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Qdrant: The Vector Database





Qdrant is a powerful vector search database designed for high-speed, scalable, and efficient nearst neighbor search over large datasets. It is build for AI, recommendation systems, and RAG-based applications, making it great choice when you need semantic search, image similarity search, and natural language processing (NLP) applications.

Storage Engine

Qdrant stores vector embeddings in a highly optimized binary format. Instead of traditional relational storage models, it uses an efficient key-value store with embedded metadata. Key aspects of its storage engine:

Indexing Algorithms

At its core, Qdrant relies on a mix of HNSW (Hierarchical Navigable Small World) and quantization techniques for indexing and retrieval.

Optimizations & Benefits

🛠️ Qdrant is optimized for real-time vector search and comes with several built-in features to enhance performance:

Downsides & Trade-offs

⚠️ Qdrant, while powerful, comes with a few trade-offs to keep in mind:

Use Cases

🔍 – Where Qdrant Shines Qdrant is a perfect fit for applications where semantic understanding, image/audio search, and recommendation systems are needed:

Main Features of QdrantClient

Collections

.getCollections() – List all collections
.getCollection(collectionName) – Get collection info
.createCollection(collectionName, options) – Create a new collection
.deleteCollection(collectionName) – Delete a collection
.updateCollection(collectionName, options) – Update configuration
.collectionExists(collectionName) – Check if a collection exists

Points (Vectors)

  • .upsert(collectionName, { points }) – Insert or update vectors
  • .delete(collectionName, { points }) – Delete points by ID
  • .search(collectionName, query) – Search using a query vector
  • .scroll(collectionName, options) – Paginated point browsing
  • .retrieve(collectionName, ids) – Get points by ID
  • .setPayload(collectionName, { points, payload }) – Set metadata (payload)
  • .overwritePayload(collectionName, { points, payload }) – Replace metadata
  • .deletePayload(collectionName, { points, keys }) – Remove specific fields
  • .clearPayload(collectionName, { points }) – Remove all metadata

Payload Indexing

  • createPayloadIndex(collectionName, field) – Create index on payload field
  • deletePayloadIndex(collectionName, field) – Remove payload index

Recommend & Filter

  • recommend(collectionName, { positive, negative, ... }) – Recommend based on similar points
  • searchBatch(collectionName, { searches }) – Perform multiple searches at once
  • recommendBatch(collectionName, { searches }) – Multiple recommendation queries

Miscellaneous

  • snapshotCollection(collectionName) – Create snapshot (for backups)
  • listCollectionSnapshots(collectionName) – List snapshots
  • deleteCollectionSnapshot(collectionName, snapshotName)
  • createFieldIndex(collectionName, field) – Create field index (for filtering)

Final Thoughts

Qdrant is one of the best vector databases if you need fast, scalable, and accurate vector search for AI-driven applications. It's particularly strong in retrieval-augmented generation (RAG) and semantic search, making it a top choice for LLM-powered systems, recommendation engines, and real-time AI applications. However, if your dataset is small or your use case doesn't require ANN-based retrieval, a traditional database with embedding search might be enough.