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Code

cookbook/11_models/lmstudio/knowledge.py
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

1

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate
2

Install LM Studio

Install LM Studio from here and download the model you want to use.
3

Install dependencies

uv pip install -U sqlalchemy pgvector pypdf agno
4

Run PgVector

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
5

Run Agent

python cookbook/11_models/lmstudio/knowledge.py