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L. Elaine Dazzio
1e527a328c
* Python: Remove unsupported memory scoping params from samples and docs Fixes #4353 The `Mem0ContextProvider` and `RedisContextProvider` no longer support `thread_id` or `scope_to_per_operation_thread_id` parameters. This commit updates the affected samples and READMEs to use only the currently supported API (`user_id`, `agent_id`, `application_id`). Changes: - mem0_sessions.py: Remove `thread_id` and `scope_to_per_operation_thread_id` from examples 1 and 2, rewrite to demonstrate user-scoped and agent-scoped memory patterns - redis_sessions.py: Update module docstring to remove references to removed thread scoping params - mem0/README.md: Update Memory Scoping docs to reflect current API - redis/README.md: Remove `thread_id` and `scope_to_per_operation_thread_id` references from docs * Address Copilot review: rename thread_scope functions, fix docstring - Rename `example_global_thread_scope` -> `example_global_memory_scope` - Rename `example_per_operation_thread_scope` -> `example_agent_scoped_memory` - Update example 2 docstring to mention `application_id` alongside `user_id` and `agent_id` since it's set in the provider config - Update module docstring scenario 2 to include `application_id` * fix: rebase onto main, address giles17 review feedback - Resolve merge conflicts by rebasing all 4 original files onto current main - Address giles17's agent review suggestions: - mem0_basic.py: update comment to remove thread_id from scoping list - mem0_oss.py: update comment to remove thread_id from scoping list - redis_sessions.py: rename Example 2 from "Agent-Scoped Memory" to "Hybrid Vector Search" to accurately describe what it demonstrates - redis/README.md: update Example 2 description to match renamed example --------- Co-authored-by: Tao Chen <taochen@microsoft.com> Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com>
75 lines
3.4 KiB
Python
75 lines
3.4 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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from mem0 import AsyncMemory
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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 local Mem0 OSS 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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# By default, local Mem0 authenticates to your OpenAI using the OPENAI_API_KEY environment variable.
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# See the Mem0 documentation for other LLM providers and authentication options.
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local_mem0_client = AsyncMemory()
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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, mem0_client=local_mem0_client)],
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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("\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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result = await agent.run(query, session=session)
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if __name__ == "__main__":
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asyncio.run(main())
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