# Agent with Knowledge (/models/providers/gateways/litellm/usage/knowledge)



## Code [#code]

```python knowledge.py
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.litellm import LiteLLM
from agno.vectordb.pgvector import PgVector

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

knowledge = Knowledge(
    vector_db=PgVector(table_name="recipes", db_url=db_url),
)
# Add content to the knowledge
knowledge.insert(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")

agent = Agent(model=LiteLLM(id="gpt-4o"), knowledge=knowledge)
agent.print_response("How to make Thai curry?", markdown=True)
```

## Usage [#usage]

<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="Set your API key">
    ```bash
    export LITELLM_API_KEY=xxx
    export OPENAI_API_KEY=xxx
    ```
  </Step>

  <Step title="Install dependencies">
    ```bash
    uv pip install -U "litellm>=1.83.0" openai agno sqlalchemy "psycopg[binary]" pgvector pypdf
    ```
  </Step>

  <Step title="Run PgVector">
    ```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
    ```
  </Step>

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

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