Qdrant: The Vector Database Built for Production RAG Pipelines
Qdrant combines HNSW indexing, hybrid search, and rich payload filtering in a Rust-native vector database that scales from Docker to multi-node Qdrant Cloud.
Most vector databases are fine for prototyping. Qdrant is built for production: it is written in Rust for memory safety and performance, uses HNSW (Hierarchical Navigable Small World) for sub-millisecond approximate nearest neighbor search, and supports hybrid search (sparse + dense vectors) natively. It also exposes a rich filtering API over payload metadata, so you can restrict similarity searches to specific users, documents, or time ranges without post-filtering.
Core Concepts
Collection: a named set of points (analogous to a table)
Point: a vector + payload + optional ID
Payload: arbitrary JSON metadata attached to each point
Vector: float array (dense) or token-score dict (sparse)
HNSW index: the default index; configured per collection
Team workspace
Ship faster with chat, meetings, and projects in one place — Zlyqor.
Combine results with Reciprocal Rank Fusion for best-of-both retrieval.
Distance Metrics
Metric
Use case
Cosine
Text embeddings (default)
Dot Product
When vectors are pre-normalized
Euclidean
Image embeddings
Qdrant Cloud
For production, Qdrant Cloud provides a managed cluster with automatic backups, horizontal scaling, and a free 1 GB tier. Switch by changing the client URL:
Practical deep-dives on LLMs, developer tools, and AI engineering. No filler. Unsubscribe any time.
// written byFIG. AUTH-01
530
Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
Open Code Review – An AI-powered code review CLI tool: A Practical Overview
Open Code Review is an open-source CLI tool from Alibaba that uses AI to review code changes. It runs locally, supports multiple LLMs, and costs about $0.01 per review. Here's a practical breakdown.
