langchain-architecture

Build autonomous AI agents with LangChain by implementing reasoning and action patterns like ReAct or OpenAI Functions. This approach enables agents to dynamically select and utilize external tools to perform multi-step workflows and act on user queries.

Memory management for LLM applications works by implementing persistent conversation context using strategies like Buffer, Summary, Entity, or VectorStore memory. These strategies allow the application to maintain coherent conversation history across multiple interactions.

Integrate external data sources into an LLM workflow by utilizing document loaders, text splitters, vector stores, and retrievers. This document processing pipeline connects external knowledge bases to the LLM for accurate information retrieval.

Orchestrate complex multi-step LLM workflows using chains by sequencing LLM calls and utilities. You can utilize different chain types such as LLMChain, SequentialChain, and RouterChain to modularize and control the execution flow of your application components.

Vector stores are needed to develop LLM applications with document processing pipelines when you want to retrieve external data efficiently. They store document embeddings, enabling the LLM to access and query large external knowledge bases during conversations.

Do not use autonomous agents for LLM application development when tasks are simple and deterministic. Chains are more suitable for predictable, linear operations, whereas agents add unnecessary complexity and latency when dynamic reasoning and tool selection are not required.