qdrant client
v1.12.1
QDRANT矢量搜索引擎的Python客戶庫。
客戶庫庫和SDK，用於QDRANT矢量搜索引擎。
庫包含所有QDRANT API的類型定義，並允許同步和異步請求。
客戶端允許直接呼叫所有QDRANT API方法。它還為經常需要操作提供了一些其他幫助方法，例如初始收集上傳。
有關更多詳細信息，請參見Quickstart！
Python客戶端API文檔可在python-client.qdrant.tech上找到
pip install qdrant-client
Python客戶端允許您在無需運行QDRANT服務器的情況下在本地模式下運行相同的代碼。
只需這樣的初始化客戶端：
from qdrant_client import QdrantClient
client = QdrantClient ( ":memory:" )
# or
client = QdrantClient ( path = "path/to/db" ) # Persists changes to disk本地模式可用於開發，原型製作和測試。
pip install qdrant-client[fastembed]
Fastembed是用於在CPU上創建快速向量嵌入的庫。它基於ONNX運行時，並允許使用類似GPU的性能對CPU進行推斷。
QDRANT客戶端可以使用快速培訓來創建嵌入並將其上傳到QDRANT。這允許簡化API並使其更直觀。
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 )快速培訓還可以利用GPU來更快地嵌入。要啟用GPU支持，請安裝
pip install ' qdrant-client[fastembed-gpu] ' from qdrant_client import QdrantClient
# Initialize the client
client = QdrantClient ( ":memory:" ) # or QdrantClient(path="path/to/db")
client . set_model ( client . DEFAULT_EMBEDDING_MODEL , providers = [ "CUDAExecutionProvider" , "CPUExecutionProvider" ])注意：
fastembed-gpu和fastembed是相互排斥的。您只能安裝其中之一。如果您以前安裝了
fastembed安裝，則可能需要從新鮮的環境開始安裝fastembed-gpu。
要連接到QDRANT服務器，只需指定主機和端口：
from qdrant_client import QdrantClient
client = QdrantClient ( host = "localhost" , port = 6333 )
# or
client = QdrantClient ( url = "http://localhost:6333" )您可以使用Docker本地運行QDrant Server：
docker run -p 6333:6333 qdrant/qdrant:latest查看QDRANT存儲庫中的更多啟動選項。
您可以註冊並使用Qdrant Cloud來獲得1GB RAM的免費層帳戶。
擁有群集和API鍵後，您可以這樣連接到它：
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>" ,
)創建一個新集合
from qdrant_client . models import Distance , VectorParams
client . create_collection (
collection_name = "my_collection" ,
vectors_config = VectorParams ( size = 100 , distance = Distance . COSINE ),
)將向量插入集合
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 )
]
)搜索類似的向量
query_vector = np . random . rand ( 100 )
hits = client . search (
collection_name = "my_collection" ,
query_vector = query_vector ,
limit = 5 # Return 5 closest points
)搜索具有過濾條件的類似向量
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
)在我們的文檔中查看更多示例！
要使用GRPC上傳（通常更快）（通常更快）的集合，請使用以下初始化：
from qdrant_client import QdrantClient
client = QdrantClient ( host = "localhost" , grpc_port = 6334 , prefer_grpc = True )從1.6.1版本開始，所有Python客戶端方法均在異步版本中提供。
要使用它，只需導入AsyncQdrantClient而不是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 ())在異步模式下支持GRPC和REST API。可以在此處找到更多示例。
該項目使用git掛鉤運行代碼格式。
安裝使用PIP3安裝pre-commit的預pip3 install pre-commit ，並設置帶有pre-commit install的掛鉤。
預先承諾需要python> = 3.8
