> ## 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.

# Knowledge

> Load a PDF of Thai recipes into a PgVector knowledge base with OllamaEmbedder and query it through an Ollama llama3.2 agent.

## Code

```python 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.ollama import Ollama
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,
        embedder=OllamaEmbedder(id="llama3.2", dimensions=3072),
    ),
)
# Add content to the knowledge
knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(model=Ollama(id="llama3.2"), knowledge=knowledge)
agent.print_response("How to make Thai curry?", markdown=True)

```

## Usage

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

**1. Install Ollama**

Follow the [Ollama installation guide](https://github.com/ollama/ollama?tab=readme-ov-file#macos) and run:

```bash theme={null}
ollama pull llama3.2
```

**2. Install dependencies**

```bash theme={null}
uv pip install -U agno sqlalchemy psycopg pgvector pypdf openai ollama
```

**3. Run Agent**

Save the code above as `knowledge.py`, then run:

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


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