The following additional index options are supported:efConstruction: Defines the number of nearest neighbors considered during index construction. The default value is 32.efsearch: Defines the number of nearest neighbors considered during index search. The default value is 32.
For information about how you can configure these options to influence the HNSW algorithm, refer to Tuning the HNSW algorithm.Retrieving a vectorstore in PG‚Äãstore = PGEmbedding(    connection_string=connection_string,    embedding_function=embeddings,    collection_name=collection_name,)retriever = store.as_retriever()retriever    VectorStoreRetriever(vectorstore=<langchain.vectorstores.pghnsw.HNSWVectoreStore object at 0x121d3c8b0>, search_type='similarity', search_kwargs={})db1 = PGEmbedding.from_existing_index(    embedding=embeddings,    collection_name=collection_name,    pre_delete_collection=False,    connection_string=connection_string,)query = "What did the president say about Ketanji Brown Jackson"docs_with_score: List[Tuple[Document, float]] = db1.similarity_search_with_score(query)for doc, score in docs_with_score:    print("-" * 80)    print("Score: ", score)    print(doc.page_content)    print("-" * 80)PreviousOpenSearchNextPGVectorWorking with vectorstore in PostgresUploading a vectorstore in PGCreate HNSW IndexRetrieving a vectorstore in PGCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Postgres Embedding is an open-source vector similarity search for Postgres that uses  Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search.
Postgres Embedding is an open-source vector similarity search for Postgres that uses  Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. ->: The following additional index options are supported:efConstruction: Defines the number of nearest neighbors considered during index construction. The default value is 32.efsearch: Defines the number of nearest neighbors considered during index search. The default value is 32.
For information about how you can configure these options to influence the HNSW algorithm, refer to Tuning the HNSW algorithm.Retrieving a vectorstore in PG‚Äãstore = PGEmbedding(    connection_string=connection_string,    embedding_function=embeddings,    collection_name=collection_name,)retriever = store.as_retriever()retriever    VectorStoreRetriever(vectorstore=<langchain.vectorstores.pghnsw.HNSWVectoreStore object at 0x121d3c8b0>, search_type='similarity', search_kwargs={})db1 = PGEmbedding.from_existing_index(    embedding=embeddings,    collection_name=collection_name,    pre_delete_collection=False,    connection_string=connection_string,)query = "What did the president say about Ketanji Brown Jackson"docs_with_score: List[Tuple[Document, float]] = db1.similarity_search_with_score(query)for doc, score in docs_with_score:    print("-" * 80)    print("Score: ", score)    print(doc.page_content)    print("-" * 80)PreviousOpenSearchNextPGVectorWorking with vectorstore in PostgresUploading a vectorstore in PGCreate HNSW IndexRetrieving a vectorstore in PGCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.