Storage and Memory

Combine PgVector knowledge, Postgres memory, session summaries, and web search on Gemini.

storage_and_memory.py
"""Run `uv pip install ddgs sqlalchemy 'psycopg[binary]' pgvector pypdf openai google.genai` to install dependencies."""

from agno.agent import Agent
from agno.db.postgres.postgres import PostgresDb
from agno.knowledge.embedder.google import GeminiEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.google import Gemini
from agno.tools.websearch import WebSearchTools
from agno.vectordb.pgvector import PgVector

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        embedder=GeminiEmbedder(),
    ),
)
# Add content to the knowledge; reruns skip re-ingesting via the content hash
knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    skip_if_exists=True,
)

agent = Agent(
    model=Gemini(id="gemini-3.7-flash"),
    tools=[WebSearchTools()],
    knowledge=knowledge,
    # Store the memories and summary in a database
    db=PostgresDb(db_url=db_url, memory_table="agent_memory"),
    update_memory_on_run=True,
    enable_session_summaries=True,
    # This setting adds a tool to search the knowledge base for information
    search_knowledge=True,
    # This setting adds a tool to get chat history
    read_chat_history=True,
    # Add the previous chat history to the messages sent to the Model.
    add_history_to_context=True,
    # This setting adds 6 previous messages from chat history to the messages sent to the LLM
    num_history_runs=6,
    markdown=True,
)
agent.print_response("Whats is the latest AI news?")

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass

The example uses Gemini for both generation and embeddings. If you already indexed the recipes table with another embedding provider, use a fresh table or deliberately reindex before switching; skip_if_exists=True can reuse existing vectors. num_history_runs=6 includes six prior runs.

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno "psycopg[binary]" ddgs google-genai pgvector pypdf sqlalchemy

Export your API keys

export GOOGLE_API_KEY="your_google_api_key_here"

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18

Run the example

Save the code above as storage_and_memory.py, then run:

python storage_and_memory.py

Full source: cookbook/90_models/google/gemini/storage_and_memory.py