How do I write a Python script to connect OpenAI embeddings directly to our SQL database to answer customer queries without paying high API markup fees?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do I write a Python script to connect OpenAI embeddings directly to our SQL database to answer customer queries without paying high API markup fees?
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 API embeddings documentation text-embedding-3-small pricing Responses API Python official docs Google ↗
pgvector PostgreSQL Python psycopg documentation official Google ↗
- text-embedding-3-small Model · developers.openai.com
- Vector embeddings · developers.openai.com
- pgvector · pypi.org
- GitHub - pgvector/pgvector-python: pgvector support for Python · github.com
- Psycopg · psycopg.org
Queries
2 queries · click a column to sort