Build a RAG Agent with LangChain – A Practical Lab for Retrieval‑Augmented Generation

The article presents a complete RAG agent built with LangChain, designed to answer questions over a user’s own DEV.to posts. It pulls posts via the DEV.to API, chunks them with markdown‑aware splitting that preserves heading metadata, and stores the vectors in a persistent Chroma database. Embeddings are generated locally with Ollama’s nomic‑embed‑text model to avoid quota limits, and the system supports both local and Google Gemini embeddings if desired. The repo includes a CLI with commands ingest, chat, and bench, and uses LangSmith for tracing and evaluation of each step.

Three retrieval strategies are benchmarked: a tool‑calling agent that decides when to retrieve, an always‑retrieve chain that fetches a passage on every turn, and a corrective RAG (CRAG) that grades the retrieved context and retries if necessary. The project also implements content‑hash dedup to skip unchanged articles during re‑ingest, persistent threads via SqliteSaver to survive restarts, and a structured repo layout with strategies/agent.py, chain.py, crag.py. Developers can run `python rag_agent.py ingest` to index their posts, then `python rag_agent.py chat` to interact with the agent, and `python rag_agent.py bench` to benchmark the strategies. The repository is hosted on GitHub and includes a detailed README, requirements.txt, and an .env.example for configuration.