LangChain in Action: How to Build Intelligent AI Applications Easily and Efficiently ?
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:
- ✅ Append-Only Log Structure – Ensures durability and high-speed writes.
- ✅ Binary Vector Storage – Reduces memory footprint while keeping retrieval fast.
- ✅ Persistent Storage (RocksDB) – Qdrant uses RocksDB under the hood to persist vector and metadata efficiently.
- ✅ Custom Memory Mapping – Allows for direct memory access to optimize vector retrieval.
Indexing Algorithms
At its core, Qdrant relies on a mix of HNSW (Hierarchical Navigable Small World) and quantization techniques for indexing and retrieval.
- 🔹HNSW (Hierarchical Navigable Small World) A state-of-the-art algorithm for approximate nearest neighbor (ANN) search. It builds a multi-layered graph structure where each node connects to its closest neighbors, ensuring fast lookups.
- 🔹Product Quantization (PQ) & Vector Compression Optional quantization techniques help reduce memory usage without sacrificing too much accuracy.
- 🔹 Scalar Quantization (SQ) Helps with efficient storage when precision is not critical. The HNSW-based indexing makes it super fast compared to brute-force search while balancing accuracy vs. speed trade-offs.
Optimizations & Benefits
🛠️ Qdrant is optimized for real-time vector search and comes with several built-in features to enhance performance:
- ✅ Multi-Tenant Support – Can handle multiple collections with isolated configurations.
- ✅ Sharding & Replication – Scales horizontally by distributing data across nodes.
- ✅ Dynamic Payload Filtering – Allows filtering by metadata alongside vector similarity.
- ✅ Custom Scoring Functions – Fine-tune how similarity is calculated beyond just cosine distance or dot product.
- ✅ Streaming Inserts & Real-Time Updates – Unlike some vector DBs that require batch updates, Qdrant supports real-time ingestion.
Downsides & Trade-offs
⚠️ Qdrant, while powerful, comes with a few trade-offs to keep in mind:
- ❌ Higher Memory Usage - HNSW - based search requires keeping a large part of the index In-Memory.
- ❌ No Native SQL - If you're used to relational databases, it requires adapting to a new query model.
- ❌ Overhead in Small Datasets - For small collections (< 100k vectors), brute-force search in a regular DB might be just as fast.
- ❌ Limited Graph Analytics - While it uses HNSW graphs internally, Qdrant isn't designed for graph traversal or complex graph queries.
- ❌ Doesn't have a built-in UI for managing collections or visualizing vector data. You have to use third party UI Tools like:
🔹 Qdrant Cloud Dashboard – If you're using Qdrant Cloud, it provides a web-based UI.
🔹 Weaviate UI – Some developers use Weaviate for similar use cases, but it's a different vector DB.
🔹 FastAPI or Streamlit – Build your own simple UI to interact with Qdrant's API.
Use Cases
🔍 – Where Qdrant Shines Qdrant is a perfect fit for applications where semantic understanding, image/audio search, and recommendation systems are needed:
- 🔹 RAG (Retrieval-Augmented Generation) – Helps AI models retrieve relevant documents or knowledge bases.
- 🔹 Semantic Search (NLP) - Powering chatbots, search engines, and FAQ bots that understand user intent.
- 🔹 Image & Video Search - Finding similar images/videos using embeddings (used in e-commerce, media, etc).
- 🔹Anomaly Detection - Spotting unusual behavior in cybersecurity, fraud detection, and predictive maintenance.
- 🔹 Recommendation Systems - Personalized content suggestions based on similarity of user behavior or preferences.
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.