Diagnosed a subtle RAG retrieval regression — fast, thorough, and precise -- 2
Budget: $30 – $250 USD
We brought in this developer to debug a production RAG pipeline that had quietly degraded after a dependency bump. The pipeline — built on LangChain, FastAPI, and Supabase pgvector — was still returning results, but retrieval quality had clearly dropped: wrong chunks, wrong document sections, irrelevant context making it into answers.
The scope was well-defined: audit our chunking config, confirm embedding model consistency between ingestion and query time, check our pgvector distance/index setup, and run live test queries to surface exactly where retrieval was going wrong. He delivered on every point. The root cause turned out to be a silent embedding model mismatch introduced during a langchain-openai upgrade — something easy to miss and painful to debug without knowing where to look.
The diagnosis came back as a clean one-pager: clear root cause, specific file and line references, and the exact code changes needed. No vague recommendations — just actionable fixes. The cosine vs. L2 config and ivfflat index sizing were also flagged and corrected as part of the review.
If you have a LangChain, pgvector, or OpenAI embeddings stack and something feels off about your retrieval quality, this is exactly the kind of focused, no-fluff diagnostic work you want. Highly recommended.
The scope was well-defined: audit our chunking config, confirm embedding model consistency between ingestion and query time, check our pgvector distance/index setup, and run live test queries to surface exactly where retrieval was going wrong. He delivered on every point. The root cause turned out to be a silent embedding model mismatch introduced during a langchain-openai upgrade — something easy to miss and painful to debug without knowing where to look.
The diagnosis came back as a clean one-pager: clear root cause, specific file and line references, and the exact code changes needed. No vague recommendations — just actionable fixes. The cosine vs. L2 config and ivfflat index sizing were also flagged and corrected as part of the review.
If you have a LangChain, pgvector, or OpenAI embeddings stack and something feels off about your retrieval quality, this is exactly the kind of focused, no-fluff diagnostic work you want. Highly recommended.