rust-vector-db

Overview

rust-vector-db is a hands-on project demonstrating the creation of a vector database in Rust, focusing on storing and retrieving high-dimensional vectors for AI applications.

Key Concepts

• Vector Embeddings: Numerical representations capturing semantic meaning of unstructured data like text, images, audio, and video.
• Vector Indexing: Organizes data using structures like KD-trees, VP-trees, or graph-based indices for efficient similarity searches.
• Distance Metrics: Cosine similarity, Euclidean distance, dot product for measuring vector similarity.

Workflow

1. Embed raw data into vectors using ML models.
2. Store vectors in the database.
3. For queries: Embed query, retrieve most similar vectors via similarity search.

Use Cases

• Recommendation systems
• Image and video search
• NLP applications

Pricing

Free and open-source.