Hey, what are the exact steps to optimize vector search accuracy in our local document retrieval setup so I can find household recipes and work files faster?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Hey, what are the exact steps to optimize vector search accuracy in our local document retrieval setup so I can find household recipes and work files faster?
1 real search · 1 real-search pass
vector search retrieval accuracy best practices hybrid search chunking metadata filtering reranking embeddings official documentationGoogle ↗
OBSERVEDReason
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
vector search retrieval accuracy best practices hybrid search chunking metadata filtering reranking embeddings official documentation Google ↗
- Hybrid search · docs.opensearch.org
- How to Perform Hybrid Search - MongoDB Vector Search · mongodb.com
- Hybrid full-text and vector search patterns | Spanner | Google Cloud Documentation · docs.cloud.google.com
- Develop a RAG Solution on Azure - Information-Retrieval Phase - Azure Architecture Center · learn.microsoft.com
- Documentation · qdrant.tech
- Documentation · docs.trychroma.com
- Website · ollama.com