qdrant-client 1.8.2

Client library for the Qdrant vector search engine

Python Client library for the Qdrant vector search engine.

Python Qdrant Client

Client library and SDK for the Qdrant vector search engine. Python Client API Documentation is available here.

Library contains type definitions for all Qdrant API and allows to make both Sync and Async requests.

Client allows calls for all Qdrant API methods directly. It also provides some additional helper methods for frequently required operations, e.g. initial collection uploading.

See QuickStart for more details!

Installation

pip install qdrant-client

Features

• Type hints for all API methods
• Local mode - use same API without running server
• REST and gRPC support
• Minimal dependencies
• Extensive Test Coverage

Local mode

Python client allows you to run same code in local mode without running Qdrant server.

Simply initialize client like this:

from qdrant_client import QdrantClient

client = QdrantClient(":memory:")
# or
client = QdrantClient(path="path/to/db")  # Persists changes to disk

Local mode is useful for development, prototyping and testing.

• You can use it to run tests in your CI/CD pipeline.
• Run it in Colab or Jupyter Notebook, no extra dependencies required. See an example
• When you need to scale, simply switch to server mode.

Fast Embeddings + Simpler API

pip install qdrant-client[fastembed]

FastEmbed is a library for creating fast vector embeddings on CPU. It is based on ONNX Runtime and allows to run inference on CPU with GPU-like performance.

Qdrant Client can use FastEmbed to create embeddings and upload them to Qdrant. This allows to simplify API and make it more intuitive.

from qdrant_client import QdrantClient

# Initialize the client
client = QdrantClient(":memory:")  # or QdrantClient(path="path/to/db")

# Prepare your documents, metadata, and IDs
docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
metadata = [
    {"source": "Langchain-docs"},
    {"source": "Linkedin-docs"},
]
ids = [42, 2]

# Use the new add method
client.add(
    collection_name="demo_collection",
    documents=docs,
    metadata=metadata,
    ids=ids
)

search_result = client.query(
    collection_name="demo_collection",
    query_text="This is a query document"
)
print(search_result)

Connect to Qdrant server

To connect to Qdrant server, simply specify host and port:

from qdrant_client import QdrantClient

client = QdrantClient(host="localhost", port=6333)
# or
client = QdrantClient(url="http://localhost:6333")

You can run Qdrant server locally with docker:

docker run -p 6333:6333 qdrant/qdrant:latest

See more launch options in Qdrant repository.

Connect to Qdrant cloud

You can register and use Qdrant Cloud to get a free tier account with 1GB RAM.

Once you have your cluster and API key, you can connect to it like this:

from qdrant_client import QdrantClient

qdrant_client = QdrantClient(
    url="https://xxxxxx-xxxxx-xxxxx-xxxx-xxxxxxxxx.us-east.aws.cloud.qdrant.io:6333",
    api_key="<your-api-key>",
)

Examples

Create a new collection

from qdrant_client.models import Distance, VectorParams

client.recreate_collection(
    collection_name="my_collection",
    vectors_config=VectorParams(size=100, distance=Distance.COSINE),
)

Insert vectors into a collection

import numpy as np
from qdrant_client.models import PointStruct

vectors = np.random.rand(100, 100)
# NOTE: consider splitting the data into chunks to avoid hitting the server's payload size limit
# or use `upload_collection` or `upload_points` methods which handle this for you
# WARNING: uploading points one-by-one is not recommended due to requests overhead
client.upsert(
    collection_name="my_collection",
    points=[
        PointStruct(
            id=idx,
            vector=vector.tolist(),
            payload={"color": "red", "rand_number": idx % 10}
        )
        for idx, vector in enumerate(vectors)
    ]
)

Search for similar vectors

query_vector = np.random.rand(100)
hits = client.search(
    collection_name="my_collection",
    query_vector=query_vector,
    limit=5  # Return 5 closest points
)

Search for similar vectors with filtering condition

from qdrant_client.models import Filter, FieldCondition, Range

hits = client.search(
    collection_name="my_collection",
    query_vector=query_vector,
    query_filter=Filter(
        must=[  # These conditions are required for search results
            FieldCondition(
                key='rand_number',  # Condition based on values of `rand_number` field.
                range=Range(
                    gte=3  # Select only those results where `rand_number` >= 3
                )
            )
        ]
    ),
    limit=5  # Return 5 closest points
)

See more examples in our Documentation!

gRPC

To enable (typically, much faster) collection uploading with gRPC, use the following initialization:

from qdrant_client import QdrantClient

client = QdrantClient(host="localhost", grpc_port=6334, prefer_grpc=True)

Async client

Starting from version 1.6.1, all python client methods are available in async version.

To use it, just import AsyncQdrantClient instead of QdrantClient:

from qdrant_client import AsyncQdrantClient, models
import numpy as np
import asyncio

async def main():
    # Your async code using QdrantClient might be put here
    client = AsyncQdrantClient(url="http://localhost:6333")

    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.

Development

This project uses git hooks to run code formatters.

Install pre-commit with pip3 install pre-commit and set up hooks with pre-commit install.

pre-commit requires python>=3.8