= {i: str(uuid.uuid4()) for i in range(len(documents))}docstore = InMemoryDocstore(    {index_to_docstore_id[i]: doc for i, doc in enumerate(documents)})db_manually = Annoy(    embeddings_func.embed_query, index, metric, docstore, index_to_docstore_id)db_manually.similarity_search_with_score("eating!", k=3)    [(Document(page_content='pizza is great', metadata={'x': 'food'}),      1.1314140558242798),     (Document(page_content='I love salad', metadata={'x': 'food'}),      1.1668788194656372),     (Document(page_content='my car', metadata={'x': 'stuff'}), 1.226445198059082)]PreviousAnalyticDBNextAtlasCreate VectorStore from textsCreate VectorStore from docsCreate VectorStore via existing embeddingsSearch via embeddingsSearch via docstore idSave and loadConstruct from scratchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
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. ->: = {i: str(uuid.uuid4()) for i in range(len(documents))}docstore = InMemoryDocstore(    {index_to_docstore_id[i]: doc for i, doc in enumerate(documents)})db_manually = Annoy(    embeddings_func.embed_query, index, metric, docstore, index_to_docstore_id)db_manually.similarity_search_with_score("eating!", k=3)    [(Document(page_content='pizza is great', metadata={'x': 'food'}),      1.1314140558242798),     (Document(page_content='I love salad', metadata={'x': 'food'}),      1.1668788194656372),     (Document(page_content='my car', metadata={'x': 'stuff'}), 1.226445198059082)]PreviousAnalyticDBNextAtlasCreate VectorStore from textsCreate VectorStore from docsCreate VectorStore via existing embeddingsSearch via embeddingsSearch via docstore idSave and loadConstruct from scratchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.