Qdrant Vector Store - Default Qdrant Filters
Source: llama_index

<a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/pinecone_metadata_filter.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a># Qdrant Vector Store - Default Qdrant FiltersExample on how to use Filters from the qdrant_client SDK directly in your Retriever / Query Engine```python%pip install llama-index-vector-stores-qdrant``````python!pip3 install llama-index qdrant_client``````pythonimport openaiimport qdrant_clientfrom IPython.display import Markdown, displayfrom llama_index.core import VectorStoreIndexfrom llama_index.core import StorageContextfrom llama_index.vector_stores.qdrant import QdrantVectorStorefrom qdrant_client.http.models import Filter, FieldCondition, MatchValueclient = qdrant_client.QdrantClient(location=":memory:")from llama_index.core.schema import TextNodenodes = [    TextNode(        text="りんごとは",        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.tech/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)```