zeusdb-vector-database 0.0.3

Blazing-fast vector DB with real-time similarity search and metadata filtering.

ZeusDB Vector Database

Meta

What is ZeusDB Vector Database?

ZeusDB Vector Database is a high-performance, Rust-powered vector database designed for blazing-fast similarity search across high-dimensional data. It enables efficient approximate nearest neighbor (ANN) search, ideal for use cases like document retrieval, semantic search, recommendation systems, and AI-powered assistants.

ZeusDB leverages the HNSW (Hierarchical Navigable Small World) algorithm for speed and accuracy, with native Python bindings for easy integration into data science and machine learning workflows. Whether you're indexing millions of vectors or running low-latency queries in production, ZeusDB offers a lightweight, extensible foundation for scalable vector search.

Features

🔍 Approximate Nearest Neighbor (ANN) search with HNSW

🔥 High-performance Rust backend

📥 Supports multiple input formats using a single, easy-to-use Python method

🗂️ Metadata-aware filtering at query time

🐍 Simple and intuitive Python API

✅ Supported Distance Metrics

Metric	Description
cosine	Cosine distance (1 - Cosine Similiarity)

📦 Installation

You can install ZeusDB Vector Database with 'uv' or alternatively using 'pip'.

Recommended (with uv):

uv pip install zeusdb-vector-database

Alternatively (using pip):

pip install zeusdb-vector-database

✨ Usage

📘 create_index_hnsw Parameters

Parameter	Type	Default	Description
dim	int	1536	Dimensionality of the vectors to be indexed. Each vector must have this length. The default dim=1536 is chosen to match the output dimensionality of OpenAI’s text-embedding-ada-002 model.
space	str	"cosine"	Distance metric used for similarity search. Options include "cosine". Additional metrics such as "l2", and "dot" will be added in future versions.
M	int	16	Number of bi-directional connections created for each new node. Higher M improves recall but increases index size and build time.
ef_construction	int	200	Size of the dynamic list used during index construction. Larger values increase indexing time and memory, but improve quality.
expected_size	int	10000	Estimated number of elements to be inserted. Used for preallocating internal data structures. Not a hard limit.

🔥 Quick Start Example

# Import the vector database module
from zeusdb_vector_database import VectorDatabase

# Instantiate the VectorDatabase class
vdb = VectorDatabase()

# Initialize and set up the database resources
index = vdb.create_index_hnsw(dim = 8, space = "cosine", M = 16, ef_construction = 200, expected_size=5)

# Upload vector records using the unified `add()` method
records = [
    {"id": "doc_001", "values": [0.1, 0.2, 0.3, 0.1, 0.4, 0.2, 0.6, 0.7], "metadata": {"author": "Alice"}},
    {"id": "doc_002", "values": [0.9, 0.1, 0.4, 0.2, 0.8, 0.5, 0.3, 0.9], "metadata": {"author": "Bob"}},
    {"id": "doc_003", "values": [0.11, 0.21, 0.31, 0.15, 0.41, 0.22, 0.61, 0.72], "metadata": {"author": "Alice"}},
    {"id": "doc_004", "values": [0.85, 0.15, 0.42, 0.27, 0.83, 0.52, 0.33, 0.95], "metadata": {"author": "Bob"}},
    {"id": "doc_005", "values": [0.12, 0.22, 0.33, 0.13, 0.45, 0.23, 0.65, 0.71], "metadata": {"author": "Alice"}},
]

result = index.add(records)

# Perform a similarity search and print the top 2 results
# Query Vector
query_vec = [0.1, 0.2, 0.3, 0.1, 0.4, 0.2, 0.6, 0.7]

# Query with no filter (all documents)
print("\n--- Querying without filter (all documents) ---")
results = index.query(vector=query_vec, filter=None, top_k=2)
for doc_id, score in results:
    print(f"{doc_id} (score={score:.4f})")

➕ Adding Vectors – Multiple Formats Supported

ZeusDB Vector Database supports multiple intuitive ways to insert data using index.add(...). All formats accept optional metadata per record.

✅ Format 1 – Single Object

index.add({
    "id": "doc1",
    "values": [0.1, 0.2],
    "metadata": {"text": "hello"}
})

print(result.summary())     # ✅ 1 inserted, ❌ 0 errors
print(result.is_success())  # True

✅ Format 2 – List of Objects

index.add([
    {"id": "doc1", "values": [0.1, 0.2], "metadata": {"text": "hello"}},
    {"id": "doc2", "values": [0.3, 0.4], "metadata": {"text": "world"}}
])

print(result.summary())       # ✅ 2 inserted, ❌ 0 errors
print(result.vector_shape)    # (2, 2)
print(result.errors)          # []

✅ Format 3 – Separate Arrays

index.add({
    "ids": ["doc1", "doc2"],
    "embeddings": [[0.1, 0.2], [0.3, 0.4]],
    "metadatas": [{"text": "hello"}, {"text": "world"}]
})
print(result)  # BatchResult(inserted=2, errors=0, shape=(2, 2))

✅ Format 4 – Using NumPy Arrays

ZeusDB also supports NumPy arrays as input for seamless integration with scientific and ML workflows.

import numpy as np

data = [
    {"id": "doc2", "values": np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32), "metadata": {"type": "blog"}},
    {"id": "doc3", "values": np.array([0.5, 0.6, 0.7, 0.8], dtype=np.float32), "metadata": {"type": "news"}},
]

result = index.add(data)

print(result.summary())   # ✅ 2 inserted, ❌ 0 errors

Each format is automatically parsed and validated internally, including support for NumPy arrays (np.ndarray). Errors and successes are returned in a structured BatchResult object for easy debugging and logging.

🧰 Additional functionality

Check the details of your HNSW index

print(index.info()) 

Output

HNSWIndex(dim=8, space=cosine, M=16, ef_construction=200, expected_size=5, vectors=5)

Add index level metadata

index.add_metadata({
  "creator": "John Smith",
  "version": "0.1",
  "created_at": "2024-01-28T11:35:55Z",
  "index_type": "HNSW",
  "embedding_model": "openai/text-embedding-ada-002",
  "dataset": "docs_corpus_v2",
  "environment": "production",
  "description": "Knowledge base index for customer support articles",
  "num_documents": "15000",
  "tags": "['support', 'docs', '2024']"
})

# View index level metadata by key
print(index.get_metadata("creator"))  

# View all index level metadata 
print(index.get_all_metadata())       

Output

John Smith
{'description': 'Knowledge base index for customer support articles', 'environment': 'production', 'embedding_model': 'openai/text-embedding-ada-002', 'creator': 'John Smith', 'tags': "['support', 'docs', '2024']", 'num_documents': '15000', 'version': '0.1', 'index_type': 'HNSW', 'dataset': 'docs_corpus_v2', 'created_at': '2024-01-28T11:35:55Z'}

List records in the index

print("\n--- Index Shows first 5 records ---")
print(index.list(number=5)) # Shows first 5 records

Output

[('doc_004', {'author': 'Bob'}), ('doc_003', {'author': 'Alice'}), ('doc_005', {'author': 'Alice'}), ('doc_002', {'author': 'Bob'}), ('doc_001', {'author': 'Alice'})]

Query with metadata filter (only Alice documents)

This pre-filters on the given metadata prior to conducting the similarity search.

print("\n--- Querying with filter: author = 'Alice' ---")
results = index.query(vector=query_vec, filter={"author": "Alice"}, top_k=5)
for doc_id, score in results:
    print(f"{doc_id} (score={score:.4f})")

Output

doc_001 (score=0.0000)
doc_003 (score=0.0010)
doc_005 (score=0.0011)

📄 License

This project is licensed under the Apache License 2.0.