ZeusDB Vector Database

ZeusDB Vector Database is a high-performance, Rust-powered vector database built for fast and scalable similarity search across high-dimensional embeddings. Designed for modern machine learning and AI workloads, it provides efficient approximate nearest neighbor (ANN) search, supports real-time querying at scale, and seamlessly transitions from in-memory performance to durable disk persistence.

Whether you’re powering document search, enabling natural language interfaces, or building custom vector-based tools, ZeusDB offers a lightweight, extensible foundation for high-performance vector retrieval. It’s also well-suited for Retrieval-Augmented Generation (RAG) pipelines, where fast and semantically rich context retrieval is critical to enhancing large language model (LLM) responses.

⭐ Features

“Start fast. Tune deep. Build for any scale.”

🐍 User-friendly Python API for adding vectors and running similarity searches

🔥 High-performance Rust backend optimized for speed and concurrency

🔍 Approximate Nearest Neighbor (ANN) search using HNSW for lightning fast results

📦 Product Quantization (PQ) for compact storage, with reranking to hold accuracy

📥 Flexible input formats, including native Python types and NumPy arrays

🗂️ Metadata filtering for precise and contextual querying

💾 Save and reload full indexes, metadata, and quantized vectors across systems

📝 Enterprise-grade logging with flexible formats and output targets

✅ Supported Distance Metrics

ZeusDB Vector Database supports the following metrics for vector similarity search. All metric names are case-insensitive, so “cosine”, “COSINE”, and “Cosine” are treated identically.

Metric	Description	Accepted Values (case-insensitive)
cosine	Cosine Distance (1 - Cosine Similarity)	“cosine”, “COSINE”, “Cosine”
l1	Manhattan distance	“l1”, “L1”
l2	Euclidean distance	“l2”, “L2”

📏 Scores vs Distances

All distance metrics in ZeusDB Vector Database return distance values, not similarity scores:

• Lower values = more similar

• A vector identical to the query scores 0.0, or a value within floating point error of it

This applies to all distance types, including cosine.

Under cosine, vectors are normalized to unit length when they are stored. A vector you read back with return_vector=True or get_records() is therefore the normalized form, not the values you supplied. Under l1 and l2 the values are stored unchanged.

A zero vector has no direction, so under cosine it sits at distance 1.0 from everything, including itself.