We want to migrate from Pinecone to a cheaper self-hosted vector database like Milvus or Qdrant without sacrificing search latency for our recommendation engine
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
We want to migrate from Pinecone to a cheaper self-hosted vector database like Milvus or Qdrant without sacrificing search latency for our recommendation engine
3 real searches · 1 real-search pass
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.
Classifying 3 queries…
Searches and sources
ChatGPT3 real searches · gpt-6
Qdrant documentation HNSW quantization filtering performance distributed deployment official Google ↗
Milvus documentation index types HNSW DiskANN IVF performance official Google ↗
Pinecone documentation query latency filtering metadata official Google ↗
- HNSW and indexing · qdrant.tech
- Index Explained Milvus v2.5.x documentation · milvus.io
- Prompt: Milvus Index Selection · milvus.io
- In-memory Index · milvus.io
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Qdrant documentation HNSW quantization filtering performance distributed deployment official OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Milvus documentation index types HNSW DiskANN IVF performance official OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Pinecone documentation query latency filtering metadata official OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |