ferres-db-python 0.2.0

Python SDK for FerresDB vector database

FerresDB Python SDK

Python SDK for interacting with FerresDB vector database.

Installation

pip install ferres-db-python

Or install from source:

cd sdk/python
pip install -e .

Authentication

All data routes (collections, points, search, API keys) require authentication. Pass the API key when creating the client; the SDK sends the Authorization: Bearer <api_key> header on every request.

client = VectorDBClient(
    base_url="http://localhost:8080",
    api_key="ferres_sk_...",  # required for protected routes
)

Without api_key, the server will respond with 401 on protected routes.

Running FerresDB with Docker

To use the SDK against a real FerresDB instance, you can run the official images.

Pull images

docker pull ferresdb/ferres-db-core
docker pull ferresdb/ferres-db-frontend

Start the backend (API)

docker run -d \
  --name ferres-db-core \
  -p 8080:8080 \
  -e PORT=8080 \
  -e STORAGE_PATH=/data \
  -e FERRESDB_API_KEYS=ferres_sk_your_key_here \
  -v ferres-data:/data \
  ferresdb/ferres-db-core

• API: http://localhost:8080

Start the frontend (dashboard)

docker run -d \
  --name ferres-db-frontend \
  -p 3000:80 \
  -e VITE_API_BASE_URL=http://localhost:8080 \
  -e VITE_API_KEY=ferres_sk_your_key_here \
  ferresdb/ferres-db-frontend

• Dashboard: http://localhost:3000

Use the SDK

With the backend running at http://localhost:8080 and the same API key:

from vector_db_client import VectorDBClient

client = VectorDBClient(
    base_url="http://localhost:8080",
    api_key="ferres_sk_your_key_here",
)
# create collections, upsert, search, etc.

Quick Start

import asyncio
from vector_db_client import VectorDBClient, Point, DistanceMetric

async def main():
    # Create client (api_key required for collections, points, etc.)
    async with VectorDBClient(
        base_url="http://localhost:8080",
        api_key="ferres_sk_...",
    ) as client:
        # Create a collection
        collection = await client.create_collection(
            name="my-collection",
            dimension=128,
            distance=DistanceMetric.COSINE,
        )

        # Upsert points
        points = [
            Point(id="1", vector=[0.1, 0.2, 0.3], metadata={"text": "hello"}),
            Point(id="2", vector=[0.4, 0.5, 0.6], metadata={"text": "world"}),
        ]
        result = await client.upsert_points("my-collection", points)
        print(f"Upserted {result.upserted} points")

        # Search for similar vectors
        results = await client.search(
            collection="my-collection",
            vector=[0.1, 0.2, 0.3],
            limit=10,
        )
        for result in results:
            print(f"ID: {result.id}, Score: {result.score}")

asyncio.run(main())

Features

• Type hints: Full type annotations for better IDE support
• Automatic retry: Exponential backoff for transient failures
• Structured logging: Uses structlog for better observability
• Automatic batching: Large upsert operations are automatically split into batches
• Async/await: Built on httpx for async operations

API Reference

VectorDBClient

__init__(base_url: str, api_key: str = None, timeout: int = 30)

Initialize the client.

• base_url: Base URL of the FerresDB server (e.g., "http://localhost:8080")
• api_key: Optional API key for authentication (recommended for all data routes)
• timeout: Request timeout in seconds

create_collection(name: str, dimension: int, distance: DistanceMetric, enable_bm25: bool = None, bm25_text_field: str = None) -> Collection

Create a new collection. Use enable_bm25=True and bm25_text_field="content" for hybrid search.

list_collections() -> List[CollectionListItem]

List all collections.

list_keys() -> List[ApiKeyInfo]

List API keys (metadata only; requires Editor/Admin). Returns id, name, key_prefix, created_at.

create_key(name: str) -> CreateKeyResponse

Create a new API key. The raw key is returned only once; store it securely.

delete_key(key_id: int) -> None

Delete an API key by id (from list_keys or create_key).

delete_collection(name: str) -> None

Delete a collection.

upsert_points(collection: str, points: List[Point]) -> UpsertResult

Upsert points into a collection. Automatically batches if more than 1000 points.

delete_points(collection: str, ids: List[str]) -> None

Delete points by IDs.

search(collection: str, vector: List[float], limit: int = 10, filter: dict = None) -> List[SearchResult]

Search for similar vectors.

Models

Point

• id: str: Point identifier
• vector: List[float]: Vector coordinates
• metadata: Dict[str, Any]: Arbitrary metadata

Collection

• name: str: Collection name
• dimension: int: Vector dimension
• distance: DistanceMetric: Distance metric

SearchResult

• id: str: Point ID
• score: float: Similarity score
• metadata: Dict[str, Any]: Point metadata

ApiKeyInfo

• id: int: Key id
• name: str: Display name
• key_prefix: str: Prefix (raw key never returned in list)
• created_at: int: Unix timestamp

CreateKeyResponse

• id: int, name: str, key: str, key_prefix: str, created_at: int — key is the raw secret (returned only on create).

Exceptions

• VectorDBError: Base exception
• CollectionNotFoundError: Collection not found (404)
• CollectionAlreadyExistsError: Collection already exists (409)
• InvalidDimensionError: Invalid dimension (400)
• InvalidPayloadError: Invalid payload (400)
• InternalError: Internal server error (500)
• ConnectionError: Connection error

Development

Install development dependencies:

pip install -e ".[dev]"

Run tests:

pytest

Format code:

black vector_db_client tests

Type checking:

mypy vector_db_client