Build a Production RAG Pipeline with LangChain and FastAPI

Stack: FastAPI, LangChain, OpenAI text-embedding-3-small plus gpt-4o, Qdrant, PostgreSQL, Celery plus Redis. Document Ingestion Celery Task: Load PDF with PyPDFLoader, split with RecursiveCharacterTextSplitter at chunk size 1000 with overlap 200, embed with OpenAIEmbeddings, store in Qdrant with metadata. Query Pipeline: User question arrives, generate query embedding, retrieve top 5 chunks from Qdrant, construct prompt with retrieved context, stream GPT-4o response to client. Production Improvements: Re-ranking with Cohere Rerank, query decomposition for complex questions, embedding cache for repeated queries, source citations in responses, rate limiting per subscription tier. FastAPI Endpoints: POST /upload for background document processing, POST /query for streaming responses, GET /documents for listing user documents. This pattern powers enterprise AI SaaS — knowledge bases, support bots, internal Q&A tools.

Strategic Implementation

Establishing a robust workflow is paramount in 2026. As the gap between development and operations continues to shrink, the tools we choose must facilitate speed WITHOUT sacrificing security or stability.

We'll continue exploring these advanced patterns in our upcoming technical deep-dives. Stay tuned for more insights into scaling infrastructure and optimizing software delivery pipelines.