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.
pip install qdrant-client
Instance a client
from qdrant_client import QdrantClient
client = QdrantClient(host="localhost", port=6333)Create a new collection
client.recreate_collection(
collection_name="my_collection",
vector_size=100
)Get info about created collection
my_collection_info = client.http.collections_api.get_collection("my_collection")
print(my_collection_info.dict())Search for similar vectors
query_vector = np.random.rand(100)
hits = client.search(
collection_name="my_collection",
query_vector=query_vector,
query_filter=None, # Don't use any filters for now, search across all indexed points
append_payload=True, # Also return a stored payload for found points
top=5 # Return 5 closest points
)Search for similar vectors with filtering condition
from qdrant_openapi_client.models.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=0.5 # Select only those results where `rand_number` >= 0.5
)
)
]
),
append_payload=True, # Also return a stored payload for found points
top=5 # Return 5 closest points
)Check out full example code