific setting.
(3) If using JSON mode, the output needs to be parsed into a JSON object.
With these challenges in mind, LangChain provides a helper function (`withStructuredOutput()`) to streamline the process.
This both binds the schema to the model as a tool and parses the output to the specified output schema.
```typescript
// Bind the schema to the model
const modelWithStructure = model.withStructuredOutput(ResponseFormatter);
// Invoke the model
const structuredOutput = await modelWithStructure.invoke(
"What is the powerhouse of the cell?"
);
// Get back the object
console.log(structuredOutput);
// { answer: "The powerhouse of the cell is the mitochondrion. Mitochondria are organelles that generate most of the cell's supply of adenosine triphosphate (ATP), which is used as a source of chemical energy.", followup_question: "What is the function of ATP in the cell?" }
```
:::info[Further reading]
For more details on usage, see our [how-to guide](/docs/how_to/structured_output/#the-.withstructuredoutput-method).
:::
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/output_parsers.mdx | # Output parsers
<span data-heading-keywords="output parser"></span>
:::note
The information here refers to parsers that take a text output from a model try to parse it into a more structured representation.
More and more models are supporting function (or tool) calling, which handles this automatically.
It is recommended to use function/tool calling rather than output parsing.
See documentation for that [here](/docs/concepts/tool_calling).
:::
Output parsers are responsible for taking the output of a model and transforming it to a more suitable format for downstream tasks.
Useful when you are using LLMs to generate structured data, or to normalize output from chat models and LLMs.
LangChain has lots of different types of output parsers. This is a list of output parsers LangChain supports. The table below has various pieces of information:
- **Name**: The name of the output parser
- **Supports Streaming**: Whether the output parser supports streaming.
- **Has Format Instructions**: Whether the output parser has format instructions. This is generally available except when (a) the desired schema is not specified in the prompt but rather in other parameters (like OpenAI function calling), or (b) when the OutputParser wraps another OutputParser.
- **Calls LLM**: Whether this output parser itself calls an LLM. This is usually only done by output parsers that attempt to correct misformatted output.
- **Input Type**: Expected input type. Most output parsers work on both strings and messages, but some (like OpenAI Functions) need a message with specific kwargs.
- **Output Type**: The output type of the object returned by the parser.
- **Description**: Our commentary on this output parser and when to use it.
| Name | Supports Streaming | Has Format Instructions | Calls LLM | Input Type | Output Type | Description |
| ------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------------- | --------- | --------------------- | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [JSON](https://api.js.langchain.com/classes/_langchain_core.output_parsers.JsonOutputParser.html) | ✅ | ✅ | | `string` \| `Message` | JSON object | Returns a JSON object as specified. Probably the most reliable output parser for getting structured data that does NOT use function calling. |
| [XML](https://api.js.langchain.com/classes/_langchain_core.output_parsers.XMLOutputParser.html) | ✅ | ✅ | | `string` \| `Message` | `object` | Returns a object of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's). |
| [CSV](https://api.js.langchain.com/classes/langchain.output_parsers.CommaSeparatedListOutputParser.html) | ✅ | ✅ | | `string` \| `Message` | `Array<string>` | Returns a list of comma separated values. |
| [OutputFixing](https://api.js.langchain.com/classes/langchain.output_parsers.OutputFixingParser.html) | | | ✅ | `string` \| `Message` | | Wraps another output parser. If that output parser errors, then this will pass the error message and the bad output to an LLM and ask it to fix the output. |
| [Datetime](https://api.js.langchain.com/classes/langchain.output_parsers.DatetimeOutputParser.html) | | ✅ | | `string` \| `Message` | `Date` | Parses response into a datetime string. |
| [Structured](https://api.js.langchain.com/classes/_langchain_core.output_parsers.StructuredOutputParser.html) | | ✅ | | `string` \| `Message` | `Record<string, string>` | An output parser that returns structured information. It is less powerful than other output parsers since it only allows for fields to be strings. This can be useful when you are working with smaller LLMs. |
For specifics on how to use output parsers, see the [relevant how-to guides here](/docs/how_to/#output-parsers).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/evaluation.mdx | # Evaluation
<span data-heading-keywords="evaluation,evaluate"></span>
Evaluation is the process of assessing the performance and effectiveness of your LLM-powered applications.
It involves testing the model's responses against a set of predefined criteria or benchmarks to ensure it meets the desired quality standards and fulfills the intended purpose.
This process is vital for building reliable applications.
[LangSmith](https://docs.smith.langchain.com/) helps with this process in a few ways:
- It makes it easier to create and curate datasets via its tracing and annotation features
- It provides an evaluation framework that helps you define metrics and run your app against your dataset
- It allows you to track results over time and automatically run your evaluators on a schedule or as part of CI/Code
To learn more, check out [this LangSmith guide](https://docs.smith.langchain.com/concepts/evaluation).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/callbacks.mdx | # Callbacks
:::note Prerequisites
- [Runnable interface](/docs/concepts/runnables)
:::
LangChain provides a callback system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.
## Callback events
| Event | Event Trigger | Associated Method |
| ---------------- | ------------------------------------------- | ---------------------- |
| Chat model start | When a chat model starts | `handleChatModelStart` |
| LLM start | When a llm starts | `handleLlmStart` |
| LLM new token | When an llm OR chat model emits a new token | `handleLlmNewToken` |
| LLM ends | When an llm OR chat model ends | `handleLlmEnd` |
| LLM errors | When an llm OR chat model errors | `handleLlmError` |
| Chain start | When a chain starts running | `handleChainStart` |
| Chain end | When a chain ends | `handleChainEnd` |
| Chain error | When a chain errors | `handleChainError` |
| Tool start | When a tool starts running | `handleToolStart` |
| Tool end | When a tool ends | `handleToolEnd` |
| Tool error | When a tool errors | `handleToolError` |
| Retriever start | When a retriever starts | `handleRetrieverStart` |
| Retriever end | When a retriever ends | `handleRetrieverEnd` |
| Retriever error | When a retriever errors | `handleRetrieverError` |
## Callback handlers
- Callback handlers implement the [BaseCallbackHandler](https://api.js.langchain.com/classes/_langchain_core.callbacks_base.BaseCallbackHandler.html) interface.
During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://api.js.langchain.com/classes/_langchain_core.callbacks_manager.BaseCallbackManager.html)) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
## Passing callbacks
The `callbacks` property is available on most objects throughout the API (Models, Tools, Agents, etc.) in two different places:
- **Request time callbacks**: Passed at the time of the request in addition to the input data.
Available on all standard `Runnable` objects. These callbacks are INHERITED by all children
of the object they are defined on. For example, `await chain.invoke({ number: 25 }, { callbacks: [handler] })`.
- **Constructor callbacks**: `const chain = new TheNameOfSomeChain({ callbacks: [handler] })`. These callbacks
are passed as arguments to the constructor of the object. The callbacks are scoped
only to the object they are defined on, and are **not** inherited by any children of the object.
:::warning
Constructor callbacks are scoped only to the object they are defined on. They are **not** inherited by children
of the object.
:::
If you're creating a custom chain or runnable, you need to remember to propagate request time
callbacks to any child objects.
For specifics on how to use callbacks, see the [relevant how-to guides here](/docs/how_to/#callbacks).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/tool_calling.mdx | # Tool calling
:::info[Prerequisites]
- [Tools](/docs/concepts/tools)
- [Chat Models](/docs/concepts/chat_models)
:::
## Overview
Many AI applications interact directly with humans. In these cases, it is appropriate for models to respond in natural language.
But what about cases where we want a model to also interact _directly_ with systems, such as databases or an API?
These systems often have a particular input schema; for example, APIs frequently have a required payload structure.
This need motivates the concept of _tool calling_. You can use [tool calling](https://platform.openai.com/docs/guides/function-calling/example-use-cases) to request model responses that match a particular schema.
:::info
You will sometimes hear the term `function calling`. We use this term interchangeably with `tool calling`.
:::
## Key concepts
**(1) Tool Creation:** Use the [tool](https://api.js.langchain.com/functions/_langchain_core.tools.tool-1.html) function to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.
**(2) Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.
**(3) Tool Calling:** When appropriate, the model can decide to call a tool and ensure its response conforms to the tool's input schema.
**(4) Tool Execution:** The tool can be executed using the arguments provided by the model.
## Recommended usage
This pseudo-code illustrates the recommended workflow for using tool calling.
Created tools are passed to `.bindTools()` method as a list.
This model can be called, as usual. If a tool call is made, model's response will contain the tool call arguments.
The tool call arguments can be passed directly to the tool.
```typescript
// Tool creation
const tools = [myTool];
// Tool binding
const modelWithTools = model.bindTools(tools);
// Tool calling
const response = await modelWithTools.invoke(userInput);
```
## Tool creation
The recommended way to create a tool is using the `tool` function.
```typescript
import { tool } from "@langchain/core/tools";
const multiply = tool(
({ a, b }: { a: number; b: number }): number => {
/**
* Multiply a and b.
*/
return a * b;
},
{
name: "multiply",
description: "Multiply two numbers",
schema: z.object({
a: z.number(),
b: z.number(),
}),
}
);
```
:::info[Further reading]
- See our conceptual guide on [tools](/docs/concepts/tools/) for more details.
- See our [model integrations](/docs/integrations/chat/) that support tool calling.
- See our [how-to guide](/docs/how_to/tool_calling/) on tool calling.
:::
For tool calling that does not require a function to execute, you can also define just the tool schema:
```typescript
const multiplyTool = {
name: "multiply",
description: "Multiply two numbers",
schema: z.object({
a: z.number(),
b: z.number(),
}),
};
```
## Tool binding
[Many](https://platform.openai.com/docs/guides/function-calling) [model providers](https://platform.openai.com/docs/guides/function-calling) support tool calling.
:::tip
See our [model integration page](/docs/integrations/chat/) for a list of providers that support tool calling.
:::
The central concept to understand is that LangChain provides a standardized interface for connecting tools to models.
The `.bindTools()` method can be used to specify which tools are available for a model to call.
```typescript
const modelWithTools = model.bindTools([toolsList]);
```
As a specific example, let's take a function `multiply` and bind it as a tool to a model that supports tool calling.
```typescript
const multiply = tool(
({ a, b }: { a: number; b: number }): number => {
/**
* Multiply a and b.
*
* @param a - first number
* @param b - second number
* @returns The product of a and b
*/
return a * b;
},
{
name: "multiply",
description: "Multiply two numbers",
schema: z.object({
a: z.number(),
b: z.number(),
}),
}
);
const llmWithTools = toolCallingModel.bindTools([multiply]);
```
## Tool calling
A key principle of tool calling is that the model decides when to use a tool based on the input's relevance. The model doesn't always need to call a tool.
For example, given an unrelated input, the model would not call the tool:
```typescript
const result = await llmWithTools.invoke("Hello world!");
```
The result would be an `AIMessage` containing the model's response in natural language (e.g., "Hello!").
However, if we pass an input _relevant to the tool_, the model should choose to call it:
```typescript
const result = await llmWithTools.invoke("What is 2 multiplied by 3?");
```
As before, the output `result` will be an `AIMessage`.
But, if the tool was called, `result` will have a `tool_calls` attribute.
This attribute includes everything needed to execute the tool, including the tool name and input arguments:
```
result.tool_calls
{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'xxx', 'type': 'tool_call'}
```
For more details on usage, see our [how-to guides](/docs/how_to/#tools)!
## Tool execution
[Tools](/docs/concepts/tools/) implement the [Runnable](/docs/concepts/runnables/) interface, which means that they can be invoked (e.g., `tool.invoke(args)`) directly.
[LangGraph](https://langchain-ai.github.io/langgraphjs/) offers pre-built components (e.g., [`ToolNode`](https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph_prebuilt.ToolNode.html)) that will often invoke the tool in behalf of the user.
:::info[Further reading]
- See our [how-to guide](/docs/how_to/tool_calling/) on tool calling.
- See the [LangGraph documentation on using ToolNode](https://langchain-ai.github.io/langgraphjs/how-tos/tool-calling/).
:::
## Best practices
When designing [tools](/docs/concepts/tools/) to be used by a model, it is important to keep in mind that:
- Models that have explicit [tool-calling APIs](/docs/concepts/tool_calling) will be better at tool calling than non-fine-tuned models.
- Models will perform better if the tools have well-chosen names and descriptions.
- Simple, narrowly scoped tools are easier for models to use than complex tools.
- Asking the model to select from a large list of tools poses challenges for the model.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/runnables.mdx | # Runnable interface
The Runnable interface is foundational for working with LangChain components, and it's implemented across many of them, such as [language models](/docs/concepts/chat_models), [output parsers](/docs/concepts/output_parsers), [retrievers](/docs/concepts/retrievers), [compiled LangGraph graphs](https://langchain-ai.github.io/langgraphjs/concepts/low_level/#compiling-your-graph) and more.
This guide covers the main concepts and methods of the Runnable interface, which allows developers to interact with various LangChain components in a consistent and predictable manner.
:::info Related Resources
- The ["Runnable" Interface API Reference](https://api.js.langchain.com/classes/_langchain_core.runnables.Runnable.html) provides a detailed overview of the Runnable interface and its methods.
- A list of built-in `Runnables` can be found in the [LangChain Core API Reference](https://api.js.langchain.com/modules/_langchain_core.runnables.html). Many of these Runnables are useful when composing custom "chains" in LangChain using the [LangChain Expression Language (LCEL)](/docs/concepts/lcel).
:::
## Overview of runnable interface
The Runnable way defines a standard interface that allows a Runnable component to be:
- [Invoked](/docs/how_to/lcel_cheatsheet/#invoke-a-runnable): A single input is transformed into an output.
- [Batched](/docs/how_to/lcel_cheatsheet/#batch-a-runnable/): Multiple inputs are efficiently transformed into outputs.
- [Streamed](/docs/how_to/lcel_cheatsheet/#stream-a-runnable): Outputs are streamed as they are produced.
- Inspected: Schematic information about Runnable's input, output, and configuration can be accessed.
- Composed: Multiple Runnables can be composed to work together using [the LangChain Expression Language (LCEL)](/docs/concepts/lcel) to create complex pipelines.
Please review the [LCEL Cheatsheet](/docs/how_to/lcel_cheatsheet) for some common patterns that involve the Runnable interface and LCEL expressions.
### Optimized parallel execution (batch)
LangChain Runnables offer a built-in `batch` API that allow you to process multiple inputs in parallel.
Using this method can significantly improve performance when needing to process multiple independent inputs, as the
processing can be done in parallel instead of sequentially.
The batching method is:
- `batch`: Process multiple inputs in parallel, returning results in the same order as the inputs.
The default implementation of `batch` executed the `invoke` method in parallel.
Some Runnables may provide their own implementations of `batch` that are optimized for their specific use case (e.g.,
rely on a `batch` API provided by a model provider).
:::tip
When processing a large number of inputs using `batch`, users may want to control the maximum number of parallel calls. This can be done by setting the `maxConcurrency` attribute in the `RunnableConfig` object. See the [RunnableConfig](/docs/concepts/runnables#RunnableConfig) for more information.
:::
## Streaming apis
<span data-heading-keywords="streaming-api"></span>
Streaming is critical in making applications based on LLMs feel responsive to end-users.
Runnables expose the following three streaming APIs:
1. [`stream`](https://api.js.langchain.com/classes/_langchain_core.runnables.Runnable.html#stream): yields the output a Runnable as it is generated.
2. [`streamEvents`](https://v03.api.js.langchain.com/classes/_langchain_core.runnables.Runnable.html#streamEvents): a more advanced streaming API that allows streaming intermediate steps and final output
3. **legacy** `streamLog`: a legacy streaming API that streams intermediate steps and final output
Please refer to the [Streaming Conceptual Guide](/docs/concepts/streaming) for more details on how to stream in LangChain.
## Input and output types
Every `Runnable` is characterized by an input and output type. These input and output types can be any TypeScript object, and are defined by the Runnable itself.
Runnable methods that result in the execution of the Runnable (e.g., `invoke`, `batch`, `stream`, `streamEvents`) work with these input and output types.
- `invoke`: Accepts an input and returns an output.
- `batch`: Accepts a list of inputs and returns a list of outputs.
- `stream`: Accepts an input and returns a generator that yields outputs.
The **input type** and **output type** vary by component:
| Component | Input Type | Output Type |
| ------------ | ---------------------------------------------------- | --------------------- |
| Prompt | `object` | `PromptValue` |
| ChatModel | a `string`, list of chat messages or a `PromptValue` | `ChatMessage` |
| LLM | a `string`, list of chat messages or a `PromptValue` | `string` |
| OutputParser | the output of an LLM or ChatModel | Depends on the parser |
| Retriever | a `string` | List of `Document`s |
| Tool | a `string` or `object`, depending on the tool | Depends on the tool |
Please refer to the individual component documentation for more information on the input and output types and how to use them.
## RunnableConfig
Any of the methods that are used to execute the runnable (e.g., `invoke`, `batch`, `stream`, `streamEvents`) accept a second argument called
`RunnableConfig` ([API Reference](https://api.js.langchain.com/interfaces/_langchain_core.runnables.RunnableConfig.html)). This argument is an object that contains configuration for the Runnable that will be used
at run time during the execution of the runnable.
A `RunnableConfig` can have any of the following properties defined:
| Attribute | Description |
| ---------------- | ------------------------------------------------------------------------------------------ |
| `runName` | Name used for the given Runnable (not inherited). |
| `runId` | Unique identifier for this call. sub-calls will get their own unique run ids. |
| `tags` | Tags for this call and any sub-calls. |
| `metadata` | Metadata for this call and any sub-calls. |
| `callbacks` | Callbacks for this call and any sub-calls. |
| `maxConcurrency` | Maximum number of parallel calls to make (e.g., used by batch). |
| `recursionLimit` | Maximum number of times a call can recurse (e.g., used by Runnables that return Runnables) |
| `configurable` | Runtime values for configurable attributes of the Runnable. |
Passing `config` to the `invoke` method is done like so:
```typescript
await someRunnable.invoke(someInput, {
runName: "myRun",
tags: ["tag1", "tag2"],
metadata: { key: "value" },
});
```
### Propagation of RunnableConfig
Many `Runnables` are composed of other Runnables, and it is important that the `RunnableConfig` is propagated to all sub-calls made by the Runnable. This allows providing run time configuration values to the parent Runnable that are inherited by all sub-calls.
If this were not the case, it would be impossible to set and propagate [callbacks](/docs/concepts/callbacks) or other configuration values like `tags` and `metadata` which
are expected to be inherited by all sub-calls.
There are two main patterns by which new `Runnables` are created:
1. Declaratively using [LangChain Expression Language (LCEL)](/docs/concepts/lcel):
```typescript
const chain = prompt.pipe(chatModel).pipe(outputParser);
```
2. Using a [custom Runnable](#custom-runnables) (e.g., `RunnableLambda`) or using the `tool` function:
```typescript
const foo = (input) => {
// Note that .invoke() is used directly here
// highlight-next-line
return barRunnable.invoke(input);
};
const fooRunnable = RunnableLambda.from(foo);
```
LangChain will try to propagate `RunnableConfig` automatically for both of the patterns.
Propagating the `RunnableConfig` manually is done like so:
```typescript
// Note the config argument
// highlight-next-line
const foo = (input, config) => {
return barRunnable.invoke(input, config);
};
const fooRunnable = RunnableLambda.from(foo);
```
### Setting custom run name, tags, and metadata
The `runName`, `tags`, and `metadata` attributes of the `RunnableConfig` object can be used to set custom values for the run name, tags, and metadata for a given Runnable.
The `runName` is a string that can be used to set a custom name for the run. This name will be used in logs and other places to identify the run. It is not inherited by sub-calls.
The `tags` and `metadata` attributes are arrays and objects, respectively, that can be used to set custom tags and metadata for the run. These values are inherited by sub-calls.
Using these attributes can be useful for tracking and debugging runs, as they will be surfaced in [LangSmith](https://docs.smith.langchain.com/) as trace attributes that you can
filter and search on.
The attributes will also be propagated to [callbacks](/docs/concepts/callbacks), and will appear in streaming APIs like [streamEvents](/docs/concepts/streaming) as part of each event in the stream.
:::note Related
- [How-to trace with LangChain](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain)
:::
### Setting run id
:::note
This is an advanced feature that is unnecessary for most users.
:::
You may need to set a custom `runId` for a given run, in case you want
to reference it later or correlate it with other systems.
The `runId` MUST be a valid UUID string and **unique** for each run. It is used to identify
the parent run, sub-class will get their own unique run ids automatically.
To set a custom `runId`, you can pass it as a key-value pair in the `config` object when invoking the Runnable:
```typescript
import { v4 as uuidv4 } from "uuid";
const runId = uuidv4();
await someRunnable.invoke(someInput, {
runId,
});
// Do something with the runId
```
### Setting recursion limit
:::note
This is an advanced feature that is unnecessary for most users.
:::
Some Runnables may return other Runnables, which can lead to infinite recursion if not handled properly. To prevent this, you can set a `recursion_limit` in the `RunnableConfig` object. This will limit the number of times a Runnable can recurse.
### Setting max concurrency
If using the `batch` methods, you can set the `maxConcurrency` attribute in the `RunnableConfig` object to control the maximum number of parallel calls to make. This can be useful when you want to limit the number of parallel calls to prevent overloading a server or API.
### Setting configurable
The `configurable` field is used to pass runtime values for configurable attributes of the Runnable.
It is used frequently in [LangGraph](/docs/concepts/architecture#langgraph) with
[LangGraph Persistence](https://langchain-ai.github.io/langgraphjs/concepts/persistence/)
and [memory](https://langchain-ai.github.io/langgraphjs/concepts/memory/).
It is used for a similar purpose in [RunnableWithMessageHistory](https://api.js.langchain.com/classes/_langchain_core.runnables.RunnableWithMessageHistory.html) to specify
a `session_id` to keep track of conversation history.
### Setting callbacks
Use this option to configure [callbacks](/docs/concepts/callbacks) for the runnable at
runtime. The callbacks will be passed to all sub-calls made by the runnable.
```typescript
await someRunnable.invoke(someInput, {
callbacks: [SomeCallbackHandler(), AnotherCallbackHandler()],
});
```
Please read the [Callbacks Conceptual Guide](/docs/concepts/callbacks) for more information on how to use callbacks in LangChain.
## Creating a runnable from a function
You may need to create a custom Runnable that runs arbitrary logic. This is especially
useful if using [LangChain Expression Language (LCEL)](/docs/concepts/lcel) to compose
multiple Runnables and you need to add custom processing logic in one of the steps.
There are two ways to create a custom Runnable from a function:
- `RunnableLambda`: Use this simple transformations where streaming is not required.
- `RunnableGenerator`: use this for more complex transformations when streaming is needed.
See the [How to run custom functions](/docs/how_to/functions) guide for more information on how to use `RunnableLambda` and `RunnableGenerator`.
:::important
Users should not try to subclass Runnables to create a new custom Runnable. It is
much more complex and error-prone than simply using `RunnableLambda` or `RunnableGenerator`.
:::
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/messages.mdx | # Messages
:::info Prerequisites
- [Chat Models](/docs/concepts/chat_models)
:::
## Overview
Messages are the unit of communication in [chat models](/docs/concepts/chat_models). They are used to represent the input and output of a chat model, as well as any additional context or metadata that may be associated with a conversation.
Each message has a **role** (e.g., "user", "assistant"), **content** (e.g., text, multimodal data), and additional metadata that can vary depending on the chat model provider.
LangChain provides a unified message format that can be used across chat models, allowing users to work with different chat models without worrying about the specific details of the message format used by each model provider.
## What inside a message?
A message typically consists of the following pieces of information:
- **Role**: The role of the message (e.g., "user", "assistant").
- **Content**: The content of the message (e.g., text, multimodal data).
- Additional metadata: id, name, [token usage](/docs/concepts/tokens) and other model-specific metadata.
### Role
Roles are used to distinguish between different types of messages in a conversation and help the chat model understand how to respond to a given sequence of messages.
| **Role** | **Description** |
| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **system** | Used to tell the chat model how to behave and provide additional context. Not supported by all chat model providers. |
| **user** | Represents input from a user interacting with the model, usually in the form of text or other interactive input. |
| **assistant** | Represents a response from the model, which can include text or a request to invoke tools. |
| **tool** | A message used to pass the results of a tool invocation back to the model after external data or processing has been retrieved. Used with chat models that support [tool calling](/docs/concepts/tool_calling). |
| **function** (legacy) | This is a legacy role, corresponding to OpenAI's legacy function-calling API. **tool** role should be used instead. |
### Content
The content of a message text or an array of objects representing [multimodal data](/docs/concepts/multimodality) (e.g., images, audio, video). The exact format of the content can vary between different chat model providers.
Currently, most chat models support text as the primary content type, with some models also supporting multimodal data. However, support for multimodal data is still limited across most chat model providers.
For more information see:
- [HumanMessage](#humanmessage) -- for content in the input from the user.
- [AIMessage](#aimessage) -- for content in the response from the model.
- [Multimodality](/docs/concepts/multimodality) -- for more information on multimodal content.
### Other Message Data
Depending on the chat model provider, messages can include other data such as:
- **ID**: An optional unique identifier for the message.
- **Name**: An optional `name` property which allows differentiate between different entities/speakers with the same role. Not all models support this!
- **Metadata**: Additional information about the message, such as timestamps, token usage, etc.
- **Tool Calls**: A request made by the model to call one or more tools> See [tool calling](/docs/concepts/tool_calling) for more information.
## Conversation Structure
The sequence of messages into a chat model should follow a specific structure to ensure that the chat model can generate a valid response.
For example, a typical conversation structure might look like this:
1. **User Message**: "Hello, how are you?"
2. **Assistant Message**: "I'm doing well, thank you for asking."
3. **User Message**: "Can you tell me a joke?"
4. **Assistant Message**: "Sure! Why did the scarecrow win an award? Because he was outstanding in his field!"
Please read the [chat history](/docs/concepts/chat_history) guide for more information on managing chat history and ensuring that the conversation structure is correct.
## LangChain Messages
LangChain provides a unified message format that can be used across all chat models, allowing users to work with different chat models without worrying about the specific details of the message format used by each model provider.
LangChain messages are classes that subclass from a [BaseMessage](https://api.js.langchain.com/classes/_langchain_core.messages.BaseMessage.html).
The five main message types are:
- [SystemMessage](#systemmessage): corresponds to **system** role
- [HumanMessage](#humanmessage): corresponds to **user** role
- [AIMessage](#aimessage): corresponds to **assistant** role
- [AIMessageChunk](#aimessagechunk): corresponds to **assistant** role, used for [streaming](/docs/concepts/streaming) responses
- [ToolMessage](#toolmessage): corresponds to **tool** role
Other important messages include:
- [RemoveMessage](#removemessage) -- does not correspond to any role. This is an abstraction, mostly used in [LangGraph](/docs/concepts/architecture#langgraph) to manage chat history.
- **Legacy** [FunctionMessage](#legacy-functionmessage): corresponds to the **function** role in OpenAI's **legacy** function-calling API.
You can find more information about **messages** in the [API Reference](https://api.js.langchain.com/modules/_langchain_core.messages.html).
### SystemMessage
A `SystemMessage` is used to prime the behavior of the AI model and provide additional context, such as instructing the model to adopt a specific persona or setting the tone of the conversation (e.g., "This is a conversation about cooking").
Different chat providers may support system message in one of the following ways:
- **Through a "system" message role**: In this case, a system message is included as part of the message sequence with the role explicitly set as "system."
- **Through a separate API parameter for system instructions**: Instead of being included as a message, system instructions are passed via a dedicated API parameter.
- **No support for system messages**: Some models do not support system messages at all.
Most major chat model providers support system instructions via either a chat message or a separate API parameter. LangChain will automatically adapt based on the provider’s capabilities. If the provider supports a separate API parameter for system instructions, LangChain will extract the content of a system message and pass it through that parameter.
If no system message is supported by the provider, in most cases LangChain will attempt to incorporate the system message's content into a HumanMessage or raise an exception if that is not possible. However, this behavior is not yet consistently enforced across all implementations, and if using a less popular implementation of a chat model (e.g., an implementation from the `@langchain/community` package) it is recommended to check the specific documentation for that model.
### HumanMessage
The `HumanMessage` corresponds to the **"user"** role. A human message represents input from a user interacting with the model.
#### Text Content
Most chat models expect the user input to be in the form of text.
```typescript
import { HumanMessage } from "@langchain/core/messages";
await model.invoke([new HumanMessage("Hello, how are you?")]);
```
:::tip
When invoking a chat model with a string as input, LangChain will automatically convert the string into a `HumanMessage` object. This is mostly useful for quick testing.
```typescript
await model.invoke("Hello, how are you?");
```
:::
#### Multi-modal Content
Some chat models accept multimodal inputs, such as images, audio, video, or files like PDFs.
Please see the [multimodality](/docs/concepts/multimodality) guide for more information.
### AIMessage
`AIMessage` is used to represent a message with the role **"assistant"**. This is the response from the model, which can include text or a request to invoke tools. It could also include other media types like images, audio, or video -- though this is still uncommon at the moment.
```typescript
import { HumanMessage } from "@langchain/core/messages";
const aiMessage = await model.invoke([new HumanMessage("Tell me a joke")]);
console.log(aiMessage);
```
```text
AIMessage({
content: "Why did the chicken cross the road?\n\nTo get to the other side!",
tool_calls: [],
response_metadata: { ... },
usage_metadata: { ... },
})
```
An `AIMessage` has the following attributes. The attributes which are **standardized** are the ones that LangChain attempts to standardize across different chat model providers. **raw** fields are specific to the model provider and may vary.
| Attribute | Standardized/Raw | Description |
| -------------------- | :--------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `content` | Raw | Usually a string, but can be a list of content blocks. See [content](#content) for details. |
| `tool_calls` | Standardized | Tool calls associated with the message. See [tool calling](/docs/concepts/tool_calling) for details. |
| `invalid_tool_calls` | Standardized | Tool calls with parsing errors associated with the message. See [tool calling](/docs/concepts/tool_calling) for details. |
| `usage_metadata` | Standardized | Usage metadata for a message, such as [token counts](/docs/concepts/tokens). See [Usage Metadata API Reference](https://api.js.langchain.com/types/_langchain_core.messages.UsageMetadata.html). |
| `id` | Standardized | An optional unique identifier for the message, ideally provided by the provider/model that created the message. |
| `response_metadata` | Raw | Response metadata, e.g., response headers, logprobs, token counts. |
#### content
The **content** property of an `AIMessage` represents the response generated by the chat model.
The content is either:
- **text** -- the norm for virtually all chat models.
- A **array of objects** -- Each object represents a content block and is associated with a `type`.
- Used by Anthropic for surfacing agent thought process when doing [tool calling](/docs/concepts/tool_calling).
- Used by OpenAI for audio outputs. Please see [multi-modal content](/docs/concepts/multimodality) for more information.
:::important
The **content** property is **not** standardized across different chat model providers, mostly because there are
still few examples to generalize from.
:::
### AIMessageChunk
It is common to [stream](/docs/concepts/streaming) responses for the chat model as they are being generated, so the user can see the response in real-time instead of waiting for the entire response to be generated before displaying it.
It is returned from the `stream`, and `streamEvents` methods of the chat model.
For example,
```typescript
for await (const chunk of model.stream([
new HumanMessage("what color is the sky?"),
])) {
console.log(chunk);
}
```
`AIMessageChunk` follows nearly the same structure as `AIMessage`, but uses a different [ToolCallChunk](https://api.js.langchain.com/types/_langchain_core.messages_tool.ToolCallChunk.html)
to be able to stream tool calling in a standardized manner.
#### Aggregating
`<Type>MessageChunks` have a `concat` method you can use, or you can import it. This is useful when you want to display the final response to the user.
```typescript
const aiMessage = chunk1.concat(chunk2).concat(chunk3).concat(...);
```
or
```typescript
import { concat } from "@langchain/core/utils/stream";
const aiMessage = concat(chunk1, chunk2);
```
### ToolMessage
This represents a message with role "tool", which contains the result of [calling a tool](/docs/concepts/tool_calling). In addition to `role` and `content`, this message has:
- a `tool_call_id` field which conveys the id of the call to the tool that was called to produce this result.
- an `artifact` field which can be used to pass along arbitrary artifacts of the tool execution which are useful to track but which should not be sent to the model.
Please see [tool calling](/docs/concepts/tool_calling) for more information.
### RemoveMessage
This is a special message type that does not correspond to any roles. It is used
for managing chat history in [LangGraph](/docs/concepts/architecture#langgraph).
Please see the following for more information on how to use the `RemoveMessage`:
- [Memory conceptual guide](https://langchain-ai.github.io/langgraphjs/concepts/memory/)
- [How to delete messages](https://langchain-ai.github.io/langgraphjs/how-tos/delete-messages/)
### (Legacy) FunctionMessage
This is a legacy message type, corresponding to OpenAI's legacy function-calling API. `ToolMessage` should be used instead to correspond to the updated tool-calling API.
## OpenAI Format
### Inputs
Chat models also accept OpenAI's format as **inputs** to chat models:
```typescript
await chatModel.invoke([
{
role: "user",
content: "Hello, how are you?",
},
{
role: "assistant",
content: "I'm doing well, thank you for asking.",
},
{
role: "user",
content: "Can you tell me a joke?",
},
]);
```
### Outputs
At the moment, the output of the model will be in terms of LangChain messages, so you will need to convert the output to the OpenAI format if you
need OpenAI format for the output as well.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/retrieval.mdx | # Retrieval
:::info[Prerequisites]
- [Retrievers](/docs/concepts/retrievers/)
- [Vector stores](/docs/concepts/vectorstores/)
- [Embeddings](/docs/concepts/embedding_models/)
- [Text splitters](/docs/concepts/text_splitters/)
:::
:::danger[Security]
Some of the concepts reviewed here utilize models to generate queries (e.g., for SQL or graph databases).
There are inherent risks in doing this.
Make sure that your database connection permissions are scoped as narrowly as possible for your application's needs.
This will mitigate, though not eliminate, the risks of building a model-driven system capable of querying databases.
For more on general security best practices, see our [security guide](/docs/security/).
:::
## Overview
Retrieval systems are fundamental to many AI applications, efficiently identifying relevant information from large datasets.
These systems accommodate various data formats:
- Unstructured text (e.g., documents) is often stored in vector stores or lexical search indexes.
- Structured data is typically housed in relational or graph databases with defined schemas.
Despite this diversity in data formats, modern AI applications increasingly aim to make all types of data accessible through natural language interfaces.
Models play a crucial role in this process by translating natural language queries into formats compatible with the underlying search index or database.
This translation enables more intuitive and flexible interactions with complex data structures.
## Key concepts
(1) **Query analysis**: A process where models transform or construct search queries to optimize retrieval.
(2) **Information retrieval**: Search queries are used to fetch information from various retrieval systems.
## Query analysis
While users typically prefer to interact with retrieval systems using natural language, retrieval systems can specific query syntax or benefit from particular keywords.
Query analysis serves as a bridge between raw user input and optimized search queries. Some common applications of query analysis include:
1. **Query Re-writing**: Queries can be re-written or expanded to improve semantic or lexical searches.
2. **Query Construction**: Search indexes may require structured queries (e.g., SQL for databases).
Query analysis employs models to transform or construct optimized search queries from raw user input.
### Query re-writing
Retrieval systems should ideally handle a wide spectrum of user inputs, from simple and poorly worded queries to complex, multi-faceted questions.
To achieve this versatility, a popular approach is to use models to transform raw user queries into more effective search queries.
This transformation can range from simple keyword extraction to sophisticated query expansion and reformulation.
Here are some key benefits of using models for query analysis in unstructured data retrieval:
1. **Query Clarification**: Models can rephrase ambiguous or poorly worded queries for clarity.
2. **Semantic Understanding**: They can capture the intent behind a query, going beyond literal keyword matching.
3. **Query Expansion**: Models can generate related terms or concepts to broaden the search scope.
4. **Complex Query Handling**: They can break down multi-part questions into simpler sub-queries.
Various techniques have been developed to leverage models for query re-writing, including:
| Name | When to use | Description |
| --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Decomposition](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | When a question can be broken down into smaller subproblems. | Decompose a question into a set of subproblems / questions, which can either be solved sequentially (use the answer from first + retrieval to answer the second) or in parallel (consolidate each answer into final answer). |
| [Step-back](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | When a higher-level conceptual understanding is required. | First prompt the LLM to ask a generic step-back question about higher-level concepts or principles, and retrieve relevant facts about them. Use this grounding to help answer the user question. [Paper](https://arxiv.org/pdf/2310.06117). |
| [HyDE](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | If you have challenges retrieving relevant documents using the raw user inputs. | Use an LLM to convert questions into hypothetical documents that answer the question. Use the embedded hypothetical documents to retrieve real documents with the premise that doc-doc similarity search can produce more relevant matches. [Paper](https://arxiv.org/abs/2212.10496). |
As an example, query decomposition can simply be accomplished using prompting and a structured output that enforces a list of sub-questions.
These can then be run sequentially or in parallel on a downstream retrieval system.
```typescript
import { z } from "zod";
import { ChatOpenAI } from "@langchain/openai";
import { SystemMessage, HumanMessage } from "@langchain/core/messages";
// Define a zod object for the structured output
const Questions = z.object({
questions: z
.array(z.string())
.describe("A list of sub-questions related to the input query."),
});
// Create an instance of the model and enforce the output structure
const model = new ChatOpenAI({ modelName: "gpt-4", temperature: 0 });
const structuredModel = model.withStructuredOutput(Questions);
// Define the system prompt
const system = `You are a helpful assistant that generates multiple sub-questions related to an input question.
The goal is to break down the input into a set of sub-problems / sub-questions that can be answers in isolation.`;
// Pass the question to the model
const question =
"What are the main components of an LLM-powered autonomous agent system?";
const questions = await structuredModel.invoke([
new SystemMessage(system),
new HumanMessage(question),
]);
```
:::tip
See our RAG from Scratch videos for a few different specific approaches:
- [Multi-query](https://youtu.be/JChPi0CRnDY?feature=shared)
- [Decomposition](https://youtu.be/h0OPWlEOank?feature=shared)
- [Step-back](https://youtu.be/xn1jEjRyJ2U?feature=shared)
- [HyDE](https://youtu.be/SaDzIVkYqyY?feature=shared)
:::
### Query construction
Query analysis also can focus on translating natural language queries into specialized query languages or filters.
This translation is crucial for effectively interacting with various types of databases that house structured or semi-structured data.
1. **Structured Data examples**: For relational and graph databases, Domain-Specific Languages (DSLs) are used to query data.
- **Text-to-SQL**: [Converts natural language to SQL](https://paperswithcode.com/task/text-to-sql) for relational databases.
- **Text-to-Cypher**: [Converts natural language to Cypher](https://neo4j.com/labs/neodash/2.4/user-guide/extensions/natural-language-queries/) for graph databases.
2. **Semi-structured Data examples**: For vectorstores, queries can combine semantic search with metadata filtering.
- **Natural Language to Metadata Filters**: Converts user queries into [appropriate metadata filters](https://docs.pinecone.io/guides/data/filter-with-metadata).
These approaches leverage models to bridge the gap between user intent and the specific query requirements of different data storage systems. Here are some popular techniques:
| Name | When to Use | Description |
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Self Query](/docs/how_to/self_query/) | If users are asking questions that are better answered by fetching documents based on metadata rather than similarity with the text. | This uses an LLM to transform user input into two things: (1) a string to look up semantically, (2) a metadata filter to go along with it. This is useful because oftentimes questions are about the METADATA of documents (not the content itself). |
| [Text to SQL](/docs/tutorials/sql_qa/) | If users are asking questions that require information housed in a relational database, accessible via SQL. | This uses an LLM to transform user input into a SQL query. |
| [Text-to-Cypher](/docs/tutorials/graph/) | If users are asking questions that require information housed in a graph database, accessible via Cypher. | This uses an LLM to transform user input into a Cypher query. |
As an example, here is how to use the `SelfQueryRetriever` to convert natural language queries into metadata filters.
```typescript
import { SelfQueryRetriever } from "langchain/retrievers/self_query";
import { AttributeInfo } from "langchain/chains/query_constructor";
import { ChatOpenAI } from "@langchain/openai";
const attributeInfo: AttributeInfo[] = schemaForMetadata;
const documentContents = "Brief summary of a movie";
const llm = new ChatOpenAI({ temperature: 0 });
const retriever = SelfQueryRetriever.fromLLM({
llm,
vectorStore,
documentContents,
attributeInfo,
});
```
:::info[Further reading]
- See our tutorials on [text-to-SQL](/docs/tutorials/sql_qa/), [text-to-Cypher](/docs/tutorials/graph/), and [query analysis for metadata filters](/docs/tutorials/query_analysis/).
- See our [blog post overview](https://blog.langchain.dev/query-construction/).
- See our RAG from Scratch video on [query construction](https://youtu.be/kl6NwWYxvbM?feature=shared).
:::
## Information retrieval
### Common retrieval systems
#### Lexical search indexes
Many search engines are based upon matching words in a query to the words in each document.
This approach is called lexical retrieval, using search [algorithms that are typically based upon word frequencies](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2).
The intution is simple: a word appears frequently both in the user’s query and a particular document, then this document might be a good match.
The particular data structure used to implement this is often an [_inverted index_](https://www.geeksforgeeks.org/inverted-index/).
This types of index contains a list of words and a mapping of each word to a list of locations at which it occurs in various documents.
Using this data structure, it is possible to efficiently match the words in search queries to the documents in which they appear.
[BM25](https://en.wikipedia.org/wiki/Okapi_BM25#:~:text=BM25%20is%20a%20bag%2Dof,slightly%20different%20components%20and%20parameters.) and [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) are [two popular lexical search algorithms](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2).
:::info[Further reading]
- See the [BM25](/docs/integrations/retrievers/bm25/) retriever integration.
:::
#### Vector indexes
Vector indexes are an alternative way to index and store unstructured data.
See our conceptual guide on [vectorstores](/docs/concepts/vectorstores/) for a detailed overview.
In short, rather than using word frequencies, vectorstores use an [embedding model](/docs/concepts/embedding_models/) to compress documents into high-dimensional vector representation.
This allows for efficient similarity search over embedding vectors using simple mathematical operations like cosine similarity.
:::info[Further reading]
- See our [how-to guide](/docs/how_to/vectorstore_retriever/) for more details on working with vectorstores.
- See our [list of vectorstore integrations](/docs/integrations/vectorstores/).
- See Cameron Wolfe's [blog post](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2) on the basics of vector search.
:::
#### Relational databases
Relational databases are a fundamental type of structured data storage used in many applications.
They organize data into tables with predefined schemas, where each table represents an entity or relationship.
Data is stored in rows (records) and columns (attributes), allowing for efficient querying and manipulation through SQL (Structured Query Language).
Relational databases excel at maintaining data integrity, supporting complex queries, and handling relationships between different data entities.
:::info[Further reading]
- See our [tutorial](/docs/tutorials/sql_qa/) for working with SQL databases.
:::
#### Graph databases
Graph databases are a specialized type of database designed to store and manage highly interconnected data.
Unlike traditional relational databases, graph databases use a flexible structure consisting of nodes (entities), edges (relationships), and properties.
This structure allows for efficient representation and querying of complex, interconnected data.
Graph databases store data in a graph structure, with nodes, edges, and properties.
They are particularly useful for storing and querying complex relationships between data points, such as social networks, supply-chain management, fraud detection, and recommendation services
:::info[Further reading]
- See our [tutorial](/docs/tutorials/graph/) for working with graph databases.
- See Neo4j's [starter kit for LangChain](https://neo4j.com/developer-blog/langchain-neo4j-starter-kit/).
:::
### Retriever
LangChain provides a unified interface for interacting with various retrieval systems through the [retriever](/docs/concepts/retrievers/) concept. The interface is straightforward:
1. Input: A query (string)
2. Output: A list of documents (standardized LangChain [Document](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html) objects)
You can create a retriever using any of the retrieval systems mentioned earlier. The query analysis techniques we discussed are particularly useful here, as they enable natural language interfaces for databases that typically require structured query languages.
For example, you can build a retriever for a SQL database using text-to-SQL conversion. This allows a natural language query (string) to be transformed into a SQL query behind the scenes.
Regardless of the underlying retrieval system, all retrievers in LangChain share a common interface. You can use them with the simple `invoke` method:
```typescript
const docs = await retriever.invoke(query);
```
:::info[Further reading]
- See our [conceptual guide on retrievers](/docs/concepts/retrievers/).
- See our [how-to guide](/docs/how_to/#retrievers) on working with retrievers.
:::
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/prompt_templates.mdx | # Prompt Templates
Prompt templates help to translate user input and parameters into instructions for a language model.
This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output.
Prompt Templates take as input an object, where each key represents a variable in the prompt template to fill in.
Prompt Templates output a PromptValue. This PromptValue can be passed to an LLM or a ChatModel, and can also be cast to a string or a list of messages.
The reason this PromptValue exists is to make it easy to switch between strings and messages.
There are a few different types of prompt templates:
## String PromptTemplates
These prompt templates are used to format a single string, and generally are used for simpler inputs.
For example, a common way to construct and use a PromptTemplate is as follows:
```typescript
import { PromptTemplate } from "@langchain/core/prompts";
const promptTemplate = PromptTemplate.fromTemplate(
"Tell me a joke about {topic}"
);
await promptTemplate.invoke({ topic: "cats" });
```
```text
StringPromptValue {
value: 'Tell me a joke about cats'
}
```
## ChatPromptTemplates
These prompt templates are used to format a list of messages. These "templates" consist of a list of templates themselves.
For example, a common way to construct and use a ChatPromptTemplate is as follows:
```typescript
import { ChatPromptTemplate } from "@langchain/core/prompts";
const promptTemplate = ChatPromptTemplate.fromMessages([
["system", "You are a helpful assistant"],
["user", "Tell me a joke about {topic}"],
]);
await promptTemplate.invoke({ topic: "cats" });
```
```text
ChatPromptValue {
messages: [
SystemMessage {
"content": "You are a helpful assistant",
"additional_kwargs": {},
"response_metadata": {}
},
HumanMessage {
"content": "Tell me a joke about cats",
"additional_kwargs": {},
"response_metadata": {}
}
]
}
```
In the above example, this ChatPromptTemplate will construct two messages when called.
The first is a system message, that has no variables to format.
The second is a HumanMessage, and will be formatted by the `topic` variable the user passes in.
## MessagesPlaceholder
<span data-heading-keywords="messagesplaceholder"></span>
This prompt template is responsible for adding a list of messages in a particular place.
In the above ChatPromptTemplate, we saw how we could format two messages, each one a string.
But what if we wanted the user to pass in a list of messages that we would slot into a particular spot?
This is how you use MessagesPlaceholder.
```typescript
import {
ChatPromptTemplate,
MessagesPlaceholder,
} from "@langchain/core/prompts";
import { HumanMessage } from "@langchain/core/messages";
const promptTemplate = ChatPromptTemplate.fromMessages([
["system", "You are a helpful assistant"],
new MessagesPlaceholder("msgs"),
]);
await promptTemplate.invoke({ msgs: [new HumanMessage("hi!")] });
```
```text
ChatPromptValue {
messages: [
SystemMessage {
"content": "You are a helpful assistant",
"additional_kwargs": {},
"response_metadata": {}
},
HumanMessage {
"content": "hi!",
"additional_kwargs": {},
"response_metadata": {}
}
]
}
```
This will produce a list of two messages, the first one being a system message, and the second one being the HumanMessage we passed in.
If we had passed in 5 messages, then it would have produced 6 messages in total (the system message plus the 5 passed in).
This is useful for letting a list of messages be slotted into a particular spot.
An alternative way to accomplish the same thing without using the `MessagesPlaceholder` class explicitly is:
```typescript
const promptTemplate = ChatPromptTemplate.fromMessages([
["system", "You are a helpful assistant"],
// highlight-next-line
["placeholder", "{msgs}"], // <-- This is the changed part
]);
```
For specifics on how to use prompt templates, see the [relevant how-to guides here](/docs/how_to/#prompt-templates).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/agents.mdx | # Agents
By themselves, language models can't take actions - they just output text. Agents are systems that take a high-level task and use an LLM as a reasoning engine to decide what actions to take and execute those actions.
[LangGraph](/docs/concepts/architecture#langgraph) is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. We recommend that you use LangGraph for building agents.
Please see the following resources for more information:
- LangGraph docs on [common agent architectures](https://langchain-ai.github.io/langgraphjs/concepts/agentic_concepts/)
- [Pre-built agents in LangGraph](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html)
## Legacy agent concept: AgentExecutor
LangChain previously introduced the `AgentExecutor` as a runtime for agents.
While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents.
As a result, we're gradually phasing out `AgentExecutor` in favor of more flexible solutions in LangGraph.
### Transitioning from AgentExecutor to langgraph
If you're currently using `AgentExecutor`, don't worry! We've prepared resources to help you:
1. For those who still need to use `AgentExecutor`, we offer a comprehensive guide on [how to use AgentExecutor](/docs/how_to/agent_executor).
2. However, we strongly recommend transitioning to LangGraph for improved flexibility and control. To facilitate this transition, we've created a detailed [migration guide](/docs/how_to/migrate_agent) to help you move from `AgentExecutor` to LangGraph seamlessly.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/vectorstores.mdx | # Vector stores
<span data-heading-keywords="vector,vectorstore,vectorstores,vector store,vector stores"></span>
:::info[Prerequisites]
- [Embeddings](/docs/concepts/embedding_models/)
- [Text splitters](/docs/concepts/text_splitters/)
:::
:::info[Note]
This conceptual overview focuses on text-based indexing and retrieval for simplicity.
However, embedding models can be [multi-modal](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings)
and vector stores can be used to store and retrieve a variety of data types beyond text.
:::
## Overview
Vector stores are specialized data stores that enable indexing and retrieving information based on vector representations.
These vectors, called [embeddings](/docs/concepts/embedding_models/), capture the semantic meaning of data that has been embedded.
Vector stores are frequently used to search over unstructured data, such as text, images, and audio, to retrieve relevant information based on semantic similarity rather than exact keyword matches.
## Integrations
LangChain has a large number of vectorstore integrations, allowing users to easily switch between different vectorstore implementations.
Please see the [full list of LangChain vectorstore integrations](/docs/integrations/vectorstores/).
## Interface
LangChain provides a standard interface for working with vector stores, allowing users to easily switch between different vectorstore implementations.
The interface consists of basic methods for writing, deleting and searching for documents in the vector store.
The key methods are:
- `addDocuments`: Add a list of texts to the vector store.
- `deleteDocuments` / `delete`: Delete a list of documents from the vector store.
- `similaritySearch`: Search for similar documents to a given query.
## Initialization
Most vectors in LangChain accept an embedding model as an argument when initializing the vector store.
We will use LangChain's [MemoryVectorStore](https://api.js.langchain.com/classes/langchain.vectorstores_memory.MemoryVectorStore.html) implementation to illustrate the API.
```typescript
import { MemoryVectorStore } from "langchain/vectorstores/memory";
// Initialize with an embedding model
const vectorStore = new MemoryVectorStore(new SomeEmbeddingModel());
```
## Adding documents
To add documents, use the `addDocuments` method.
This API works with a list of [Document](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html) objects.
`Document` objects all have `pageContent` and `metadata` attributes, making them a universal way to store unstructured text and associated metadata.
```typescript
import { Document } from "@langchain/core/documents";
const document1 = new Document(
pageContent: "I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
metadata: { source: "tweet" },
)
const document2 = new Document(
pageContent: "The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
metadata: { source: "news" },
)
const documents = [document1, document2]
await vectorStore.addDocuments(documents)
```
You should usually provide IDs for the documents you add to the vector store, so
that instead of adding the same document multiple times, you can update the existing document.
```typescript
await vectorStore.addDocuments(documents, { ids: ["doc1", "doc2"] });
```
## Delete
To delete documents, use the `deleteDocuments` method which takes a list of document IDs to delete.
```typescript
await vectorStore.deleteDocuments(["doc1"]);
```
or the `delete` method:
```typescript
await vectorStore.deleteDocuments({ ids: ["doc1"] });
```
## Search
Vector stores embed and store the documents that added.
If we pass in a query, the vectorstore will embed the query, perform a similarity search over the embedded documents, and return the most similar ones.
This captures two important concepts: first, there needs to be a way to measure the similarity between the query and _any_ [embedded](/docs/concepts/embedding_models/) document.
Second, there needs to be an algorithm to efficiently perform this similarity search across _all_ embedded documents.
### Similarity metrics
A critical advantage of embeddings vectors is they can be compared using many simple mathematical operations:
- **Cosine Similarity**: Measures the cosine of the angle between two vectors.
- **Euclidean Distance**: Measures the straight-line distance between two points.
- **Dot Product**: Measures the projection of one vector onto another.
The choice of similarity metric can sometimes be selected when initializing the vectorstore. Please refer
to the documentation of the specific vectorstore you are using to see what similarity metrics are supported.
:::info[Further reading]
- See [this documentation](https://developers.google.com/machine-learning/clustering/dnn-clustering/supervised-similarity) from Google on similarity metrics to consider with embeddings.
- See Pinecone's [blog post](https://www.pinecone.io/learn/vector-similarity/) on similarity metrics.
- See OpenAI's [FAQ](https://platform.openai.com/docs/guides/embeddings/faq) on what similarity metric to use with OpenAI embeddings.
:::
### Similarity search
Given a similarity metric to measure the distance between the embedded query and any embedded document, we need an algorithm to efficiently search over _all_ the embedded documents to find the most similar ones.
There are various ways to do this. As an example, many vectorstores implement [HNSW (Hierarchical Navigable Small World)](https://www.pinecone.io/learn/series/faiss/hnsw/), a graph-based index structure that allows for efficient similarity search.
Regardless of the search algorithm used under the hood, the LangChain vectorstore interface has a `similaritySearch` method for all integrations.
This will take the search query, create an embedding, find similar documents, and return them as a list of [Documents](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html).
```typescript
const query = "my query";
const docs = await vectorstore.similaritySearch(query);
```
Many vectorstores support search parameters to be passed with the `similaritySearch` method. See the documentation for the specific vectorstore you are using to see what parameters are supported.
As an example [Pinecone](https://api.js.langchain.com/classes/_langchain_pinecone.PineconeStore.html#similaritySearch) several parameters that are important general concepts:
Many vectorstores support [the `k`](/docs/integrations/vectorstores/pinecone/#query-directly), which controls the number of Documents to return, and `filter`, which allows for filtering documents by metadata.
- `query (string) – Text to look up documents similar to.`
- `k (number) – Number of Documents to return. Defaults to 4.`
- `filter (Record<string, any> | undefined) – Object of argument(s) to filter on metadata`
:::info[Further reading]
- See the [how-to guide](/docs/how_to/vectorstores/) for more details on how to use the `similaritySearch` method.
- See the [integrations page](/docs/integrations/vectorstores/) for more details on arguments that can be passed in to the `similaritySearch` method for specific vectorstores.
:::
### Metadata filtering
While vectorstore implement a search algorithm to efficiently search over _all_ the embedded documents to find the most similar ones, many also support filtering on metadata.
This allows structured filters to reduce the size of the similarity search space. These two concepts work well together:
1. **Semantic search**: Query the unstructured data directly, often using via embedding or keyword similarity.
2. **Metadata search**: Apply structured query to the metadata, filtering specific documents.
Vector store support for metadata filtering is typically dependent on the underlying vector store implementation.
Here is example usage with [Pinecone](/docs/integrations/vectorstores/pinecone/#query-directly), showing that we filter for all documents that have the metadata key `source` with value `tweet`.
```typescript
await vectorstore.similaritySearch(
"LangChain provides abstractions to make working with LLMs easy",
2,
{
// The arguments of this field are provider specific.
filter: { source: "tweet" },
}
);
```
:::info[Further reading]
- See Pinecone's [documentation](https://docs.pinecone.io/guides/data/filter-with-metadata) on filtering with metadata.
- See the [list of LangChain vectorstore integrations](/docs/integrations/retrievers/self_query/) that support metadata filtering.
:::
## Advanced search and retrieval techniques
While algorithms like HNSW provide the foundation for efficient similarity search in many cases, additional techniques can be employed to improve search quality and diversity.
For example, maximal marginal relevance is a re-ranking algorithm used to diversify search results, which is applied after the initial similarity search to ensure a more diverse set of results.
| Name | When to use | Description |
| ----------------------------------------------------------------------------------------------------------------- | ----------------------------------------- | ----------------------------------------------------------------------------------------------------- |
| [Maximal Marginal Relevance (MMR)](/docs/integrations/vectorstores/pinecone/#maximal-marginal-relevance-searches) | When needing to diversify search results. | MMR attempts to diversify the results of a search to avoid returning similar and redundant documents. |
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/tools.mdx | # Tools
:::info Prerequisites
- [Chat models](/docs/concepts/chat_models/)
:::
## Overview
The **tool** abstraction in LangChain associates a TypeScript **function** with a **schema** that defines the function's **name**, **description** and **input**.
**Tools** can be passed to [chat models](/docs/concepts/chat_models) that support [tool calling](/docs/concepts/tool_calling) allowing the model to request the execution of a specific function with specific inputs.
## Key concepts
- Tools are a way to encapsulate a function and its schema in a way that can be passed to a chat model.
- Create tools using the [tool](https://api.js.langchain.com/functions/_langchain_core.tools.tool-1.html) function, which simplifies the process of tool creation, supporting the following:
- Defining tools that return **artifacts** (e.g. images, etc.)
- Hiding input arguments from the schema (and hence from the model) using **injected tool arguments**.
## Tool interface
The tool interface is defined in the [`StructuredTool`](https://api.js.langchain.com/classes/_langchain_core.tools.StructuredTool.html) class which is a subclass of the [Runnable Interface](/docs/concepts/runnables).
The key attributes that correspond to the tool's **schema**:
- **name**: The name of the tool.
- **description**: A description of what the tool does.
- **args**: Property that returns the JSON schema for the tool's arguments.
The key methods to execute the function associated with the **tool**:
- **invoke**: Invokes the tool with the given arguments.
## Create tools using the `tool` function
The recommended way to create tools is using the [tool](https://api.js.langchain.com/functions/_langchain_core.tools.tool-1.html) function. This function is designed to simplify the process of tool creation and should be used in most cases.
```typescript
import { tool } from "@langchain/core/tools";
import { z } from "zod";
const multiply = tool(
({ a, b }: { a: number; b: number }): number => {
/**
* Multiply two numbers.
*/
return a * b;
},
{
name: "multiply",
description: "Multiply two numbers",
schema: z.object({
a: z.number(),
b: z.number(),
}),
}
);
```
For more details on how to create tools, see the [how to create custom tools](/docs/how_to/custom_tools/) guide.
:::note
LangChain has a few other ways to create tools; e.g., by sub-classing the [`StructuredTool`](https://api.js.langchain.com/classes/_langchain_core.tools.StructuredTool.html) class or by using `StructuredTool`. These methods are shown in the [how to create custom tools guide](/docs/how_to/custom_tools/), but
we generally recommend using the `tool` function for most cases.
:::
## Use the tool directly
Once you have defined a tool, you can use it directly by calling the function. For example, to use the `multiply` tool defined above:
```typescript
await multiply.invoke({ a: 2, b: 3 });
```
### Inspect
You can also inspect the tool's schema and other properties:
```typescript
console.log(multiply.name); // multiply
console.log(multiply.description); // Multiply two numbers.
```
:::note
If you're using pre-built LangChain or LangGraph components like [createReactAgent](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html),you might not need to interact with tools directly. However, understanding how to use them can be valuable for debugging and testing. Additionally, when building custom LangGraph workflows, you may find it necessary to work with tools directly.
:::
## Configuring the schema
The `tool` function offers additional options to configure the schema of the tool (e.g., modify name, description
or parse the function's doc-string to infer the schema).
Please see the [API reference for tool](https://api.js.langchain.com/functions/_langchain_core.tools.tool-1.html) for more details and review the [how to create custom tools](/docs/how_to/custom_tools/) guide for examples.
## Tool artifacts
**Tools** are utilities that can be called by a model, and whose outputs are designed to be fed back to a model. Sometimes, however, there are artifacts of a tool's execution that we want to make accessible to downstream components in our chain or agent, but that we don't want to expose to the model itself. For example if a tool returns a custom object, a dataframe or an image, we may want to pass some metadata about this output to the model without passing the actual output to the model. At the same time, we may want to be able to access this full output elsewhere, for example in downstream tools.
```typescript
const someTool = tool(({ ... }) => {
// do something
}, {
// ... tool schema args
// Set the returnType to "content_and_artifact"
responseFormat: "content_and_artifact"
});
```
See [how to return artifacts from tools](/docs/how_to/tool_artifacts/) for more details.
### RunnableConfig
You can use the `RunnableConfig` object to pass custom run time values to tools.
If you need to access the [RunnableConfig](/docs/concepts/runnables/#RunnableConfig) object from within a tool. This can be done by using the `RunnableConfig` in the tool's function signature.
```typescript
import { RunnableConfig } from "@langchain/core/runnables";
const someTool = tool(
async (args: any, config: RunnableConfig): Promise<[string, any]> => {
/**
* Tool that does something.
*/
},
{
name: "some_tool",
description: "Tool that does something",
schema: z.object({ ... }),
returnType: "content_and_artifact"
}
);
await someTool.invoke(..., { configurable: { value: "some_value" } });
```
The `config` will not be part of the tool's schema and will be injected at runtime with appropriate values.
## Best practices
When designing tools to be used by models, keep the following in mind:
- Tools that are well-named, correctly-documented and properly type-hinted are easier for models to use.
- Design simple and narrowly scoped tools, as they are easier for models to use correctly.
- Use chat models that support [tool-calling](/docs/concepts/tool_calling) APIs to take advantage of tools.
## Toolkits
<span data-heading-keywords="toolkit,toolkits"></span>
LangChain has a concept of **toolkits**. This a very thin abstraction that groups tools together that
are designed to be used together for specific tasks.
### Interface
All Toolkits expose a `getTools` method which returns a list of tools. You can therefore do:
```typescript
// Initialize a toolkit
const toolkit = new ExampleTookit(...)
// Get list of tools
const tools = toolkit.getTools()
```
## Related resources
See the following resources for more information:
- [API Reference for `tool`](https://api.js.langchain.com/functions/_langchain_core.tools.tool-1.html)
- [How to create custom tools](/docs/how_to/custom_tools/)
- [How to pass run time values to tools](/docs/how_to/tool_runtime/)
- [All LangChain tool how-to guides](https://docs.langchain.com/docs/how_to/#tools)
- [Additional how-to guides that show usage with LangGraph](https://langchain-ai.github.io/langgraphjs/how-tos/tool-calling/)
- Tool integrations, see the [tool integration docs](https://docs.langchain.com/docs/integrations/tools/).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/rag.mdx | # Retrieval augmented generation (rag)
:::info[Prerequisites]
- [Retrieval](/docs/concepts/retrieval/)
:::
## Overview
Retrieval Augmented Generation (RAG) is a powerful technique that enhances [language models](/docs/concepts/chat_models/) by combining them with external knowledge bases.
RAG addresses [a key limitation of models](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise): models rely on fixed training datasets, which can lead to outdated or incomplete information.
When given a query, RAG systems first search a knowledge base for relevant information.
The system then incorporates this retrieved information into the model's prompt.
The model uses the provided context to generate a response to the query.
By bridging the gap between vast language models and dynamic, targeted information retrieval, RAG is a powerful technique for building more capable and reliable AI systems.
## Key concepts
(1) **Retrieval system**: Retrieve relevant information from a knowledge base.
(2) **Adding external knowledge**: Pass retrieved information to a model.
## Retrieval system
Model's have internal knowledge that is often fixed, or at least not updated frequently due to the high cost of training.
This limits their ability to answer questions about current events, or to provide specific domain knowledge.
To address this, there are various knowledge injection techniques like [fine-tuning](https://hamel.dev/blog/posts/fine_tuning_valuable.html) or continued pre-training.
Both are [costly](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise) and often [poorly suited](https://www.anyscale.com/blog/fine-tuning-is-for-form-not-facts) for factual retrieval.
Using a retrieval system offers several advantages:
- **Up-to-date information**: RAG can access and utilize the latest data, keeping responses current.
- **Domain-specific expertise**: With domain-specific knowledge bases, RAG can provide answers in specific domains.
- **Reduced hallucination**: Grounding responses in retrieved facts helps minimize false or invented information.
- **Cost-effective knowledge integration**: RAG offers a more efficient alternative to expensive model fine-tuning.
:::info[Further reading]
See our conceptual guide on [retrieval](/docs/concepts/retrieval/).
:::
## Adding external knowledge
With a retrieval system in place, we need to pass knowledge from this system to the model.
A RAG pipeline typically achieves this following these steps:
- Receive an input query.
- Use the retrieval system to search for relevant information based on the query.
- Incorporate the retrieved information into the prompt sent to the LLM.
- Generate a response that leverages the retrieved context.
As an example, here's a simple RAG workflow that passes information from a [retriever](/docs/concepts/retrievers/) to a [chat model](/docs/concepts/chat_models/):
```typescript
import { ChatOpenAI } from "@langchain/openai";
// Define a system prompt that tells the model how to use the retrieved context
const systemPrompt = `You are an assistant for question-answering tasks.
Use the following pieces of retrieved context to answer the question.
If you don't know the answer, just say that you don't know.
Use three sentences maximum and keep the answer concise.
Context: {context}:`;
// Define a question
const question =
"What are the main components of an LLM-powered autonomous agent system?";
// Retrieve relevant documents
const docs = await retriever.invoke(question);
// Combine the documents into a single string
const docsText = docs.map((d) => d.pageContent).join("");
// Populate the system prompt with the retrieved context
const systemPromptFmt = systemPrompt.replace("{context}", docsText);
// Create a model
const model = new ChatOpenAI({
model: "gpt-4o",
temperature: 0,
});
// Generate a response
const questions = await model.invoke([
{
role: "system",
content: systemPromptFmt,
},
{
role: "user",
content: question,
},
]);
```
:::info[Further reading]
RAG a deep area with many possible optimization and design choices:
- See [this excellent blog](https://cameronrwolfe.substack.com/p/a-practitioners-guide-to-retrieval?utm_source=profile&utm_medium=reader2) from Cameron Wolfe for a comprehensive overview and history of RAG.
- See our [RAG how-to guides](/docs/how_to/#qa-with-rag).
- See our RAG [tutorials](/docs/tutorials/#working-with-external-knowledge).
- See our RAG from Scratch course, with [code](https://github.com/langchain-ai/rag-from-scratch) and [video playlist](https://www.youtube.com/playlist?list=PLfaIDFEXuae2LXbO1_PKyVJiQ23ZztA0x).
- Also, see our RAG from Scratch course [on Freecodecamp](https://youtu.be/sVcwVQRHIc8?feature=shared).
:::
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/t.ipynb | import {
ChatPromptTemplate,
MessagesPlaceholder,
} from "@langchain/core/prompts";
import { HumanMessage } from "@langchain/core/messages";
const promptTemplate = ChatPromptTemplate.fromMessages([
["system", "You are a helpful assistant"],
new MessagesPlaceholder("msgs"),
]);
await promptTemplate.invoke({ msgs: [new HumanMessage("hi!")] }); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/tracing.mdx | # Tracing
<span data-heading-keywords="trace,tracing"></span>
A trace is essentially a series of steps that your application takes to go from input to output.
Traces contain individual steps called `runs`. These can be individual calls from a model, retriever,
tool, or sub-chains.
Tracing gives you observability inside your chains and agents, and is vital in diagnosing issues.
For a deeper dive, check out [this LangSmith conceptual guide](https://docs.smith.langchain.com/concepts/tracing).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/text_splitters.mdx | # Text splitters
<span data-heading-keywords="text splitter,text splitting"></span>
:::info[Prerequisites]
- [Documents](/docs/concepts/retrievers/#interface)
- Tokenization(/docs/concepts/tokens)
:::
## Overview
Document splitting is often a crucial preprocessing step for many applications.
It involves breaking down large texts into smaller, manageable chunks.
This process offers several benefits, such as ensuring consistent processing of varying document lengths, overcoming input size limitations of models, and improving the quality of text representations used in retrieval systems.
There are several strategies for splitting documents, each with its own advantages.
## Key concepts
Text splitters split documents into smaller chunks for use in downstream applications.
## Why split documents?
There are several reasons to split documents:
- **Handling non-uniform document lengths**: Real-world document collections often contain texts of varying sizes. Splitting ensures consistent processing across all documents.
- **Overcoming model limitations**: Many embedding models and language models have maximum input size constraints. Splitting allows us to process documents that would otherwise exceed these limits.
- **Improving representation quality**: For longer documents, the quality of embeddings or other representations may degrade as they try to capture too much information. Splitting can lead to more focused and accurate representations of each section.
- **Enhancing retrieval precision**: In information retrieval systems, splitting can improve the granularity of search results, allowing for more precise matching of queries to relevant document sections.
- **Optimizing computational resources**: Working with smaller chunks of text can be more memory-efficient and allow for better parallelization of processing tasks.
Now, the next question is _how_ to split the documents into chunks! There are several strategies, each with its own advantages.
:::info[Further reading]
- See Greg Kamradt's [chunkviz](https://chunkviz.up.railway.app/) to visualize different splitting strategies discussed below.
:::
## Approaches
### Length-based
The most intuitive strategy is to split documents based on their length. This simple yet effective approach ensures that each chunk doesn't exceed a specified size limit.
Key benefits of length-based splitting:
- Straightforward implementation
- Consistent chunk sizes
- Easily adaptable to different model requirements
Types of length-based splitting:
- **Token-based**: Splits text based on the number of tokens, which is useful when working with language models.
- **Character-based**: Splits text based on the number of characters, which can be more consistent across different types of text.
Example implementation using LangChain's `CharacterTextSplitter` with character based splitting:
```typescript
import { CharacterTextSplitter } from "@langchain/textsplitters";
const textSplitter = new CharacterTextSplitter({
chunkSize: 100,
chunkOverlap: 0,
});
const texts = await textSplitter.splitText(document);
```
:::info[Further reading]
- See the how-to guide for [token-based](/docs/how_to/split_by_token/) splitting.
- See the how-to guide for [character-based](/docs/how_to/character_text_splitter/) splitting.
:::
### Text-structured based
Text is naturally organized into hierarchical units such as paragraphs, sentences, and words.
We can leverage this inherent structure to inform our splitting strategy, creating split that maintain natural language flow, maintain semantic coherence within split, and adapts to varying levels of text granularity.
LangChain's [`RecursiveCharacterTextSplitter`](/docs/how_to/recursive_text_splitter/) implements this concept:
- The `RecursiveCharacterTextSplitter` attempts to keep larger units (e.g., paragraphs) intact.
- If a unit exceeds the chunk size, it moves to the next level (e.g., sentences).
- This process continues down to the word level if necessary.
Here is example usage:
```typescript
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
const textSplitter = new RecursiveCharacterTextSplitter({
chunkSize: 100,
chunkOverlap: 0,
});
const texts = await textSplitter.splitText(document);
```
:::info[Further reading]
- See the how-to guide for [recursive text splitting](/docs/how_to/recursive_text_splitter/).
:::
### Document-structured based
Some documents have an inherent structure, such as HTML, Markdown, or JSON files.
In these cases, it's beneficial to split the document based on its structure, as it often naturally groups semantically related text.
Key benefits of structure-based splitting:
- Preserves the logical organization of the document
- Maintains context within each chunk
- Can be more effective for downstream tasks like retrieval or summarization
Examples of structure-based splitting:
- **Markdown**: Split based on headers (e.g., #, ##, ###)
- **HTML**: Split using tags
- **JSON**: Split by object or array elements
- **Code**: Split by functions, classes, or logical blocks
:::info[Further reading]
- See the how-to guide for [Code splitting](/docs/how_to/code_splitter/).
:::
### Semantic meaning based
Unlike the previous methods, semantic-based splitting actually considers the _content_ of the text.
While other approaches use document or text structure as proxies for semantic meaning, this method directly analyzes the text's semantics.
There are several ways to implement this, but conceptually the approach is split text when there are significant changes in text _meaning_.
As an example, we can use a sliding window approach to generate embeddings, and compare the embeddings to find significant differences:
- Start with the first few sentences and generate an embedding.
- Move to the next group of sentences and generate another embedding (e.g., using a sliding window approach).
- Compare the embeddings to find significant differences, which indicate potential "break points" between semantic sections.
This technique helps create chunks that are more semantically coherent, potentially improving the quality of downstream tasks like retrieval or summarization.
:::info[Further reading]
- See Greg Kamradt's [notebook](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb) showcasing semantic splitting.
:::
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/example_selectors.mdx | # Example selectors
:::note Prerequisites
- [Chat models](/docs/concepts/chat_models/)
- [Few-shot prompting](/docs/concepts/few_shot_prompting/)
:::
## Overview
One common prompting technique for achieving better performance is to include examples as part of the prompt. This is known as [few-shot prompting](/docs/concepts/few_shot_prompting).
This gives the [language model](/docs/concepts/chat_models/) concrete examples of how it should behave.
Sometimes these examples are hardcoded into the prompt, but for more advanced situations it may be nice to dynamically select them.
**Example Selectors** are classes responsible for selecting and then formatting examples into prompts.
> See the [API reference for more information.](https://v03.api.js.langchain.com/classes/_langchain_core.example_selectors.BaseExampleSelector.html)
## Related resources
- [Example selector how-to guides](/docs/how_to/#example-selectors)
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/streaming.mdx | # Streaming
:::info Prerequisites
- [Runnable Interface](/docs/concepts/runnables)
- [Chat Models](/docs/concepts/chat_models)
:::
**Streaming** is crucial for enhancing the responsiveness of applications built on [LLMs](/docs/concepts/chat_models). By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
## Overview
Generating full responses from [LLMs](/docs/concepts/chat_models) often incurs a delay of several seconds, which becomes more noticeable in complex applications with multiple model calls. Fortunately, LLMs generate responses iteratively, allowing for intermediate results to be displayed as they are produced. By streaming these intermediate outputs, LangChain enables smoother UX in LLM-powered apps and offers built-in support for streaming at the core of its design.
In this guide, we'll discuss streaming in LLM applications and explore how LangChain's streaming APIs facilitate real-time output from various components in your application.
## What to stream in LLM applications
In applications involving LLMs, several types of data can be streamed to improve user experience by reducing perceived latency and increasing transparency. These include:
### 1. Streaming LLM outputs
The most common and critical data to stream is the output generated by the LLM itself. LLMs often take time to generate full responses, and by streaming the output in real-time, users can see partial results as they are produced. This provides immediate feedback and helps reduce the wait time for users.
### 2. Streaming pipeline or workflow progress
Beyond just streaming LLM output, it’s useful to stream progress through more complex workflows or pipelines, giving users a sense of how the application is progressing overall. This could include:
- **In LangGraph Workflows:**
With [LangGraph](/docs/concepts/architecture#langgraph), workflows are composed of nodes and edges that represent various steps. Streaming here involves tracking changes to the **graph state** as individual **nodes** request updates. This allows for more granular monitoring of which node in the workflow is currently active, giving real-time updates about the status of the workflow as it progresses through different stages.
- **In LCEL Pipelines:**
Streaming updates from an [LCEL](/docs/concepts/lcel) pipeline involves capturing progress from individual **sub-runnables**. For example, as different steps or components of the pipeline execute, you can stream which sub-runnable is currently running, providing real-time insight into the overall pipeline's progress.
Streaming pipeline or workflow progress is essential in providing users with a clear picture of where the application is in the execution process.
### 3. Streaming custom data
In some cases, you may need to stream **custom data** that goes beyond the information provided by the pipeline or workflow structure. This custom information is injected within a specific step in the workflow, whether that step is a tool or a LangGraph node. For example, you could stream updates about what a tool is doing in real-time or the progress through a LangGraph node. This granular data, which is emitted directly from within the step, provides more detailed insights into the execution of the workflow and is especially useful in complex processes where more visibility is needed.
## Streaming APIs
LangChain two main APIs for streaming output in real-time. These APIs are supported by any component that implements the [Runnable Interface](/docs/concepts/runnables), including [LLMs](/docs/concepts/chat_models), [compiled LangGraph graphs](https://langchain-ai.github.io/langgraphjs/concepts/low_level/), and any Runnable generated with [LCEL](/docs/concepts/lcel).
1. [`stream`](https://api.js.langchain.com/classes/_langchain_core.runnables.Runnable.html#stream): Use to stream outputs from individual Runnables (e.g., a chat model) as they are generated or stream any workflow created with LangGraph.
2. [`streamEvents`](https://api.js.langchain.com/classes/_langchain_core.runnables.Runnable.html#streamEvents): Use this API to get access to custom events and intermediate outputs from LLM applications built entirely with [LCEL](/docs/concepts/lcel). Note that this API is available, but not needed when working with LangGraph.
:::note
In addition, there is a **legacy** [streamLog](https://api.js.langchain.com/classes/_langchain_core.runnables.Runnable.html#streamLog) API. This API is not recommended for new projects it is more complex and less feature-rich than the other streaming APIs.
:::
### `stream()`
The `stream()` method returns an iterator that yields chunks of output synchronously as they are produced. You can use a `for await` loop to process each chunk in real-time. For example, when using an LLM, this allows the output to be streamed incrementally as it is generated, reducing the wait time for users.
The type of chunk yielded by the `stream()` methods depends on the component being streamed. For example, when streaming from an [LLM](/docs/concepts/chat_models) each component will be an [`AIMessageChunk`](/docs/concepts/messages#aimessagechunk); however, for other components, the chunk may be different.
The `stream()` method returns an iterator that yields these chunks as they are produced. For example,
```typescript
for await (const chunk of await component.stream(someInput)) {
// IMPORTANT: Keep the processing of each chunk as efficient as possible.
// While you're processing the current chunk, the upstream component is
// waiting to produce the next one. For example, if working with LangGraph,
// graph execution is paused while the current chunk is being processed.
// In extreme cases, this could even result in timeouts (e.g., when llm outputs are
// streamed from an API that has a timeout).
console.log(chunk);
}
```
#### Usage with chat models
When using `stream()` with chat models, the output is streamed as [`AIMessageChunks`](/docs/concepts/messages#aimessagechunk) as it is generated by the LLM. This allows you to present or process the LLM's output incrementally as it's being produced, which is particularly useful in interactive applications or interfaces.
#### Usage with LangGraph
[LangGraph](/docs/concepts/architecture#langgraph) compiled graphs are [Runnables](/docs/concepts/runnables) and support the standard streaming APIs.
When using the _stream_ and methods with LangGraph, you can **one or more** [streaming mode](https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph_pregel.Pregel.html#streamMode) which allow you to control the type of output that is streamed. The available streaming modes are:
- **"values"**: Emit all values of the [state](https://langchain-ai.github.io/langgraphjs/concepts/low_level/) for each step.
- **"updates"**: Emit only the node name(s) and updates that were returned by the node(s) after each step.
- **"debug"**: Emit debug events for each step.
- **"messages"**: Emit LLM [messages](/docs/concepts/messages) [token-by-token](/docs/concepts/tokens).
For more information, please see:
- [LangGraph streaming conceptual guide](https://langchain-ai.github.io/langgraphjs/concepts/streaming/) for more information on how to stream when working with LangGraph.
- [LangGraph streaming how-to guides](https://langchain-ai.github.io/langgraphjs/how-tos/#streaming) for specific examples of streaming in LangGraph.
#### Usage with LCEL
If you compose multiple Runnables using [LangChain’s Expression Language (LCEL)](/docs/concepts/lcel), the `stream()` methods will, by convention, stream the output of the last step in the chain. This allows the final processed result to be streamed incrementally. **LCEL** tries to optimize streaming latency in pipelines such that the streaming results from the last step are available as soon as possible.
### `streamEvents`
<span data-heading-keywords="streamEvents,stream_events,stream events"></span>
:::tip
Use the `streamEvents` API to access custom data and intermediate outputs from LLM applications built entirely with [LCEL](/docs/concepts/lcel).
While this API is available for use with [LangGraph](/docs/concepts/architecture#langgraph) as well, it is usually not necessary when working with LangGraph, as the `stream` methods provide comprehensive streaming capabilities for LangGraph graphs.
:::
For chains constructed using **LCEL**, the `.stream()` method only streams the output of the final step from te chain. This might be sufficient for some applications, but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of the chain alongside the final output. For example, you may want to return sources alongside the final generation when building a chat-over-documents app.
There are ways to do this [using callbacks](/docs/concepts/callbacks), or by constructing your chain in such a way that it passes intermediate
values to the end with something like chained [`.assign()`](/docs/how_to/passthrough/) calls, but LangChain also includes an
`.streamEvents()` method that combines the flexibility of callbacks with the ergonomics of `.stream()`. When called, it returns an iterator
which yields [various types of events](/docs/how_to/streaming/#event-reference) that you can filter and process according
to the needs of your project.
Here's one small example that prints just events containing streamed chat model output:
```typescript
import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({ model: "claude-3-sonnet-20240229" });
const prompt = ChatPromptTemplate.fromTemplate("tell me a joke about {topic}");
const parser = StringOutputParser();
const chain = prompt.pipe(model).pipe(parser);
for await (const event of await chain.streamEvents(
{ topic: "parrot" },
{ version: "v2" }
)) {
if (event.event === "on_chat_model_stream") {
console.log(event);
}
}
```
You can roughly think of it as an iterator over callback events (though the format differs) - and you can use it on almost all LangChain components!
See [this guide](/docs/how_to/streaming/#using-stream-events) for more detailed information on how to use `.streamEvents()`, including a table listing available events.
## Writing custom data to the stream
To write custom data to the stream, you will need to choose one of the following methods based on the component you are working with:
1. [dispatch_events](https://api.js.langchain.com/functions/_langchain_core.callbacks_dispatch.dispatchCustomEvent.html#) can be used to write custom data that will be surfaced through the **streamEvents** API. See [how to dispatch custom callback events](/docs/how_to/callbacks_custom_events/#stream-events-api) for more information.
## "Auto-Streaming" Chat Models
LangChain simplifies streaming from [chat models](/docs/concepts/chat_models) by automatically enabling streaming mode in certain cases, even when you're not explicitly calling the streaming methods. This is particularly useful when you use the non-streaming `invoke` method but still want to stream the entire application, including intermediate results from the chat model.
### How It Works
When you call the `invoke` method on a chat model, LangChain will automatically switch to streaming mode if it detects that you are trying to stream the overall application.
Under the hood, it'll have `invoke` use the `stream` method to generate its output. The result of the invocation will be the same as far as the code that was using `invoke` is concerned; however, while the chat model is being streamed, LangChain will take care of invoking `on_llm_new_token` events in LangChain's [callback system](/docs/concepts/callbacks). These callback events
allow LangGraph `stream` and `streamEvents` to surface the chat model's output in real-time.
Example:
```typescript
const node = (state) => {
...
// The code below uses the invoke method, but LangChain will
// automatically switch to streaming mode
// when it detects that the overall
// application is being streamed.
ai_message = model.invoke(state["messages"])
...
for await (const chunk of await compiledGraph.stream(..., { streamMode: "messages" })) {
// ... do something
}
}
```
## Related Resources
Please see the following how-to guides for specific examples of streaming in LangChain:
- [LangGraph conceptual guide on streaming](https://langchain-ai.github.io/langgraphjs/concepts/streaming/)
- [LangGraph streaming how-to guides](https://langchain-ai.github.io/langgraphjs/how-tos/#streaming)
- [How to stream runnables](/docs/how_to/streaming/): This how-to guide goes over common streaming patterns with LangChain components (e.g., chat models) and with [LCEL](/docs/concepts/lcel).
- [How to stream chat models](/docs/how_to/chat_streaming/)
- [How to stream tool calls](/docs/how_to/tool_streaming/)
For writing custom data to the stream, please see the following resources:
- If using LCEL, see [how to dispatch custom callback events](/docs/how_to/callbacks_custom_events/#stream-events-api).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/few_shot_prompting.mdx | # Few-shot prompting
:::note Prerequisites
- [Chat models](/docs/concepts/chat_models/)
:::
## Overview
One of the most effective ways to improve model performance is to give a model examples of
what you want it to do. The technique of adding example inputs and expected outputs
to a model prompt is known as "few-shot prompting". The technique is based on the
[Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) paper.
There are a few things to think about when doing few-shot prompting:
1. How are examples generated?
2. How many examples are in each prompt?
3. How are examples selected at runtime?
4. How are examples formatted in the prompt?
Here are the considerations for each.
## 1. Generating examples
The first and most important step of few-shot prompting is coming up with a good dataset of examples. Good examples should be relevant at runtime, clear, informative, and provide information that was not already known to the model.
At a high-level, the basic ways to generate examples are:
- Manual: a person/people generates examples they think are useful.
- Better model: a better (presumably more expensive/slower) model's responses are used as examples for a worse (presumably cheaper/faster) model.
- User feedback: users (or labelers) leave feedback on interactions with the application and examples are generated based on that feedback (for example, all interactions with positive feedback could be turned into examples).
- LLM feedback: same as user feedback but the process is automated by having models evaluate themselves.
Which approach is best depends on your task. For tasks where a small number core principles need to be understood really well, it can be valuable hand-craft a few really good examples.
For tasks where the space of correct behaviors is broader and more nuanced, it can be useful to generate many examples in a more automated fashion so that there's a higher likelihood of there being some highly relevant examples for any runtime input.
**Single-turn v.s. multi-turn examples**
Another dimension to think about when generating examples is what the example is actually showing.
The simplest types of examples just have a user input and an expected model output. These are single-turn examples.
One more complex type if example is where the example is an entire conversation, usually in which a model initially responds incorrectly and a user then tells the model how to correct its answer.
This is called a multi-turn example. Multi-turn examples can be useful for more nuanced tasks where its useful to show common errors and spell out exactly why they're wrong and what should be done instead.
## 2. Number of examples
Once we have a dataset of examples, we need to think about how many examples should be in each prompt.
The key tradeoff is that more examples generally improve performance, but larger prompts increase costs and latency.
And beyond some threshold having too many examples can start to confuse the model.
Finding the right number of examples is highly dependent on the model, the task, the quality of the examples, and your cost and latency constraints.
Anecdotally, the better the model is the fewer examples it needs to perform well and the more quickly you hit steeply diminishing returns on adding more examples.
But, the best/only way to reliably answer this question is to run some experiments with different numbers of examples.
## 3. Selecting examples
Assuming we are not adding our entire example dataset into each prompt, we need to have a way of selecting examples from our dataset based on a given input. We can do this:
- Randomly
- By (semantic or keyword-based) similarity of the inputs
- Based on some other constraints, like token size
LangChain has a number of [`ExampleSelectors`](/docs/concepts/example_selectors) which make it easy to use any of these techniques.
Generally, selecting by semantic similarity leads to the best model performance. But how important this is is again model and task specific, and is something worth experimenting with.
## 4. Formatting examples
Most state-of-the-art models these days are chat models, so we'll focus on formatting examples for those. Our basic options are to insert the examples:
- In the system prompt as a string
- As their own messages
If we insert our examples into the system prompt as a string, we'll need to make sure it's clear to the model where each example begins and which parts are the input versus output. Different models respond better to different syntaxes, like [ChatML](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chat-markup-language), XML, TypeScript, etc.
If we insert our examples as messages, where each example is represented as a sequence of Human, AI messages, we might want to also assign [names](/docs/concepts/messages) to our messages like `"example_user"` and `"example_assistant"` to make it clear that these messages correspond to different actors than the latest input message.
**Formatting tool call examples**
One area where formatting examples as messages can be tricky is when our example outputs have tool calls. This is because different models have different constraints on what types of message sequences are allowed when any tool calls are generated.
- Some models require that any `AIMessage` with tool calls be immediately followed by `ToolMessage`s for every tool call,
- Some models additionally require that any `ToolMessage`s be immediately followed by an `AIMessage` before the next `HumanMessage`,
- Some models require that tools are passed in to the model if there are any tool calls / `ToolMessage`s in the chat history.
These requirements are model-specific and should be checked for the model you are using. If your model requires `ToolMessage`s after tool calls and/or `AIMessage`s after `ToolMessage`s and your examples only include expected tool calls and not the actual tool outputs, you can try adding dummy `ToolMessage`s / `AIMessage`s to the end of each example with generic contents to satisfy the API constraints.
In these cases it's especially worth experimenting with inserting your examples as strings versus messages, as having dummy messages can adversely affect certain models.
You can see a case study of how Anthropic and OpenAI respond to different few-shot prompting techniques on two different tool calling benchmarks [here](https://blog.langchain.dev/few-shot-prompting-to-improve-tool-calling-performance/).
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/concepts/document_loaders.mdx | # Document loaders
<span data-heading-keywords="document loader,document loaders"></span>
:::info[Prerequisites]
- [Document loaders API reference](/docs/how_to/#document-loaders)
:::
Document loaders are designed to load document objects. LangChain has hundreds of integrations with various data sources to load data from: Slack, Notion, Google Drive, etc.
## Integrations
You can find available integrations on the [Document loaders integrations page](/docs/integrations/document_loaders/).
## Interface
Documents loaders implement the [BaseLoader interface](https://api.js.langchain.com/classes/_langchain_core.document_loaders_base.BaseDocumentLoader.html).
Each DocumentLoader has its own specific parameters, but they can all be invoked in the same way with the `.load` method or `.lazy_load`.
Here's a simple example:
```typescript
import { CSVLoader } from "@langchain/community/document_loaders/fs/csv";
const loader = new CSVLoader(
... // <-- Integration specific parameters here
);
const data = await loader.load();
```
## Related resources
Please see the following resources for more information:
- [How-to guides for document loaders](/docs/how_to/#document-loaders)
- [Document API reference](https://api.js.langchain.com/classes/_langchain_core.documents.Document.html)
- [Document loaders integrations](/docs/integrations/document_loaders/)
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/summarization.mdx | ---
hide_table_of_contents: true
sidebar_position: 6
---
# Summarize Text
A common use case is wanting to summarize long documents.
This naturally runs into the context window limitations.
Unlike in question-answering, you can't just do some semantic search hacks to only select the chunks of text most relevant to the question (because, in this case, there is no particular question - you want to summarize everything).
So what do you do then?
To get started, we would recommend checking out the summarization chain, which attacks this problem in a recursive manner.
- [Summarization Chain](https://js.langchain.com/v0.1/docs/modules/chains/popular/summarize)
## Example
Here's an example of how you can use the [RefineDocumentsChain](https://js.langchain.com/v0.1/docs/modules/chains/document/refine) to summarize documents loaded from a YouTube video:
import CodeBlock from "@theme/CodeBlock";
import LoadDocuments from "@examples/use_cases/youtube/podcast_summary.ts";
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/anthropic @langchain/core
```
<CodeBlock language="typescript">{LoadDocuments}</CodeBlock>
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/qa_chat_history.ipynb | // @lc-docs-hide-cell
import { ChatOpenAI } from "@langchain/openai";
const llm = new ChatOpenAI({ model: "gpt-4o" });import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { createRetrievalChain } from "langchain/chains/retrieval";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
// 1. Load, chunk and index the contents of the blog to create a retriever.
const loader = new CheerioWebBaseLoader(
"https://lilianweng.github.io/posts/2023-06-23-agent/",
{
selector: ".post-content, .post-title, .post-header"
}
);
const docs = await loader.load();
const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 200 });
const splits = await textSplitter.splitDocuments(docs);
const vectorstore = await MemoryVectorStore.fromDocuments(splits, new OpenAIEmbeddings());
const retriever = vectorstore.asRetriever();
// 2. Incorporate the retriever into a question-answering chain.
const systemPrompt =
"You are an assistant for question-answering tasks. " +
"Use the following pieces of retrieved context to answer " +
"the question. If you don't know the answer, say that you " +
"don't know. Use three sentences maximum and keep the " +
"answer concise." +
"\n\n" +
"{context}";
const prompt = ChatPromptTemplate.fromMessages([
["system", systemPrompt],
["human", "{input}"],
]);
const questionAnswerChain = await createStuffDocumentsChain({
llm,
prompt,
});
const ragChain = await createRetrievalChain({
retriever,
combineDocsChain: questionAnswerChain,
});const response = await ragChain.invoke({ input: "What is Task Decomposition?" });
console.log(response.answer);import { createHistoryAwareRetriever } from "langchain/chains/history_aware_retriever";
import { MessagesPlaceholder } from "@langchain/core/prompts";
const contextualizeQSystemPrompt =
"Given a chat history and the latest user question " +
"which might reference context in the chat history, " +
"formulate a standalone question which can be understood " +
"without the chat history. Do NOT answer the question, " +
"just reformulate it if needed and otherwise return it as is.";
const contextualizeQPrompt = ChatPromptTemplate.fromMessages([
["system", contextualizeQSystemPrompt],
new MessagesPlaceholder("chat_history"),
["human", "{input}"],
]);
const historyAwareRetriever = await createHistoryAwareRetriever({
llm,
retriever,
rephrasePrompt: contextualizeQPrompt,
});const qaPrompt = ChatPromptTemplate.fromMessages([
["system", systemPrompt],
new MessagesPlaceholder("chat_history"),
["human", "{input}"],
]);
const questionAnswerChain2 = await createStuffDocumentsChain({
llm,
prompt: qaPrompt,
});
const ragChain2 = await createRetrievalChain({
retriever: historyAwareRetriever,
combineDocsChain: questionAnswerChain2,
});import { BaseMessage, HumanMessage, AIMessage } from "@langchain/core/messages";
let chatHistory: BaseMessage[] = [];
const question = "What is Task Decomposition?";
const aiMsg1 = await ragChain2.invoke({ input: question, chat_history: chatHistory });
chatHistory = chatHistory.concat([
new HumanMessage(question),
new AIMessage(aiMsg1.answer),
]);
const secondQuestion = "What are common ways of doing it?";
const aiMsg2 = await ragChain2.invoke({ input: secondQuestion, chat_history: chatHistory });
console.log(aiMsg2.answer);import { RunnableWithMessageHistory } from "@langchain/core/runnables";
import { ChatMessageHistory } from "langchain/stores/message/in_memory";
const demoEphemeralChatMessageHistoryForChain = new ChatMessageHistory();
const conversationalRagChain = new RunnableWithMessageHistory({
runnable: ragChain2,
getMessageHistory: (_sessionId) => demoEphemeralChatMessageHistoryForChain,
inputMessagesKey: "input",
historyMessagesKey: "chat_history",
outputMessagesKey: "answer",
})const result1 = await conversationalRagChain.invoke(
{ input: "What is Task Decomposition?" },
{ configurable: { sessionId: "abc123" } }
);
console.log(result1.answer);const result2 = await conversationalRagChain.invoke(
{ input: "What are common ways of doing it?" },
{ configurable: { sessionId: "abc123" } }
);
console.log(result2.answer);import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
import { createHistoryAwareRetriever } from "langchain/chains/history_aware_retriever";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { createRetrievalChain } from "langchain/chains/retrieval";
import { RunnableWithMessageHistory } from "@langchain/core/runnables";
import { ChatMessageHistory } from "langchain/stores/message/in_memory";
import { BaseChatMessageHistory } from "@langchain/core/chat_history";
const llm2 = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
// Construct retriever
const loader2 = new CheerioWebBaseLoader(
"https://lilianweng.github.io/posts/2023-06-23-agent/",
{
selector: ".post-content, .post-title, .post-header"
}
);
const docs2 = await loader2.load();
const textSplitter2 = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 200 });
const splits2 = await textSplitter2.splitDocuments(docs2);
const vectorstore2 = await MemoryVectorStore.fromDocuments(splits2, new OpenAIEmbeddings());
const retriever2 = vectorstore2.asRetriever();
// Contextualize question
const contextualizeQSystemPrompt2 =
"Given a chat history and the latest user question " +
"which might reference context in the chat history, " +
"formulate a standalone question which can be understood " +
"without the chat history. Do NOT answer the question, " +
"just reformulate it if needed and otherwise return it as is.";
const contextualizeQPrompt2 = ChatPromptTemplate.fromMessages([
["system", contextualizeQSystemPrompt2],
new MessagesPlaceholder("chat_history"),
["human", "{input}"],
]);
const historyAwareRetriever2 = await createHistoryAwareRetriever({
llm: llm2,
retriever: retriever2,
rephrasePrompt: contextualizeQPrompt2
});
// Answer question
const systemPrompt2 =
"You are an assistant for question-answering tasks. " +
"Use the following pieces of retrieved context to answer " +
"the question. If you don't know the answer, say that you " +
"don't know. Use three sentences maximum and keep the " +
"answer concise." +
"\n\n" +
"{context}";
const qaPrompt2 = ChatPromptTemplate.fromMessages([
["system", systemPrompt2],
new MessagesPlaceholder("chat_history"),
["human", "{input}"],
]);
const questionAnswerChain3 = await createStuffDocumentsChain({
llm,
prompt: qaPrompt2,
});
const ragChain3 = await createRetrievalChain({
retriever: historyAwareRetriever2,
combineDocsChain: questionAnswerChain3,
});
// Statefully manage chat history
const store2: Record<string, BaseChatMessageHistory> = {};
function getSessionHistory2(sessionId: string): BaseChatMessageHistory {
if (!(sessionId in store2)) {
store2[sessionId] = new ChatMessageHistory();
}
return store2[sessionId];
}
const conversationalRagChain2 = new RunnableWithMessageHistory({
runnable: ragChain3,
getMessageHistory: getSessionHistory2,
inputMessagesKey: "input",
historyMessagesKey: "chat_history",
outputMessagesKey: "answer",
});
// Example usage
const query2 = "What is Task Decomposition?";
for await (const s of await conversationalRagChain2.stream(
{ input: query2 },
{ configurable: { sessionId: "unique_session_id" } }
)) {
console.log(s);
console.log("----");
}import { createRetrieverTool } from "langchain/tools/retriever";
const tool = createRetrieverTool(
retriever,
{
name: "blog_post_retriever",
description: "Searches and returns excerpts from the Autonomous Agents blog post.",
}
)
const tools = [tool]console.log(await tool.invoke({ query: "task decomposition" }))import { createReactAgent } from "@langchain/langgraph/prebuilt";
const agentExecutor = createReactAgent({ llm, tools });const query = "What is Task Decomposition?";
for await (const s of await agentExecutor.stream(
{ messages: [new HumanMessage(query)] }
)) {
console.log(s);
console.log("----");
}import { MemorySaver } from "@langchain/langgraph";
const memory = new MemorySaver();
const agentExecutorWithMemory = createReactAgent({ llm, tools, checkpointSaver: memory });const config = { configurable: { thread_id: "abc123" } };
for await (const s of await agentExecutorWithMemory.stream(
{ messages: [new HumanMessage("Hi! I'm bob")] },
config
)) {
console.log(s);
console.log("----");
}for await (const s of await agentExecutorWithMemory.stream(
{ messages: [new HumanMessage(query)] },
config
)) {
console.log(s);
console.log("----");
}const query3 = "What according to the blog post are common ways of doing it? redo the search";
for await (const s of await agentExecutorWithMemory.stream(
{ messages: [new HumanMessage(query3)] },
config
)) {
console.log(s);
console.log("----");
}import { ChatOpenAI } from "@langchain/openai";
import { MemorySaver } from "@langchain/langgraph";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { createRetrieverTool } from "langchain/tools/retriever";
const memory3 = new MemorySaver();
const llm3 = new ChatOpenAI({ model: "gpt-4o", temperature: 0 });
// Construct retriever
const loader3 = new CheerioWebBaseLoader(
"https://lilianweng.github.io/posts/2023-06-23-agent/",
{
selector: ".post-content, .post-title, .post-header"
}
);
const docs3 = await loader3.load();
const textSplitter3 = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 200 });
const splits3 = await textSplitter3.splitDocuments(docs3);
const vectorstore3 = await MemoryVectorStore.fromDocuments(splits3, new OpenAIEmbeddings());
const retriever3 = vectorstore3.asRetriever();
// Build retriever tool
const tool3 = createRetrieverTool(
retriever3,
{
name: "blog_post_retriever",
description: "Searches and returns excerpts from the Autonomous Agents blog post.",
}
);
const tools3 = [tool3];
const agentExecutor3 = createReactAgent({ llm: llm3, tools: tools3, checkpointSaver: memory3 }); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/rag.ipynb | import "cheerio";
import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory"
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { pull } from "langchain/hub";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
const loader = new CheerioWebBaseLoader(
"https://lilianweng.github.io/posts/2023-06-23-agent/"
);
const docs = await loader.load();
const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 200 });
const splits = await textSplitter.splitDocuments(docs);
const vectorStore = await MemoryVectorStore.fromDocuments(splits, new OpenAIEmbeddings());
// Retrieve and generate using the relevant snippets of the blog.
const retriever = vectorStore.asRetriever();
const prompt = await pull<ChatPromptTemplate>("rlm/rag-prompt");
const llm = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
const ragChain = await createStuffDocumentsChain({
llm,
prompt,
outputParser: new StringOutputParser(),
})
const retrievedDocs = await retriever.invoke("what is task decomposition")await ragChain.invoke({
question: "What is task decomposition?",
context: retrievedDocs,
});import { formatDocumentsAsString } from "langchain/util/document";
import { RunnableSequence, RunnablePassthrough } from "@langchain/core/runnables";
const declarativeRagChain = RunnableSequence.from([
{
context: retriever.pipe(formatDocumentsAsString),
question: new RunnablePassthrough(),
},
prompt,
llm,
new StringOutputParser()
]);await declarativeRagChain.invoke("What is task decomposition?")const pTagSelector = "p";
const cheerioLoader = new CheerioWebBaseLoader(
"https://lilianweng.github.io/posts/2023-06-23-agent/",
{
selector: pTagSelector
}
);
const loadedDocs = await cheerioLoader.load();
console.log(loadedDocs[0].pageContent.length)console.log(loadedDocs[0].pageContent)const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 1000, chunkOverlap: 200
});
const allSplits = await splitter.splitDocuments(loadedDocs);console.log(allSplits.length);console.log(allSplits[0].pageContent.length);allSplits[10].metadataimport { MemoryVectorStore } from "langchain/vectorstores/memory"
import { OpenAIEmbeddings } from "@langchain/openai";
const inMemoryVectorStore = await MemoryVectorStore.fromDocuments(allSplits, new OpenAIEmbeddings());const vectorStoreRetriever = inMemoryVectorStore.asRetriever({ k: 6, searchType: "similarity" });const retrievedDocuments = await vectorStoreRetriever.invoke("What are the approaches to task decomposition?");console.log(retrievedDocuments.length);console.log(retrievedDocuments[0].pageContent);import { ChatPromptTemplate } from "@langchain/core/prompts";
import { pull } from "langchain/hub";
const ragPrompt = await pull<ChatPromptTemplate>("rlm/rag-prompt");const exampleMessages = await ragPrompt.invoke({ context: "filler context", question: "filler question" });
exampleMessagesconsole.log(exampleMessages.messages[0].content);import { StringOutputParser } from "@langchain/core/output_parsers";
import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables";
import { formatDocumentsAsString } from "langchain/util/document";
const runnableRagChain = RunnableSequence.from([
{
context: vectorStoreRetriever.pipe(formatDocumentsAsString),
question: new RunnablePassthrough(),
},
ragPrompt,
llm,
new StringOutputParser(),
]);for await (const chunk of await runnableRagChain.stream("What is task decomposition?")) {
console.log(chunk);
}import { PromptTemplate } from "@langchain/core/prompts";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
const customTemplate = `Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use three sentences maximum and keep the answer as concise as possible.
Always say "thanks for asking!" at the end of the answer.
{context}
Question: {question}
Helpful Answer:`;
const customRagPrompt = PromptTemplate.fromTemplate(customTemplate);
const customRagChain = await createStuffDocumentsChain({
llm: llm,
prompt: customRagPrompt,
outputParser: new StringOutputParser(),
})
const context = await vectorStoreRetriever.invoke("what is task decomposition");
await customRagChain.invoke({
question: "What is Task Decomposition?",
context,
}); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/llm_chain.ipynb | // @lc-docs-hide-cell
import { ChatOpenAI } from '@langchain/openai';
const model = new ChatOpenAI({
model: "gpt-4o",
temperature: 0,
})import { HumanMessage, SystemMessage } from "@langchain/core/messages"
const messages = [
new SystemMessage("Translate the following from English into Italian"),
new HumanMessage("hi!"),
];
await model.invoke(messages)import { StringOutputParser } from "@langchain/core/output_parsers";
const parser = new StringOutputParser();const result = await model.invoke(messages)await parser.invoke(result)const chain = model.pipe(parser);await chain.invoke(messages)import { ChatPromptTemplate } from "@langchain/core/prompts"const systemTemplate = "Translate the following into {language}:"const promptTemplate = ChatPromptTemplate.fromMessages(
[
["system", systemTemplate],
["user", "{text}"]
]
)const promptValue = await promptTemplate.invoke({ language: "italian", text: "hi" })
promptValuepromptValue.toChatMessages()const llmChain = promptTemplate.pipe(model).pipe(parser);await llmChain.invoke({ language: "italian", text: "hi" }) |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/index.mdx | ---
sidebar_position: 0
sidebar_class_name: hidden
---
# Tutorials
New to LangChain/LangGraph or to LLM app development in general? Read this material to quickly get up and running.
## Basics
- [Build a Simple LLM Application with LCEL](/docs/tutorials/llm_chain)
- [Build a Chatbot](/docs/tutorials/chatbot)
- [Build an Agent with LangGraph.js](https://langchain-ai.github.io/langgraphjs/tutorials/quickstart/)
## Working with external knowledge
- [Build a Retrieval Augmented Generation (RAG) Application](/docs/tutorials/rag)
- [Build a Conversational RAG Application](/docs/tutorials/qa_chat_history)
- [Build a Question/Answering system over SQL data](/docs/tutorials/sql_qa)
- [Build a Query Analysis System](/docs/tutorials/query_analysis)
- [Build a local RAG application](/docs/tutorials/local_rag)
- [Build a Question Answering application over a Graph Database](/docs/tutorials/graph)
- [Build a PDF ingestion and Question/Answering system](/docs/tutorials/pdf_qa/)
## Specialized tasks
- [Build an Extraction Chain](/docs/tutorials/extraction)
- [Classify text into labels](/docs/tutorials/classification)
- [Summarize text](/docs/tutorials/summarization)
## LangGraph.js
LangGraph.js is an extension of LangChain aimed at
building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
LangGraph.js documentation is currently hosted on a separate site.
You can peruse [LangGraph.js tutorials here](https://langchain-ai.github.io/langgraphjs/tutorials/).
## LangSmith
LangSmith allows you to closely trace, monitor and evaluate your LLM application.
It seamlessly integrates with LangChain, and you can use it to inspect and debug individual steps of your chains as you build.
LangSmith documentation is hosted on a separate site.
You can peruse [LangSmith tutorials here](https://docs.smith.langchain.com/tutorials/).
### Evaluation
LangSmith helps you evaluate the performance of your LLM applications. The below tutorial is a great way to get started:
- [Evaluate your LLM application](https://docs.smith.langchain.com/tutorials/Developers/evaluation)
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/classification.ipynb | import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI } from "@langchain/openai";
import { z } from "zod";
const taggingPrompt = ChatPromptTemplate.fromTemplate(
`Extract the desired information from the following passage.
Only extract the properties mentioned in the 'Classification' function.
Passage:
{input}
`
);
const classificationSchema = z.object({
sentiment: z.string().describe("The sentiment of the text"),
aggressiveness: z.number().int().min(1).max(10).describe(
"How aggressive the text is on a scale from 1 to 10"
),
language: z.string().describe("The language the text is written in"),
});
// LLM
const llm = new ChatOpenAI({
temperature: 0,
model: "gpt-3.5-turbo-0125",
});
// Name is optional, but gives the models more clues as to what your schema represents
const llmWihStructuredOutput = llm.withStructuredOutput(classificationSchema, { name: "extractor" })
const taggingChain = taggingPrompt.pipe(llmWihStructuredOutput);const input = "Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!"
await taggingChain.invoke({ input })import { z } from "zod";
const classificationSchema = z.object({
sentiment: z.enum(["happy", "neutral", "sad"]).describe("The sentiment of the text"),
aggressiveness: z.number().int().min(1).max(5).describe(
"describes how aggressive the statement is, the higher the number the more aggressive"
),
language: z.enum(["spanish", "english", "french", "german", "italian"]).describe("The language the text is written in"),
});const taggingPrompt = ChatPromptTemplate.fromTemplate(
`Extract the desired information from the following passage.
Only extract the properties mentioned in the 'Classification' function.
Passage:
{input}
`
)
// LLM
const llm = new ChatOpenAI({
temperature: 0,
model: "gpt-3.5-turbo-0125",
});
const llmWihStructuredOutput = llm.withStructuredOutput(classificationSchema, { name: "extractor" })
const chain = taggingPrompt.pipe(llmWihStructuredOutput);const input = "Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!"
await chain.invoke({ input })const input = "Estoy muy enojado con vos! Te voy a dar tu merecido!"
await chain.invoke({ input })const input = "Weather is ok here, I can go outside without much more than a coat"
await chain.invoke({ input }) |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/graph.ipynb | import "neo4j-driver";
import { Neo4jGraph } from "@langchain/community/graphs/neo4j_graph";
const url = process.env.NEO4J_URI;
const username = process.env.NEO4J_USER;
const password = process.env.NEO4J_PASSWORD;
const graph = await Neo4jGraph.initialize({ url, username, password });
// Import movie information
const moviesQuery = `LOAD CSV WITH HEADERS FROM
'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies_small.csv'
AS row
MERGE (m:Movie {id:row.movieId})
SET m.released = date(row.released),
m.title = row.title,
m.imdbRating = toFloat(row.imdbRating)
FOREACH (director in split(row.director, '|') |
MERGE (p:Person {name:trim(director)})
MERGE (p)-[:DIRECTED]->(m))
FOREACH (actor in split(row.actors, '|') |
MERGE (p:Person {name:trim(actor)})
MERGE (p)-[:ACTED_IN]->(m))
FOREACH (genre in split(row.genres, '|') |
MERGE (g:Genre {name:trim(genre)})
MERGE (m)-[:IN_GENRE]->(g))`
await graph.query(moviesQuery);await graph.refreshSchema()
console.log(graph.getSchema())import { GraphCypherQAChain } from "langchain/chains/graph_qa/cypher";
import { ChatOpenAI } from "@langchain/openai";
const llm = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0 })
const chain = GraphCypherQAChain.fromLLM({
llm,
graph,
});
const response = await chain.invoke({ query: "What was the cast of the Casino?" })
console.log(response) |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/pdf_qa.ipynb | import "pdf-parse"; // Peer dep
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
const loader = new PDFLoader("../../data/nke-10k-2023.pdf");
const docs = await loader.load();
console.log(docs.length);console.log(docs[0].pageContent.slice(0, 100));
console.log(docs[0].metadata)import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "@langchain/openai";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
const textSplitter = new RecursiveCharacterTextSplitter({
chunkSize: 1000,
chunkOverlap: 200,
});
const splits = await textSplitter.splitDocuments(docs);
const vectorstore = await MemoryVectorStore.fromDocuments(splits, new OpenAIEmbeddings());
const retriever = vectorstore.asRetriever();import { createRetrievalChain } from "langchain/chains/retrieval";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { ChatPromptTemplate } from "@langchain/core/prompts";
const systemTemplate = [
`You are an assistant for question-answering tasks. `,
`Use the following pieces of retrieved context to answer `,
`the question. If you don't know the answer, say that you `,
`don't know. Use three sentences maximum and keep the `,
`answer concise.`,
`\n\n`,
`{context}`,
].join("");
const prompt = ChatPromptTemplate.fromMessages([
["system", systemTemplate],
["human", "{input}"],
]);
const questionAnswerChain = await createStuffDocumentsChain({ llm: model, prompt });
const ragChain = await createRetrievalChain({ retriever, combineDocsChain: questionAnswerChain });
const results = await ragChain.invoke({
input: "What was Nike's revenue in 2023?",
});
console.log(results);console.log(results.context[0].pageContent);console.log(results.context[0].metadata); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/query_analysis.ipynb | import "chromadb";
import { OpenAIEmbeddings } from "@langchain/openai";
import { Chroma } from "@langchain/community/vectorstores/chroma";
const embeddings = new OpenAIEmbeddings({
model: "text-embedding-3-small"
});
const vectorStore = await Chroma.fromExistingCollection(embeddings, {
collectionName: "yt-videos",
});const searchResults = await vectorStore.similaritySearch("how do I build a RAG agent");
console.log(searchResults[0].metadata.title);
console.log(searchResults[0].pageContent.slice(0, 500));const specificSearchResults = await vectorStore.similaritySearch("videos on RAG published in 2023");
console.log(specificSearchResults[0].metadata.title);
console.log(specificSearchResults[0].metadata.publish_year);
console.log(specificSearchResults[0].pageContent.slice(0, 500));import { z } from 'zod';
const searchSchema = z.object({
query: z.string().describe("Similarity search query applied to video transcripts."),
publish_year: z.number().optional().describe("Year of video publication."),
}).describe("Search over a database of tutorial videos about a software library.");import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI } from "@langchain/openai";
import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables";
const system = `You are an expert at converting user questions into database queries.
You have access to a database of tutorial videos about a software library for building LLM-powered applications.
Given a question, return a list of database queries optimized to retrieve the most relevant results.
If there are acronyms or words you are not familiar with, do not try to rephrase them.`;
const prompt = ChatPromptTemplate.fromMessages([
["system", system],
["human", "{question}"]
]);
const llm = new ChatOpenAI({
model: "gpt-3.5-turbo-0125",
temperature: 0,
});
const structuredLLM = llm.withStructuredOutput(searchSchema, {
name: "search",
});
const queryAnalyzer = RunnableSequence.from([
{
question: new RunnablePassthrough(),
},
prompt,
structuredLLM,
]);console.log(await queryAnalyzer.invoke("How do I build a rag agent"));console.log(await queryAnalyzer.invoke("videos on RAG published in 2023"));import { DocumentInterface } from "@langchain/core/documents";
const retrieval = async (input: { query: string, publish_year?: number }): Promise<DocumentInterface[]> => {
let _filter: Record<string, any> = {};
if (input.publish_year) {
// This syntax is specific to Chroma
// the vector database we are using.
_filter = {
publish_year: {
"$eq": input.publish_year
}
};
}
return vectorStore.similaritySearch(input.query, undefined, _filter);
};import { RunnableLambda } from "@langchain/core/runnables";
const retrievalChain = queryAnalyzer.pipe(new RunnableLambda({
func: async (input) => retrieval(input as unknown as { query: string, publish_year?: number })
}));const results = await retrievalChain.invoke("RAG tutorial published in 2023");console.log(results.map((doc) => ({ title: doc.metadata.title, year: doc.metadata.publish_date }))); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/sql_qa.mdx | # Build a Question/Answering system over SQL data
:::info Prerequisites
This guide assumes familiarity with the following concepts:
- [Chaining runnables](/docs/how_to/sequence/)
- [Chat models](/docs/concepts/chat_models)
- [Tools](/docs/concepts/tools)
- [Agents](/docs/concepts/agents)
:::
In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database.
These systems will allow us to ask a question about the data in a SQL database and get back a natural language answer.
The main difference between the two is that our agent can query the database in a loop as many time as it needs to answer the question.
## ⚠️ Security note ⚠️
Building Q&A systems of SQL databases can require executing model-generated SQL queries. There are inherent risks in doing this.
Make sure that your database connection permissions are always scoped as narrowly as possible for your chain/agent's needs.
This will mitigate though not eliminate the risks of building a model-driven system. For more on general security best practices, see [here](/docs/security).
## Architecture
At a high-level, the steps of most SQL chain and agent are:
1. **Convert question to SQL query**: Model converts user input to a SQL query.
2. **Execute SQL query**: Execute the SQL query
3. **Answer the question**: Model responds to user input using the query results.
## Setup
First, get required packages and set environment variables:
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm i langchain @langchain/community @langchain/openai @langchain/core
```
We default to OpenAI models in this guide.
```bash
export OPENAI_API_KEY=<your key>
# Uncomment the below to use LangSmith. Not required, but recommended for debugging and observability.
# export LANGCHAIN_API_KEY=<your key>
# export LANGCHAIN_TRACING_V2=true
# Reduce tracing latency if you are not in a serverless environment
# export LANGCHAIN_CALLBACKS_BACKGROUND=true
```
import CodeBlock from "@theme/CodeBlock";
import DbCheck from "@examples/use_cases/sql/db_check.ts";
<CodeBlock language="typescript">{DbCheck}</CodeBlock>
Great! We've got a SQL database that we can query. Now let's try hooking it up to an LLM.
## Chain
Let's create a simple chain that takes a question, turns it into a SQL query, executes the query, and uses the result to answer the original question.
### Convert question to SQL query
The first step in a SQL chain or agent is to take the user input and convert it to a SQL query. LangChain comes with a built-in chain for this: [`createSqlQueryChain`](https://api.js.langchain.com/functions/langchain.chains_sql_db.createSqlQueryChain.html)
import QuickstartChainExample from "@examples/use_cases/sql/quickstart_chain.ts";
<CodeBlock language="typescript">{QuickstartChainExample}</CodeBlock>
We can look at the [LangSmith trace](https://smith.langchain.com/public/6d8f0213-9f02-498e-aeb2-ec774e324e2c/r) to get a better understanding of what this chain is doing.
We can also inspect the chain directly for its prompts. Looking at the prompt (below), we can see that it is:
- Dialect-specific. In this case it references SQLite explicitly.
- Has definitions for all the available tables.
- Has three examples rows for each table.
This technique is inspired by papers like [this](https://arxiv.org/pdf/2204.00498.pdf), which suggest showing examples rows and being explicit about tables improves performance.
We can also inspect the full prompt via the LangSmith trace:
### Execute SQL query
Now that we've generated a SQL query, we'll want to execute it.
This is the most dangerous part of creating a SQL chain. Consider carefully if it is OK to run automated queries over your data.
Minimize the database connection permissions as much as possible.
Consider adding a human approval step to you chains before query execution (see below).
We can use the [`QuerySqlTool`](https://api.js.langchain.com/classes/langchain.tools_sql.QuerySqlTool.html) to easily add query execution to our chain:
import QuickstartExecuteExample from "@examples/use_cases/sql/quickstart_execute_sql.ts";
<CodeBlock language="typescript">{QuickstartExecuteExample}</CodeBlock>
:::tip
See a LangSmith trace of the chain above [here](https://smith.langchain.com/public/3cbcf6f2-a55b-4701-a2e3-9928e4747328/r).
:::
### Answer the question
Now that we have a way to automatically generate and execute queries, we just need to combine the original question and SQL query result to generate a final answer.
We can do this by passing question and result to the LLM once more:
import QuickstartAnswerExample from "@examples/use_cases/sql/quickstart_answer_question.ts";
<CodeBlock language="typescript">{QuickstartAnswerExample}</CodeBlock>
:::tip
See a LangSmith trace of the chain above [here](https://smith.langchain.com/public/d130ce1f-1fce-4192-921e-4b522884ec1a/r).
:::
### Next steps
For more complex query-generation, we may want to create few-shot prompts or add query-checking steps. For advanced techniques like this and more check out:
- [Prompting strategies](/docs/how_to/sql_prompting): Advanced prompt engineering techniques.
- [Query checking](/docs/how_to/sql_query_checking): Add query validation and error handling.
- [Large databases](/docs/how_to/sql_large_db): Techniques for working with large databases.
## Agents
LangChain offers a number of tools and functions that allow you to create SQL Agents which can provide a more flexible way of interacting with SQL databases. The main advantages of using SQL Agents are:
- It can answer questions based on the databases' schema as well as on the databases' content (like describing a specific table).
- It can recover from errors by running a generated query, catching the traceback and regenerating it correctly.
- It can answer questions that require multiple dependent queries.
- It will save tokens by only considering the schema from relevant tables.
- To initialize the agent, we use [`createOpenAIToolsAgent`](https://api.js.langchain.com/functions/langchain.agents.createOpenAIToolsAgent.html) function.
This agent contains the [`SqlToolkit`](https://api.js.langchain.com/classes/langchain.agents_toolkits_sql.SqlToolkit.html) which contains tools to:
- Create and execute queries
- Check query syntax
- Retrieve table descriptions
- … and more
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/local_rag.ipynb | import "cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";const loader = new CheerioWebBaseLoader(
"https://lilianweng.github.io/posts/2023-06-23-agent/"
);
const docs = await loader.load();
const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 500, chunkOverlap: 0 });
const allSplits = await textSplitter.splitDocuments(docs);
console.log(allSplits.length)import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
const embeddings = new OllamaEmbeddings();
const vectorStore = await MemoryVectorStore.fromDocuments(allSplits, embeddings);const question = "What are the approaches to Task Decomposition?";
const docs = await vectorStore.similaritySearch(question);
console.log(docs.length)import { ChatOllama } from "@langchain/ollama";
const ollamaLlm = new ChatOllama({
baseUrl: "http://localhost:11434", // Default value
model: "llama2", // Default value
});
const response = await ollamaLlm.invoke("Simulate a rap battle between Stephen Colbert and John Oliver");
console.log(response.content);import { RunnableSequence } from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { PromptTemplate } from "@langchain/core/prompts";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
const prompt = PromptTemplate.fromTemplate("Summarize the main themes in these retrieved docs: {context}");
const chain = await createStuffDocumentsChain({
llm: ollamaLlm,
outputParser: new StringOutputParser(),
prompt,
})const question = "What are the approaches to Task Decomposition?";
const docs = await vectorStore.similaritySearch(question);
await chain.invoke({
context: docs,
});import { pull } from "langchain/hub";
import { ChatPromptTemplate } from "@langchain/core/prompts";
const ragPrompt = await pull<ChatPromptTemplate>("rlm/rag-prompt");
const chain = await createStuffDocumentsChain({
llm: ollamaLlm,
outputParser: new StringOutputParser(),
prompt: ragPrompt,
});console.log(ragPrompt.promptMessages.map((msg) => msg.prompt.template).join("\n"));await chain.invoke({ context: docs, question });import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables";
import { formatDocumentsAsString } from "langchain/util/document";
const retriever = vectorStore.asRetriever();
const qaChain = RunnableSequence.from([
{
context: (input: { question: string }, callbacks) => {
const retrieverAndFormatter = retriever.pipe(formatDocumentsAsString);
return retrieverAndFormatter.invoke(input.question, callbacks);
},
question: new RunnablePassthrough(),
},
ragPrompt,
ollamaLlm,
new StringOutputParser(),
]);
await qaChain.invoke({ question }); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/extraction.ipynb | import { z } from "zod";
const personSchema = z.object({
name: z.string().nullish().describe('The name of the person'),
hair_color: z.string().nullish().describe("The color of the person's hair if known"),
height_in_meters: z.string().nullish().describe('Height measured in meters'),
});import { ChatPromptTemplate } from "@langchain/core/prompts";
// Define a custom prompt to provide instructions and any additional context.
// 1) You can add examples into the prompt template to improve extraction quality
// 2) Introduce additional parameters to take context into account (e.g., include metadata
// about the document from which the text was extracted.)
const prompt = ChatPromptTemplate.fromMessages(
[
[
"system",
`You are an expert extraction algorithm.
Only extract relevant information from the text.
If you do not know the value of an attribute asked to extract,
return null for the attribute's value.`,
],
// Please see the how-to about improving performance with
// reference examples.
// ["placeholder", "{examples}"],
["human", "{text}"],
],
);import { ChatAnthropic } from "@langchain/anthropic";
const llm = new ChatAnthropic({
model: "claude-3-sonnet-20240229",
temperature: 0
})
const runnable = prompt.pipe(llm.withStructuredOutput(personSchema))
const text = "Alan Smith is 6 feet tall and has blond hair."
await runnable.invoke({ text })const runnableWithName = prompt.pipe(llm.withStructuredOutput(personSchema, { name: "person" }));
const text2 = "Alan Smith is 6 feet tall and has blond hair.";
await runnableWithName.invoke({ text: text2 });import { z } from "zod";
const person = z.object({
name: z.string().nullish().describe('The name of the person'),
hair_color: z.string().nullish().describe("The color of the person's hair if known"),
height_in_meters: z.number().nullish().describe('Height measured in meters'),
});
const dataSchema = z.object({
people: z.array(person).describe('Extracted data about people'),
});const peopleExtractionChain = prompt.pipe(llm.withStructuredOutput(dataSchema))
const text3 = "My name is Jeff, my hair is black and i am 6 feet tall. Anna has the same color hair as me."
await peopleExtractionChain.invoke({ text: text3 }) |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/tutorials/chatbot.ipynb | // @lc-docs-hide-cell
import { ChatOpenAI } from "@langchain/openai";
const llm = new ChatOpenAI({ model: "gpt-4o-mini" })await llm.invoke([{ role: "user", content: "Hi im bob" }])await llm.invoke([{ role: "user", content: "Whats my name" }])await llm.invoke([
{ role: "user", content: "Hi! I'm Bob" },
{ role: "assistant", content: "Hello Bob! How can I assist you today?" },
{ role: "user", content: "What's my name?" }
]);import { START, END, MessagesAnnotation, StateGraph, MemorySaver } from "@langchain/langgraph";
// Define the function that calls the model
const callModel = async (state: typeof MessagesAnnotation.State) => {
const response = await llm.invoke(state.messages);
return { messages: response };
};
// Define a new graph
const workflow = new StateGraph(MessagesAnnotation)
// Define the node and edge
.addNode("model", callModel)
.addEdge(START, "model")
.addEdge("model", END);
// Add memory
const memory = new MemorySaver();
const app = workflow.compile({ checkpointer: memory });import { v4 as uuidv4 } from "uuid";
const config = { configurable: { thread_id: uuidv4() } };const input = [
{
role: "user",
content: "Hi! I'm Bob.",
}
]
const output = await app.invoke({ messages: input }, config)
// The output contains all messages in the state.
// This will long the last message in the conversation.
console.log(output.messages[output.messages.length - 1]);const input2 = [
{
role: "user",
content: "What's my name?",
}
]
const output2 = await app.invoke({ messages: input2 }, config)
console.log(output2.messages[output2.messages.length - 1]);const config2 = { configurable: { thread_id: uuidv4() } }
const input3 = [
{
role: "user",
content: "What's my name?",
}
]
const output3 = await app.invoke({ messages: input3 }, config2)
console.log(output3.messages[output3.messages.length - 1]);const output4 = await app.invoke({ messages: input2 }, config)
console.log(output4.messages[output4.messages.length - 1]);import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
const prompt = ChatPromptTemplate.fromMessages([
["system", "You talk like a pirate. Answer all questions to the best of your ability."],
new MessagesPlaceholder("messages"),
]);import { START, END, MessagesAnnotation, StateGraph, MemorySaver } from "@langchain/langgraph";
// Define the function that calls the model
const callModel2 = async (state: typeof MessagesAnnotation.State) => {
// highlight-start
const chain = prompt.pipe(llm);
const response = await chain.invoke(state);
// highlight-end
// Update message history with response:
return { messages: [response] };
};
// Define a new graph
const workflow2 = new StateGraph(MessagesAnnotation)
// Define the (single) node in the graph
.addNode("model", callModel2)
.addEdge(START, "model")
.addEdge("model", END);
// Add memory
const app2 = workflow2.compile({ checkpointer: new MemorySaver() });const config3 = { configurable: { thread_id: uuidv4() } }
const input4 = [
{
role: "user",
content: "Hi! I'm Jim.",
}
]
const output5 = await app2.invoke({ messages: input4 }, config3)
console.log(output5.messages[output5.messages.length - 1]);const input5 = [
{
role: "user",
content: "What is my name?"
}
]
const output6 = await app2.invoke({ messages: input5 }, config3)
console.log(output6.messages[output6.messages.length - 1]);const prompt2 = ChatPromptTemplate.fromMessages([
["system", "You are a helpful assistant. Answer all questions to the best of your ability in {language}."],
new MessagesPlaceholder("messages"),
]);import { START, END, StateGraph, MemorySaver, MessagesAnnotation, Annotation } from "@langchain/langgraph";
// Define the State
const GraphAnnotation = Annotation.Root({
...MessagesAnnotation.spec,
language: Annotation<string>(),
});
// Define the function that calls the model
const callModel3 = async (state: typeof GraphAnnotation.State) => {
const chain = prompt2.pipe(llm);
const response = await chain.invoke(state);
return { messages: [response] };
};
const workflow3 = new StateGraph(GraphAnnotation)
.addNode("model", callModel3)
.addEdge(START, "model")
.addEdge("model", END);
const app3 = workflow3.compile({ checkpointer: new MemorySaver() });const config4 = { configurable: { thread_id: uuidv4() } }
const input6 = {
messages: [
{
role: "user",
content: "Hi im bob"
}
],
language: "Spanish"
}
const output7 = await app3.invoke(input6, config4)
console.log(output7.messages[output7.messages.length - 1]);const input7 = {
messages: [
{
role: "user",
content: "What is my name?"
}
],
}
const output8 = await app3.invoke(input7, config4)
console.log(output8.messages[output8.messages.length - 1]);import { SystemMessage, HumanMessage, AIMessage, trimMessages } from "@langchain/core/messages"
const trimmer = trimMessages({
maxTokens: 10,
strategy: "last",
tokenCounter: (msgs) => msgs.length,
includeSystem: true,
allowPartial: false,
startOn: "human",
})
const messages = [
new SystemMessage("you're a good assistant"),
new HumanMessage("hi! I'm bob"),
new AIMessage("hi!"),
new HumanMessage("I like vanilla ice cream"),
new AIMessage("nice"),
new HumanMessage("whats 2 + 2"),
new AIMessage("4"),
new HumanMessage("thanks"),
new AIMessage("no problem!"),
new HumanMessage("having fun?"),
new AIMessage("yes!"),
]
await trimmer.invoke(messages)const callModel4 = async (state: typeof GraphAnnotation.State) => {
const chain = prompt2.pipe(llm);
// highlight-start
const trimmedMessage = await trimmer.invoke(state.messages);
const response = await chain.invoke({ messages: trimmedMessage, language: state.language });
// highlight-end
return { messages: [response] };
};
const workflow4 = new StateGraph(GraphAnnotation)
.addNode("model", callModel4)
.addEdge(START, "model")
.addEdge("model", END);
const app4 = workflow4.compile({ checkpointer: new MemorySaver() });const config5 = { configurable: { thread_id: uuidv4() }}
const input8 = {
// highlight-next-line
messages: [...messages, new HumanMessage("What is my name?")],
language: "English"
}
const output9 = await app4.invoke(
input8,
config5,
)
console.log(output9.messages[output9.messages.length - 1]);const config6 = { configurable: { thread_id: uuidv4() }}
const input9 = {
// highlight-next-line
messages: [...messages, new HumanMessage("What math problem did I ask?")],
language: "English"
}
const output10 = await app4.invoke(
input9,
config6,
)
console.log(output10.messages[output10.messages.length - 1]); |
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/contributing/integrations.mdx | ---
sidebar_position: 5
---
# Contribute Integrations
To begin, make sure you have all the dependencies outlined in guide on [Contributing Code](/docs/contributing/code/).
There are a few different places you can contribute integrations for LangChain:
- **Community**: For lighter-weight integrations that are primarily maintained by LangChain and the Open Source Community.
- **Partner Packages**: For independent packages that are co-maintained by LangChain and a partner.
For the most part, new integrations should be added to the Community package. Partner packages require more maintenance as separate packages, so please confirm with the LangChain team before creating a new partner package.
In the following sections, we'll walk through how to contribute to each of these packages from a fake company, `Parrot Link AI`.
## Community package
The `@langchain/community` package is in `libs/langchain-community` and contains most integrations.
It can be installed with e.g. `npm install @langchain/community`, and exported members can be imported with code like
```ts
import { ChatParrotLink } from "@langchain/community/chat_models/parrot_link";
import { ParrotLinkLLM } from "@langchain/community/llms/parrot_link";
import { ParrotLinkVectorStore } from "@langchain/community/vectorstores/parrot_link";
```
The `@langchain/community` package relies on manually-installed dependent packages, so you will see errors
if you try to import a package that is not installed. In our fake example, if you tried to import `ParrotLinkLLM` without installing `parrot-link-sdk`, you would see an error telling you that the package failed to import.
Let's say we wanted to implement a chat model for Parrot Link AI. We would create a new file in `libs/langchain-community/src/chat_models/parrot_link.ts` with something like the following code:
```ts
import {
SimpleChatModel,
} from "@langchain/core/language_models/chat_models";
export class ChatParrotLink extends SimpleChatModel {
...
```
Tests are colocated in the `src/` directory, so you could write them in files like the below:
- Unit tests: `libs/langchain-community/src/chat_models/tests/parrot_link.test.ts`
- Integration tests: `libs/langchain-community/src/chat_models/tests/parrot_link.int.test.ts`
Unit tests should not have any external API calls or require any environment variables.
You should add documentation to:
- `docs/core_docs/docs/integrations/chat/parrot_link.mdx`
## Partner package in LangChain repo
Partner packages can be hosted in the `LangChain` monorepo.
Partner packages in the `LangChain` repo should be placed under `libs/langchain-{partner}`
A package is
installed by users with `npm install @langchain/{partner}`, and the package members
can be imported with code like:
```ts
import { X } from "@langchain/{partner}";
```
### Set up a new package
To set up a new partner package, you can use [`create-langchain-integration`](https://github.com/langchain-ai/langchainjs/blob/main/libs/create-langchain-integration/),
a utility that will automatically scaffold a repo with support for both ESM + CJS entrypoints. You can run it like this within the `libs/` folder:
```bash
cd libs/
npx create-langchain-integration
```
Then, follow the prompts to name your package.
The default package will include stubs for a Chat Model, an LLM, and/or a Vector Store. You should delete any of the files you won't use and remove them from `index.ts`.
### Dependencies
If your package needs dependencies, such as your company's SDK, you can add them to your package's `package.json` file as normal:
```bash
npm install parrot-link-sdk
```
### Write Unit and Integration Tests
Some basic tests are presented in the `src/tests/` directory. You should add more tests to cover your package's functionality.
For information on running and implementing tests, see the [Testing guide](/docs/contributing/testing/).
### Write documentation
Please copy and use the appropriate template from here:
https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-scripts/src/cli/docs/templates
You should place the notebooks with examples
in the relevant `docs/core_docs/docs/integrations` directory in the monorepo root.
### (If Necessary) Deprecate community integration
Note: this is only necessary if you're migrating an existing community integration into
a partner package. If the component you're integrating is net-new to LangChain (i.e.
not already in the `community` package), you can skip this step.
Let's pretend we migrated our `ChatParrotLink` chat model from the community package to
the partner package. We would need to deprecate the old model in the community package.
We can do this using a `@deprecated` TSDoc comment.
Before our change, our chat model might look like this:
```ts
class ChatParrotLink extends SimpleChatModel {
...
```
After our change, it would look like this:
```ts
/** @deprecated Install and import from `@langchain/parrot-link` instead. */
class ChatParrotLink extends SimpleChatModel {
...
```
You should do this for _each_ component that you're migrating to the partner package.
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/contributing/testing.mdx | ---
sidebar_position: 2
---
# Testing
In general, tests should be added within a `tests/` folder alongside the modules they
are testing.
**Unit tests** cover modular logic that does not require calls to outside APIs.
If you add new logic, please add a unit test.
Unit tests should be called `*.test.ts`.
To run only unit tests, run:
```bash
yarn test
```
### Running a single test
To run a single test, run the following from within a workspace:
```bash
yarn test:single /path/to/yourtest.test.ts
```
This is useful for developing individual features.
**Integration tests** cover logic that requires making calls to outside APIs (often integration with other services).
If you add support for a new external API, please add a new integration test.
Integration tests should be called `*.int.test.ts`.
Note that most integration tests require credentials or other setup. You will likely need to set up a `langchain/.env` or `libs/langchain-community/.env` file
like the example [here](https://github.com/langchain-ai/langchainjs/blob/main/langchain/.env.example).
We generally recommend only running integration tests with `yarn test:single`, but if you want to run all integration tests, run:
```bash
yarn test:integration
```
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs | lc_public_repos/langchainjs/docs/core_docs/docs/contributing/repo_structure.mdx | ---
sidebar_position: 0.5
---
# Repository Structure
If you plan on contributing to LangChain code or documentation, it can be useful
to understand the high level structure of the repository.
LangChain is organized as a [monorepo](https://en.wikipedia.org/wiki/Monorepo) that contains multiple packages.
Here's the structure visualized as a tree:
```text
.
├── docs
│ ├── core_docs # Contains content for the documentation here: https://js.langchain.com/
│ ├── api_refs # Contains content for the API refs here: https://api.js.langchain.com/
├── langchain # Main package
│ ├── src/**/tests/*.test.ts/ # Unit tests (present in each package not shown for brevity)
│ ├── src/**/tests/*.int.test.ts/ # Integration tests (present in each package not shown for brevity)
├── langchain # Base interfaces for key abstractions
├── libs # Community packages
│ ├── langchain-community # Third-party integrations
│ ├── langchain-partner-1
│ ├── langchain-partner-2
│ ├── ...
```
The root directory also contains the following files:
- `package.json`: Dependencies for building docs and linting docs.
There are other files in the root directory level, but their presence should be self-explanatory. Feel free to browse around!
## Documentation
The `/docs` directory contains the content for the documentation that is shown
at https://js.langchain.com/ and the associated API Reference https://api.js.langchain.com/
See the [documentation](/docs/contributing/documentation/style_guide) guidelines to learn how to contribute to the documentation.
## Code
The `/libs` directory contains the code for the LangChain packages.
To learn more about how to contribute code see the following guidelines:
- [Code](./code.mdx) Learn how to develop in the LangChain codebase.
- [Integrations](./integrations.mdx) to learn how to contribute to third-party integrations to langchain-community or to start a new partner package.
- [Testing](./testing.mdx) guidelines to learn how to write tests for the packages.
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sidebar_position: 0
---
# Welcome Contributors
Hi there! Thank you for even being interested in contributing to LangChain.
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
## 🗺️ Guidelines
### 👩💻 Ways to contribute
There are many ways to contribute to LangChain. Here are some common ways people contribute:
- [**Documentation**](/docs/contributing/documentation/style_guide): Help improve our docs, including this one!
- [**Code**](./code.mdx): Help us write code, fix bugs, or improve our infrastructure.
- [**Integrations**](./integrations.mdx): Help us integrate with your favorite vendors and tools.
- [**Discussions**](https://github.com/langchain-ai/langchainjs/discussions): Help answer usage questions and discuss issues with users.
### 🚩 GitHub Issues
Our [issues](https://github.com/langchain-ai/langchainjs/issues) page is kept up to date with bugs, improvements, and feature requests.
There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help organize issues.
If you start working on an issue, please assign it to yourself.
If you are adding an issue, please try to keep it focused on a single, modular bug/improvement/feature.
If two issues are related, or blocking, please link them rather than combining them.
We will try to keep these issues as up-to-date as possible, though
with the rapid rate of development in this field some may get out of date.
If you notice this happening, please let us know.
### 💭 GitHub Discussions
We have a [discussions](https://github.com/langchain-ai/langchainjs/discussions) page where users can ask usage questions, discuss design decisions, and propose new features.
If you are able to help answer questions, please do so! This will allow the maintainers to spend more time focused on development and bug fixing.
### 🙋 Getting Help
Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is
smooth for future contributors.
In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
we do not want these to get in the way of getting good code into the codebase.
# 🌟 Recognition
If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.
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sidebar_position: 1
---
# Contribute Code
To contribute to this project, please follow the ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
Please do not try to push directly to this repo unless you are a maintainer.
Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
maintainers.
Pull requests cannot land without passing the formatting, linting, and testing checks first. See [Testing](#testing) and
[Formatting and Linting](#formatting-and-linting) for how to run these checks locally.
It's essential that we maintain great documentation and testing. If you:
- Fix a bug
- Add a relevant unit or integration test when possible. These live in `**/tests/*.test.ts` and `**/tests/*.int.test.ts/`.
- Make an improvement
- Update any affected example notebooks and documentation. These live in `docs`.
- Update unit and integration tests when relevant.
- Add a feature
- Add a demo notebook/MDX file in `docs/core_docs/docs`.
- Add unit and integration tests.
We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the
best way to get our attention.
## 🚀 Quick Start
This quick start guide explains how to run the repository locally.
For a [development container](https://containers.dev/), see the [.devcontainer folder](https://github.com/langchain-ai/langchainjs/tree/main/.devcontainer).
### 🏭 Release process
As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
a developer and published to [npm](https://www.npmjs.com/package/langchain).
LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
#### Integration releases
The release script can be executed only while on a fresh `main` branch, with no un-committed changes, from the package root. If working from a fork of the repository, make sure to sync the forked `main` branch with the upstream `main` branch first.
You can invoke the script by calling `yarn release`. If new dependencies have been added to the integration package, install them first (i.e. run `yarn`, then `yarn release`).
There are three parameters which can be passed to this script, one required and two optional.
- **Required**: `<workspace name>`. eg: `@langchain/core` The name of the package to release. Can be found in the `name` value of the package's `package.json`
- **Optional**: `--bump-deps` eg `--bump-deps` Will find all packages in the repo which depend on this workspace and checkout a new branch, update the dep version, run yarn install, commit & push to new branch. Generally, this is not necessary.
- **Optional**: `--tag <tag>` eg `--tag beta` Add a tag to the NPM release. Useful if you want to push a release candidate.
This script automatically bumps the package version, creates a new release branch with the changes, pushes the branch to GitHub, uses `release-it` to automatically release to NPM, and more depending on the flags passed.
Halfway through this script, you'll be prompted to enter an NPM OTP (typically from an authenticator app). This value is not stored anywhere and is only used to authenticate the NPM release.
> **Note** Unless releasing `langchain`, `no` should be answered to all prompts following `Publish @langchain/<package> to npm?`. Then, the change should be manually committed with the following commit message: `<package>[patch]: Release <new version>`. E.g.: `groq[patch]: Release 0.0.1`.
Docker must be running if releasing one of `langchain`, `@langchain/core` or `@langchain/community`. These packages run LangChain's export tests, which run inside docker containers.
Full example: `yarn release @langchain/core`.
### 🛠️ Tooling
This project uses the following tools, which are worth getting familiar
with if you plan to contribute:
- **[yarn](https://yarnpkg.com/) (v3.4.1)** - dependency management
- **[eslint](https://eslint.org/)** - enforcing standard lint rules
- **[prettier](https://prettier.io/)** - enforcing standard code formatting
- **[jest](https://jestjs.io/)** - testing code
- **[TypeDoc](https://typedoc.org/)** - reference doc generation from
comments
- **[Docusaurus](https://docusaurus.io/)** - static site generation for documentation
## 🚀 Quick Start
Clone this repo, then cd into it:
```bash
cd langchainjs
```
Next, try running the following common tasks:
## ✅ Common Tasks
Our goal is to make it as easy as possible for you to contribute to this project.
All of the below commands should be run from within a workspace directory (e.g. `langchain`, `libs/langchain-community`) unless otherwise noted.
```bash
cd langchain
```
Or, if you are working on a community integration:
```bash
cd libs/langchain-community
```
### Setup
**Prerequisite**: Node version 18+ is required. Please check node version `node -v` and update it if required.
To get started, you will need to install the dependencies for the project. To do so, run:
```bash
yarn
```
Then, you will need to switch directories into `langchain-core` and build core by running:
```bash
cd ../langchain-core
yarn
yarn build
```
### Linting
We use [eslint](https://eslint.org/) to enforce standard lint rules.
To run the linter, run:
```bash
yarn lint
```
### Formatting
We use [prettier](https://prettier.io) to enforce code formatting style.
To run the formatter, run:
```bash
yarn format
```
To just check for formatting differences, without fixing them, run:
```bash
yarn format:check
```
### Testing
In general, tests should be added within a `tests/` folder alongside the modules they
are testing.
**Unit tests** cover modular logic that does not require calls to outside APIs.
If you add new logic, please add a unit test.
Unit tests should be called `*.test.ts`.
To run only unit tests, run:
```bash
yarn test
```
#### Running a single test
To run a single test, run the following from within a workspace:
```bash
yarn test:single /path/to/yourtest.test.ts
```
This is useful for developing individual features.
**Integration tests** cover logic that requires making calls to outside APIs (often integration with other services).
If you add support for a new external API, please add a new integration test.
Integration tests should be called `*.int.test.ts`.
Note that most integration tests require credentials or other setup. You will likely need to set up a `langchain/.env` or `libs/langchain-community/.env` file
like the example [here](https://github.com/langchain-ai/langchainjs/blob/main/langchain/.env.example).
We generally recommend only running integration tests with `yarn test:single`, but if you want to run all integration tests, run:
```bash
yarn test:integration
```
### Building
To build the project, run:
```bash
yarn build
```
### Adding an Entrypoint
LangChain exposes multiple subpaths the user can import from, e.g.
```typescript
import { OpenAI } from "langchain/llms/openai";
```
We call these subpaths "entrypoints". In general, you should create a new entrypoint if you are adding a new integration with a 3rd party library. If you're adding self-contained functionality without any external dependencies, you can add it to an existing entrypoint.
In order to declare a new entrypoint that users can import from, you
should edit the `langchain/langchain.config.js` or `libs/langchain-community/langchain.config.js` file. To add an
entrypoint `tools` that imports from `tools/index.ts` you'd add
the following to the `entrypoints` key inside the `config` variable:
```typescript
// ...
entrypoints: {
// ...
tools: "tools/index",
},
// ...
```
If you're adding a new integration which requires installing a third party dependency, you must add the entrypoint to the `requiresOptionalDependency` array, also located inside `langchain/langchain.config.js` or `libs/langchain-community/langchain.config.js`.
```typescript
// ...
requiresOptionalDependency: [
// ...
"tools/index",
],
// ...
```
This will make sure the entrypoint is included in the published package,
and in generated documentation.
## Documentation
### Contribute Documentation
#### Install dependencies
##### Note: you only need to follow these steps if you are building the docs site locally.
1. [Quarto](https://quarto.org/) - package that converts Jupyter notebooks (`.ipynb` files) into `.mdx` files for serving in Docusaurus.
2. `yarn build --filter=core_docs` - It's as simple as that! (or you can simply run `yarn build` from `docs/core_docs/`)
All notebooks are converted to `.md` files and automatically gitignored. If you would like to create a non notebook doc, it must be a `.mdx` file.
### Writing Notebooks
When adding new dependencies inside the notebook you must update the import map inside `deno.json` in the root of the LangChain repo.
This is required because the notebooks use the Deno runtime, and Deno formats imports differently than Node.js.
Example:
```typescript
// Import in Node:
import { z } from "zod";
// Import in Deno:
import { z } from "npm:/zod";
```
See examples inside `deno.json` for more details.
Docs are largely autogenerated by [TypeDoc](https://typedoc.org/) from the code.
For that reason, we ask that you add good documentation to all classes and methods.
Similar to linting, we recognize documentation can be annoying. If you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
Documentation and the skeleton lives under the `docs/` folder. Example code is imported from under the `examples/` folder.
### Running examples
If you add a new major piece of functionality, it is helpful to add an
example to showcase how to use it. Most of our users find examples to be the
most helpful kind of documentation.
Examples can be added in the `examples/src` directory, e.g.
`examples/src/path/to/example`. This
example can then be invoked with `yarn example path/to/example` at the top
level of the repo.
To run examples that require an environment variable, you'll need to add a `.env` file under `examples/.env`.
### Build Documentation Locally
To generate and view the documentation locally, change to the project root and run `yarn` to ensure dependencies get installed
in both the `docs/` and `examples/` workspaces:
```bash
cd ..
yarn
```
Then run:
```bash
yarn docs
```
## Advanced
**Environment tests** test whether LangChain works across different JS environments, including Node.js (both ESM and CJS), Edge environments (eg. Cloudflare Workers), and browsers (using Webpack).
To run the environment tests with Docker, run the following command from the project root:
```bash
yarn test:exports:docker
```
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sidebar_position: 6
sidebar_label: FAQ
---
# Frequently Asked Questions
## Pull Requests (PRs)
### How do I allow maintainers to edit my PR?
When you submit a pull request, there may be additional changes
necessary before merging it. Oftentimes, it is more efficient for the
maintainers to make these changes themselves before merging, rather than asking you
to do so in code review.
By default, most pull requests will have a
`✅ Maintainers are allowed to edit this pull request.`
badge in the right-hand sidebar.
If you do not see this badge, you may have this setting off for the fork you are
pull-requesting from. See [this Github docs page](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork)
for more information.
Notably, Github doesn't allow this setting to be enabled for forks in **organizations** ([issue](https://github.com/orgs/community/discussions/5634)).
If you are working in an organization, we recommend submitting your PR from a personal
fork in order to enable this setting.
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sidebar_label: "Style guide"
---
# LangChain Documentation Style Guide
## Introduction
As LangChain continues to grow, the surface area of documentation required to cover it continues to grow too.
This page provides guidelines for anyone writing documentation for LangChain, as well as some of our philosophies around
organization and structure.
## Philosophy
LangChain's documentation aspires to follow the [Diataxis framework](https://diataxis.fr).
Under this framework, all documentation falls under one of four categories:
- **Tutorials**: Lessons that take the reader by the hand through a series of conceptual steps to complete a project.
- An example of this is our [LCEL streaming guide](/docs/how_to/streaming).
- Our guides on [custom components](/docs/how_to/custom_chat) is another one.
- **How-to guides**: Guides that take the reader through the steps required to solve a real-world problem.
- The clearest examples of this are our [Use case](/docs/how_to/#use-cases) pages.
- **Reference**: Technical descriptions of the machinery and how to operate it.
- Our [Runnable](/docs/how_to/#langchain-expression-language-lcel) pages is an example of this.
- The [API reference pages](https://api.js.langchain.com/) are another.
- **Explanation**: Explanations that clarify and illuminate a particular topic.
Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
## Taxonomy
Keeping the above in mind, we have sorted LangChain's docs into categories. It is helpful to think in these terms
when contributing new documentation:
### Getting started
The [getting started section](/docs/introduction) includes a high-level introduction to LangChain, a quickstart that
tours LangChain's various features, and logistical instructions around installation and project setup.
It contains elements of **How-to guides** and **Explanations**.
### Use cases
[Use cases](/docs/how_to/#use-cases) are guides that are meant to show how to use LangChain to accomplish a specific task (RAG, information extraction, etc.).
The quickstarts should be good entrypoints for first-time LangChain developers who prefer to learn by getting something practical prototyped,
then taking the pieces apart retrospectively. These should mirror what LangChain is good at.
The quickstart pages here should fit the **How-to guide** category, with the other pages intended to be **Explanations** of more
in-depth concepts and strategies that accompany the main happy paths.
:::note
The below sections are listed roughly in order of increasing level of abstraction.
:::
### Expression Language
[LangChain Expression Language (LCEL)](/docs/how_to/#langchain-expression-language-lcel) is the fundamental way that most LangChain components fit together, and this section is designed to teach
developers how to use it to build with LangChain's primitives effectively.
This section should contains **Tutorials** that teach how to stream and use LCEL primitives for more abstract tasks, **Explanations** of specific behaviors,
and some **References** for how to use different methods in the Runnable interface.
### Components
The [how to section](/docs/how_to) covers concepts one level of abstraction higher than LCEL.
Abstract base classes like `BaseChatModel` and `BaseRetriever` should be covered here, as well as core implementations of these base classes,
such as `ChatPromptTemplate` and `RecursiveCharacterTextSplitter`. Customization guides belong here too.
This section should contain mostly conceptual **Tutorials**, **References**, and **Explanations** of the components they cover.
:::note
As a general rule of thumb, everything covered in the `Expression Language` and `Components` sections (with the exception of the `Composition` section of components) should
cover only components that exist in `@langchain/core`.
:::
### Integrations
The [integrations](/docs/integrations/platforms/) are specific implementations of components. These often involve third-party APIs and services.
If this is the case, as a general rule, these are maintained by the third-party partner.
This section should contain mostly **Explanations** and **References**, though the actual content here is more flexible than other sections and more at the
discretion of the third-party provider.
:::note
Concepts covered in `Integrations` should generally exist in `@langchain/community` or specific partner packages.
:::
### Tutorials and Ecosystem
The [Tutorials](/docs/tutorials) and [Ecosystem](https://docs.smith.langchain.com) sections should contain guides that address higher-level problems than the sections above.
This includes, but is not limited to, considerations around productionization and development workflows.
These should contain mostly **How-to guides**, **Explanations**, and **Tutorials**.
### API references
LangChain's API references. Should act as **References** (as the name implies) with some **Explanation**-focused content as well.
## Sample developer journey
We have set up our docs to assist a new developer to LangChain. Let's walk through the intended path:
- The developer lands on https://js.langchain.com, and reads through the introduction and the diagram.
- If they are just curious, they may be drawn to the [Quickstart](/docs/tutorials/llm_chain) to get a high-level tour of what LangChain contains.
- If they have a specific task in mind that they want to accomplish, they will be drawn to the Use-Case section. The use-case should provide a good, concrete hook that shows the value LangChain can provide them and be a good entrypoint to the framework.
- They can then move to learn more about the fundamentals of LangChain through the Expression Language sections.
- Next, they can learn about LangChain's various components and integrations.
- Finally, they can get additional knowledge through the Guides.
This is only an ideal of course - sections will inevitably reference lower or higher-level concepts that are documented in other sections.
## Guidelines
Here are some other guidelines you should think about when writing and organizing documentation.
### Linking to other sections
Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
to allow a developer to learn more about an unfamiliar topic inline.
This includes linking to the API references as well as conceptual sections!
### Conciseness
In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
re-explain it, unless the concept you are documenting presents some new wrinkle.
Be concise, including in code samples.
### General style
- Use active voice and present tense whenever possible.
- Use examples and code snippets to illustrate concepts and usage.
- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically.
- Use bullet points and numbered lists to break down information into easily digestible chunks.
- Use tables (especially for **Reference** sections) and diagrams often to present information visually.
- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages.
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keywords: [bedrock]
---
# AWS
All functionality related to the [Amazon AWS](https://aws.amazon.com/) platform.
## Chat Models
### Bedrock
See a [usage example](/docs/integrations/chat/bedrock).
```typescript
import { BedrockChat } from "@langchain/community/chat_models/bedrock";
```
## LLMs
### Bedrock
See a [usage example](/docs/integrations/llms/bedrock).
```typescript
import { Bedrock } from "@langchain/community/llms/bedrock";
```
### SageMaker Endpoint
> [Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a system that can build, train, and deploy machine learning (ML) models with fully managed infrastructure, tools, and workflows.
We use `SageMaker` to host our model and expose it as the `SageMaker Endpoint`.
See a [usage example](/docs/integrations/llms/aws_sagemaker).
```typescript
import {
SagemakerEndpoint,
SageMakerLLMContentHandler,
} from "@langchain/community/llms/sagemaker_endpoint";
```
## Text Embedding Models
### Bedrock
See a [usage example](/docs/integrations/text_embedding/bedrock).
```typescript
import { BedrockEmbeddings } from "@langchain/aws";
```
## Document loaders
### AWS S3 Directory and File
> [Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.
> [AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) >[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)
See a [usage example for S3FileLoader](/docs/integrations/document_loaders/web_loaders/s3).
```bash npm2yarn
npm install @aws-sdk/client-s3
```
```typescript
import { S3Loader } from "@langchain/community/document_loaders/web/s3";
```
## Memory
### AWS DynamoDB
> [AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html)
> is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability.
We have to configure the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html).
```bash npm2yarn
npm install @aws-sdk/client-dynamodb
```
See a [usage example](/docs/integrations/memory/dynamodb).
```typescript
import { DynamoDBChatMessageHistory } from "@langchain/community/stores/message/dynamodb";
```
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All functionality related to Anthropic models.
[Anthropic](https://www.anthropic.com/) is an AI safety and research company, and is the creator of Claude.
This page covers all integrations between Anthropic models and LangChain.
## Prompting Best Practices
Anthropic models have several prompting best practices compared to OpenAI models.
**System Messages may only be the first message**
Anthropic models require any system messages to be the first one in your prompts.
## `ChatAnthropic`
`ChatAnthropic` is a subclass of LangChain's `ChatModel`, meaning it works best with `ChatPromptTemplate`.
You can import this wrapper with the following code:
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/anthropic @langchain/core
```
```typescript
import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({});
```
When working with ChatModels, it is preferred that you design your prompts as `ChatPromptTemplate`s.
Here is an example below of doing that:
```typescript
import { ChatPromptTemplate } from "langchain/prompts";
const prompt = ChatPromptTemplate.fromMessages([
["system", "You are a helpful chatbot"],
["human", "Tell me a joke about {topic}"],
]);
```
You can then use this in a chain as follows:
```typescript
const chain = prompt.pipe(model);
await chain.invoke({ topic: "bears" });
```
See the [chat model integration page](/docs/integrations/chat/anthropic/) for more examples, including multimodal inputs.
|
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keywords: [azure]
---
import CodeBlock from "@theme/CodeBlock";
# Microsoft
All functionality related to `Microsoft Azure` and other `Microsoft` products.
## Chat Models
### Azure OpenAI
See a [usage example](/docs/integrations/chat/azure)
import AzureChatOpenAI from "@examples/models/chat/integration_azure_openai.ts";
<UnifiedModelParamsTooltip></UnifiedModelParamsTooltip>
<CodeBlock language="typescript">{AzureChatOpenAI}</CodeBlock>
## LLM
### Azure OpenAI
> [Microsoft Azure](https://en.wikipedia.org/wiki/Microsoft_Azure), often referred to as `Azure` is a cloud computing platform run by `Microsoft`, which offers access, management, and development of applications and services through global data centers. It provides a range of capabilities, including software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). `Microsoft Azure` supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems.
> [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) is a cloud service to help you quickly develop generative AI experiences with a diverse set of prebuilt and curated models from OpenAI, Meta and beyond.
LangChain.js supports integration with [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) using the new Azure integration in the [OpenAI SDK](https://github.com/openai/openai-node).
You can learn more about Azure OpenAI and its difference with the OpenAI API on [this page](https://learn.microsoft.com/azure/ai-services/openai/overview). If you don't have an Azure account, you can [create a free account](https://azure.microsoft.com/free/) to get started.
You'll need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following [this guide](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).
Once you have your instance running, make sure you have the name of your instance and key. You can find the key in the Azure Portal, under the "Keys and Endpoint" section of your instance.
If you're using Node.js, you can define the following environment variables to use the service:
```bash
AZURE_OPENAI_API_INSTANCE_NAME=<YOUR_INSTANCE_NAME>
AZURE_OPENAI_API_DEPLOYMENT_NAME=<YOUR_DEPLOYMENT_NAME>
AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=<YOUR_EMBEDDINGS_DEPLOYMENT_NAME>
AZURE_OPENAI_API_KEY=<YOUR_KEY>
AZURE_OPENAI_API_VERSION="2024-02-01"
```
:::info
You can find the list of supported API versions in the [Azure OpenAI documentation](https://learn.microsoft.com/azure/ai-services/openai/reference).
:::
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @langchain/core
```
See a [usage example](/docs/integrations/llms/azure).
import AzureOpenAI from "@examples/models/llm/azure_openai.ts";
import UnifiedModelParamsTooltip from "@mdx_components/unified_model_params_tooltip.mdx";
<UnifiedModelParamsTooltip></UnifiedModelParamsTooltip>
<CodeBlock language="typescript">{AzureOpenAI}</CodeBlock>
## Text Embedding Models
### Azure OpenAI
See a [usage example](/docs/integrations/text_embedding/azure_openai)
import AzureOpenAIEmbeddings from "@examples/models/embeddings/azure_openai.ts";
<UnifiedModelParamsTooltip></UnifiedModelParamsTooltip>
<CodeBlock language="typescript">{AzureOpenAIEmbeddings}</CodeBlock>
## Vector stores
### Azure AI Search
> [Azure AI Search](https://azure.microsoft.com/products/ai-services/ai-search) (formerly known as Azure Search and Azure Cognitive Search) is a distributed, RESTful search engine optimized for speed and relevance on production-scale workloads on Azure. It supports also vector search using the [k-nearest neighbor](https://en.wikipedia.org/wiki/Nearest_neighbor_search) (kNN) algorithm and also [semantic search](https://learn.microsoft.com/azure/search/semantic-search-overview).
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install -S @langchain/community @langchain/core @azure/search-documents
```
See a [usage example](/docs/integrations/vectorstores/azure_aisearch).
```typescript
import { AzureAISearchVectorStore } from "@langchain/community/vectorstores/azure_aisearch";
```
### Azure Cosmos DB for NoSQL
> [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/) provides support for querying items with flexible schemas and native support for JSON. It now offers vector indexing and search. This feature is designed to handle high-dimensional vectors, enabling efficient and accurate vector search at any scale. You can now store vectors directly in the documents alongside your data. Each document in your database can contain not only traditional schema-free data, but also high-dimensional vectors as other properties of the documents.
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/azure-cosmosdb @langchain/core
```
See a [usage example](/docs/integrations/vectorstores/azure_cosmosdb_nosql).
```typescript
import { AzureCosmosDBNoSQLVectorStore } from "@langchain/azure-cosmosdb";
```
### Azure Cosmos DB for MongoDB vCore
> [Azure Cosmos DB for MongoDB vCore](https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/) makes it easy to create a database with full native MongoDB support. You can apply your MongoDB experience and continue to use your favorite MongoDB drivers, SDKs, and tools by pointing your application to the API for MongoDB vCore account’s connection string. Use vector search in Azure Cosmos DB for MongoDB vCore to seamlessly integrate your AI-based applications with your data that’s stored in Azure Cosmos DB.
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/azure-cosmosdb @langchain/core
```
See a [usage example](/docs/integrations/vectorstores/azure_cosmosdb_mongodb).
```typescript
import { AzureCosmosDBMongoDBVectorStore } from "@langchain/azure-cosmosdb";
```
## Semantic Cache
### Azure Cosmos DB NoSQL Semantic Cache
> The Semantic Cache feature is supported with Azure Cosmos DB for NoSQL integration, enabling users to retrieve cached responses based on semantic similarity between the user input and previously cached results. It leverages [AzureCosmosDBNoSQLVectorStore](/docs/integrations/vectorstores/azure_cosmosdb_nosql), which stores vector embeddings of cached prompts. These embeddings enable similarity-based searches, allowing the system to retrieve relevant cached results.
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/azure-cosmosdb @langchain/core
```
See a [usage example](/docs/integrations/llm_caching/azure_cosmosdb_nosql).
```typescript
import { AzureCosmosDBNoSQLSemanticCache } from "@langchain/azure-cosmosdb";
```
## Chat Message History
### Azure Cosmos DB NoSQL Chat Message History
> The AzureCosmosDBNoSQLChatMessageHistory uses Cosmos DB to store chat message history. For longer-term persistence across chat sessions, you can swap out the default in-memory `chatHistory` that backs chat memory classes like `BufferMemory`.
```bash npm2yarn
npm install @langchain/azure-cosmosdb @langchain/core
```
See [usage example](/docs/integrations/memory/azure_cosmosdb_nosql.mdx).
```typescript
import { AzureCosmosDBNoSQLChatMessageHistory } from "@langchain/azure-cosmosdb";
```
## Document loaders
### Azure Blob Storage
> [Azure Blob Storage](https://learn.microsoft.com/azure/storage/blobs/storage-blobs-introduction) is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data.
> [Azure Files](https://learn.microsoft.com/azure/storage/files/storage-files-introduction) offers fully managed
> file shares in the cloud that are accessible via the industry standard Server Message Block (`SMB`) protocol,
> Network File System (`NFS`) protocol, and `Azure Files REST API`. `Azure Files` are based on the `Azure Blob Storage`.
`Azure Blob Storage` is designed for:
- Serving images or documents directly to a browser.
- Storing files for distributed access.
- Streaming video and audio.
- Writing to log files.
- Storing data for backup and restore, disaster recovery, and archiving.
- Storing data for analysis by an on-premises or Azure-hosted service.
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/community @langchain/core @azure/storage-blob
```
See a [usage example for the Azure Blob Storage](/docs/integrations/document_loaders/web_loaders/azure_blob_storage_container).
```typescript
import { AzureBlobStorageContainerLoader } from "@langchain/community/document_loaders/web/azure_blob_storage_container";
```
See a [usage example for the Azure Files](/docs/integrations/document_loaders/web_loaders/azure_blob_storage_file).
```typescript
import { AzureBlobStorageFileLoader } from "@langchain/community/document_loaders/web/azure_blob_storage_file";
```
## Tools
### Azure Container Apps Dynamic Sessions
> [Azure Container Apps dynamic sessions](https://learn.microsoft.com/azure/container-apps/sessions) provide fast access to secure sandboxed environments that are ideal for running code or applications that require strong isolation from other workloads.
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/azure-dynamic-sessions @langchain/core
```
See a [usage example](/docs/integrations/tools/azure_dynamic_sessions).
```typescript
import { SessionsPythonREPLTool } from "@langchain/azure-dynamic-sessions";
```
|
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sidebar_position: 0
sidebar_class_name: hidden
---
# Providers
LangChain integrates with many providers.
## Partner Packages
These providers have standalone `@langchain/{provider}` packages for improved versioning, dependency management and testing.
- [Anthropic](https://www.npmjs.com/package/@langchain/anthropic)
- [Cloudflare](https://www.npmjs.com/package/@langchain/cloudflare)
- [Cohere](https://www.npmjs.com/package/@langchain/cohere)
- [Exa](https://www.npmjs.com/package/@langchain/exa)
- [Google GenAI](https://www.npmjs.com/package/@langchain/google-genai)
- [Google VertexAI](https://www.npmjs.com/package/@langchain/google-vertexai)
- [Google VertexAI Web](https://www.npmjs.com/package/@langchain/google-vertexai-web)
- [Groq](https://www.npmjs.com/package/@langchain/groq)
- [MistralAI](https://www.npmjs.com/package/@langchain/mistralai)
- [MongoDB](https://www.npmjs.com/package/@langchain/mongodb)
- [Nomic](https://www.npmjs.com/package/@langchain/nomic)
- [OpenAI](https://www.npmjs.com/package/@langchain/openai)
- [Pinecone](https://www.npmjs.com/package/@langchain/pinecone)
- [Qdrant](https://www.npmjs.com/package/@langchain/qdrant)
- [Redis](https://www.npmjs.com/package/@langchain/redis)
- [Weaviate](https://www.npmjs.com/package/@langchain/weaviate)
- [Yandex](https://www.npmjs.com/package/@langchain/yandex)
- [Azure CosmosDB](https://www.npmjs.com/package/@langchain/azure-cosmosdb)
- [xAI](https://www.npmjs.com/package/@langchain/xai)
|
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keywords: [gemini, gemini-pro]
---
# Google
Functionality related to [Google Cloud Platform](https://cloud.google.com/)
## Chat models
### Gemini Models
Access Gemini models such as `gemini-1.5-pro` and `gemini-1.5-flex` through the [`ChatGoogleGenerativeAI`](/docs/integrations/chat/google_generativeai),
or if using VertexAI, via the [`ChatVertexAI`](/docs/integrations/chat/google_vertex_ai) class.
import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<Tabs>
<TabItem value="genai" label="GenAI" default>
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/google-genai @langchain/core
```
Configure your API key.
```
export GOOGLE_API_KEY=your-api-key
```
```typescript
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
const model = new ChatGoogleGenerativeAI({
model: "gemini-pro",
maxOutputTokens: 2048,
});
// Batch and stream are also supported
const res = await model.invoke([
[
"human",
"What would be a good company name for a company that makes colorful socks?",
],
]);
```
Gemini vision models support image inputs when providing a single human message. For example:
```typescript
const visionModel = new ChatGoogleGenerativeAI({
model: "gemini-pro-vision",
maxOutputTokens: 2048,
});
const image = fs.readFileSync("./hotdog.jpg").toString("base64");
const input2 = [
new HumanMessage({
content: [
{
type: "text",
text: "Describe the following image.",
},
{
type: "image_url",
image_url: `data:image/png;base64,${image}`,
},
],
}),
];
const res = await visionModel.invoke(input2);
```
:::tip
Click [here](/docs/integrations/chat/google_generativeai) for the `@langchain/google-genai` specific integration docs
:::
</TabItem>
<TabItem value="vertexai" label="VertexAI" default>
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/google-vertexai @langchain/core
```
Then, you'll need to add your service account credentials, either directly as a `GOOGLE_VERTEX_AI_WEB_CREDENTIALS` environment variable:
```
GOOGLE_VERTEX_AI_WEB_CREDENTIALS={"type":"service_account","project_id":"YOUR_PROJECT-12345",...}
```
or as a file path:
```
GOOGLE_VERTEX_AI_WEB_CREDENTIALS_FILE=/path/to/your/credentials.json
```
```typescript
import { ChatVertexAI } from "@langchain/google-vertexai";
// Or, if using the web entrypoint:
// import { ChatVertexAI } from "@langchain/google-vertexai-web";
const model = new ChatVertexAI({
model: "gemini-1.0-pro",
maxOutputTokens: 2048,
});
// Batch and stream are also supported
const res = await model.invoke([
[
"human",
"What would be a good company name for a company that makes colorful socks?",
],
]);
```
Gemini vision models support image inputs when providing a single human message. For example:
```typescript
const visionModel = new ChatVertexAI({
model: "gemini-pro-vision",
maxOutputTokens: 2048,
});
const image = fs.readFileSync("./hotdog.png").toString("base64");
const input2 = [
new HumanMessage({
content: [
{
type: "text",
text: "Describe the following image.",
},
{
type: "image_url",
image_url: `data:image/png;base64,${image}`,
},
],
}),
];
const res = await visionModel.invoke(input2);
```
:::tip
Click [here](/docs/integrations/chat/google_vertex_ai) for the `@langchain/google-vertexai` specific integration docs
:::
</TabItem>
</Tabs>
The value of `image_url` must be a base64 encoded image (e.g., `data:image/png;base64,abcd124`).
### Non-Gemini Models
See above for setting up authentication through Vertex AI to use these models.
[Anthropic](/docs/integrations/chat/anthropic) Claude models are also available through
the [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude)
platform. See [here](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude)
for more information about enabling access to the models and the model names to use.
PaLM models are no longer supported.
## Vector Store
### Vertex AI Vector Search
> [Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview),
> formerly known as Vertex AI Matching Engine, provides the industry's leading high-scale
> low latency vector database. These vector databases are commonly
> referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.
```typescript
import { MatchingEngine } from "langchain/vectorstores/googlevertexai";
```
## Tools
### Google Search
- Set up a Custom Search Engine, following [these instructions](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search)
- Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables `GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively
There exists a `GoogleCustomSearch` utility which wraps this API. To import this utility:
```typescript
import { GoogleCustomSearch } from "langchain/tools";
```
We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:
```typescript
const tools = [new GoogleCustomSearch({})];
// Pass this variable into your agent.
```
|
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keywords: [openai]
---
# OpenAI
All functionality related to OpenAI
> [OpenAI](https://en.wikipedia.org/wiki/OpenAI) is American artificial intelligence (AI) research laboratory
> consisting of the non-profit `OpenAI Incorporated`
> and its for-profit subsidiary corporation `OpenAI Limited Partnership`.
> `OpenAI` conducts AI research with the declared intention of promoting and developing a friendly AI.
> `OpenAI` systems run on an `Azure`-based supercomputing platform from `Microsoft`.
> The [OpenAI API](https://platform.openai.com/docs/models) is powered by a diverse set of models with different capabilities and price points.
>
> [ChatGPT](https://chat.openai.com) is the Artificial Intelligence (AI) chatbot developed by `OpenAI`.
## Installation and Setup
- Get an OpenAI api key and set it as an environment variable (`OPENAI_API_KEY`)
## Chat model
See a [usage example](/docs/integrations/chat/openai).
```typescript
import { ChatOpenAI } from "@langchain/openai";
```
## LLM
See a [usage example](/docs/integrations/llms/openai).
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @langchain/core
```
```typescript
import { OpenAI } from "@langchain/openai";
```
## Text Embedding Model
See a [usage example](/docs/integrations/text_embedding/openai)
```typescript
import { OpenAIEmbeddings } from "@langchain/openai";
```
## Chain
```typescript
import { OpenAIModerationChain } from "langchain/chains";
```
|
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import { OpenAIEmbeddings } from "@langchain/openai";
import { createClient } from "redis";
const embeddings = new OpenAIEmbeddings({
model: "text-embedding-3-small",
});
const client = createClient({
url: process.env.REDIS_URL ?? "redis://localhost:6379",
});
await client.connect();
const vectorStore = new RedisVectorStore(embeddings, {
redisClient: client,
indexName: "langchainjs-testing",
});import type { Document } from "@langchain/core/documents";
const document1: Document = {
pageContent: "The powerhouse of the cell is the mitochondria",
metadata: { type: "example" }
};
const document2: Document = {
pageContent: "Buildings are made out of brick",
metadata: { type: "example" }
};
const document3: Document = {
pageContent: "Mitochondria are made out of lipids",
metadata: { type: "example" }
};
const document4: Document = {
pageContent: "The 2024 Olympics are in Paris",
metadata: { type: "example" }
}
const documents = [document1, document2, document3, document4];
await vectorStore.addDocuments(documents);const similaritySearchResults = await vectorStore.similaritySearch("biology", 2);
for (const doc of similaritySearchResults) {
console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}const similaritySearchWithScoreResults = await vectorStore.similaritySearchWithScore("biology", 2)
for (const [doc, score] of similaritySearchWithScoreResults) {
console.log(`* [SIM=${score.toFixed(3)}] ${doc.pageContent} [${JSON.stringify(doc.metadata)}]`);
}const retriever = vectorStore.asRetriever({
k: 2,
});
await retriever.invoke("biology");await vectorStore.delete({ deleteAll: true });await client.disconnect(); |
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sidebar_class_name: node-only
---
import CodeBlock from "@theme/CodeBlock";
# Astra DB
:::tip Compatibility
Only available on Node.js.
:::
DataStax [Astra DB](https://astra.datastax.com/register) is a serverless vector-capable database built on [Apache Cassandra](https://cassandra.apache.org/_/index.html) and made conveniently available through an easy-to-use JSON API.
## Setup
1. Create an [Astra DB account](https://astra.datastax.com/register).
2. Create a [vector enabled database](https://astra.datastax.com/createDatabase).
3. Grab your `API Endpoint` and `Token` from the Database Details.
4. Set up the following env vars:
```bash
export ASTRA_DB_APPLICATION_TOKEN=YOUR_ASTRA_DB_APPLICATION_TOKEN_HERE
export ASTRA_DB_ENDPOINT=YOUR_ASTRA_DB_ENDPOINT_HERE
export ASTRA_DB_COLLECTION=YOUR_ASTRA_DB_COLLECTION_HERE
export OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE
```
Where `ASTRA_DB_COLLECTION` is the desired name of your collection
6. Install the Astra TS Client & the LangChain community package
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @datastax/astra-db-ts @langchain/community @langchain/core
```
## Indexing docs
import Example from "@examples/indexes/vector_stores/astra.ts";
<CodeBlock language="typescript">{Example}</CodeBlock>
## Vector Types
Astra DB supports `cosine` (the default), `dot_product`, and `euclidean` similarity search; this is defined when the
vector store is first created as part of the `CreateCollectionOptions`:
```typescript
vector: {
dimension: number;
metric?: "cosine" | "euclidean" | "dot_product";
};
```
## Related
- Vector store [conceptual guide](/docs/concepts/#vectorstores)
- Vector store [how-to guides](/docs/how_to/#vectorstores)
|
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Neo4j is an open-source graph database with integrated support for vector similarity search.
It supports:
- approximate nearest neighbor search
- Euclidean similarity and cosine similarity
- Hybrid search combining vector and keyword searches
## Setup
To work with Neo4j Vector Index, you need to install the `neo4j-driver` package:
```bash npm2yarn
npm install neo4j-driver
```
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @langchain/community @langchain/core
```
### Setup a `Neo4j` self hosted instance with `docker-compose`
`Neo4j` provides a prebuilt Docker image that can be used to quickly setup a self-hosted Neo4j database instance.
Create a file below named `docker-compose.yml`:
import CodeBlock from "@theme/CodeBlock";
import DockerExample from "@examples/indexes/vector_stores/neo4j_vector/docker-compose.example.yml";
<CodeBlock language="yml" name="docker-compose.yml">
{DockerExample}
</CodeBlock>
And then in the same directory, run `docker compose up` to start the container.
You can find more information on how to setup `Neo4j` on their [website](https://neo4j.com/docs/operations-manual/current/installation/).
## Usage
import Example from "@examples/indexes/vector_stores/neo4j_vector/neo4j_vector.ts";
One complete example of using `Neo4jVectorStore` is the following:
<CodeBlock language="typescript">{Example}</CodeBlock>
### Use retrievalQuery parameter to customize responses
import RetrievalExample from "@examples/indexes/vector_stores/neo4j_vector/neo4j_vector_retrieval.ts";
<CodeBlock language="typescript">{RetrievalExample}</CodeBlock>
### Instantiate Neo4jVectorStore from existing graph
import ExistingGraphExample from "@examples/indexes/vector_stores/neo4j_vector/neo4j_vector_existinggraph.ts";
<CodeBlock language="typescript">{ExistingGraphExample}</CodeBlock>
### Metadata filtering
import MetadataExample from "@examples/indexes/vector_stores/neo4j_vector/neo4j_vector_metadata.ts";
<CodeBlock language="typescript">{MetadataExample}</CodeBlock>
# Disclaimer ⚠️
_Security note_: Make sure that the database connection uses credentials
that are narrowly-scoped to only include necessary permissions.
Failure to do so may result in data corruption or loss, since the calling
code may attempt commands that would result in deletion, mutation
of data if appropriately prompted or reading sensitive data if such
data is present in the database.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this tool.
For example, creating read only users for the database is a good way to
ensure that the calling code cannot mutate or delete data.
See the [security page](/docs/security) for more information.
## Related
- Vector store [conceptual guide](/docs/concepts/#vectorstores)
- Vector store [how-to guides](/docs/how_to/#vectorstores)
|
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sidebar_class_name: node-only
---
import CodeBlock from "@theme/CodeBlock";
# ClickHouse
:::tip Compatibility
Only available on Node.js.
:::
[ClickHouse](https://clickhouse.com/) is a robust and open-source columnar database that is used for handling analytical queries and efficient storage, ClickHouse is designed to provide a powerful combination of vector search and analytics.
## Setup
1. Launch a ClickHouse cluster. Refer to the [ClickHouse Installation Guide](https://clickhouse.com/docs/en/getting-started/install/) for details.
2. After launching a ClickHouse cluster, retrieve the `Connection Details` from the cluster's `Actions` menu. You will need the host, port, username, and password.
3. Install the required Node.js peer dependency for ClickHouse in your workspace.
You will need to install the following peer dependencies:
```bash npm2yarn
npm install -S @clickhouse/client mysql2
```
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @langchain/community @langchain/core
```
## Index and Query Docs
import InsertExample from "@examples/indexes/vector_stores/clickhouse_fromTexts.ts";
<CodeBlock language="typescript">{InsertExample}</CodeBlock>
## Query Docs From an Existing Collection
import SearchExample from "@examples/indexes/vector_stores/clickhouse_search.ts";
<CodeBlock language="typescript">{SearchExample}</CodeBlock>
## Related
- Vector store [conceptual guide](/docs/concepts/#vectorstores)
- Vector store [how-to guides](/docs/how_to/#vectorstores)
|
0 | lc_public_repos/langchainjs/docs/core_docs/docs/integrations | lc_public_repos/langchainjs/docs/core_docs/docs/integrations/vectorstores/typesense.mdx | # Typesense
Vector store that utilizes the Typesense search engine.
### Basic Usage
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>
```bash npm2yarn
npm install @langchain/openai @langchain/community @langchain/core
```
```typescript
import {
Typesense,
TypesenseConfig,
} from "@lanchain/community/vectorstores/typesense";
import { OpenAIEmbeddings } from "@langchain/openai";
import { Client } from "typesense";
import { Document } from "@langchain/core/documents";
const vectorTypesenseClient = new Client({
nodes: [
{
// Ideally should come from your .env file
host: "...",
port: 123,
protocol: "https",
},
],
// Ideally should come from your .env file
apiKey: "...",
numRetries: 3,
connectionTimeoutSeconds: 60,
});
const typesenseVectorStoreConfig = {
// Typesense client
typesenseClient: vectorTypesenseClient,
// Name of the collection to store the vectors in
schemaName: "your_schema_name",
// Optional column names to be used in Typesense
columnNames: {
// "vec" is the default name for the vector column in Typesense but you can change it to whatever you want
vector: "vec",
// "text" is the default name for the text column in Typesense but you can change it to whatever you want
pageContent: "text",
// Names of the columns that you will save in your typesense schema and need to be retrieved as metadata when searching
metadataColumnNames: ["foo", "bar", "baz"],
},
// Optional search parameters to be passed to Typesense when searching
searchParams: {
q: "*",
filter_by: "foo:[fooo]",
query_by: "",
},
// You can override the default Typesense import function if you want to do something more complex
// Default import function:
// async importToTypesense<
// T extends Record<string, unknown> = Record<string, unknown>
// >(data: T[], collectionName: string) {
// const chunkSize = 2000;
// for (let i = 0; i < data.length; i += chunkSize) {
// const chunk = data.slice(i, i + chunkSize);
// await this.caller.call(async () => {
// await this.client
// .collections<T>(collectionName)
// .documents()
// .import(chunk, { action: "emplace", dirty_values: "drop" });
// });
// }
// }
import: async (data, collectionName) => {
await vectorTypesenseClient
.collections(collectionName)
.documents()
.import(data, { action: "emplace", dirty_values: "drop" });
},
} satisfies TypesenseConfig;
/**
* Creates a Typesense vector store from a list of documents.
* Will update documents if there is a document with the same id, at least with the default import function.
* @param documents list of documents to create the vector store from
* @returns Typesense vector store
*/
const createVectorStoreWithTypesense = async (documents: Document[] = []) =>
Typesense.fromDocuments(
documents,
new OpenAIEmbeddings(),
typesenseVectorStoreConfig
);
/**
* Returns a Typesense vector store from an existing index.
* @returns Typesense vector store
*/
const getVectorStoreWithTypesense = async () =>
new Typesense(new OpenAIEmbeddings(), typesenseVectorStoreConfig);
// Do a similarity search
const vectorStore = await getVectorStoreWithTypesense();
const documents = await vectorStore.similaritySearch("hello world");
// Add filters based on metadata with the search parameters of Typesense
// will exclude documents with author:JK Rowling, so if Joe Rowling & JK Rowling exists, only Joe Rowling will be returned
vectorStore.similaritySearch("Rowling", undefined, {
filter_by: "author:!=JK Rowling",
});
// Delete a document
vectorStore.deleteDocuments(["document_id_1", "document_id_2"]);
```
### Constructor
Before starting, create a schema in Typesense with an id, a field for the vector and a field for the text. Add as many other fields as needed for the metadata.
- `constructor(embeddings: Embeddings, config: TypesenseConfig)`: Constructs a new instance of the `Typesense` class.
- `embeddings`: An instance of the `Embeddings` class used for embedding documents.
- `config`: Configuration object for the Typesense vector store.
- `typesenseClient`: Typesense client instance.
- `schemaName`: Name of the Typesense schema in which documents will be stored and searched.
- `searchParams` (optional): Typesense search parameters. Default is `{ q: '*', per_page: 5, query_by: '' }`.
- `columnNames` (optional): Column names configuration.
- `vector` (optional): Vector column name. Default is `'vec'`.
- `pageContent` (optional): Page content column name. Default is `'text'`.
- `metadataColumnNames` (optional): Metadata column names. Default is an empty array `[]`.
- `import` (optional): Replace the default import function for importing data to Typesense. This can affect the functionality of updating documents.
### Methods
- `async addDocuments(documents: Document[]): Promise<void>`: Adds documents to the vector store. The documents will be updated if there is a document with the same ID.
- `static async fromDocuments(docs: Document[], embeddings: Embeddings, config: TypesenseConfig): Promise<Typesense>`: Creates a Typesense vector store from a list of documents. Documents are added to the vector store during construction.
- `static async fromTexts(texts: string[], metadatas: object[], embeddings: Embeddings, config: TypesenseConfig): Promise<Typesense>`: Creates a Typesense vector store from a list of texts and associated metadata. Texts are converted to documents and added to the vector store during construction.
- `async similaritySearch(query: string, k?: number, filter?: Record<string, unknown>): Promise<Document[]>`: Searches for similar documents based on a query. Returns an array of similar documents.
- `async deleteDocuments(documentIds: string[]): Promise<void>`: Deletes documents from the vector store based on their IDs.
## Related
- Vector store [conceptual guide](/docs/concepts/#vectorstores)
- Vector store [how-to guides](/docs/how_to/#vectorstores)
|
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