​
代码

cookbook/agent_concepts/knowledge/vector_dbs/weaviate_db/weaviate_db_hybrid_search.py
import typer
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
from rich.prompt import Prompt

vector_db = Weaviate(
    collection="recipes",
    search_type=SearchType.hybrid,
    vector_index=VectorIndex.HNSW,
    distance=Distance.COSINE,
    local=False,  # 如果使用 Weaviate Cloud，设置为 True，如果使用本地实例，设置为 False
    hybrid_search_alpha=0.6, # 调整用于混合搜索的 alpha 值（0.0-1.0，默认为 0.5），其中 0 是纯关键词搜索，1 是纯向量搜索
)

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

def weaviate_agent(user: str = "user"):
    agent = Agent(
        user_id=user,
        knowledge=knowledge_base,
        search_knowledge=True,
    )

    while True:
        message = Prompt.ask(f"[bold] :sunglasses: {user} [/bold]")
        if message in ("exit", "bye"):
            break
        agent.print_response(message)


if __name__ == "__main__":
    # 首次运行后注释掉此行
    knowledge_base.load(recreate=True)

    typer.run(weaviate_agent)

​
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 weaviate-client tantivy pypdf openai agno
4

运行 Agent

python cookbook/agent_concepts/knowledge/vector_dbs/weaviate_db/weaviate_db_hybrid_search.py