assistance. Humanitarian assistance. \n\nWe are giving more than $1 Billion in direct assistance to Ukraine. \n\nAnd we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering.  \n\nLet me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine.  \n\nOur forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies ‚Äì in the event that Putin decides to keep moving west.', metadata={'source': '../../../state_of_the_union.txt'})]vector_store_from_docs = Annoy.from_documents(docs, embeddings_func)query = "What did the president say about Ketanji Brown Jackson"docs = vector_store_from_docs.similarity_search(query)print(docs[0].page_content[:100])    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights AcCreate VectorStore via existing embeddings‚Äãembs = embeddings_func.embed_documents(texts)data = list(zip(texts, embs))vector_store_from_embeddings = Annoy.from_embeddings(data, embeddings_func)vector_store_from_embeddings.similarity_search_with_score("food", k=3)    [(Document(page_content='pizza is great', metadata={}), 1.0944390296936035),     (Document(page_content='I love salad', metadata={}), 1.1273186206817627),     (Document(page_content='my car', metadata={}), 1.1580758094787598)]Search via embeddings‚Äãmotorbike_emb = embeddings_func.embed_query("motorbike")vector_store.similarity_search_by_vector(motorbike_emb, k=3)    [Document(page_content='my car', metadata={}),     Document(page_content='a dog', metadata={}),     Document(page_content='pizza is great', metadata={})]vector_store.similarity_search_with_score_by_vector(motorbike_emb, k=3)    [(Document(page_content='my car', metadata={}), 1.0870471000671387),     (Document(page_content='a dog', metadata={}), 1.2095637321472168),     (Document(page_content='pizza is great', metadata={}), 1.3254905939102173)]Search via docstore
Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.
Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data. ->: assistance. Humanitarian assistance. \n\nWe are giving more than $1 Billion in direct assistance to Ukraine. \n\nAnd we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering.  \n\nLet me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine.  \n\nOur forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies ‚Äì in the event that Putin decides to keep moving west.', metadata={'source': '../../../state_of_the_union.txt'})]vector_store_from_docs = Annoy.from_documents(docs, embeddings_func)query = "What did the president say about Ketanji Brown Jackson"docs = vector_store_from_docs.similarity_search(query)print(docs[0].page_content[:100])    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights AcCreate VectorStore via existing embeddings‚Äãembs = embeddings_func.embed_documents(texts)data = list(zip(texts, embs))vector_store_from_embeddings = Annoy.from_embeddings(data, embeddings_func)vector_store_from_embeddings.similarity_search_with_score("food", k=3)    [(Document(page_content='pizza is great', metadata={}), 1.0944390296936035),     (Document(page_content='I love salad', metadata={}), 1.1273186206817627),     (Document(page_content='my car', metadata={}), 1.1580758094787598)]Search via embeddings‚Äãmotorbike_emb = embeddings_func.embed_query("motorbike")vector_store.similarity_search_by_vector(motorbike_emb, k=3)    [Document(page_content='my car', metadata={}),     Document(page_content='a dog', metadata={}),     Document(page_content='pizza is great', metadata={})]vector_store.similarity_search_with_score_by_vector(motorbike_emb, k=3)    [(Document(page_content='my car', metadata={}), 1.0870471000671387),     (Document(page_content='a dog', metadata={}), 1.2095637321472168),     (Document(page_content='pizza is great', metadata={}), 1.3254905939102173)]Search via docstore