✨ A community-curated collection of tutorials, projects, integrations, benchmarks, articles, and practical resources for building with Qdrant.
Unofficial and community-maintained, not affiliated with Qdrant.
Awesome Qdrant Resources brings together practical learning materials for Qdrant—from foundational tutorials to hybrid search, quantization, embeddings, integrations, scaling, and production operations.
Whether you're exploring vector search for the first time or improving an existing Qdrant deployment, this collection is designed to help you find useful, working examples quickly.
New to Qdrant? Here's how to get started:
- First time with Qdrant? Start with our Qdrant 101 tutorial - ✅ Ready!
- Text chunking with Qdrant? Try the Chonkie Integration - ✅ Ready!
- Multimodal Search? Check out Gemini Embeddings - ✅ Ready!
- Efficient Embeddings? Learn about Matryoshka MRL - ✅ Ready!
- Vector Compression? Read about Google TurboQuant - ✅ Ready!
- Quantization tradeoffs? Compare them in the Quantization Face-Off - ✅ Ready!
- Exploring Qdrant 1.19? Run the Qdrant 1.19 hands-on demos - ✅ Ready!
- Multi-tenant collections? Read the Multitenancy Playbook - ✅ Ready!
- Embedding without external API calls? See FastEmbed Cloud Inference - ✅ Ready!
- Filtered search at low selectivity? Benchmark ACORN vs filtered HNSW - ✅ Ready!
- Hybrid search in Go? Try bm25-go-hybrid - ✅ Ready!
- Chonkie Integration - Complete text chunking tutorial with Qdrant handshake
- Qdrant-101 - Foundational Qdrant concepts and getting started
- Gemini Embeddings - Multimodal Semantic Search with Gemini 2
- Matryoshka MRL - Matryoshka Representation Learning (MRL) concepts
- TurboQuant - High-recall vector quantization and memory-efficient search
- Qdrant 1.19 Hands-On - Runnable demos for Turbo4, memory tiers, prefix matching, and parallel scrolling
- Qdrant 1.20 Hands-On - Runnable demos for BM25 on full-text indexes, the code tokenizer, per-element nested indexes, and turbo16/turbo8
- Haystack + Qdrant - Qdrant Cloud integration with semantic retrieval and metadata filtering
- FastEmbed Cloud Inference - Embedding models alongside Qdrant with no external API calls: FastEmbed, a local sidecar, and Qdrant Cloud's built-in inference, benchmarked on real data
- RAG Applications - RAG resources and the practical Just-RAG implementation
- Agent Workflows - LangGraph implementations (in development)
- Image Recommendations - Computer vision search systems (planned)
- Embeddings - Matryoshka embeddings and advanced strategies (in development)
| Status | Project | Description | Key Features |
|---|---|---|---|
| ✅ | Quantization Face-Off | Head-to-head benchmark of Qdrant quantization (f32 / SQ / PQ / BQ) on 1M-scale text embeddings | Recall vs latency vs RAM, rescoring sweeps, cost analysis, W&B telemetry |
| ✅ | Chonkie Integration | Text chunking with seamless Qdrant integration | Multiple chunking strategies, performance comparison |
| ✅ | Qdrant 1.19 Hands-On | Runnable examples for the Qdrant 1.19 release | Turbo4, memory tiers, prefix filters, parallel scrolling |
| ✅ | Qdrant 1.20 Hands-On | Runnable examples for the Qdrant 1.20 release | BM25 without sparse vectors, code tokenizer, per-element indexes, turbo16/turbo8 |
| ✅ | Multitenancy Playbook | Shared tenant indexing, tiered shards, and per-tenant IDF in Qdrant 1.19 | Docker benchmarks of latency and recall, BM25/IDF ranking comparisons, decision framework |
| ✅ | FastEmbed Cloud Inference | CPU/GPU/Qdrant Cloud embedding benchmarks on real data, no external API calls | Real-data indexing throughput, query latency, retrieval quality (hit-rate@10, MRR@10) across three deployment patterns |
| ✅ | ACORN vs Filtered HNSW | Benchmark of Qdrant 1.16's ACORN search against vanilla filtered HNSW across filter selectivity from 0.1% to 90% | Recall@10 and latency vs selectivity, hnsw_ef sweep, charts from raw results |
| 🚧 | Image Recommendations | Image similarity search systems (in development) | Computer vision, embeddings, similarity search |
| 🚧 | Agent Workflows | AI agent implementations (planned) | LangGraph, agentic RAG, vector memory |
| Status | Integration | Description | Tutorial |
|---|---|---|---|
| ✅ | Chonkie | Advanced text chunking with Qdrant handshake | Tutorial |
| ✅ | FastEmbed | Fast, lightweight embedding library | Guide + benchmarks |
| ✅ | bm25-go-hybrid | Dense + sparse (BM25) hybrid search in Go, using a FastEmbed-compatible sparse encoder | Demo |
| ✅ | Haystack | Qdrant Cloud as vector database inside a Haystack pipeline, with payload indexing and metadata filtering | Tutorial |
| 🚧 | LangChain | Python framework for LLM applications | Coming Soon |
| 🚧 | LlamaIndex | Data framework for LLM applications | Coming Soon |
Blog posts behind these tutorials, plus deep dives with no repo counterpart — see ARTICLES.md for the full, ordered index.
Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go, .NET, and Java.
- Repository: qdrant/skills
- Scope: practical workflows for building, operating, and upgrading Qdrant-backed applications
- Use cases: retrieval quality tuning, operational monitoring, production deployment, and SDK-specific implementation guidance
We welcome contributions to Awesome Qdrant Resources! 🎉
We're passionate about contributing to the community and sharing knowledge with developers exploring vector databases and Qdrant.
Please check our CONTRIBUTING.md for detailed guidelines.
Awesome Qdrant Resources — built with ❤️ by and for the Qdrant community.