Azure Cosmos DB MongoDB vCore 集成

代码

cookbook/agent_concepts/knowledge/vector_dbs/mongo_db/cosmos_mongodb_vcore.py

import urllib.parse
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.mongodb import MongoDb

# Azure Cosmos DB MongoDB connection string
"""
Example connection strings:
"mongodb+srv://<username>:<encoded_password>@cluster0.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"
"""
mdb_connection_string = f"mongodb+srv://<username>:<encoded_password>@cluster0.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=MongoDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        search_index_name="recipes",
        cosmos_compatibility=True,
    ),
)

# Comment out after first run
knowledge_base.load(recreate=True)

# Create and use the agent
agent = Agent(knowledge=knowledge_base, 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。

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. 安装库

   pip install -U pymongo pypdf openai agno
   
3. 运行 Agent

   Mac

   python cookbook/agent_concepts/knowledge/vector_dbs/mongo_db/cosmos_mongodb_vcore.py