Qdrant スキル - クイックスタート

Semantic search with a vector database like Qdrant enables storing and retrieving high-dimensional embeddings based on similarity. Launch Qdrant via Docker or CLI, create a collection with 1536 dimensions for OpenAI embeddings, and use cosine-distance matching to find semantically similar content without keyword matching.

Yes. Qdrant stores conversation history, user behavior patterns, and system events as embedded vectors with payload metadata. AI agents query this collection by semantic similarity and filter by session_id or timestamp, enabling retrieval of relevant past interactions for context-aware responses.

Qdrant supports filtering search results by payload fields such as event_type, timestamp, and session_id. Apply score thresholds and payload conditions during the search query to return only vectors matching both similarity and metadata criteria, narrowing results to specific contexts or time windows.

Use the Qdrant quick start: launch the server via Docker or Vantage CLI, create a collection named 'vantage_memory' with 1536 dimensions and cosine-distance configuration, then upsert vector points with OpenAI embeddings and payload data to begin storing and searching embeddings immediately.

Yes. Qdrant v1.x integrates with OpenAI embeddings (1536-dimensional vectors) and supports the Rust client for collection management, upsert_points operations, and search workflows, enabling full-featured semantic retrieval within agent systems and production applications.

Use payload filtering when you need to narrow results by context—such as retrieving memories from a specific session or recent timeframe. Pure vector similarity searches all stored embeddings for semantic relevance. Combine both to balance recall across your entire vector store while constraining results to relevant subsets.