Code
cookbook/11_models/lmstudio/knowledge.py
Usage
1
Set up your virtual environment
2
Install LM Studio
Install LM Studio from here and download the
model you want to use.
3
Install dependencies
4
Run PgVector
5
Run Agent
Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
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)
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 dependencies
uv pip install -U sqlalchemy pgvector pypdf agno
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
Run Agent
python cookbook/11_models/lmstudio/knowledge.py
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