Quickstart

Quickstart

This guide will have you inserting and searching vectors in under 60 seconds.

Prerequisites

Make sure VortexDB is running (see Installation).

Insert Your First Vector

Python SDK

from vortexdb import VortexDB, DenseVector, Payload, Similarity

# Connect to VortexDB
db = VortexDB(
    grpc_url="localhost:50051",
    api_key="your-secure-password"
)

# Insert a vector with a text payload
point_id = db.insert(
    vector=DenseVector([0.1, 0.2, 0.3, 0.4]),
    payload=Payload.text("Hello, VortexDB!")
)
print(f"Inserted point: {point_id}")

# Clean up
db.close()

HTTP API

curl -X POST http://localhost:3000/points \
  -H "Content-Type: application/json" \
  -d '{
    "vector": [0.1, 0.2, 0.3, 0.4],
    "payload": {
      "content_type": "Text",
      "content": "Hello, VortexDB!"
    }
  }'

Response:

{
  "point_id": "550e8400-e29b-41d4-a716-446655440000"
}

gRPC

// Using grpcurl
grpcurl -plaintext \
  -H "authorization: your-secure-password" \
  -d '{
    "vector": {"values": [0.1, 0.2, 0.3, 0.4]},
    "payload": {"content_type": 1, "content": "Hello, VortexDB!"}
  }' \
  localhost:50051 vectordb.VectorDB/InsertVector

Search for Similar Vectors

Python SDK

from vortexdb import VortexDB, DenseVector, Similarity

db = VortexDB(
    grpc_url="localhost:50051",
    api_key="your-secure-password"
)

# Search for 5 most similar vectors using cosine similarity
results = db.search(
    vector=DenseVector([0.1, 0.2, 0.3, 0.4]),
    similarity=Similarity.COSINE,
    limit=5
)

print(f"Found {len(results)} similar vectors:")
for point_id in results:
    print(f"  - {point_id}")

db.close()

HTTP API

curl -X POST http://localhost:3000/points/search \
  -H "Content-Type: application/json" \
  -d '{
    "vector": [0.1, 0.2, 0.3, 0.4],
    "similarity": "Cosine",
    "limit": 5
  }'

Response:

{
  "results": [
    "550e8400-e29b-41d4-a716-446655440000",
    "6ba7b810-9dad-11d1-80b4-00c04fd430c8"
  ]
}

Retrieve a Point

Python SDK

# Get the point you just inserted
point = db.get(point_id=point_id)

if point:
    print(point.pretty())
    # Output:
    # Point ID: 550e8400-e29b-41d4-a716-446655440000
    # Vector: [0.1, 0.2, 0.3, 0.4]
    # Payload: Hello, VortexDB!

HTTP API

curl http://localhost:3000/points/550e8400-e29b-41d4-a716-446655440000

Response:

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "vector": [0.1, 0.2, 0.3, 0.4],
  "payload": {
    "content_type": "Text",
    "content": "Hello, VortexDB!"
  }
}

Delete a Point

Python SDK

db.delete(point_id=point_id)
print("Point deleted successfully")

HTTP API

curl -X DELETE http://localhost:3000/points/550e8400-e29b-41d4-a716-446655440000

Complete Example

Here’s a complete example using the Python SDK with context manager:

from vortexdb import VortexDB, DenseVector, Payload, Similarity

# Using context manager for automatic cleanup
with VortexDB(grpc_url="localhost:50051", api_key="secret") as db:
    # Insert some vectors
    vectors = [
        ([0.1, 0.2, 0.3, 0.4], "First document"),
        ([0.2, 0.3, 0.4, 0.5], "Second document"),
        ([0.9, 0.8, 0.7, 0.6], "Third document"),
    ]

    point_ids = []
    for vec, text in vectors:
        pid = db.insert(
            vector=DenseVector(vec),
            payload=Payload.text(text)
        )
        point_ids.append(pid)
        print(f"Inserted: {text} -> {pid}")

    # Search for vectors similar to the first one
    results = db.search(
        vector=DenseVector([0.15, 0.25, 0.35, 0.45]),
        similarity=Similarity.COSINE,
        limit=2
    )

    print(f"\nTop 2 similar vectors:")
    for pid in results:
        point = db.get(point_id=pid)
        print(f"  - {point.payload.content}")

Next Steps

Architecture

Understand how VortexDB works under the hood

Indexers

Choose the right index for your use case

Python SDK

Python client documentation

API Reference

Explore the complete API