> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent with Knowledge

> Give an LMStudio agent a PgVector knowledge base loaded from a Thai recipes PDF and answer questions with markdown output.

## Code

```python lmstudio_knowledge.py theme={null}
from agno.agent import Agent
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 = 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=LMStudio(id="qwen2.5-7b-instruct-1m"), knowledge=knowledge)
agent.print_response("How to make Thai curry?", markdown=True)
```

## Usage

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

**1. Install LM Studio**

[Install LM Studio](https://lmstudio.ai/download), download and load `qwen2.5-7b-instruct-1m`, then start the local server on port `1234`.

**2. Install dependencies**

```bash theme={null}
uv pip install -U agno openai pgvector pypdf "psycopg[binary]" sqlalchemy
```

**3. 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 \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18
```

**4. Export the OpenAI API key**

The knowledge base uses Agno's default OpenAI embedder.

```bash theme={null}
export OPENAI_API_KEY=your_openai_api_key_here
```

**5. Save the example**

Save the code above as `lmstudio_knowledge.py`.

**6. Run Agent**

```bash theme={null}
python lmstudio_knowledge.py
```


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