semantic-search

Semantic code search uses vector embeddings and AST extraction to index Python functions and classes, allowing you to locate relevant code by meaning rather than exact text matching.

Yes, you can search production data messages and facts using a pgvector-backed PostgreSQL index with configurable time and confidence filters to retrieve relevant user data.

The best way to deploy a vector embedding search index is using the Dockerized deployment provided, which centralizes the embedding index for fast code and production data search.

Yes, semantic search relies on OpenAI embeddings to index and trace webhooks by meaning, identifying relevant events within indexed datasets without requiring exact text matches.

You find code elements by meaning using AST-based code extraction and vector embeddings, which index the semantic meaning of your codebase to retrieve relevant items regardless of exact text.

You should use pgvector for semantic search when you need to locate code, messages, or facts by conceptual meaning rather than exact keywords, especially when tracing webhooks or discovering code elements.