In order to do that, we can initialize it directly.store = PGVector(    collection_name=COLLECTION_NAME,    connection_string=CONNECTION_STRING,    embedding_function=embeddings,)Add documents‚ÄãWe can add documents to the existing vectorstore.store.add_documents([Document(page_content="foo")])    ['048c2e14-1cf3-11ee-8777-e65801318980']docs_with_score = db.similarity_search_with_score("foo")docs_with_score[0]    (Document(page_content='foo', metadata={}), 3.3203430005457335e-09)docs_with_score[1]    (Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she‚Äôs been nominated, she‚Äôs received a broad range of support‚Äîfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}),     0.2404395365581814)Overriding a vectorstore‚ÄãIf you have an existing collection, you override it by doing from_documents and setting pre_delete_collection = Truedb = PGVector.from_documents(    documents=docs,    embedding=embeddings,    collection_name=COLLECTION_NAME,    connection_string=CONNECTION_STRING,    pre_delete_collection=True,)docs_with_score = db.similarity_search_with_score("foo")docs_with_score[0]    (Document(page_content='A former top
PGVector is an open-source vector similarity search for Postgres
PGVector is an open-source vector similarity search for Postgres ->: In order to do that, we can initialize it directly.store = PGVector(    collection_name=COLLECTION_NAME,    connection_string=CONNECTION_STRING,    embedding_function=embeddings,)Add documents‚ÄãWe can add documents to the existing vectorstore.store.add_documents([Document(page_content="foo")])    ['048c2e14-1cf3-11ee-8777-e65801318980']docs_with_score = db.similarity_search_with_score("foo")docs_with_score[0]    (Document(page_content='foo', metadata={}), 3.3203430005457335e-09)docs_with_score[1]    (Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she‚Äôs been nominated, she‚Äôs received a broad range of support‚Äîfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}),     0.2404395365581814)Overriding a vectorstore‚ÄãIf you have an existing collection, you override it by doing from_documents and setting pre_delete_collection = Truedb = PGVector.from_documents(    documents=docs,    embedding=embeddings,    collection_name=COLLECTION_NAME,    connection_string=CONNECTION_STRING,    pre_delete_collection=True,)docs_with_score = db.similarity_search_with_score("foo")docs_with_score[0]    (Document(page_content='A former top