← Back to Embeddings & RAG overview

Hand-written tutorials, ordered as a recommended learning path.

1. 21 Self-Query Retrievers Build self-query retrievers that translate natural language into structured metadata filters plus a semantic query for more precise RAG results. 4 min read →
2. 22 LangSmith Tracing Use LangSmith to trace, debug, and evaluate RAG pipelines step by step, from instrumentation to dataset replay and regression detection. 4 min read →
3. 23 RAG vs FT A practical comparison of RAG and fine-tuning, with guidance on when to choose each, and when to combine them in production systems. 4 min read →
4. 24 RAG Retrieval Practical retrieval strategies for RAG: chunking, hybrid search, reranking, query rewriting, metadata filtering, and evaluation patterns that actually move the needle. 5 min read →
5. 25 Vector DBs A grounded comparison of vector databases for RAG and semantic search: pgvector, Pinecone, Weaviate, Qdrant, Milvus, and Chroma, with guidance on when each shines. 5 min read →
6. 26 pgvector Use pgvector to run embeddings, similarity search, and hybrid retrieval inside Postgres. Schemas, indexes, and a working Python pipeline. 4 min read →
7. 27 Pinecone Tutorial Build a working vector search pipeline with Pinecone in Python. Indexes, upserts, metadata filters, hybrid search, and patterns for production RAG. 4 min read →