> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-agui.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent with Knowledge

## Code

```python cookbook/11_models/lmstudio/knowledge.py theme={null}
from agno.agent import Agent
from agno.knowledge.embedder.ollama import OllamaEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.lmstudio import LMStudio
from agno.vectordb.pgvector import PgVector

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

knowledge_base = Knowledge(
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        embedder=OllamaEmbedder(id="llama3.2", dimensions=3072),
    ),
)
knowledge_base.insert(
  url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(
    model=LMStudio(id="qwen2.5-7b-instruct-1m"),
    knowledge=knowledge_base,
    )
agent.print_response("How to make Thai curry?", markdown=True)
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install LM Studio">
    Install LM Studio from [here](https://lmstudio.ai/download) and download the
    model you want to use.
  </Step>

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U sqlalchemy pgvector pypdf agno
    ```
  </Step>

  <Step title="Run PgVector">
    ```bash theme={null}
    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 \
      agnohq/pgvector:16
    ```
  </Step>

  <Step title="Run Agent">
    ```bash theme={null}
    python cookbook/11_models/lmstudio/knowledge.py
    ```
  </Step>
</Steps>