grepai-storage-postgres

This skill covers using PostgreSQL with the pgvector extension as the storage backend for GrepAI. Then enable the extension: Set the environment variable: DSN components: GrepAI automatically creates these tables: For better vector search performance: For large indices, tune the IVFFlat index: Rule of thumb: lists = sqrt(rows) PostgreSQL handles concurrent access automatically: All team members point to the same database: For isolated projects, use separate databases: ❌ Problem: FATAL: password authentication failed ✅ Solution: Check DSN credentials and pg_hba.conf ❌ Problem: ERROR: extension "vector" is not available ✅ Solution: Install pgvector: ❌ Problem: ERROR: type "vector" does not exist ✅ Solution: Enable extension in the database: ❌ Problem: Connection refused ✅ Solution: ❌ Problem: Slow searches ✅ Solution: PostgreSQL storage status: - Team environments with shared index - Large codebases (10K+ files) - Need concurrent access - Integration with existing PostgreSQL infrastructure - PostgreSQL 14+ with pgvector extension - Database user with create table permissions - Network access to PostgreSQL server - Supabase: pgvector included by default - Neon: pgvector available - AWS RDS: Install pgvector extension - Azure Database: pgvector available - user : Database username - password : Database password - host : Server hostname or IP - 5432 : Port (default: 5432) -...