QdrantDB 混合搜索

代码

cookbook/agent_concepts/knowledge/vector_dbs/qdrant_db/qdrant_db_hybrid_search.py

from typing import Optional

import typer
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.search import SearchType
from agno.vectordb.qdrant import Qdrant
from rich.prompt import Prompt

COLLECTION_NAME = "thai-recipes"

vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333", search_type=SearchType.hybrid)

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


def qdrantdb_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__":
    # 首次运行时注释掉此行
    knowledge_base.load(recreate=True)

    typer.run(qdrantdb_agent)

用法

1. 创建虚拟环境

   打开 Terminal 并创建一个 python 虚拟环境。

   Mac

   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 qdrant-client fastembed tantivy pypdf openai agno
   
4. 运行 Agent

   Mac

   python cookbook/agent_concepts/knowledge/vector_dbs/qdrant_db/qdrant_db_hybrid_search.py