LangChain
7 pieces on LangChain, including 7 step-by-step guides.
Guides
Generate Synthetic RAG Test Sets With Ragas
Build, save and reuse a Ragas knowledge graph to generate single-hop and multi-hop evaluation queries from Markdown documents.
Build an Agentic RAG System with smolagents and ChromaDB
Use smolagents CodeAgent to run multi-step retrieval over a ChromaDB vector store, letting the agent plan its own search strategy.
Graph RAG with Neo4j and OpenAI: Build a Product Search Pipeline
Combine Neo4j property graphs, GPT-4o entity extraction, and cosine similarity to build a multi-strategy product retrieval pipeline.
Build a Self-Correcting RAG Agent with LangGraph and Milvus
Wire adaptive routing, corrective RAG, and self-RAG into a stateful LangGraph agent backed by Milvus Lite — no OpenAI key required.
Beyond Prompt Engineering: Deep-Dive RAG for Intent Detection & Slot Filling in AI Agents
Stop relying on massive, static context prompts. Learn how to combine Retrieval-Augmented Generation (RAG), vector search (FAISS), and history compaction to achieve scalable intent detection and slot filling.
Local RAG Tutorial: LangChain, Ollama & ChromaDB with Ragas
Build a fully local, private RAG system using Ollama, ChromaDB, and LangChain. Includes a Python Streamlit app, a Node.js variant, and automated evaluation with Ragas.
Getting Started with LangChain in 2026: Build a RAG Agent and a RAG Chain
Build retrieval-augmented generation two ways with LangChain 1.x: an agent that calls a retrieval tool, and a chain that injects context every time.