Qdrant Python Client Documentation¶
Client library for the Qdrant vector search engine.
Library contains type definitions for all Qdrant API and allows to make both Sync and Async requests.
Pydantic is used for describing request models and httpx for handling http queries.
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.
Installation¶
pip install qdrant-client
Examples¶
Instance a client
from qdrant_client import QdrantClient
client = QdrantClient(host="localhost", port=6333)
Create a new collection
from qdrant_client.models import VectorParams, Distance
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
)
Async Client¶
Starting from version 1.6.1, all python client methods are available in async version.
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.
Highlighted Classes¶
Indices and tables¶
Examples
API Reference
Complete Docs
