mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Giles Odigwe
6f6ee61834
* Python: Fix broken samples and add missing READMEs - simple_context_provider: move instructions kwarg into options dict - suspend_resume_session: use OpenAIChatCompletionClient for in-memory demo - foundry_chat_client_with_hosted_mcp: move store kwarg into options dict - Add README.md for context_providers and conversations sample folders Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix additional sample issues in context_providers - mem0_basic: send preferences query before sleep so Mem0 can learn them, print result from new session recall - mem0_sessions: add session for multi-turn conversation in agent-scoped example, remove user_id from agent-scoped provider (Mem0 API stores memories without user_id when agent_id is provided), use single message for storing preferences - redis_basics: print retrieved context messages instead of raw object - redis_sessions: add missing load_dotenv() call - redis_basics/redis_sessions: fix docstrings referencing wrong client type - azure_redis_conversation: replace duplicate copyright with load_dotenv() Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix broken link in declarative README openai_responses_agent.py was renamed to openai_agent.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
85 lines
3.8 KiB
Python
85 lines
3.8 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import uuid
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from agent_framework import Agent, tool
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.mem0 import Mem0ContextProvider
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from azure.identity.aio import AzureCliCredential
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
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# see samples/02-agents/tools/function_tool_with_approval.py
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# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
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@tool(approval_mode="never_require")
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def retrieve_company_report(company_code: str, detailed: bool) -> str:
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if company_code != "CNTS":
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raise ValueError("Company code not found")
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if not detailed:
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return "CNTS is a company that specializes in technology."
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return (
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"CNTS is a company that specializes in technology. "
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"It had a revenue of $10 million in 2022. It has 100 employees."
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)
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async def main() -> None:
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"""Example of memory usage with Mem0 context provider."""
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print("=== Mem0 Context Provider Example ===")
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# Each record in Mem0 should be associated with agent_id or user_id or application_id.
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# In this example, we associate Mem0 records with user_id.
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user_id = str(uuid.uuid4())
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# For Azure authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
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# authentication option.
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# For Mem0 authentication, set Mem0 API key via "api_key" parameter or MEM0_API_KEY environment variable.
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async with (
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AzureCliCredential() as credential,
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Agent(
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client=FoundryChatClient(credential=credential),
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name="FriendlyAssistant",
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instructions="You are a friendly assistant.",
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tools=retrieve_company_report,
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context_providers=[Mem0ContextProvider(source_id="mem0", user_id=user_id)],
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) as agent,
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):
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# First ask the agent to retrieve a company report with no previous context.
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# The agent will not be able to invoke the tool, since it doesn't know
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# the company code or the report format, so it should ask for clarification.
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query = "Please retrieve my company report"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result}\n")
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# Now tell the agent the company code and the report format that you want to use
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# and it should be able to invoke the tool and return the report.
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query = "I always work with CNTS and I always want a detailed report format. Please remember and retrieve it."
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result}\n")
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# Mem0 processes and indexes memories asynchronously.
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# Wait for memories to be indexed before querying in a new thread.
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# In production, consider implementing retry logic or using Mem0's
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# eventual consistency handling instead of a fixed delay.
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print("Waiting for memories to be processed...")
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await asyncio.sleep(15) # Empirically determined delay for Mem0 indexing
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print("\nRequest within a new session:")
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# Create a new session for the agent.
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# The new session has no context of the previous conversation.
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session = agent.create_session()
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# Since we have the mem0 component in the session, the agent should be able to
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# retrieve the company report without asking for clarification, as it will
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# be able to remember the user preferences from Mem0 component.
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query = "Please retrieve my company report"
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print(f"User: {query}")
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result = await agent.run(query, session=session)
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print(f"Agent: {result}")
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if __name__ == "__main__":
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asyncio.run(main())
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