What is the best way to optimize vector search accuracy in our production database today because our current results are completely missing relevant context?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the best way to optimize vector search accuracy in our production database today because our current results are completely missing relevant context?
1 real search · 1 real-search pass
production vector search retrieval accuracy hybrid search reranking chunking embeddings metadata filtering best practices pgvector documentationGoogle ↗
OBSERVEDComparison
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
production vector search retrieval accuracy hybrid search reranking chunking embeddings metadata filtering best practices pgvector documentation Google ↗
- Embeddings · pydantic.dev
- Design a RAG Data Ingestion Pipeline · datadank.com
- Best Practices for Advanced PostgreSQL Vector Workloads · docs.digitalocean.com
- Best Practices for PostgreSQL Vector Search · docs.digitalocean.com
- EDB Docs - EDB Postgres AI Database v7 · enterprisedb.com
- About hybrid search | Gemini Enterprise Agent Platform | Google Cloud Documentation · docs.cloud.google.com
- Retrieval and reranking · learnaiengineering.dev