Overview
LangChain provides a set of tools and abstractions for 'chaining' together different components of an LLM application, such as prompts, models, and external data sources.
Key Components
- Chains: Sequences of operations (e.g., Prompt -> LLM -> Output Parser).
- Agents: Systems that use an LLM to decide which actions to take and in what order.
- Memory: Tools for maintaining state and context across multiple interactions.
- Retrievers: Interfaces for fetching data from vector databases or other sources.
Impact
LangChain has become the de facto standard for building complex AI applications, enabling developers to quickly move from simple prompts to sophisticated agents.