Building a demo RAG script in a Jupyter notebook takes 10 minutes, but scaling a production Retrieval-Augmented Generation (RAG) pipeline to handle millions of documents with sub-second latency and zero hallucinations requires rigorous engineering.
Key Steps in Production RAG Architecture
- Document Ingestion & Semantic Chunking: Splitting documents based on semantic boundaries rather than arbitrary character limits.
- Dense Vector Embedding: Converting text chunks into high-dimensional vectors using OpenAI text-embedding-3-large or Cohere Embed.
- Vector Indexing with Pinecone: Storing embeddings with metadata filtering (tenant ID, department, access level).
- Hybrid Search & Re-Ranking: Combining vector similarity with BM25 keyword matching and Cohere Rerank to maximize top-k relevance.
- FastAPI Inference Endpoint: Asynchronous streaming endpoints delivering real-time responses to client applications.
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