PDF Agent with Storage

import typer
from typing import Optional, List
from phi.agent import Agent
from phi.storage.agent.postgres import PgAgentStorage
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid),
)
# Load the knowledge base: Comment out after first run
knowledge_base.load(upsert=True)

storage = PgAgentStorage(table_name="pdf_agent", db_url=db_url)


def pdf_agent(new: bool = False, user: str = "user"):
    session_id: Optional[str] = None

    if not new:
        existing_sessions: List[str] = storage.get_all_session_ids(user)
        if len(existing_sessions) > 0:
            session_id = existing_sessions[0]

    agent = Agent(
        session_id=session_id,
        user_id=user,
        knowledge=knowledge_base,
        storage=storage,
        # Show tool calls in the response
        show_tool_calls=True,
        # Enable the agent to read the chat history
        read_chat_history=True,
    )
    if session_id is None:
        session_id = agent.session_id
        print(f"Started Session: {session_id}\n")
    else:
        print(f"Continuing Session: {session_id}\n")

    # Runs the agent as a cli app
    agent.cli_app(markdown=True)


if __name__ == "__main__":
    typer.run(pdf_agent)