Embeddings + Vector DB for RAG

Gateway in front of both the embedding model and the chat model — the full RAG loop with Pinecone, Qdrant, or pgvector, including the embedding-cost question.

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Tip: RAG with the gateway in front of both the embedding call and the chat call is the cleanest seam — one place for cost, one place for retries, one place to swap providers.

What this use case covers

  • The full RAG loop: chunk → embed (via gateway) → store (Pinecone / Qdrant / pgvector) → retrieve → augment → chat (via gateway).
  • Gateway in front of the embedding model, not just the chat model — so embedding cost and rate limits land in the same dashboards.
  • The embedding-cost question: run once, cache forever (with proper version keys).
  • A short comparison of Pinecone vs Qdrant vs pgvector for the use case at hand.

Why it matters

RAG is the most common LLM workload after chat. Without the gateway in the embedding path, half the cost and half the failure modes are invisible.

Coming soon

Implementation coming soon. This use case will ship as a runnable folder in infinite-containers/lite-llm, with a step-by-step lesson here and a companion post and video. The companion courses on Qdrant, Pinecone, and Milvus are good prerequisites. Track the full publishing roadmap in USE-CASES.md.