What are the top five best practices for optimizing vector search accuracy when using OpenAI embeddings for knowledge bases
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the top five best practices for optimizing vector search accuracy when using OpenAI embeddings for knowledge bases
2 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 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
OpenAI embeddings guide text-embedding-3 dimensions shortening embeddings cosine similarity recommendations Google ↗
OpenAI cookbook retrieval augmented generation chunking hybrid search reranking evaluation embeddings knowledge base Google ↗
- Retrieval · developers.openai.com
- Contextual retrieval in AI systems \ Anthropic · anthropic.com
- How to parse PDF docs for RAG · developers.openai.com
- GitHub - openai/openai-knowledge-retrieval: OS repo for Knowledge Retrieval starter kit · github.com
- Optimizing LLM Accuracy · developers.openai.com
- openai-cookbook/examples/Search_reranking_with_cross-encoders.ipynb at main · openai/openai-cookbook · github.com
- Vector embeddings · developers.openai.com
- Embeddings FAQ · help.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI embeddings guide text-embedding-3 dimensions shortening embeddings cosine similarity recommendations OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
OpenAI cookbook retrieval augmented generation chunking hybrid search reranking evaluation embeddings knowledge base OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |