Supabase & PostgreSQL Expert Skill

This skill provides patterns for implementing RAG logic with Supabase and the pgvector extension.

📂 Storage Pattern (Postgres)

• Tables:

- documents: Stores source document text and global metadata. - chunks: Stores text fragments, embedding (vector), and a foreign key document_id (UUID).

• Relationships: Ensure a foreign key relationship with ON DELETE CASCADE from chunks to documents for clean deletions.

🔍 Search Patterns

• Semantic Search (pgvector):

- Assumes a stored procedure matchchunks exists in the database. - Parameters: queryembedding (vector), matchthreshold (float), matchcount (int). - Use self.client.rpc("matchchunks", rpcparams).execute().

• Text Search (WFTS):

- Use Postgres full-text search capabilities. - Pattern: self.client.table("chunks").select("...").filter("content", "wfts", query).range(0, limit - 1).execute().

🛠️ Code Standards

• Client: Use the supabase Python library (create_client).
• UUIDs: PostgreSQL IDs are typically UUIDs (strings in Python).
• Error Handling: Verify that the match_chunks RPC is properly defined in the database schema before use.
• Config: The threshold for semantic matches is usually configurable in the repository's init.