Can someone explain how to tune distance metrics like cosine versus euclidean to improve vector search accuracy in my product catalog
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Can someone explain how to tune distance metrics like cosine versus euclidean to improve vector search accuracy in my product catalog
1 real search · 1 real-search pass
vector database cosine similarity euclidean distance normalized embeddings official documentationGoogle ↗
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
vector database cosine similarity euclidean distance normalized embeddings official documentation Google ↗
- Vector embeddings · developers.openai.com
- Choose among vector distance functions to measure vector embeddings similarity | Spanner | Google Cloud Documentation · docs.cloud.google.com
- Vector Relevance and Ranking - Azure AI Search · learn.microsoft.com
- Candidate generation overview | Machine Learning | Google for Developers · developers.google.com