The returned distance score is cosine distance. Therefore, a lower score is better.query = "What did the president say about Ketanji Brown Jackson"found_docs = qdrant.similarity_search_with_score(query)document, score = found_docs[0]print(document.page_content)print(f"\nScore: {score}")    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.         Tonight, 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.         One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And 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.        Score: 0.8153784913324512Metadata filtering‚ÄãQdrant has an extensive filtering system with rich type support. It is also possible to use the filters in Langchain, by passing an additional param to both the similarity_search_with_score and similarity_search methods.from qdrant_client.http import models as restquery = "What did the president say about Ketanji Brown Jackson"found_docs = qdrant.similarity_search_with_score(query, filter=rest.Filter(...))Maximum marginal relevance search (MMR)‚ÄãIf you'd like to look up for some similar documents, but you'd also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.query = "What did the president say about Ketanji Brown Jackson"found_docs = qdrant.max_marginal_relevance_search(query, k=2, fetch_k=10)for i, doc in
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. ->: The returned distance score is cosine distance. Therefore, a lower score is better.query = "What did the president say about Ketanji Brown Jackson"found_docs = qdrant.similarity_search_with_score(query)document, score = found_docs[0]print(document.page_content)print(f"\nScore: {score}")    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.         Tonight, 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.         One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And 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.        Score: 0.8153784913324512Metadata filtering‚ÄãQdrant has an extensive filtering system with rich type support. It is also possible to use the filters in Langchain, by passing an additional param to both the similarity_search_with_score and similarity_search methods.from qdrant_client.http import models as restquery = "What did the president say about Ketanji Brown Jackson"found_docs = qdrant.similarity_search_with_score(query, filter=rest.Filter(...))Maximum marginal relevance search (MMR)‚ÄãIf you'd like to look up for some similar documents, but you'd also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.query = "What did the president say about Ketanji Brown Jackson"found_docs = qdrant.max_marginal_relevance_search(query, k=2, fetch_k=10)for i, doc in