Code
knowledge.py
Usage
1
Set up your virtual environment
2
Install Ollama
Follow the Ollama installation guide and run:
3
Install dependencies
4
Run Agent
Save the code above as
knowledge.py, then run:Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Load a PDF of Thai recipes into a PgVector knowledge base with OllamaEmbedder and query it through an Ollama llama3.2 agent.
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)
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
Install Ollama
ollama pull llama3.2
Install dependencies
uv pip install -U agno sqlalchemy psycopg pgvector pypdf openai ollama
Run Agent
knowledge.py, then run:python knowledge.py
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