id stringlengths 14 15 | text stringlengths 101 5.26k | source stringlengths 57 120 |
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cb1ecfa06586-4 | If the fact is false, explain why.
> Finished chain.
> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.
Checked Assertions:
"""
- The Greenland Sea is an outlyi... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
cb1ecfa06586-5 | > Entering new SequentialChain chain...
> Entering new LLMChain chain...
Prompt after formatting:
Given some text, extract a list of facts from the text.
Format your output as a bulleted list.
Text:
"""
The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelag... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
cb1ecfa06586-6 | """
Result: False
===
Checked Assertions:"""
- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True
- It has an area of 465,000 square miles. True
- It is an arm of the Arctic Ocean. True
- It is covered almost entirely by water, some... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
cb1ecfa06586-7 | """
Original Summary:
"""
The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glacier... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
cb1ecfa06586-8 | - Birds can lay eggs
- Birds are mammals
"""
For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".
If the fact is false, explain why.
> Finished chain.
> Entering new LLMChain chain...
Prompt after formatting:
Bel... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
cb1ecfa06586-9 | Below are some assertions that have been fact checked and are labeled as true or false.
If all of the assertions are true, return "True". If any of the assertions are false, return "False".
Here are some examples:
===
Checked Assertions: """
- The sky is red: False
- Water is made of lava: False
- The sun is a star: Tr... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
06c165ab987f-0 | .ipynb
.pdf
Moderation
Contents
How to use the moderation chain
How to append a Moderation chain to an LLMChain
Moderation#
This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. ... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/moderation.html |
06c165ab987f-1 | if results["flagged"]:
error_str = f"The following text was found that violates OpenAI's content policy: {text}"
return error_str
return text
custom_moderation = CustomModeration()
custom_moderation.run("This is okay")
'This is okay'
custom_moderation.run("I will kill you")
"The fol... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/moderation.html |
5f4c30e4fd45-0 | .ipynb
.pdf
Summarization
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The custom MapReduceChain
The refine Chain
Summarization#
This notebook walks through how to use LangChain for summarization over a list of documents. It covers three different chain types: stuff, map_reduce, and refine. F... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/summarize.html |
5f4c30e4fd45-1 | chain.run(docs)
" In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and releasing oil from its Strategic... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/summarize.html |
5f4c30e4fd45-2 | "\n\nIl Presidente Biden ha lottato per passare l'American Rescue Plan per aiutare le persone che soffrivano a causa della pandemia. Il piano ha fornito sollievo economico immediato a milioni di americani, ha aiutato a mettere cibo sulla loro tavola, a mantenere un tetto sopra le loro teste e a ridurre il costo dell'as... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/summarize.html |
5f4c30e4fd45-3 | while j >= gap and input_list[j - gap] > temp:
input_list[j] = input_list[j - gap]
j = j-gap
input_list[j] = temp
gap = gap//2
return input_list
"""
map_reduce.run(input_text=code, question="Which function has a better time complexity?")
Created a chunk of size 247, which is longer than the spec... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/summarize.html |
5f4c30e4fd45-4 | "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are ... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/summarize.html |
5f4c30e4fd45-5 | "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso ... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/summarize.html |
62e631e7e688-0 | .ipynb
.pdf
Question Answering with Sources
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
The map-rerank Chain
Question Answering with Sources#
This notebook walks through how to use LangChain for question answering with sources over a list of documents. It covers four differe... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/qa_with_sources.html |
62e631e7e688-1 | Intermediate Steps
We can also return the intermediate steps for map_reduce chains, should we want to inspect them. This is done with the return_intermediate_steps variable.
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True)
chain({"input_documents": docs,... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/qa_with_sources.html |
62e631e7e688-2 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': "\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked him for his service and praised his c... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/qa_with_sources.html |
62e631e7e688-3 | '\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal public defender, and... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/qa_with_sources.html |
62e631e7e688-4 | "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'impor... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/qa_with_sources.html |
62e631e7e688-5 | result["output_text"]
' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.'
result["intermediate_steps"]
[{'answer': ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.',
'score': '100'},
... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/qa_with_sources.html |
b6d4ef9794e8-0 | .ipynb
.pdf
Question Answering
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
The map-rerank Chain
Question Answering#
This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: stuff, map_reduce, ... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/question_answering.html |
b6d4ef9794e8-1 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': [' "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice B... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/question_answering.html |
b6d4ef9794e8-2 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': ['\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.',
'\nThe president said that he wanted to honor Justice Breyer for his dedication t... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/question_answering.html |
b6d4ef9794e8-3 | "\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottol... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/question_answering.html |
b6d4ef9794e8-4 | Score: [score between 0 and 100]
Begin!
Context:
---------
{context}
---------
Question: {question}
Helpful Answer In Italian:"""
PROMPT = PromptTemplate(
template=prompt_template,
input_variables=["context", "question"],
output_parser=output_parser,
)
chain = load_qa_chain(OpenAI(temperature=0), chain_type... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/question_answering.html |
c12d869881a1-0 | .ipynb
.pdf
Hypothetical Document Embeddings
Contents
Multiple generations
Using our own prompts
Using HyDE
Hypothetical Document Embeddings#
This notebook goes over how to use Hypothetical Document Embeddings (HyDE), as described in this paper.
At a high level, HyDE is an embedding technique that takes queries, gene... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/hyde.html |
c12d869881a1-1 | previous
Graph QA
next
Question Answering with Sources
Contents
Multiple generations
Using our own prompts
Using HyDE
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/hyde.html |
20e420244fba-0 | .ipynb
.pdf
Chat Over Documents with Chat History
Contents
Pass in chat history
Using a different model for condensing the question
Return Source Documents
ConversationalRetrievalChain with search_distance
ConversationalRetrievalChain with map_reduce
ConversationalRetrievalChain with Question Answering with sources
C... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/chat_vector_db.html |
20e420244fba-1 | Using a different model for condensing the question#
This chain has two steps. First, it condenses the current question and the chat history into a standalone question. This is neccessary to create a standanlone vector to use for retrieval. After that, it does retrieval and then answers the question using retrieval aug... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/chat_vector_db.html |
20e420244fba-2 | result['answer']
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fra... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/chat_vector_db.html |
20e420244fba-3 | result['answer']
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad r... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/chat_vector_db.html |
4118f7b83d16-0 | .ipynb
.pdf
Retrieval Question/Answering
Contents
Chain Type
Custom Prompts
Return Source Documents
Retrieval Question/Answering#
This example showcases question answering over an index.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter imp... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/vector_db_qa.html |
4118f7b83d16-1 | qa.run(query)
" Il presidente ha detto che Ketanji Brown Jackson è una delle menti legali più importanti del paese, che continuerà l'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani."
Return Source Documents#
Additionally,... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/vector_db_qa.html |
4118f7b83d16-2 | Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards ... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/vector_db_qa.html |
eb8d4c0fcd60-0 | .ipynb
.pdf
Graph QA
Contents
Create the graph
Querying the graph
Save the graph
Graph QA#
This notebook goes over how to do question answering over a graph data structure.
Create the graph#
In this section, we construct an example graph. At the moment, this works best for small pieces of text.
from langchain.indexes... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/graph_qa.html |
f3ef3940b40f-0 | .ipynb
.pdf
Vector DB Text Generation
Contents
Prepare Data
Set Up Vector DB
Set Up LLM Chain with Custom Prompt
Generate Text
Vector DB Text Generation#
This notebook walks through how to use LangChain for text generation over a vector index. This is useful if we want to generate text that is able to draw from a lar... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
f3ef3940b40f-1 | print(chain.apply(inputs))
generate_blog_post("environment variables")
[{'text': '\n\nEnvironment variables are a great way to store and access sensitive information in your Deno applications. Deno offers built-in support for environment variables with `Deno.env`, and you can also use a `.env` file to store and access ... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
f3ef3940b40f-2 | previous
Retrieval Question Answering with Sources
next
API Chains
Contents
Prepare Data
Set Up Vector DB
Set Up LLM Chain with Custom Prompt
Generate Text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
5c28abc9ab19-0 | .ipynb
.pdf
Analyze Document
Contents
Summarize
Question Answering
Analyze Document#
The AnalyzeDocumentChain is more of an end to chain. This chain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain. This can be used as more of an end-to-end chain.
with open("../../state_of_th... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/analyze_document.html |
383d5ab25b8c-0 | .ipynb
.pdf
Retrieval Question Answering with Sources
Contents
Chain Type
Retrieval Question Answering with Sources#
This notebook goes over how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index.
from langch... | https://langchain.readthedocs.io/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
3e7ded4185d8-0 | .ipynb
.pdf
Callbacks
Contents
Callbacks
How to use callbacks
When do you want to use each of these?
Using an existing handler
Creating a custom handler
Async Callbacks
Using multiple handlers, passing in handlers
Tracing and Token Counting
Tracing
Token Counting
Callbacks#
LangChain provides a callbacks system that ... | https://langchain.readthedocs.io/en/latest/modules/callbacks/getting_started.html |
3e7ded4185d8-1 | Constructor callbacks: defined in the constructor, eg. LLMChain(callbacks=[handler]), which will be used for all calls made on that object, and will be scoped to that object only, eg. if you pass a handler to the LLMChain constructor, it will not be used by the Model attached to that chain.
Request callbacks: defined i... | https://langchain.readthedocs.io/en/latest/modules/callbacks/getting_started.html |
3e7ded4185d8-2 | My custom handler, token: Because
My custom handler, token: it
My custom handler, token: saw
My custom handler, token: the
My custom handler, token: salad
My custom handler, token: dressing
My custom handler, token: !
My custom handler, token:
AIMessage(content='Why did the tomato turn red? Because it saw the sa... | https://langchain.readthedocs.io/en/latest/modules/callbacks/getting_started.html |
3e7ded4185d8-3 | However, in many cases, it is advantageous to pass in handlers instead when running the object. When we pass through CallbackHandlers using the callbacks keyword arg when executing an run, those callbacks will be issued by all nested objects involved in the execution. For example, when a handler is passed through to an... | https://langchain.readthedocs.io/en/latest/modules/callbacks/getting_started.html |
3e7ded4185d8-4 | on_new_token ...
on_new_token num
on_new_token expr
on_new_token .
on_new_token evaluate
on_new_token ("
on_new_token 2
on_new_token **
on_new_token 0
on_new_token .
on_new_token 235
on_new_token ")
on_new_token ...
on_new_token
on_new_token
on_chain_start LLMChain
on_llm_start OpenAI
on_llm_start (I'm the second han... | https://langchain.readthedocs.io/en/latest/modules/callbacks/getting_started.html |
3e7ded4185d8-5 | Action: Search
Action Input: "Olivia Wilde boyfriend"
Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles afte... | https://langchain.readthedocs.io/en/latest/modules/callbacks/getting_started.html |
3e7ded4185d8-6 | > Entering new AgentExecutor chain...
> Entering new AgentExecutor chain...
I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.
Action: Search
Action Input: "Formula 1 Grand Prix Winner" I need to find out who won the US Open men's final in 2019 and then calculate his age r... | https://langchain.readthedocs.io/en/latest/modules/callbacks/getting_started.html |
1425f7798b06-0 | .rst
.pdf
Toolkits
Toolkits#
Note
Conceptual Guide
This section of documentation covers agents with toolkits - eg an agent applied to a particular use case.
See below for a full list of agent toolkits
Azure Cognitive Services Toolkit
CSV Agent
Gmail Toolkit
Jira
JSON Agent
OpenAPI agents
Natural Language APIs
Pandas Da... | https://langchain.readthedocs.io/en/latest/modules/agents/toolkits.html |
9b828695685b-0 | .rst
.pdf
Agent Executors
Agent Executors#
Note
Conceptual Guide
Agent executors take an agent and tools and use the agent to decide which tools to call and in what order.
In this part of the documentation we cover other related functionality to agent executors
How to combine agents and vectorstores
How to use the asyn... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_executors.html |
59b4052a0ce4-0 | .ipynb
.pdf
Getting Started
Getting Started#
Agents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning to the user.
When used correctly agents can be extremely powerful. The purpose of this notebook is to show you how to easily us... | https://langchain.readthedocs.io/en/latest/modules/agents/getting_started.html |
1e1ebdc5bbc2-0 | .ipynb
.pdf
Plan and Execute
Contents
Plan and Execute
Imports
Tools
Planner, Executor, and Agent
Run Example
Plan and Execute#
Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by BabyAGI and then the “Plan-and-Solve” paper.
The ... | https://langchain.readthedocs.io/en/latest/modules/agents/plan_and_execute.html |
1e1ebdc5bbc2-1 | > Entering new AgentExecutor chain...
Action:
```
{
"action": "Calculator",
"action_input": "28 ** 0.43"
}
```
> Entering new LLMMathChain chain...
28 ** 0.43
```text
28 ** 0.43
```
...numexpr.evaluate("28 ** 0.43")...
Answer: 4.1906168361987195
> Finished chain.
Observation: Answer: 4.1906168361987195
Thought:The ... | https://langchain.readthedocs.io/en/latest/modules/agents/plan_and_execute.html |
241f7000ec40-0 | .rst
.pdf
Agents
Agents#
Note
Conceptual Guide
In this part of the documentation we cover the different types of agents, disregarding which specific tools they are used with.
For a high level overview of the different types of agents, see the below documentation.
Agent Types
For documentation on how to create a custom ... | https://langchain.readthedocs.io/en/latest/modules/agents/agents.html |
fffec0c16796-0 | .rst
.pdf
Tools
Tools#
Note
Conceptual Guide
Tools are ways that an agent can use to interact with the outside world.
For an overview of what a tool is, how to use them, and a full list of examples, please see the getting started documentation
Getting Started
Next, we have some examples of customizing and generically w... | https://langchain.readthedocs.io/en/latest/modules/agents/tools.html |
ec41ddb2cbb4-0 | .ipynb
.pdf
Custom LLM Agent
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
Adding Memory
Custom LLM Agent#
This notebook goes through how to create your own custom LLM agent.
An LLM agent consists of three parts:
PromptTemplate... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_llm_agent.html |
ec41ddb2cbb4-1 | # Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
return self.template.format(**kwargs)
prompt = CustomPromptTemplate(
template=template,
tools=tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variab... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_llm_agent.html |
ec41ddb2cbb4-2 | Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lot... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_llm_agent.html |
469de9739106-0 | .ipynb
.pdf
Custom LLM Agent (with a ChatModel)
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
Custom LLM Agent (with a ChatModel)#
This notebook goes through how to create your own custom agent based on a chat model.
An LLM cha... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_llm_chat_agent.html |
469de9739106-1 | # Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
# Create a list of tool names for the tools provided
... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_llm_chat_agent.html |
469de9739106-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_llm_chat_agent.html |
204e9ceeb356-0 | .ipynb
.pdf
Custom MultiAction Agent
Custom MultiAction Agent#
This notebook goes through how to create your own custom agent.
An agent consists of two parts:
- Tools: The tools the agent has available to use.
- The agent class itself: this decides which action to take.
In this notebook we walk through how to create a ... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_multi_action_agent.html |
d119d5b4129b-0 | .ipynb
.pdf
Custom Agent with Tool Retrieval
Contents
Set up environment
Set up tools
Tool Retriever
Prompt Template
Output Parser
Set up LLM, stop sequence, and the agent
Use the Agent
Custom Agent with Tool Retrieval#
This notebook builds off of this notebook and assumes familiarity with how agents work.
The novel ... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html |
d119d5b4129b-1 | get_tools("whats the number 13?")
[Tool(name='foo-13', description='a silly function that you can use to get more information about the number 13', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, cor... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html |
d119d5b4129b-2 | # Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action ... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html |
eb41659584f1-0 | .ipynb
.pdf
Custom MRKL Agent
Contents
Custom LLMChain
Multiple inputs
Custom MRKL Agent#
This notebook goes through how to create your own custom MRKL agent.
A MRKL agent consists of three parts:
- Tools: The tools the agent has available to use.
- LLMChain: The LLMChain that produces the text that is parsed in a ce... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_mrkl_agent.html |
eb41659584f1-1 | agent_executor.run("How many people live in canada as of 2023?")
> Entering new AgentExecutor chain...
Thought: I need to find out the population of Canada
Action: Search
Action Input: Population of Canada 2023
Observation: The current population of Canada is 38,661,927 as of Sunday, April 16, 2023, based on Worldomete... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_mrkl_agent.html |
e2bf473c27bd-0 | .ipynb
.pdf
Custom Agent
Custom Agent#
This notebook goes through how to create your own custom agent.
An agent consists of two parts:
- Tools: The tools the agent has available to use.
- The agent class itself: this decides which action to take.
In this notebook we walk through how to create a custom agent.
from langc... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/custom_agent.html |
020d66a60fd5-0 | .md
.pdf
Agent Types
Contents
zero-shot-react-description
react-docstore
self-ask-with-search
conversational-react-description
Agent Types#
Agents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning a response to the user.
Here a... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/agent_types.html |
94fdead7eb30-0 | .ipynb
.pdf
Conversation Agent
Conversation Agent#
This notebook walks through using an agent optimized for conversation. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/conversational_agent.html |
3920e70f51d5-0 | .ipynb
.pdf
Structured Tool Chat Agent
Contents
Initialize Tools
Adding in memory
Structured Tool Chat Agent#
This notebook walks through using a chat agent capable of using multi-input tools.
Older agents are configured to specify an action input as a single string, but this agent can use the provided tools’ args_sc... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/structured_chat.html |
3920e70f51d5-1 | TL;DR
We recently open-sourced an auto-evaluator tool for grading LLM question-answer chains. We are now releasing an open source, free to use hosted app and API to expand usability. Below we discuss a few opportunities to further improve May 1, 2023 5 min read Callbacks Improvements TL;DR: We're announcing improvement... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/structured_chat.html |
3920e70f51d5-2 | The reason we like Supabase so much is that Apr 8, 2023 2 min read Announcing our $10M seed round led by Benchmark It was only six months ago that we released the first version of LangChain, but it seems like several years. When we launched, generative AI was starting to go mainstream: stable diffusion had just been re... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/structured_chat.html |
3920e70f51d5-3 | What does this mean? It means that all your favorite prompts, chains, and agents are all recreatable in TypeScript natively. Both the Python version and TypeScript version utilize the same serializable format, meaning that artifacts can seamlessly be shared between languages. As an Feb 17, 2023 2 min read Streaming Sup... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/structured_chat.html |
7bbd873be40a-0 | .ipynb
.pdf
Self Ask With Search
Self Ask With Search#
This notebook showcases the Self Ask With Search chain.
from langchain import OpenAI, SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
tools = [
Tool(... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/self_ask_with_search.html |
82e6e73de4ae-0 | .ipynb
.pdf
MRKL Chat
MRKL Chat#
This notebook showcases using an agent to replicate the MRKL chain using an agent optimized for chat models.
This uses the example Chinook database.
To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at t... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/mrkl_chat.html |
82e6e73de4ae-1 | }
```
Observation: Alanis Morissette
Thought:Now that I know the artist's name, I can use the FooBar DB tool to find out if they are in the database and what albums of theirs are in it.
Action:
```
{
"action": "FooBar DB",
"action_input": "What albums does Alanis Morissette have in the database?"
}
```
> Entering n... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/mrkl_chat.html |
423324c54048-0 | .ipynb
.pdf
ReAct
ReAct#
This notebook showcases using an agent to implement the ReAct logic.
from langchain import OpenAI, Wikipedia
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.agents.react.base import DocstoreExplorer
docstore=DocstoreExplorer(Wikipedia())... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/react.html |
a0bc0623318e-0 | .ipynb
.pdf
Conversation Agent (for Chat Models)
Conversation Agent (for Chat Models)#
This notebook walks through using an agent optimized for conversation, using ChatModels. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may w... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/chat_conversation_agent.html |
a0bc0623318e-1 | }
> Finished chain.
'Cloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%.'
previous
Custom Agent with Tool Retrieval
next
Conversation Agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/chat_conversation_agent.html |
37aef4dd68a1-0 | .ipynb
.pdf
MRKL
MRKL#
This notebook showcases using an agent to replicate the MRKL chain.
This uses the example Chinook database.
To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at the root of this repository.
from langchain import L... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/mrkl.html |
37aef4dd68a1-1 | What albums by Alanis Morissette are in the FooBar database?
SQLQuery:
/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:191: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Ple... | https://langchain.readthedocs.io/en/latest/modules/agents/agents/examples/mrkl.html |
a98863dae490-0 | .ipynb
.pdf
Multi-Input Tools
Contents
Multi-Input Tools with a string format
Multi-Input Tools#
This notebook shows how to use a tool that requires multiple inputs with an agent. The recommended way to do so is with the StructuredTool class.
import os
os.environ["LANGCHAIN_TRACING"] = "true"
from langchain import Op... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/multi_input_tool.html |
878ef5333bfe-0 | .ipynb
.pdf
Defining Custom Tools
Contents
Completely New Tools - String Input and Output
Tool dataclass
Subclassing the BaseTool class
Using the tool decorator
Custom Structured Tools
StructuredTool dataclass
Subclassing the BaseTool
Using the decorator
Modify existing tools
Defining the priorities among Tools
Using... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/custom_tools.html |
878ef5333bfe-1 | Action Input: "Camila Morrone age"
Observation: 25 years
Thought:Now that I have her age, I need to calculate her age raised to the 0.43 power
Action: Calculator
Action Input: 25^(0.43)
> Entering new LLMMathChain chain...
25^(0.43)```text
25**(0.43)
```
...numexpr.evaluate("25**(0.43)")...
Answer: 3.991298452658078
> ... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/custom_tools.html |
878ef5333bfe-2 | from langchain.tools import tool
@tool
def search_api(query: str) -> str:
"""Searches the API for the query."""
return f"Results for query {query}"
search_api
You can also provide arguments like the tool name and whether to return directly.
@tool("search", return_direct=True)
def search_api(query: str) -> str:
... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/custom_tools.html |
878ef5333bfe-3 | return search_wrapper.run(query)
async def _arun(self, query: str, engine: str = "google", gl: str = "us", hl: str = "en", run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("custom_search does not support async")
... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/custom_tools.html |
878ef5333bfe-4 | agent.run("what is the most famous song of christmas")
> Entering new AgentExecutor chain...
I should use a music search engine to find the answer
Action: Music Search
Action Input: most famous song of christmas'All I Want For Christmas Is You' by Mariah Carey. I now know the final answer
Final Answer: 'All I Want For... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/custom_tools.html |
878ef5333bfe-5 | Action: Search_tool3
Action Input: "Leo DiCaprio girlfriend"
Observation: Leonardo DiCaprio and Gigi Hadid were recently spotted at a pre-Oscars party, sparking interest once again in their rumored romance. The Revenant actor and the model first made headlines when they were spotted together at a New York Fashion Week ... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/custom_tools.html |
4e8aac2d530d-0 | .md
.pdf
Getting Started
Contents
List of Tools
Getting Started#
Tools are functions that agents can use to interact with the world.
These tools can be generic utilities (e.g. search), other chains, or even other agents.
Currently, tools can be loaded with the following snippet:
from langchain.agents import load_tool... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/getting_started.html |
4e8aac2d530d-1 | Notes: A natural language connection to the TMDB API (https://api.themoviedb.org/3), specifically the /search/movie endpoint.
Requires LLM: Yes
Extra Parameters: tmdb_bearer_token (your Bearer Token to access this endpoint - note that this is different from the API key)
google-search
Tool Name: Search
Tool Description:... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/getting_started.html |
8fa46b33b6b6-0 | .ipynb
.pdf
Tool Input Schema
Tool Input Schema#
By default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic.
from typing import Any, Dict
from langchain.agents import AgentType, initialize_agent... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/tool_input_validation.html |
8fa46b33b6b6-1 | 694 # We then call the tool on the tool input to get an observation
--> 695 observation = tool.run(
696 agent_action.tool_input,
697 verbose=self.verbose,
698 color=color,
699 **tool_run_kwargs,
700 )
701 else:
702 tool_run_kwargs = self.agent.... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/tool_input_validation.html |
8ba763380ace-0 | .ipynb
.pdf
Gradio Tools
Contents
Using a tool
Using within an agent
Gradio Tools#
There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM’s fingers 🦾
Specifically, gradio-tools is a Python library for converting Gradio apps into tools that can be leveraged by a lar... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/examples/gradio_tools.html |
8ba763380ace-1 | Job Status: Status.STARTING eta: None
Due to heavy traffic on this app, the prediction will take approximately 73 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis)
Job Status: Status.IN_QUEUE eta: 73.89824726581574... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/examples/gradio_tools.html |
8d8e4ee4b834-0 | .ipynb
.pdf
ChatGPT Plugins
ChatGPT Plugins#
This example shows how to use ChatGPT Plugins within LangChain abstractions.
Note 1: This currently only works for plugins with no auth.
Note 2: There are almost certainly other ways to do this, this is just a first pass. If you have better ideas, please open a PR!
from lang... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/examples/chatgpt_plugins.html |
8d8e4ee4b834-1 | Observation: {"products":[{"name":"Lacoste Men's Pack of Plain T-Shirts","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202043025/Clothing/Lacoste-Men-s-Pack-of-Plain-T-Shirts/?utm_source=openai","price":"$26.60","attributes":["Material:Cotton","Target Group:Man","Color:White,Black"]},{"name":"Hanes Men's Ultima... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/examples/chatgpt_plugins.html |
3d7d03d2161c-0 | .ipynb
.pdf
Search Tools
Contents
Google Serper API Wrapper
SerpAPI
GoogleSearchAPIWrapper
SearxNG Meta Search Engine
Search Tools#
This notebook shows off usage of various search tools.
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from l... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/examples/search_tools.html |
3d7d03d2161c-1 | Thought: I now know the current weather conditions in Pomfret.
Final Answer: Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%.
> Finished AgentExecutor chain.
'Showers early becoming a steady light rain later in the... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/examples/search_tools.html |
84ab24a8de5f-0 | .ipynb
.pdf
Requests
Contents
Inside the tool
Requests#
The web contains a lot of information that LLMs do not have access to. In order to easily let LLMs interact with that information, we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.
from langchain.agents im... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/examples/requests.html |
84ab24a8de5f-1 | '<!doctype html><html itemscope="" itemtype="http://schema.org/WebPage" lang="en"><head><meta content="Search the world\'s information, including webpages, images, videos and more. Google has many special features to help you find exactly what you\'re looking for." name="description"><meta content="noodp" name="robots"... | https://langchain.readthedocs.io/en/latest/modules/agents/tools/examples/requests.html |
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