What steps should I take to optimize vector search accuracy in Pinecone for our legal document retrieval chatbot?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What steps should I take to optimize vector search accuracy in Pinecone for our legal document retrieval chatbot?
1 real search · 1 real-search pass
Pinecone documentation vector search hybrid search reranking metadata filtering namespaces indexing best practices legal retrievalGoogle ↗
OBSERVEDOther / navigational
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
Pinecone documentation vector search hybrid search reranking metadata filtering namespaces indexing best practices legal retrieval Google ↗
- How Pinecone Works · sdk.pinecone.io
- Introducing the Pinecone Documents API · pinecone.io
- pinecone-ts-client/guides/data-operations/metadata-filtering.md at main · pinecone-io/pinecone-ts-client · github.com
- Pinecone Metadata Filtering · pinecone.io
- Refine Retrieval Quality with Pinecone Rerank · pinecone.io
- Reranking results · sdk.pinecone.io
- How Pinecone Works: Architecture and Engineering Deep Dive · pinecone.io
- Upserting and querying vectors · sdk.pinecone.io