as all the other vector stores, is a LangChain Retriever, by using cosine similarity. retriever = qdrant.as_retriever()retriever    VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='similarity', search_kwargs={})It might be also specified to use MMR as a search strategy, instead of similarity.retriever = qdrant.as_retriever(search_type="mmr")retriever    VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='mmr', search_kwargs={})query = "What did the president say about Ketanji Brown Jackson"retriever.get_relevant_documents(query)[0]    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})Customizing Qdrant‚ÄãThere are some options to use an existing Qdrant collection within your Langchain application. In such cases you may need to define how to map Qdrant point into the Langchain Document.Named vectors‚ÄãQdrant supports multiple vectors per point by named vectors. Langchain requires just a single embedding per document and, by default, uses a single vector. However, if you work with a collection created externally or want to have
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. ->: as all the other vector stores, is a LangChain Retriever, by using cosine similarity. retriever = qdrant.as_retriever()retriever    VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='similarity', search_kwargs={})It might be also specified to use MMR as a search strategy, instead of similarity.retriever = qdrant.as_retriever(search_type="mmr")retriever    VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='mmr', search_kwargs={})query = "What did the president say about Ketanji Brown Jackson"retriever.get_relevant_documents(query)[0]    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})Customizing Qdrant‚ÄãThere are some options to use an existing Qdrant collection within your Langchain application. In such cases you may need to define how to map Qdrant point into the Langchain Document.Named vectors‚ÄãQdrant supports multiple vectors per point by named vectors. Langchain requires just a single embedding per document and, by default, uses a single vector. However, if you work with a collection created externally or want to have