# Storage and Memory (/examples/models/google/gemini/storage-and-memory)



```python title="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 [#run-the-example]

<Steps>
    <Step title="Set up your virtual environment">
      <CodeBlockTabs defaultValue="Mac">
        <CodeBlockTabsList>
          <CodeBlockTabsTrigger value="Mac">
            Mac
          </CodeBlockTabsTrigger>

          <CodeBlockTabsTrigger value="Windows">
            Windows
          </CodeBlockTabsTrigger>
        </CodeBlockTabsList>

        <CodeBlockTab value="Mac">
          ```bash
          uv venv --python 3.12
          source .venv/bin/activate
          ```
        </CodeBlockTab>

        <CodeBlockTab value="Windows">
          ```bash
          uv venv --python 3.12
          .venv\Scripts\activate
          ```
        </CodeBlockTab>
      </CodeBlockTabs>
    </Step>

  <Step title="Install dependencies">
    ```bash
    uv pip install -U agno "psycopg[binary]" ddgs google-genai pgvector pypdf sqlalchemy
    ```
  </Step>

  <Step title="Export your API keys">
    <CodeBlockTabs defaultValue="Mac/Linux">
      <CodeBlockTabsList>
        <CodeBlockTabsTrigger value="Mac/Linux">
          Mac/Linux
        </CodeBlockTabsTrigger>

        <CodeBlockTabsTrigger value="Windows">
          Windows
        </CodeBlockTabsTrigger>
      </CodeBlockTabsList>

      <CodeBlockTab value="Mac/Linux">
        ```bash
        export GOOGLE_API_KEY="your_google_api_key_here"
        ```
      </CodeBlockTab>

      <CodeBlockTab value="Windows">
        ```bash
        $Env:GOOGLE_API_KEY="your_google_api_key_here"
        ```
      </CodeBlockTab>
    </CodeBlockTabs>
  </Step>

    <Step title="Run PgVector">
      <CodeBlockTabs defaultValue="macOS / Linux">
        <CodeBlockTabsList>
          <CodeBlockTabsTrigger value="macOS / Linux">
            macOS / Linux
          </CodeBlockTabsTrigger>

          <CodeBlockTabsTrigger value="Windows">
            Windows
          </CodeBlockTabsTrigger>
        </CodeBlockTabsList>

        <CodeBlockTab value="macOS / Linux">
          ```bash
          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
          ```
        </CodeBlockTab>

        <CodeBlockTab value="Windows">
          ```powershell
          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
          ```
        </CodeBlockTab>
      </CodeBlockTabs>
    </Step>

  <Step title="Run the example">
    Save the code above as `storage_and_memory.py`, then run:

    ```bash
    python storage_and_memory.py
    ```
  </Step>
</Steps>

Full source: [cookbook/90\_models/google/gemini/storage\_and\_memory.py](https://github.com/agno-agi/agno/blob/8f36eaf2d18e91afa7b327eec66a3cd3685dcb87/cookbook/90_models/google/gemini/storage_and_memory.py)