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:

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