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
HTTP API
gRPC

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()

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"
}

// 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
HTTP API

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()

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
HTTP API

# 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!

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
HTTP API

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

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

Getting Started

Concepts

Quickstart

Quickstart

Prerequisites

Insert Your First Vector

Search for Similar Vectors

Retrieve a Point

Delete a Point

Complete Example

Next Steps

Architecture

Indexers

Python SDK

API Reference

​Quickstart

​Prerequisites

​Insert Your First Vector

​Search for Similar Vectors

​Retrieve a Point

​Delete a Point

​Complete Example

​Next Steps

Architecture

Indexers

Python SDK

API Reference

Quickstart

Prerequisites

Insert Your First Vector

Search for Similar Vectors

Retrieve a Point

Delete a Point

Complete Example

Next Steps