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⚡ Building applications with LLMs through composability ⚡
0f9b4bf244
**Description:** The current implementation of `DynamoDBChatMessageHistory` updates the `History` attribute for a given chat history record by first extracting the existing contents into memory, appending the new message, and then using the `put_item` method to put the record back. This has the effect of overwriting any additional attributes someone may want to include in the record, like chat session metadata. This PR suggests changing from using `put_item` to using `update_item` instead which will keep any other attributes in the record untouched. The change is backward compatible since 1. `update_item` is an "upsert" operation, creating the record if it doesn't already exist, otherwise updating it 2. It only touches the db insert call and passes the exact same information. The rest of the class is left untouched **Dependencies:** None **Tests and docs:** No unit tests currently exist for the `DynamoDBChatMessageHistory` class. This PR adds the file `libs/community/tests/unit_tests/chat_message_histories/test_dynamodb_chat_message_history.py` to test the `add_message` and `clear` methods. I wanted to use the moto library to mock DynamoDB calls but I could not get poetry to resolve it so I mocked those calls myself in the test. Therefore, no test dependencies were added. The change was tested on a test DynamoDB table as well. The first three images below show the current behavior. First a message is added to chat history, then a value is inserted in the record in some other attribute, and finally another message is added to the record, destroying the other attribute.    The next three images show the new behavior. Once again a value is added to an attribute other than the History attribute, but now when the followup message is added it does not destroy that other attribute. The History attribute itself is unaffected by this change.    The doc located at `docs/docs/integrations/memory/aws_dynamodb.ipynb` required no changes and was tested as well. |
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🦜️🔗 LangChain
⚡ Build context-aware reasoning applications ⚡
Looking for the JS/TS library? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. Fill out this form to speak with our sales team.
Quick Install
With pip:
pip install langchain
With conda:
conda install langchain -c conda-forge
🤔 What is LangChain?
LangChain is a framework for developing applications powered by large language models (LLMs).
For these applications, LangChain simplifies the entire application lifecycle:
- Open-source libraries: Build your applications using LangChain's open-source components and third-party integrations. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support.
- Productionization: Inspect, monitor, and evaluate your apps with LangSmith so that you can constantly optimize and deploy with confidence.
- Deployment: Turn your LangGraph applications into production-ready APIs and Assistants with LangGraph Platform.
Open-source libraries
langchain-core: Base abstractions.- Integration packages (e.g.
langchain-openai,langchain-anthropic, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers. langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.langchain-community: Third-party integrations that are community maintained.- LangGraph: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, Introduction to LangGraph, available here.
Productionization:
- LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
Deployment:
- LangGraph Platform: Turn your LangGraph applications into production-ready APIs and Assistants.
🧱 What can you build with LangChain?
❓ Question answering with RAG
- Documentation
- End-to-end Example: Chat LangChain and repo
🧱 Extracting structured output
- Documentation
- End-to-end Example: SQL Llama2 Template
🤖 Chatbots
- Documentation
- End-to-end Example: Web LangChain (web researcher chatbot) and repo
And much more! Head to the Tutorials section of the docs for more.
🚀 How does LangChain help?
The main value props of the LangChain libraries are:
- Components: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not.
- Easy orchestration with LangGraph: LangGraph,
built on top of
langchain-core, has built-in support for messages, tools, and other LangChain abstractions. This makes it easy to combine components into production-ready applications with persistence, streaming, and other key features. Check out the LangChain tutorials page for examples.
Components
Components fall into the following modules:
📃 Model I/O
This includes prompt management and a generic interface for chat models, including a consistent interface for tool-calling and structured output across model providers.
📚 Retrieval
Retrieval Augmented Generation involves loading data from a variety of sources, preparing it, then searching over (a.k.a. retrieving from) it for use in the generation step.
🤖 Agents
Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangGraph makes it easy to use LangChain components to build both custom and built-in LLM agents.
📖 Documentation
Please see here for full documentation, which includes:
- Introduction: Overview of the framework and the structure of the docs.
- Tutorials: If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
- How-to guides: Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
- Conceptual guide: Conceptual explanations of the key parts of the framework.
- API Reference: Thorough documentation of every class and method.
🌐 Ecosystem
- 🦜🛠️ LangSmith: Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
- 🦜🕸️ LangGraph: Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
- 🦜🕸️ LangGraph Platform: Deploy LLM applications built with LangGraph into production.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see here.