And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. 
We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  
We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  
We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. 
We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
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Working with vectorstore in PG#
Uploading a vectorstore in PG#
data=docs
api_key=os.environ['OPENAI_API_KEY']
db = PGVector.from_documents(
    documents=docs,
    embedding=embeddings,
    collection_name=collection_name,
    connection_string=connection_string,
    distance_strategy=DistanceStrategy.COSINE,
    openai_api_key=api_key,
    pre_delete_collection=False 
)
Retrieving a vectorstore in PG#
connection_string = CONNECTION_STRING 
embedding=embeddings
collection_name="state_of_the_union"
from langchain.vectorstores.pgvector import DistanceStrategy
store = PGVector(
    connection_string=connection_string, 
    embedding_function=embedding, 
    collection_name=collection_name,
    distance_strategy=DistanceStrategy.COSINE
)
retriever = store.as_retriever()
print(retriever)
vectorstore=<langchain.vectorstores.pgvector.PGVector object at 0x7fe9a1b1c670> search_type='similarity' search_kwargs={}
# When we have an existing PG VEctor 
DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.EUCLIDEAN
db1 = PGVector.from_existing_index(
    embedding=embeddings,
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/vectorstores/examples/pgvector.html