Qdrant 向量存储 - 默认 Qdrant 过滤器¶

如何在检索器 / 查询引擎中直接使用 qdrant_client SDK 中的过滤器示例

输入 [ ]

%pip install llama-index-vector-stores-qdrant

%pip install llama-index-vector-stores-qdrant

输入 [ ]

!pip3 install llama-index qdrant_client

!pip3 install llama-index qdrant_client

输入 [ ]

import openai
import qdrant_client
from IPython.display import Markdown, display
from llama_index.core import VectorStoreIndex
from llama_index.core import StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client.http.models import Filter, FieldCondition, MatchValue

client = qdrant_client.QdrantClient(location=":memory:")
from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text="りんごとは",
        metadata={"author": "Tanaka", "fruit": "apple", "city": "Tokyo"},
    ),
    TextNode(
        text="Was ist Apfel?",
        metadata={"author": "David", "fruit": "apple", "city": "Berlin"},
    ),
    TextNode(
        text="Orange like the sun",
        metadata={"author": "Jane", "fruit": "orange", "city": "Hong Kong"},
    ),
    TextNode(
        text="Grape is...",
        metadata={"author": "Jane", "fruit": "grape", "city": "Hong Kong"},
    ),
    TextNode(
        text="T-dot > G-dot",
        metadata={"author": "George", "fruit": "grape", "city": "Toronto"},
    ),
    TextNode(
        text="6ix Watermelons",
        metadata={
            "author": "George",
            "fruit": "watermelon",
            "city": "Toronto",
        },
    ),
]

openai.api_key = "YOUR_API_KEY"
vector_store = QdrantVectorStore(
    client=client, collection_name="fruit_collection"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes, storage_context=storage_context)


# Use filters directly from qdrant_client python library
# View python examples here for more info https://qdrant.org.cn/documentation/concepts/filtering/

filters = Filter(
    should=[
        Filter(
            must=[
                FieldCondition(
                    key="fruit",
                    match=MatchValue(value="apple"),
                ),
                FieldCondition(
                    key="city",
                    match=MatchValue(value="Tokyo"),
                ),
            ]
        ),
        Filter(
            must=[
                FieldCondition(
                    key="fruit",
                    match=MatchValue(value="grape"),
                ),
                FieldCondition(
                    key="city",
                    match=MatchValue(value="Toronto"),
                ),
            ]
        ),
    ]
)

retriever = index.as_retriever(vector_store_kwargs={"qdrant_filters": filters})

response = retriever.retrieve("Who makes grapes?")
for node in response:
    print("node", node.score)
    print("node", node.text)
    print("node", node.metadata)

import openai import qdrant_client from IPython.display import Markdown, display from llama_index.core import VectorStoreIndex from llama_index.core import StorageContext from llama_index.vector_stores.qdrant import QdrantVectorStore from qdrant_client.http.models import Filter, FieldCondition, MatchValue client = qdrant_client.QdrantClient(location=":memory:") from llama_index.core.schema import TextNode nodes = [ TextNode( text="りんごとは", metadata={"author": "Tanaka", "fruit": "apple", "city": "Tokyo"}, ), TextNode( text="Was ist Apfel?", metadata={"author": "David", "fruit": "apple", "city": "Berlin"}, ), TextNode( text="Orange like the sun", metadata={"author": "Jane", "fruit": "orange", "city": "Hong Kong"}, ), TextNode( text="Grape is...", metadata={"author": "Jane", "fruit": "grape", "city": "Hong Kong"}, ), TextNode( text="T-dot > G-dot", metadata={"author": "George", "fruit": "grape", "city": "Toronto"}, ), TextNode( text="6ix Watermelons", metadata={ "author": "George", "fruit": "watermelon", "city": "Toronto", }, ), ] openai.api_key = "YOUR_API_KEY" vector_store = QdrantVectorStore( client=client, collection_name="fruit_collection" ) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex(nodes, storage_context=storage_context) # Use filters directly from qdrant_client python library # View python examples here for more info https://qdrant.org.cn/documentation/concepts/filtering/ filters = Filter( should=[ Filter( must=[ FieldCondition( key="fruit", match=MatchValue(value="apple"), ), FieldCondition( key="city", match=MatchValue(value="Tokyo"), ), ] ), Filter( must=[ FieldCondition( key="fruit", match=MatchValue(value="grape"), ), FieldCondition( key="city", match=MatchValue(value="Toronto"), ), ] ), ] ) retriever = index.as_retriever(vector_store_kwargs={"qdrant_filters": filters}) response = retriever.retrieve("Who makes grapes?") for node in response: print("node", node.score) print("node", node.text) print("node", node.metadata)