What are experts recommending for optimizing vector search accuracy when dealing with high-dimensional embeddings in 2024?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are experts recommending for optimizing vector search accuracy when dealing with high-dimensional embeddings in 2024?
2 real searches · 1 real-search pass
2024 expert recommendations improve vector search accuracy high dimensional embeddings HNSW IVF PQ reranking normalization evaluation recall at kGoogle ↗
OBSERVEDInstruction
2024 vector database best practices high dimensional embeddings dimensionality reduction quantization reranking hybrid search accuracyGoogle ↗
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 (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
2024 expert recommendations improve vector search accuracy high dimensional embeddings HNSW IVF PQ reranking normalization evaluation recall at k Google ↗
2024 vector database best practices high dimensional embeddings dimensionality reduction quantization reranking hybrid search accuracy Google ↗
- LeanVec: Searching vectors faster by making them fit · lacuna.tiptreesystems.com
- Vector Compression · docs.nvidia.com
- Binary quantization in Azure AI Search: optimized storage and faster search · techcommunity.microsoft.com
- Vector Database Management Techniques and Systems · dbgroup.cs.tsinghua.edu.cn
- Selecting a vector store for Amazon Bedrock Knowledge Bases · aws.amazon.com.cdn.amazon.com
- What is Vector Search? · docs.nvidia.com
- Vector engine best practices · d1.awsstatic.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
2024 expert recommendations improve vector search accuracy high dimensional embeddings HNSW IVF PQ reranking normalization evaluation recall at k OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 | |
2024 vector database best practices high dimensional embeddings dimensionality reduction quantization reranking hybrid search accuracy OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 |