from langchain_core.documents import Document
from langchain_core.embeddings import DeterministicFakeEmbedding
from langchain_postgres import PGEngine, PGVectorStore

# Replace the connection string with your own Postgres connection string
CONNECTION_STRING = "postgresql+psycopg3://langchain:langchain@localhost:6024/langchain"
engine = PGEngine.from_connection_string(url=CONNECTION_STRING)

# Replace the vector size with your own vector size
VECTOR_SIZE = 768
embedding = DeterministicFakeEmbedding(size=VECTOR_SIZE)

TABLE_NAME = "my_doc_collection"

engine.init_vectorstore_table(
    table_name=TABLE_NAME,
    vector_size=VECTOR_SIZE,
)

store = PGVectorStore.create_sync(
    engine=engine,
    table_name=TABLE_NAME,
    embedding_service=embedding,
)

docs = [
    Document(page_content="Apples and oranges"),
    Document(page_content="Cars and airplanes"),
    Document(page_content="Train")
]

store.add_documents(docs)

query = "I'd like a fruit."
docs = store.similarity_search(query)
print(docs)

import uuid

from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
from langchain_postgres import PostgresChatMessageHistory
import psycopg

# Establish a synchronous connection to the database
# (or use psycopg.AsyncConnection for async)
conn_info = ... # Fill in with your connection info
sync_connection = psycopg.connect(conn_info)

# Create the table schema (only needs to be done once)
table_name = "chat_history"
PostgresChatMessageHistory.create_tables(sync_connection, table_name)

session_id = str(uuid.uuid4())

# Initialize the chat history manager
chat_history = PostgresChatMessageHistory(
    table_name,
    session_id,
    sync_connection=sync_connection
)

# Add messages to the chat history
chat_history.add_messages([
    SystemMessage(content="Meow"),
    AIMessage(content="woof"),
    HumanMessage(content="bark"),
])

print(chat_history.messages)