Agent with Knowledge

Code

lmstudio_knowledge.py

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

Set up your virtual environment

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

uv venv --python 3.12
.venv\Scripts\activate

Install LM Studio

Install LM Studio, download and load qwen2.5-7b-instruct-1m, then start the local server on port 1234.

Install dependencies

uv pip install -U agno openai pgvector pypdf "psycopg[binary]" sqlalchemy

Run PgVector

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

Export the OpenAI API key

The knowledge base uses Agno’s default OpenAI embedder.

export OPENAI_API_KEY=your_openai_api_key_here

Save the example

Save the code above as lmstudio_knowledge.py.

Run Agent

python lmstudio_knowledge.py

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Agent with Knowledge

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