​
代码

cookbook/models/lmstudio/knowledge.py
from agno.agent import Agent
from agno.embedder.ollama import OllamaEmbedder
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.models.lmstudio import LMStudio
from agno.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        embedder=OllamaEmbedder(id="llama3.2", dimensions=3072),
    ),
)
knowledge_base.load(recreate=True)  # 首次运行后注释掉此行

agent = Agent(
    model=LMStudio(id="qwen2.5-7b-instruct-1m"),
    knowledge=knowledge_base,
    show_tool_calls=True,
)
agent.print_response("How to make Thai curry?", markdown=True)

​
用法

1

创建虚拟环境

打开 Terminal 并创建一个 python 虚拟环境。

python3 -m venv .venv
source .venv/bin/activate
2

安装 LM Studio

从这里安装 LM Studio 并下载您想使用的模型。

3

安装库

pip install -U sqlalchemy pgvector pypdf agno
4

运行 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

运行代理

python cookbook/models/lmstudio/knowledge.py