Check Qdrant connectivity, active embedding model, reranker status, and server version to confirm correct RAG configuration. Start here when the pipeline seems broken or unreachable.
Retrieve all distinct entities (users, agents, runs) that currently hold memories. Uses server-side aggregation via Qdrant Facet API with scroll+dedupe fallback for compatibility.
Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.
Upload and process documents into a Qdrant collection using specified embedding services like OpenAI or Ollama. Define text chunk size and overlap for efficient semantic search integration.