This repo contains a collection of tutorials, demos, and how-to guides on how to use Qdrant and adjacent technologies.
| Example | Description | Technologies |
|---|---|---|
| Agentic RAG with Qdrant and CrewAI | Build a vector-search-powered agentic workflow to extract insights from meeting recordings | Docker, Qdrant, CrewAI, Claude |
| Qdrant 101 - Getting Started | Introduction to semantic search and the recommendation API of Qdrant | NumPy, Faker |
| Qdrant 101 - Image Data | Use semantic search for accurate skin cancer image comparison with Qdrant and CLIP | CLIP, PyTorch, datasets |
| Qdrant 101 - Text Data | Introduction to the intersection of Vector Databases and Natural Language Processing | transformers, datasets, GPT-2, Sentence Transformers, PyTorch |
| Qdrant 101 - Audio Data | Introduction to audio embeddings, audio data, and music recommendation systems | transformers, librosa, openl3, panns_inference, streamlit, datasets, PyTorch |
| Build a Semantic Search Engine in 5 Minutes | Quick starter tutorial that builds a semantic search engine for science-fiction books | Qdrant, OpenAI, FastEmbed |
| Semantic Search for Sci-Fi Books | Semantic search over a top-100 set of science-fiction books | Qdrant, FastEmbed |
| Language Model QA with Qdrant — ColPali and Binary Quantization | Document retrieval with Vision Language Models and binary quantization | ColPali, Qdrant, CLIP, PyTorch |
| ColPali and ColQwen2 PDF Retrieval at Scale | Visual document retrieval over PDFs without OCR using vision-language embeddings | ColPali, ColQwen2, Qdrant |
| Ecommerce - reverse image search | Notebook demonstrating how to implement a reverse image search for ecommerce | CLIP, semantic image search, Sentence-Transformers |
| Ecommerce Search in Golang | Semantic product search using Qdrant with cloud inference for embeddings | Go, Qdrant Cloud |
| Multimodal Search with LlamaIndex | Multimodal image + caption embeddings stored and retrieved in a shared embedding space | LlamaIndex, Qdrant, multimodal embeddings |
| Multi-Representation Search | Step-by-step build-up of a retrieval pipeline using a document's multiple representations | Qdrant, FastEmbed, dense + multi-vector |
| Multivector Representation | Use Qdrant multi-vector search with dense and late-interaction (ColBERT-style) embeddings for retrieval and reranking | Qdrant, late-interaction / ColBERT embeddings |
| Binary Quantization with Qdrant | Demonstrate and evaluate the search performance of Qdrant with binary quantization | Qdrant Cloud, binary quantization |
| GraphRAG with Neo4j, Qdrant, and OpenAI | Extract graph relationships from text, store them in Neo4j, and enrich retrieval with Qdrant vector search | GraphRAG, Neo4j, Qdrant, OpenAI |
| Minimal Dependency RAG with DeepSeek and Qdrant | Build a lightweight RAG pipeline with the DeepSeek model and Qdrant | DeepSeek, Qdrant, RAG |
| Question Answering with LangChain and Qdrant, Without Boilerplate | Retrieval-augmented question answering pipeline with LangChain, FastEmbed, and Qdrant | LangChain, FastEmbed, Qdrant, Claude/GPT |
| LLM QA with Qdrant — DSPy Medical Bot | Build a medical Q&A bot using DSPy and Qdrant | DSPy, Qdrant, medicine |
| Extractive Question Answering with Qdrant | Build an extractive question-answering system that pulls answers from context | Qdrant, retriever model, reader model |
| QA which is always updated: Recency and Cohere using Llama Index | Keep your QA system always using updated information | Llama Index, OpenAI Embeddings, Cohere Reranker |
| Choosing a Fusion Method: RRF, DBSF, and FormulaQuery | Compare RRF, DBSF, and FormulaQuery for hybrid (dense + sparse) search | Qdrant, RRF, DBSF, FormulaQuery |
| Customizing Relevance Feedback | Customize the Relevance Feedback Query, add it to your pipeline, and evaluate gains | Qdrant, relevance feedback |
| Collaborative Filtering and MovieLens | A notebook demonstrating how to build a collaborative filtering system using Qdrant | Sparse Vectors, Qdrant, MovieLens |
| Collaborative Filtering (via Sparse Vectors) | Build a collaborative filtering recommender using sparse vector representations | Sparse Vectors, Qdrant, MovieLens |
| Self-Query on Wine Reviews | Semantic search and filtering over ~120k wine reviews | Qdrant, Python |
| Use semantic search to navigate your codebase | Implement a semantic search application for code search tasks | Qdrant, Python, sentence-transformers, Jina |
| Time-Based Sharding of Social Media | Shard social-media post data by time for efficient retrieval | Qdrant, time-based sharding |
| Incremental Embedding Updates | Sync embeddings with changing raw text data | Qdrant Cloud, Python |
| Qdrant Data Preparation Webinar | Transform raw data into an AI-ready format using Qdrant and FastEmbed | Qdrant, FastEmbed, RAG |
| Qdrant Essentials Course | Multi-day course notebooks: semantic recommendation, HNSW performance tuning, hybrid and sparse search, LAION-400M large-scale ingestion, and Universal Query | Qdrant, HNSW, FastEmbed, ColBERT |
| Qdrant Multi-Vector Search Course | Multi-module course on late-interaction (ColPali) multi-vector search: basics, MaxSim, how ColPali works, quantization, pooling, and multi-stage retrieval pipelines | Qdrant, FastEmbed, ColPali, ColBERT, quantization |