Storage

Agents use storage to persist sessions by storing them in a database.

Agents come with built-in memory, but it only lasts while the session is active. To continue conversations across sessions, we store agent sessions in a database like PostgreSQL.

The general syntax for adding storage to an Agent looks like:

from bitca.agent import Agent
from bitca.model.openai import OpenAIChat
from bitca.tools.duckduckgo import DuckDuckGo
from bitca.storage.agent.postgres import PgAgentStorage

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    storage=PgAgentStorage(table_name="agent_sessions", db_url="postgresql+psycopg://ai:ai@localhost:5532/ai"),
    tools=[DuckDuckGo()],
    show_tool_calls=True,
    add_history_to_messages=True,
)
agent.print_response("How many people live in Canada?")
agent.print_response("What is their national anthem called?")
agent.print_response("Which country are we speaking about?")

Example

1

Run Postgres

Install docker desktop and run Postgres on port 5532 using:

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  bitca/pgvector:16

2

Create an Agent with Storage

Create a file agent_with_storage.py with the following contents

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

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://bitca-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 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,
        # We can also automatically add the chat history to the messages sent to the model
        # But giving the model the chat history is not always useful, so we give it a tool instead
        # to only use when needed.
        # add_history_to_messages=True,
        # Number of historical responses to add to the messages.
        # num_history_responses=3,
    )
    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)

3

Run the agent

Install libraries

MacWindows

pip install -U bitca openai pgvector pypdf "psycopg[binary]" sqlalchemy

Run the agent

python agent_with_storage.py

Now the agent continues across sessions. Ask a question:

How do I make pad thai?

Then message bye to exit, start the app again and ask:

What was my last message?

4

Start a new run

Run the agent_with_storage.py file with the --new flag to start a new run.

python agent_with_storage.py --new

Params

Parameter	Type	Default	Description
storage	Optional[AgentStorage]	None	Storage mechanism for the agent, if applicable.