Topic
LangChain RAG with Vector Databases
15 posts on langchain rag with vector databases — part of framework tutorials on the n4n AI blog.
Supabase pgvector plus LangChain for RAG
Build a production RAG pipeline with Supabase pgvector and LangChain — from schema design to retrieval tuning, with runnable code at each step.
Retrieval-augmented generation with LangChain and Qdrant
Build a production-ready RAG pipeline with LangChain and Qdrant — from document ingestion to retrieval and generation, with verification steps at each stage.
Reranking retrieved documents in a LangChain RAG pipeline
Learn how to add reranking to your LangChain RAG pipeline with step-by-step code examples using cross-encoders and Cohere rerank for better retrieval quality.
LangChain RAG with Redis as the vector store
Build a production-ready RAG pipeline using LangChain and Redis as the vector store, with complete code and verification steps.
LangChain and Milvus: scaling RAG to millions of documents
A practical guide to scaling LangChain RAG pipelines with Milvus to millions of documents, covering collection design, embedding pipelines, retrieval strategies, and operational pitfalls.
LangChain RAG with Weaviate: a step-by-step guide
Build production-ready RAG with LangChain and Weaviate — from ingestion to retrieval, with code, pitfalls, and scaling tradeoffs.
LangChain RAG evaluation with RAGAS and n4n.ai
A step-by-step guide to evaluating LangChain RAG pipelines with RAGAS, including synthetic dataset generation, metric selection, and CI integration.
Hybrid search in LangChain: combining BM25 and embeddings
A practical guide to implementing hybrid search in LangChain by combining BM25 keyword search with dense vector embeddings for better retrieval accuracy.
FAISS vector search in LangChain for local RAG prototypes
Build a local RAG prototype with LangChain and FAISS — complete setup, indexing, retrieval, and generation steps with runnable code.
Using pgvector with LangChain for production RAG
Practical guide to building langchain pgvector production rag systems: schema, ingestion, tuning, and pitfalls for serving real traffic on Postgres.
LangChain RAG tutorial: Chroma plus n4n.ai models
Build a retrieval-augmented generation pipeline with LangChain and Chroma using n4n.ai models via an OpenAI-compatible endpoint. Hands-on tutorial with runnable code.
Comparing Pinecone and Chroma for LangChain RAG apps
Head-to-head comparison of Pinecone vs Chroma for LangChain RAG apps across cost, latency, ergonomics, and limits, with a verdict per use case.
Chunking strategies for LangChain RAG pipelines
A practical guide to langchain rag chunking strategies: fixed-size, semantic, and recursive splitting with code, tradeoffs, and common pitfalls.
Build a RAG pipeline with LangChain and Pinecone
Build a langchain pinecone rag pipeline from scratch: install deps, chunk docs, embed to Pinecone, query with LangChain retrieval chains, and verify.
Build a RAG chatbot with LangChain, LanceDB, and n4n.ai
A practical end-to-end tutorial for engineers: build a LangChain LanceDB n4n.ai RAG chatbot with vector search and OpenAI-compatible LLM calls.
More topics in framework tutorials
- Building Chatbots with Vercel AI SDK & Next.js15
- LangGraph Multi-Agent Workflows15
- LlamaIndex Query Engines for RAG15
- AutoGen Multi-Agent Conversations & Group Chat14
- Choosing an AI Framework by Use Case14
- CrewAI Agent Roles & Task Design14
- CrewAI Real-World Crew Examples14
- Haystack RAG Pipelines14
- LangChain Agents & Tool Calling14
- LangChain Multi-Model Fallback & Routing14
- LangChain.js for Node & TypeScript14
- One Backend, Every Model: Swapping GPT-5, Claude, Gemini & Llama Across Frameworks14