Python Client library for the Qdrant vector search engine.
Python Qdrant Client
Client library and SDK for the Qdrant vector search engine.
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 diskLocal 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:latestSee 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)
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
Async methods are available in raw autogenerated clients. Usually, you don't need to use them directly, but if you need extra performance, you can access them directly.
Async gRPC
Example of using raw async gRPC client:
from qdrant_client import QdrantClient, grpc
client = QdrantClient(prefer_grpc=True, timeout=3.0)
grpc_collections = client.async_grpc_collections
res = await grpc_collections.List(grpc.ListCollectionsRequest(), timeout=1.0)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