Introduction to Qdrant (Vector Database) Using Python

Install and run Quadrant, a fast open source vector database with rest and gRPC APIs. Explore built-in similarity metrics, replication, sharding, snapshots, and Docker Compose and Python client setup.

Learn the quadrant data storage model with collections and segments, covering vector and payload storage, appendable versus non appendable segments, and the role of write-ahead logging and Rocksdb persistence.

Explore Qdrant collections in a Python workflow, creating, updating, listing, and deleting collections with vectors and payloads, using cosine, dot, Euclidean, and Manhattan similarity metrics, including sparse and named vectors.

Learn how to manage points in the Quadrant vector database, including collections, named and unnamed vectors, payloads, and operations to insert, update, delete, and search based on vector similarity.

Load a real-world dataset snapshot into qdrant, exploring vectors, json payloads, and the web ui to manage collections, snapshots, and point details for learning and experimentation.

Learn to perform vector similarity search in Qdrant using Python, filtering results by payload with must, should, and must not clauses, and fine-tuning results with filtering classes.

Learn vector similarity search in Qdrant with Python, applying filtering conditions like match, match any, and match text, plus nested, range, and geo filters.

Explore payload indexes in quadrant to speed retrieval by indexing vector and payload fields, including keyword, integer, float, boolean, geo, full text search, and nested field support.

Explore navigable small world graphs (NSW) for fast, high-recall vector searches. Quadrant builds NSW indexes in the background and tunes m and f construct to balance accuracy and memory.

Explore vector quantization in Qdrant, including scalar, binary, and product quantization, and learn how lossy compression reduces storage and speeds up search for high-dimensional vectors.

Explore product quantization for high-dimensional vectors by splitting into sub vectors, clustering, and using centroid IDs to enable efficient similarity search with quantization parameters like ignore, rescore, and oversampling.

Create, list, and delete snapshots for a collection in Qdrant, a vector database, using Python. Understand that snapshots are compressed archives of a collection's data and configuration.

Configure a quadrant server with yaml or environment variables, merge multiple configs at startup, tune storage, performance, hnsw index, network, TLS, API keys, and telemetry for secure, efficient vector search.

Discover how quadrant uses vacuum, merge, and indexing optimizers configured in the settings to manage deleted data, segment consolidation, and index sizing for efficient vector search.

Explore asynchronous programming in Python using the Qdrant async Python client to enable non-blocking i/o, event loops, and efficient concurrent database operations.

Demonstrate end-to-end Qdrant usage with TensorFlow by generating 512-d embeddings using the universal sentence encoder, inserting SMS data into an SMS collection, and performing a similarity search with Euclidean distance.

Explore Qdrant and OpenAI embeddings to perform a text similarity search on the Spotify Trax dataset, filtering by artist, and using cosine distance with payloads like album, track, and popularity.

Explore building a question-and-answer system with Qdrant vector store and LangChain, using sentence-transformer embeddings, context data, and OpenAI to retrieve and summarize relevant information.

Congratulations on completing this course; share your rating and feedback to help improve it, then learn next steps to showcase your Qdrant skills with Python on real-time vector database projects.