Zoro-DB

Zoro-DB is a high-performance vector database designed for similarity search workloads. It provides a RESTfull API for managing vector collections and executing fast approximate nearest neighbor (ANN) searches.

Zoro-DB is suitable for:

• Semantic search
• Recommendation systems
• RAG (Retrieval-Augmented Generation)
• Embedding similarity / clustering

The fastest way to start Zoro-DB is with Docker:

docker run -p 6464:6464 ghcr.io/rajathshttgr/zoro-db:dev

Access the API at http://localhost:6464

For advanced setup such as persistent storage, detached mode, and development workflows, see the DEVELOPER_GUIDE.

Install the official Python client:

pip install zoro-client

from zoro_client import ZoroClient

client = ZoroClient(host="localhost", port=6464)
# or
client = ZoroClient(url="http://localhost:6464")

Zoro-DB supports externally generated embeddings and direct vector storage.

Example workflow using the Python SDK:

from zoro_client import VectorConfig, Distance
import numpy as np

# Create a collection
client.create_collection(
    collection_name="test",
    vector_config=VectorConfig(size=100, distance=Distance.COSINE)
)

# Generate example vectors
vectors = np.random.rand(5, 100).tolist()

payloads = [
    {"document": "LangChain integration"},
    {"document": "LlamaIndex integration"},
    {"document": "Hybrid search"},
    {"document": "Fast ANN search"},
    {"document": "Python for Machine Learning"},
]

# Insert vectors
client.upsert_points(
    collection_name="test",
    vectors=vectors,
    ids=[12, 4, 34, 23, 2],
    payloads=payloads
)

# Search
results = client.search(
    collection_name="test",
    query_vector=np.random.rand(100).tolist(),
    limit=2
)

print(results)

For full SDK usage, parameters, and examples see the SDK DOCUMENTATION.

Zoro-DB provides a RESTful interface that can be used from any programming language.

curl -X POST http://localhost:6464/collections/:collection_name \
  -H "Content-Type: application/json" \
  -d '{
  "vectors": {
    "size": 10,
    "distance": "Cosine"
  }
}'

curl -X PUT http://localhost:6464/collections/:collection_name/points \
  -H "Content-Type: application/json" \
  -d '{
  "points": [
    {
      "id": 12,
      "payload": {
        "document": "How to reset a forgotten password",
        "tag": "authentication"
      },
      "vector": [0.12, 0.34, 0.53, 0.63, 0.23, 0.91, 0.11, 0.42, 0.77, 0.08]
    },
    {
      "id": 24,
      "payload": {
        "document": "Best movies to watch on this weekend",
        "tag": "entertainment"
      },
      "vector": [0.91, 0.11, 0.42, 0.77, 0.08, 0.12, 0.34, 0.53, 0.63, 0.23]
    }
  ]
}
'

curl -X POST http://localhost:6464/collections/:collection_name/points/search \
  -H "Content-Type: application/json" \
  -d '{
  "vector": [0.22, 0.34, 0.53, 0.63, 0.23, 0.81, 0.11, 0.42, 0.77, 0.08],
  "limit": 2,
}'

Example Response

{
  "result": [
    {
      "id": 12,
      "score": 0.86,
      "payload": {
        "document": "How to reset a forgotten password",
        "tag": "authentication"
      }
    },
    {
      "id": 28,
      "score": 0.62,
      "payload": { "document": "forgot my password", "tag": "authentication" }
    }
  ],
  "status": "ok",
  "time": 0.000094741
}

For the complete list of endpoints, request schemas, and response formats see the API DOCUMENTATION.

Contributions, ideas, and feedback are always welcome. If you find a bug, want to suggest a feature, or improve documentation, feel free to open an issue or submit a pull request. For contribution guidelines and development workflow see CONTRIBUTING.md.

Thank you for your interest in Zoro-DB.