Building a Production-Ready RAG Application with LangChain, pgvector, and Gemini

Short summary

Step-by-step guide to building production RAG APIs using LangChain, PostgreSQL pgvector, and Google Gemini. Covers document ingestion, text chunking with metadata enrichment, vector embedding, similarity search, and LLM-based answer generation with citations. Includes working code for all pipeline components with practical debugging notes.

• •RAG pipeline: ingest documents → embed chunks → vector similarity search → generate LLM answers with citations
• •Complete working code using FastAPI, LangChain, PostgreSQL pgvector, and Gemini 2.5 Flash
• •Practical tips: remove PostgreSQL null constraints, enrich chunks with metadata to preserve context during chunking

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