Mem0

Mem0 mengekstrak memori tahan lama dari percakapan agen dan mengambil memori yang relevan dalam proses selanjutnya. Gunakan cakupan pengguna, agen, atau aplikasi yang stabil saat memori harus tersedia di seluruh sesi.

Integrasi ini menggunakan pola memori: ini mengekstrak dan mengekstrak kembali informasi tahan lama yang dipilih daripada memutar ulang transkrip percakapan lengkap.

Important

Mem0 adalah sistem pihak ketiga. Tinjau penanganan data, retensi, batas wilayah, dan ketentuan layanannya sebelum mengirim data aplikasi.

Nota

Integrasi Mem0 saat ini tidak tersedia untuk .NET Agent Framework.

Pasang paketnya 

pip install agent-framework-mem0 --pre

Atur MEM0_API_KEY atau teruskan kunci API secara langsung. Menggunakan kembali hal yang sama user_id membuat memori tersedia di seluruh sesi.

async def main() -> None:
    """Example of memory usage with Mem0 context provider."""
    print("=== Mem0 Context Provider Example ===")
    # Each record in Mem0 should be associated with agent_id or user_id or application_id.
    # In this example, we associate Mem0 records with user_id.
    user_id = str(uuid.uuid4())
    # For Azure authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    # For Mem0 authentication, set Mem0 API key via "api_key" parameter or MEM0_API_KEY environment variable.
    async with (
        AzureCliCredential() as credential,
        Agent(
            client=FoundryChatClient(credential=credential),
            name="FriendlyAssistant",
            instructions="You are a friendly assistant.",
            tools=retrieve_company_report,
            context_providers=[Mem0ContextProvider(source_id="mem0", user_id=user_id, search_user_id=user_id)],
        ) as agent,
    ):
        # First ask the agent to retrieve a company report with no previous context.
        # The agent will not be able to invoke the tool, since it doesn't know
        # the company code or the report format, so it should ask for clarification.
        query = "Please retrieve my company report"
        print(f"User: {query}")
        result = await agent.run(query)
        print(f"Agent: {result}\n")
        # Now tell the agent the company code and the report format that you want to use
        # and it should be able to invoke the tool and return the report.
        query = "I always work with CNTS and I always want a detailed report format. Please remember and retrieve it."
        print(f"User: {query}")
        result = await agent.run(query)
        print(f"Agent: {result}\n")

        # Mem0 processes and indexes memories asynchronously.
        # Wait for memories to be indexed before querying in a new thread.
        # In production, consider implementing retry logic or using Mem0's
        # eventual consistency handling instead of a fixed delay.
        print("Waiting for memories to be processed...")
        await asyncio.sleep(15)  # Empirically determined delay for Mem0 indexing
        print("\nRequest within a new session:")
        # Create a new session for the agent.
        # The new session has no context of the previous conversation.
        session = agent.create_session()
        # Since we have the mem0 component in the session, the agent should be able to
        # retrieve the company report without asking for clarification, as it will
        # be able to remember the user preferences from Mem0 component.
        query = "Please retrieve my company report"
        print(f"User: {query}")
        result = await agent.run(query, session=session)
        print(f"Agent: {result}")

Mem0 memproses memori secara asinkron. Dalam produksi, gunakan penanganan konsistensi coba lagi atau sadar layanan alih-alih mengandalkan penundaan tetap.

Nota

Integrasi Mem0 saat ini tidak tersedia untuk Agent Framework Go. Lihat repositori Agent Framework Go untuk status terbaru.

Langkah berikutnya

Microsoft Foundry

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