Python Client Library for the Arc Vector Search Engine
Project description
arc-vector-python
Python library and client for arc-vector database
Installation
// remote install
pip install arc-vector-python
// local install by package
pip install arc_vector_python-1.6.2.tar.gz
Features
- Type hints for all API methods
- Local mode - use same API without running server
- REST and gRPC support
- Minimal dependencies
Connect to ArcVector Server
To connect to Qdrant server, simply specify host and port:
from arc_vector_client import ArcVectorClient
from arc_vector_client.models import Distance, VectorParams
# REST
client = ArcVectorClient(url="http://localhost:8333")
# gRPC
client = ArcVectorClient(host="localhost", grpc_port=8334, prefer_grpc=True)
Async client
Starting from version 1.6.2, all python client methods are available in async version.
To use it, just import AsyncArcVectorClient
instead of ArcVectorClient
:
from arc_vector_client import AsyncArcVectorClient, models
import numpy as np
import asyncio
async def main():
# Your async code using ArcVectorClient might be put here
client = AsyncArcVectorClient(url="http://localhost:8333")
# client = AsyncArcVectorClient(host="localhost", grpc_port=8334, prefer_grpc=True)
await client.create_collection(
collection_name="my_collection",
vectors_config=models.VectorParams(size=10, distance=models.Distance.COSINE),
)
await client.upsert(
collection_name="my_collection",
points=[
models.PointStruct(
id=i,
vector=np.random.rand(10).tolist(),
)
for i in range(100)
],
)
res = await client.search(
collection_name="my_collection",
query_vector=np.random.rand(10).tolist(), # type: ignore
limit=10,
)
print(res)
asyncio.run(main())
Both, gRPC and REST API are supported in async mode. More examples can be found here.
Project details
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