a collection created externally or want to have the named vector used, you can configure it by providing its name.Qdrant.from_documents(    docs,    embeddings,    location=":memory:",    collection_name="my_documents_2",    vector_name="custom_vector",)As a Langchain user, you won't see any difference whether you use named vectors or not. Qdrant integration will handle the conversion under the hood.Metadata‚ÄãQdrant stores your vector embeddings along with the optional JSON-like payload. Payloads are optional, but since LangChain assumes the embeddings are generated from the documents, we keep the context data, so you can extract the original texts as well.By default, your document is going to be stored in the following payload structure:{    "page_content": "Lorem ipsum dolor sit amet",    "metadata": {        "foo": "bar"    }}You can, however, decide to use different keys for the page content and metadata. That's useful if you already have a collection that you'd like to reuse.Qdrant.from_documents(    docs,    embeddings,    location=":memory:",    collection_name="my_documents_2",    content_payload_key="my_page_content_key",    metadata_payload_key="my_meta",)    <langchain.vectorstores.qdrant.Qdrant at 0x7fc4e2baa230>PreviousPineconeNextRedisConnecting to Qdrant from LangChainLocal modeOn-premise server deploymentQdrant CloudRecreating the collectionSimilarity searchSimilarity search with scoreMetadata filteringMaximum marginal relevance search (MMR)Qdrant as a RetrieverCustomizing QdrantNamed vectorsMetadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.
Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications. ->: a collection created externally or want to have the named vector used, you can configure it by providing its name.Qdrant.from_documents(    docs,    embeddings,    location=":memory:",    collection_name="my_documents_2",    vector_name="custom_vector",)As a Langchain user, you won't see any difference whether you use named vectors or not. Qdrant integration will handle the conversion under the hood.Metadata‚ÄãQdrant stores your vector embeddings along with the optional JSON-like payload. Payloads are optional, but since LangChain assumes the embeddings are generated from the documents, we keep the context data, so you can extract the original texts as well.By default, your document is going to be stored in the following payload structure:{    "page_content": "Lorem ipsum dolor sit amet",    "metadata": {        "foo": "bar"    }}You can, however, decide to use different keys for the page content and metadata. That's useful if you already have a collection that you'd like to reuse.Qdrant.from_documents(    docs,    embeddings,    location=":memory:",    collection_name="my_documents_2",    content_payload_key="my_page_content_key",    metadata_payload_key="my_meta",)    <langchain.vectorstores.qdrant.Qdrant at 0x7fc4e2baa230>PreviousPineconeNextRedisConnecting to Qdrant from LangChainLocal modeOn-premise server deploymentQdrant CloudRecreating the collectionSimilarity searchSimilarity search with scoreMetadata filteringMaximum marginal relevance search (MMR)Qdrant as a RetrieverCustomizing QdrantNamed vectorsMetadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.