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qualityA multi-tenant service that automatically monitors Supabase database changes, generates OpenAI embeddings, and maintains synchronized vector search capabilities for each tenant's projects.
Why this server?
This service monitors Supabase database changes, generates OpenAI embeddings, and maintains synchronized vector search capabilities, essential for pgvector functionality.
Why this server?
This server provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, making it relevant for pgvector-based applications.
Why this server?
This template allows for creating MCP servers with direct access to PostgreSQL databases, which is where pgvector would be implemented.
Why this server?
An MCP server to enable database queries and operations on Postgres databases via PostgREST, where pgvector can be incorporated.
Why this server?
This server is focused on PostgreSQL database operations, a foundation for integrating vector embeddings.
Why this server?
This server enables interactions with PostgreSQL databases, allowing schema exploration and query execution, a foundation for pgvector implementation.