​
代码

cookbook/reasoning/tools/knowledge_tools.py

from agno.agent import Agent
from agno.embedder.openai import OpenAIEmbedder
from agno.knowledge.url import UrlKnowledge
from agno.models.openai import OpenAIChat
from agno.tools.knowledge import KnowledgeTools
from agno.vectordb.lancedb import LanceDb, SearchType

# 创建一个包含来自 URL 的信息的知识库
agno_docs = UrlKnowledge(
    urls=["https://docs.agno.com/llms-full.txt"],
    # 使用 LanceDB 作为向量数据库，并将嵌入存储在 `agno_docs` 表中
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

knowledge_tools = KnowledgeTools(
    knowledge=agno_docs,
    think=True,
    search=True,
    analyze=True,
    add_few_shot=True,
)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[knowledge_tools],
    show_tool_calls=True,
    markdown=True,
)

if __name__ == "__main__":
    # 加载知识库，首次运行时可注释掉
    agno_docs.load(recreate=True)
    agent.print_response("How do I build multi-agent teams with Agno?", stream=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

设置您的 API 密钥

export OPENAI_API_KEY=xxx
3

安装库

pip install -U openai lancedb tantivy sqlalchemy agno
4

运行示例

python cookbook/reasoning/tools/knowledge_tools.py