Qdrant

What is Qdrant?

Qdrant is an open-source vector database and vector similarity AI search engine, founded by Andre Zayarni in Berlin, Germany, in 2021. Developed in Rust, it supports the conversion of various multimodal data into vectors and enables efficient storage and retrieval. With its high performance and low storage requirements, Qdrant is widely used in areas such as personalized recommendation, text and image recognition, and real-time data analysis. In January 2024, Qdrant completed a $28 million Series A funding round, led by Spark Capital.

The main features of Qdrant

• Vector Storage: Qdrant can efficiently store high-dimensional vector data, making it suitable for handling large-scale datasets.
• Similarity Search: Users can quickly retrieve vectors similar to the input vector, which is highly useful in recommendation systems and content matching.
• Multimodal Data Processing: Supports converting different modalities of data such as text, images, audio, and video into vectors, enabling cross-modal search and analysis.
• Real-time Retrieval: Provides fast retrieval capabilities, suitable for application scenarios requiring real-time feedback.

How to Use Qdrant

• Visit the website: Visit the Qdrant official website (qdrant.tech).
• Install Qdrant: Pull the Qdrant image using Docker and run it.
• Install the Qdrant client: Install the Python client via pip.
• Initialize the Qdrant client: Initialize the Qdrant client in Python and connect it to the Qdrant service.
• Create a collection: Create the schema of the collection and create the collection in Qdrant.
• Insert vector data: Insert vector data into the collection.
• Create an index: Create an index using the Python client.
• Search for vectors: Run basic search queries.
• Clean up resources: After completing the operations, clean up resources to ensure that system resources are released.