​
代码

cookbook/agent_concepts/vector_dbs/weaviate_db.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.search import SearchType
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate

vector_db = Weaviate(
    collection="recipes",
    search_type=SearchType.hybrid,
    vector_index=VectorIndex.HNSW,
    distance=Distance.COSINE,
    local=True,  # 如果使用 Weaviate Cloud 则设置为 False，如果使用本地实例则设置为 True
)
# 创建知识库
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)
knowledge_base.load(recreate=False)  # 首次运行后注释掉此行

# 创建并使用 Agent
agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
    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

安装库

pip install -U weaviate-client pypdf openai agno
3

运行 Agent

python cookbook/agent_concepts/vector_dbs/weaviate_db.py