LangChain is a Python framework designed to facilitate the development of applications powered by LLMs (Large Language Models). It enables developers to build end-to-end, composable workflows that involve LLM interaction, integration with data sources, memory, tools, and agentic decision-making.
Purpose: LangChain abstracts and orchestrates the logic needed to:
- Prompt LLMs effectively
- Chain together multiple LLM calls or components
- Maintain LLM Memory of interactions
- Interact with external tools and APIs
- Build autonomous or semi-autonomous Agentic Solutions
- Enable retrieval and indexing over documents
Core Modules:
- Models: Interface layer to various LLM providers (e.g., OpenAI, Cohere).
- Prompts: Templates and logic for prompt creation and formatting.
- Chains: Composable sequences of LLM calls, enabling structured workflows.
- LLM Memory: Mechanisms to persist context across interactions.
- Indexes: Tools for embedding and retrieving documents using vector stores.
- Agents and Tools: Constructs for dynamic decision-making, allowing the LLM to choose tools (e.g., calculator, web search) at runtime.
Example Application:
- Pandas DataFrame Agent: An agent that enables natural language interaction with tabular data using pandas and LLM reasoning.
Use Cases:
- Chatbot that remembers prior inputs within a session
- Querying a CSV file with natural language
- Summarizing notes or documents interactively
- A lightweight research assistant that chooses between tools to answer a question