langchain-architecture

To build a RAG system with LangChain, load your documents, split them into chunks, create a vector store, and set up a retrieval QA chain. This approach integrates external knowledge bases to ground LLM responses in factual data.

Managing conversation history in LLM applications requires robust memory management to maintain state across interactions. LangChain provides modular memory components that store and retrieve context, enabling long-term conversational AI coherence.

Orchestrating multi-step workflows in an LLM application involves sequencing LLM calls and utilities through chain orchestration. This process links modular components together to execute automated tasks and complex data retrieval pipelines.

Yes, you can create autonomous agents that reason, act, and use tools with LangChain. Agent development allows your LLM application to browse the web, access internal documents, and execute automated tasks independently.

LangChain supports modular state management for complex AI systems by providing structured components for memory and chains. This addresses the need for robust conversation history tracking and context preservation across long queries.