not get enough documents to filter from.results = db.similarity_search("foo", filter=dict(page=1), k=1, fetch_k=4)for doc in results:    print(f"Content: {doc.page_content}, Metadata: {doc.metadata}")    Content: foo, Metadata: {'page': 1}Delete‚ÄãYou can also delete ids. Note that the ids to delete should be the ids in the docstore.db.delete([db.index_to_docstore_id[0]])    True# Is now missing0 in db.index_to_docstore_id    FalsePreviousEpsillaNextHologresSimilarity Search with scoreSaving and loadingMergingSimilarity Search with filteringDeleteCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.
Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. ->: not get enough documents to filter from.results = db.similarity_search("foo", filter=dict(page=1), k=1, fetch_k=4)for doc in results:    print(f"Content: {doc.page_content}, Metadata: {doc.metadata}")    Content: foo, Metadata: {'page': 1}Delete‚ÄãYou can also delete ids. Note that the ids to delete should be the ids in the docstore.db.delete([db.index_to_docstore_id[0]])    True# Is now missing0 in db.index_to_docstore_id    FalsePreviousEpsillaNextHologresSimilarity Search with scoreSaving and loadingMergingSimilarity Search with filteringDeleteCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.