text: a vectorstoreâ€‹If 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 litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since sheâ€™s been nominated, sheâ€™s received a broad range of supportâ€”from 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, weâ€™ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n\nWeâ€™ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n\nWeâ€™re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWeâ€™re 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'}),     0.2404115088144465)Using a VectorStore as a Retrieverâ€‹retriever = store.as_retriever()print(retriever)    tags=None metadata=None vectorstore=<langchain.vectorstores.pgvector.PGVector object at 0x29f94f880> search_type='similarity'