from langchain_community.vectorstores.pgvector import PGVector
import os
# try:
#     CONNECTION_STRING = PGVector.connection_string_from_db_params(
#     driver="psycopg2",
#     host=os.getenv("POSTGRES_HOST"),
#     port=int(os.getenv("POSTGRES_PORT", 5432)),
#     database=os.getenv("POSTGRES_DB"),
#     user=os.getenv("POSTGRES_USER"),
#     password=os.getenv("POSTGRES_PASSWORD"),
# )  
#     print("Successfully established the connection")
# except Exception as e:
#     print("Error in establishing the connection with DB: {e}")
print("Entered here")
from dotenv import load_dotenv
load_dotenv()
from langchain_google_genai import GoogleGenerativeAIEmbeddings
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
from sqlalchemy import create_engine
from langchain_postgres.vectorstores import PGVector

from langchain_core.documents import Document
document_1 = Document(page_content="fddsdfoo", metadata={"baz": "bar"})
document_2 = Document(page_content="thufeed", metadata={"bar": "baz"})
document_3 = Document(page_content="i wefsill be deleted :(")

documents = [document_1, document_2, document_3]
ids = ["1", "2", "3"]
engine = create_engine(os.environ['CONNECTION_STRING'])
vector_store = PGVector.from_documents(
            documents=documents,
            embedding=embeddings,
            connection=os.environ['CONNECTION_STRING'],
            collection_name="collection_name",
            use_jsonb=True,

            
        )
# vector_store.add_documents(documents=documents, ids=ids)


print("Stored babe")