​
代码

cookbook/agent_concepts/vector_dbs/qdrant_db.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.qdrant import Qdrant

COLLECTION_NAME = "thai-recipes"

vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)

knowledge_base.load(recreate=False)  # 首次运行时注释掉此行

agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
agent.print_response("列出制作马萨曼咖喱鸡的食材", 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

启动 Qdrant

docker run -p 6333:6333 -p 6334:6334 \
  -v $(pwd)/qdrant_storage:/qdrant/storage:z \
  qdrant/qdrant
3

安装库

pip install -U qdrant-client pypdf openai agno
4

运行 Agent

python cookbook/agent_concepts/vector_dbs/qdrant_db.py