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

if not client.collection_exists("my_collection"):
   client.create_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")

   if not await client.collection_exists("my_collection"):
      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

Quickstart

API Reference

Complete Docs

Complete Client API Docs

Qdrant Python Client Documentation¶

Installation¶

Examples¶

Async Client¶

Highlighted Classes¶

Indices and tables¶

Qdrant

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