LangChain is an open-source framework for creating applications powered by large language models (LLMs). LangChain is designed around the principles of being data-aware and agentic, allowing large language models (LLLMs) to interact with its environment and connect to external data sources. The goal is to empower developers to build more powerful and differentiated applications.
LangChain is a framework for developing applications powered by language models.
LangChain Features & Integrations
LangChain comes with a number of features and integrations that cater to many of use cases. A few key integrations include:
- Cloud storage (Amazon, Google, and Microsoft Azure)
Web scraping subsystems
- Text processing tools
- Code analysis
- Code generation and debugging
- Google Drive operations
- Web search
- Large language models
- PDF file operations
- Database management.
LangChain can also read from more than 50 document types and data sources.
LangChain Core Concepts
LangChain works by "chaining" together different components to create advanced applications with LLMs. The core components include:
- Models: Supported model types and integrations.
- Prompts: Prompt management, optimization, and serialization.
- Memory: State that is persisted between calls of a chain/agent.
- Indexes: Interfaces and integrations for loading, querying and updating external data.
- Chains: Structured sequences of calls (to an LLM or to a different utility).
- Agents: Chains in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes it, and observes the outcome until the high-level directive is complete.
- Callbacks: Tools for logging and streaming the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.
LangChain Use Cases
LangChain can be used in a variety of ways, including:
- Autonomous Agents: Long-running agents that take many steps in an attempt to accomplish an objective.
- Agent Simulations: Evaluation of agents' long-range reasoning and planning abilities.
- Personal Assistants: Personal assistants that take actions, remember interactions, and have knowledge about your data.
- Question Answering: Answering questions over specific documents, utilizing the information in those documents to construct an answer.
- Chatbots: Natural language processing for conversational agents.
- Querying Tabular Data: Using language models to query structured data.
- Code Understanding: Using language models to analyze code.
- Interacting with APIs: Enabling language models to interact with APIs.
- Extraction: Extracting structured information from text.
- Summarization: Compressing longer documents.
- Evaluation: Using language models themselves to evaluate generative models.
Getting Started with LangChain
Getting started with LangChain involves a few simple steps detailed in the Quickstart Guide. The documentation also includes tutorials created by community experts, reference documentation, and detailed descriptions of each module and concept.
LangChain integrates with many different LLMs, systems, and products, fostering a vibrant and thriving ecosystem. The ecosystem includes guides on how other products can be used with LangChain, a list of repositories that use LangChain, and a collection of instructions for deploying LangChain apps.
Below you can find additional tutorials and resources we've created on LangChain:
Please note this pages serves informational purposes only and does not constitute an endorsement of any AI tool. Some company descriptions are assisted by our GPT-4 research assistant and is provided without any expressed or implied warranties.