id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
d2d9be274d33-90 | (langchain.llms.Cohere class method)
(langchain.llms.CTransformers class method)
(langchain.llms.Databricks class method)
(langchain.llms.DeepInfra class method)
(langchain.llms.FakeListLLM class method)
(langchain.llms.ForefrontAI class method)
(langchain.llms.GooglePalm class method)
(langchain.llms.GooseAI class met... | https://python.langchain.com/en/latest/genindex.html |
d2d9be274d33-91 | (langchain.llms.VertexAI class method)
(langchain.llms.Writer class method)
upsert_messages() (langchain.memory.CosmosDBChatMessageHistory method)
url (langchain.document_loaders.MathpixPDFLoader property)
(langchain.llms.Beam attribute)
(langchain.retrievers.ChatGPTPluginRetriever attribute)
(langchain.retrievers.Remo... | https://python.langchain.com/en/latest/genindex.html |
d2d9be274d33-92 | (langchain.chains.VectorDBQAWithSourcesChain attribute)
(langchain.prompts.example_selector.SemanticSimilarityExampleSelector attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
vectorstore_info (langchain.agents.agent_toolkits.VectorStoreTool... | https://python.langchain.com/en/latest/genindex.html |
d2d9be274d33-93 | (langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PipelineAI attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.Replicate attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.SagemakerEndpoint attribute)
(langchain.llms.Sel... | https://python.langchain.com/en/latest/genindex.html |
d2d9be274d33-94 | Wikipedia (class in langchain.docstore)
WikipediaLoader (class in langchain.document_loaders)
wolfram_alpha_appid (langchain.utilities.WolframAlphaAPIWrapper attribute)
writer_api_key (langchain.llms.Writer attribute)
writer_org_id (langchain.llms.Writer attribute)
Y
YoutubeLoader (class in langchain.document_loaders)
... | https://python.langchain.com/en/latest/genindex.html |
71977294a4af-0 | Search
Error
Please activate JavaScript to enable the search functionality.
Ctrl+K
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/search.html |
b82e9ef85278-0 | .rst
.pdf
API References
API References#
Full documentation on all methods, classes, and APIs in LangChain.
Models
Prompts
Indexes
Memory
Chains
Agents
Utilities
Experimental Modules
previous
Installation
next
Models
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/reference.html |
ea1ee61d6e72-0 | .rst
.pdf
Integrations
Contents
Integrations by Module
All Integrations
Integrations#
LangChain integrates with many LLMs, systems, and products.
Integrations by Module#
Integrations grouped by the core LangChain module they map to:
LLM Providers
Chat Model Providers
Text Embedding Model Providers
Document Loader Int... | https://python.langchain.com/en/latest/integrations.html |
ea1ee61d6e72-1 | Writer
Yeager.ai
Zilliz
previous
Experimental Modules
next
AI21 Labs
Contents
Integrations by Module
All Integrations
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/integrations.html |
b3abbef79756-0 | .rst
.pdf
Prompts
Contents
Getting Started
Go Deeper
Prompts#
Note
Conceptual Guide
The new way of programming models is through prompts.
A “prompt” refers to the input to the model.
This input is rarely hard coded, but rather is often constructed from multiple components.
A PromptTemplate is responsible for the cons... | https://python.langchain.com/en/latest/modules/prompts.html |
ce4d451d4c11-0 | .rst
.pdf
Models
Contents
Getting Started
Go Deeper
Models#
Note
Conceptual Guide
This section of the documentation deals with different types of models that are used in LangChain.
On this page we will go over the model types at a high level,
but we have individual pages for each model type.
The pages contain more de... | https://python.langchain.com/en/latest/modules/models.html |
a3adb47bf4e2-0 | .rst
.pdf
Memory
Memory#
Note
Conceptual Guide
By default, Chains and Agents are stateless,
meaning that they treat each incoming query independently (as are the underlying LLMs and chat models).
In some applications (chatbots being a GREAT example) it is highly important
to remember previous interactions, both at a sh... | https://python.langchain.com/en/latest/modules/memory.html |
6c6aff63f9c0-0 | .rst
.pdf
Chains
Chains#
Note
Conceptual Guide
Using an LLM in isolation is fine for some simple applications,
but many more complex ones require chaining LLMs - either with each other or with other experts.
LangChain provides a standard interface for Chains, as well as some common implementations of chains for ease of... | https://python.langchain.com/en/latest/modules/chains.html |
43d084bac0f9-0 | .rst
.pdf
Agents
Contents
Action Agents
Plan-and-Execute Agents
Agents#
Note
Conceptual Guide
Some applications will require not just a predetermined chain of calls to LLMs/other tools,
but potentially an unknown chain that depends on the user’s input.
In these types of chains, there is a “agent” which has access to ... | https://python.langchain.com/en/latest/modules/agents.html |
43d084bac0f9-1 | The different abstractions involved in agents are as follows:
Agent: this is where the logic of the application lives. Agents expose an interface that takes in user input along with a list of previous steps the agent has taken, and returns either an AgentAction or AgentFinish
AgentAction corresponds to the tool to use ... | https://python.langchain.com/en/latest/modules/agents.html |
43d084bac0f9-2 | Agents
In this section we cover the different types of agents LangChain supports natively.
We then cover how to modify and create your own agents.
Toolkits
In this section we go over the various toolkits that LangChain supports out of the box,
and how to create an agent from them.
Agent Executor
In this section we go o... | https://python.langchain.com/en/latest/modules/agents.html |
f671efad0206-0 | .rst
.pdf
Indexes
Contents
Go Deeper
Indexes#
Note
Conceptual Guide
Indexes refer to ways to structure documents so that LLMs can best interact with them.
This module contains utility functions for working with documents, different types of indexes, and then examples for using those indexes in chains.
The most common... | https://python.langchain.com/en/latest/modules/indexes.html |
f671efad0206-1 | previous
Zep Memory
next
Getting Started
Contents
Go Deeper
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/indexes.html |
b84b95f739a7-0 | .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://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-1 | CallbackHandlers are objects that implement the CallbackHandler interface, which has a method for each event that can be subscribed to. The CallbackManager will call the appropriate method on each handler when the event is triggered.
class BaseCallbackHandler:
"""Base callback handler that can be used to handle cal... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-2 | def on_tool_end(self, output: str, **kwargs: Any) -> Any:
"""Run when tool ends running."""
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> Any:
"""Run when tool errors."""
def on_text(self, text: str, **kwargs: Any) -> Any:
"""Run on a... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-3 | The verbose argument is available on most objects throughout the API (Chains, Models, Tools, Agents, etc.) as a constructor argument, eg. LLMChain(verbose=True), and it is equivalent to passing a ConsoleCallbackHandler to the callbacks argument of that object and all child objects. This is useful for debugging, as it w... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-4 | # First, let's explicitly set the StdOutCallbackHandler in `callbacks`
chain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler])
chain.run(number=2)
# Then, let's use the `verbose` flag to achieve the same result
chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
chain.run(number=2)
# Finally, let's use the req... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-5 | chat([HumanMessage(content="Tell me a joke")])
My custom handler, token:
My custom handler, token: Why
My custom handler, token: did
My custom handler, token: the
My custom handler, token: tomato
My custom handler, token: turn
My custom handler, token: red
My custom handler, token: ?
My custom handler, token: Be... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-6 | self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when chain starts running."""
print("zzzz....")
await asyncio.sleep(0.3)
class_name = serialized["name"]
print("Hi! I just woke up. Your llm is starting")
async def on_llm_end(self, resp... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-7 | Sync handler being called in a `thread_pool_executor`: token: they
Sync handler being called in a `thread_pool_executor`: token: make
Sync handler being called in a `thread_pool_executor`: token: up
Sync handler being called in a `thread_pool_executor`: token: everything
Sync handler being called in a `thread_pool_... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-8 | from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks import tracing_enabled
from langchain.llms import OpenAI
# First, define custom callback handler implementations
class MyCustomHandlerOne(BaseCallbackHandler):
def on_llm_start(
self, serialized: Dict[str, Any], pr... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-9 | handler1 = MyCustomHandlerOne()
handler2 = MyCustomHandlerTwo()
# Setup the agent. Only the `llm` will issue callbacks for handler2
llm = OpenAI(temperature=0, streaming=True, callbacks=[handler2])
tools = load_tools(["llm-math"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRI... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-10 | on_chain_start LLMChain
on_llm_start OpenAI
on_llm_start (I'm the second handler!!) OpenAI
on_new_token
on_new_token ```text
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 num
on_new_token expr
on_new_token .
on_n... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-11 | Using a context manager with tracing_enabled() to trace a particular block of code.
Note if the environment variable is set, all code will be traced, regardless of whether or not it’s within the context manager.
import os
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks impo... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-12 | Action: Search
Action Input: "US Open men's final 2019 winner"
Observation: Rafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...
Thought: I need to find out the age of the winner
Action: Search
Action Input: "Rafa... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-13 | Action: Calculator
Action Input: 29^0.23
Observation: Answer: 2.169459462491557
Thought: I now know the final answer.
Final Answer: Harry Styles is Olivia Wilde's boyfriend and his current age raised to the 0.23 power is 2.169459462491557.
> Finished chain.
# Now, we unset the environment variable and use a context man... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-14 | Thought: I now know the final answer
Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.
> Finished chain.
> Entering new AgentExecutor chain...
I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the ... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-15 | task = asyncio.create_task(agent.arun(questions[0])) # this should not be traced
with tracing_enabled() as session:
assert session
tasks = [agent.arun(q) for q in questions[1:3]] # these should be traced
await asyncio.gather(*tasks)
await task
> Entering new AgentExecutor chain...
> Entering new AgentExec... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-16 | Action: Search
Action Input: "Rafael Nadal age"36 years I need to find out Harry Styles' age.
Action: Search
Action Input: "Harry Styles age" I need to find out Lewis Hamilton's age
Action: Search
Action Input: "Lewis Hamilton Age"29 years I need to calculate the age raised to the 0.334 power
Action: Calculator
Action ... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
b84b95f739a7-17 | with get_openai_callback() as cb:
await asyncio.gather(
*[llm.agenerate(["What is the square root of 4?"]) for _ in range(3)]
)
assert cb.total_tokens == total_tokens * 3
# The context manager is concurrency safe
task = asyncio.create_task(llm.agenerate(["What is the square root of 4?"]))
with get_opena... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
d503e05cc4f2-0 | .ipynb
.pdf
Getting Started
Contents
ChatMessageHistory
ConversationBufferMemory
Using in a chain
Saving Message History
Getting Started#
This notebook walks through how LangChain thinks about memory.
Memory involves keeping a concept of state around throughout a user’s interactions with an language model. A user’s i... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
d503e05cc4f2-1 | history.messages
[HumanMessage(content='hi!', additional_kwargs={}),
AIMessage(content='whats up?', additional_kwargs={})]
ConversationBufferMemory#
We now show how to use this simple concept in a chain. We first showcase ConversationBufferMemory which is just a wrapper around ChatMessageHistory that extracts the mess... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
d503e05cc4f2-2 | Current conversation:
Human: Hi there!
AI:
> Finished chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation betw... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
d503e05cc4f2-3 | Human: Tell me about yourself.
AI:
> Finished chain.
" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers."
Saving ... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
e85304beb2f7-0 | .rst
.pdf
How-To Guides
Contents
Types
Usage
How-To Guides#
Types#
The first set of examples all highlight different types of memory.
ConversationBufferMemory
ConversationBufferWindowMemory
Entity Memory
Conversation Knowledge Graph Memory
ConversationSummaryMemory
ConversationSummaryBufferMemory
ConversationTokenBuf... | https://python.langchain.com/en/latest/modules/memory/how_to_guides.html |
ca8e9923a2a8-0 | .ipynb
.pdf
ConversationTokenBufferMemory
Contents
Using in a chain
ConversationTokenBufferMemory#
ConversationTokenBufferMemory keeps a buffer of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions.
Let’s first walk through how to use the ut... | https://python.langchain.com/en/latest/modules/memory/types/token_buffer.html |
ca8e9923a2a8-1 | > Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
... | https://python.langchain.com/en/latest/modules/memory/types/token_buffer.html |
ca8e9923a2a8-2 | AI: Sounds like a productive day! What kind of documentation are you writing?
Human: For LangChain! Have you heard of it?
AI:
> Finished chain.
" Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're wr... | https://python.langchain.com/en/latest/modules/memory/types/token_buffer.html |
db06f765b064-0 | .ipynb
.pdf
ConversationBufferMemory
Contents
Using in a chain
ConversationBufferMemory#
This notebook shows how to use ConversationBufferMemory. This memory allows for storing of messages and then extracts the messages in a variable.
We can first extract it as a string.
from langchain.memory import ConversationBuffe... | https://python.langchain.com/en/latest/modules/memory/types/buffer.html |
db06f765b064-1 | Current conversation:
Human: Hi there!
AI:
> Finished chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation betw... | https://python.langchain.com/en/latest/modules/memory/types/buffer.html |
db06f765b064-2 | Human: Tell me about yourself.
AI:
> Finished chain.
" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers."
And tha... | https://python.langchain.com/en/latest/modules/memory/types/buffer.html |
04d311734441-0 | .ipynb
.pdf
ConversationBufferWindowMemory
Contents
Using in a chain
ConversationBufferWindowMemory#
ConversationBufferWindowMemory keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so ... | https://python.langchain.com/en/latest/modules/memory/types/buffer_window.html |
04d311734441-1 | memory=ConversationBufferWindowMemory(k=2),
verbose=True
)
conversation_with_summary.predict(input="Hi, what's up?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from ... | https://python.langchain.com/en/latest/modules/memory/types/buffer_window.html |
04d311734441-2 | Current conversation:
Human: Hi, what's up?
AI: Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?
Human: What's their issues?
AI: The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.
Human: Is... | https://python.langchain.com/en/latest/modules/memory/types/buffer_window.html |
6c4fe9bed78f-0 | .ipynb
.pdf
ConversationSummaryBufferMemory
Contents
Using in a chain
ConversationSummaryBufferMemory#
ConversationSummaryBufferMemory combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses bot... | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
6c4fe9bed78f-1 | from langchain.chains import ConversationChain
conversation_with_summary = ConversationChain(
llm=llm,
# We set a very low max_token_limit for the purposes of testing.
memory=ConversationSummaryBufferMemory(llm=OpenAI(), max_token_limit=40),
verbose=True
)
conversation_with_summary.predict(input="Hi, w... | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
6c4fe9bed78f-2 | > Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
... | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
6c4fe9bed78f-3 | AI:
> Finished chain.
' Oh, okay. What is LangChain?'
previous
ConversationSummaryMemory
next
ConversationTokenBufferMemory
Contents
Using in a chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
694a2210699e-0 | .ipynb
.pdf
VectorStore-Backed Memory
Contents
Initialize your VectorStore
Create your the VectorStoreRetrieverMemory
Using in a chain
VectorStore-Backed Memory#
VectorStoreRetrieverMemory stores memories in a VectorDB and queries the top-K most “salient” docs every time it is called.
This differs from most of the ot... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
694a2210699e-1 | memory = VectorStoreRetrieverMemory(retriever=retriever)
# When added to an agent, the memory object can save pertinent information from conversations or used tools
memory.save_context({"input": "My favorite food is pizza"}, {"output": "thats good to know"})
memory.save_context({"input": "My favorite sport is soccer"},... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
694a2210699e-2 | memory=memory,
verbose=True
)
conversation_with_summary.predict(input="Hi, my name is Perry, what's up?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
694a2210699e-3 | conversation_with_summary.predict(input="Whats my favorite food")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a que... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
694a2210699e-4 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
65bc0897d1d3-0 | .ipynb
.pdf
Entity Memory
Contents
Using in a chain
Inspecting the memory store
Entity Memory#
This notebook shows how to work with a memory module that remembers things about specific entities. It extracts information on entities (using LLMs) and builds up its knowledge about that entity over time (also using LLMs).... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-1 | 'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}
Using in a chain#
Let’s now use it in a chain!
from langchain.chains import ConversationChain
from langchain.memory import ConversationEntityMemory
from langchain.memory.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from pydantic import BaseM... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-2 | Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.
Context:
{'Deven': 'Deven is wor... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-3 | You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the ... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-4 | You are an assistant to a human, powered by a large language model trained by OpenAI.
You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-5 | AI: That sounds like a great project! What kind of project are they working on?
Human: They are trying to add more complex memory structures to Langchain
AI: That sounds like an interesting project! What kind of memory structures are they trying to add?
Last line:
Human: They are adding in a key-value store for entit... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-6 | Context:
{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.', 'Sam': 'Sam is working on a hackathon project with Deven, t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-7 | {'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur.',
'Deven': 'Deven is working on a hackathon project with Sam, which they are '
'entering into a hackathon. They are trying to add more complex '
'memory structures to Langchain, including a key-value store for '
'e... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-8 | You are an assistant to a human, powered by a large language model trained by OpenAI.
You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-9 | Human: What do you know about Deven & Sam?
AI: Deven and Sam are working on a hackathon project together, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be working hard on this project and have a great idea for how ... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-10 | 'memory structures, including a key-value store for entities '
'mentioned so far in the conversation.',
'Sam': 'Sam is working on a hackathon project with Deven, trying to add more '
'complex memory structures to Langchain, including a key-value store '
'for entities mentioned so far in t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-11 | Context:
{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation, and seem to be working hard on this project with a great idea... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
65bc0897d1d3-12 | Last line:
Human: What do you know about Sam?
You:
> Finished chain.
' Sam is the founder of a successful company called Daimon. He is also working on a hackathon project with Deven to add more complex memory structures to Langchain. They seem to have a great idea for how the key-value store can help.'
previous
Convers... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
afd2c2b97e5a-0 | .ipynb
.pdf
ConversationSummaryMemory
Contents
Initializing with messages
Using in a chain
ConversationSummaryMemory#
Now let’s take a look at using a slightly more complex type of memory - ConversationSummaryMemory. This type of memory creates a summary of the conversation over time. This can be useful for condensin... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
afd2c2b97e5a-1 | history.add_user_message("hi")
history.add_ai_message("hi there!")
memory = ConversationSummaryMemory.from_messages(llm=OpenAI(temperature=0), chat_memory=history, return_messages=True)
memory.buffer
'\nThe human greets the AI, to which the AI responds with a friendly greeting.'
Using in a chain#
Let’s walk through an ... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
afd2c2b97e5a-2 | Human: Tell me more about it!
AI:
> Finished chain.
" Sure! The customer is having trouble with their computer not connecting to the internet. I'm helping them troubleshoot the issue and figure out what the problem is. So far, we've tried resetting the router and checking the network settings, but the issue still persi... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
3ff1990f44d4-0 | .ipynb
.pdf
Conversation Knowledge Graph Memory
Contents
Using in a chain
Conversation Knowledge Graph Memory#
This type of memory uses a knowledge graph to recreate memory.
Let’s first walk through how to use the utilities
from langchain.memory import ConversationKGMemory
from langchain.llms import OpenAI
llm = Open... | https://python.langchain.com/en/latest/modules/memory/types/kg.html |
3ff1990f44d4-1 | Let’s now use this in a chain!
llm = OpenAI(temperature=0)
from langchain.prompts.prompt import PromptTemplate
from langchain.chains import ConversationChain
template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.
If ... | https://python.langchain.com/en/latest/modules/memory/types/kg.html |
3ff1990f44d4-2 | > Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.
If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses info... | https://python.langchain.com/en/latest/modules/memory/types/kg.html |
f4d1bbbad5cb-0 | .ipynb
.pdf
Motörhead Memory
Contents
Setup
Motörhead Memory#
Motörhead is a memory server implemented in Rust. It automatically handles incremental summarization in the background and allows for stateless applications.
Setup#
See instructions at Motörhead for running the server locally.
from langchain.memory.motorhe... | https://python.langchain.com/en/latest/modules/memory/examples/motorhead_memory.html |
f4d1bbbad5cb-1 | Human: whats my name?
AI:
> Finished chain.
' You said your name is Bob. Is that correct?'
llm_chain.run("whats for dinner?")
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.
Human: hi im bob
AI: Hi Bob, nice to meet you! How are you doing today?
Human: wh... | https://python.langchain.com/en/latest/modules/memory/examples/motorhead_memory.html |
2e5bf5efdbc6-0 | .ipynb
.pdf
How to add memory to a Multi-Input Chain
How to add memory to a Multi-Input Chain#
Most memory objects assume a single input. In this notebook, we go over how to add memory to a chain that has multiple inputs. As an example of such a chain, we will add memory to a question/answering chain. This chain takes ... | https://python.langchain.com/en/latest/modules/memory/examples/adding_memory_chain_multiple_inputs.html |
2e5bf5efdbc6-1 | {context}
{chat_history}
Human: {human_input}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["chat_history", "human_input", "context"],
template=template
)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff... | https://python.langchain.com/en/latest/modules/memory/examples/adding_memory_chain_multiple_inputs.html |
456f5439730d-0 | .ipynb
.pdf
How to add Memory to an LLMChain
How to add Memory to an LLMChain#
This notebook goes over how to use the Memory class with an LLMChain. For the purposes of this walkthrough, we will add the ConversationBufferMemory class, although this can be any memory class.
from langchain.memory import ConversationBuff... | https://python.langchain.com/en/latest/modules/memory/examples/adding_memory.html |
456f5439730d-1 | Human: Hi there my friend
AI: Hi there, how are you doing today?
Human: Not to bad - how are you?
Chatbot:
> Finished LLMChain chain.
" I'm doing great, thank you for asking!"
previous
VectorStore-Backed Memory
next
How to add memory to a Multi-Input Chain
By Harrison Chase
© Copyright 2023, Harrison Chase.... | https://python.langchain.com/en/latest/modules/memory/examples/adding_memory.html |
bf929e9f2269-0 | .ipynb
.pdf
Momento
Momento#
This notebook goes over how to use Momento Cache to store chat message history using the MomentoChatMessageHistory class. See the Momento docs for more detail on how to get set up with Momento.
Note that, by default we will create a cache if one with the given name doesn’t already exist.
Yo... | https://python.langchain.com/en/latest/modules/memory/examples/momento_chat_message_history.html |
dc735e5359f6-0 | .ipynb
.pdf
Adding Message Memory backed by a database to an Agent
Adding Message Memory backed by a database to an Agent#
This notebook goes over adding memory to an Agent where the memory uses an external message store. Before going through this notebook, please walkthrough the following notebooks, as this will build... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
dc735e5359f6-1 | {chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"]
)
Now we can create the ChatMessageHistory backed by the database.
message_history = RedisChatMessageHistory(... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
dc735e5359f6-2 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
dc735e5359f6-3 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered corr... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
dc735e5359f6-4 | Action: Search
Action Input: National Anthem of Canada
Observation: Jun 7, 2010 ... https://twitter.com/CanadaImmigrantCanadian National Anthem O Canada in HQ - complete with lyrics, captions, vocals & music.LYRICS:O Canada! Nov 23, 2022 ... After 100 years of tradition, O Canada was proclaimed Canada's national anthem... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
dc735e5359f6-5 | Thought: I now know the final answer.
Final Answer: The national anthem of Canada is called "O Canada".
> Finished AgentExecutor chain.
'The national anthem of Canada is called "O Canada".'
We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name o... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
dc735e5359f6-6 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
dc735e5359f6-7 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
agent_without_memory.run("what is their national anthem called?")
> Entering new AgentExecutor chain...
Thought: I should look up the an... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
dc735e5359f6-8 | Action: Search
Action Input: national anthem of [country]
Observation: Most nation states have an anthem, defined as "a song, as of praise, devotion, or patriotism"; most anthems are either marches or hymns in style. List of all countries around the world with its national anthem. ... Title and lyrics in the language o... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
dc735e5359f6-9 | Thought: I now know the final answer
Final Answer: The national anthem of [country] is [name of anthem].
> Finished AgentExecutor chain.
'The national anthem of [country] is [name of anthem].'
previous
How to add Memory to an Agent
next
Cassandra Chat Message History
By Harrison Chase
© Copyright 2023, Harri... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
1b8237072c06-0 | .ipynb
.pdf
Zep Memory
Contents
REACT Agent Chat Message History Example
Initialize the Zep Chat Message History Class and initialize the Agent
Add some history data
Run the agent
Inspect the Zep memory
Vector search over the Zep memory
Zep Memory#
REACT Agent Chat Message History Example#
This notebook demonstrates ... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
1b8237072c06-1 | session_id = str(uuid4()) # This is a unique identifier for the user
# Load your OpenAI key from a .env file
from dotenv import load_dotenv
load_dotenv()
True
Initialize the Zep Chat Message History Class and initialize the Agent#
ddg = DuckDuckGoSearchRun()
tools = [ddg]
# Set up Zep Chat History
zep_chat_history = Z... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
1b8237072c06-2 | "The most well-known adaptation of Octavia Butler's work is the FX series"
" Kindred, based on her novel of the same name."
),
},
{"role": "human", "content": "Who were her contemporaries?"},
{
"role": "ai",
"content": (
"Octavia Butler's contemporaries includ... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
1b8237072c06-3 | ),
},
]
for msg in test_history:
zep_chat_history.append(
HumanMessage(content=msg["content"])
if msg["role"] == "human"
else AIMessage(content=msg["content"])
)
Run the agent#
Doing so will automatically add the input and response to the Zep memory.
agent_chain.run(
input="WWhat... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
1b8237072c06-4 | Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.
{'role': 'human', 'content': 'What awards did she win?', 'uuid': '9fa75c3c-edae-41e3-b9bc-9fcf16b523c9', 'created_at': '2023-05-25T15:09:41.91662Z', 'token_count': 8}
{'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur F... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.