设置
请按照 Azure Cosmos DB 设置指南 中的说明获取连接字符串。 安装 MongoDB 包:示例
agent_with_knowledge.py
MongoDB 参数
collection_name: 数据库中集合的名称。db_url: MongoDB 数据库的连接字符串。search_index_name: 要使用的搜索索引的名称。cosmos_compatibility: 设置为True以兼容 Azure Cosmos DB。
Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
pip install "pymongo[srv]"
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 连接字符串
"""
示例连接字符串:
"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,
),
)
# 首次运行后注释掉
knowledge_base.load(recreate=True)
# 创建并使用代理
agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
agent.print_response("How to make Thai curry?", markdown=True)
collection_name: 数据库中集合的名称。db_url: MongoDB 数据库的连接字符串。search_index_name: 要使用的搜索索引的名称。cosmos_compatibility: 设置为 True 以兼容 Azure Cosmos DB。