> This is a page from the ElevenLabs documentation. For a complete page index, fetch https://elevenlabs.io/docs/llms.txt. For the full documentation in a single file, fetch https://elevenlabs.io/docs/llms-full.txt. # 模拟对话 > **Warning** > > 本指南及其使用的端点已弃用。请改用 > [Simulation](/docs/zh/eleven-agents/customization/agent-testing#simulation-testing) 智能体测试类型。 ## 概述 ElevenLabs Agents API 支持你模拟和评估与 AI 智能体进行的文本对话。本指南将介绍如何使用模拟对话端点（[批量](/docs/zh/api-reference/agents/simulate-conversation) 和 [流式](/docs/zh/api-reference/agents/simulate-conversation-stream)）实现端到端模拟测试 workflow，帮助你精细测试并改进智能体表现，确保其达到预期的交互目标。 ## 前提条件 * 已在 ElevenLabs Agents 中配置智能体（[在此创建](/docs/zh/eleven-agents/quickstart)） * ElevenLabs API 密钥，可在[控制台中创建](https://elevenlabs.io/app/settings/api-keys) ## 实现模拟测试 Workflow #### 确定初始评估参数 查看智能体的对话历史，找出表现不佳的案例。基于这些对话，为将与你的智能体互动的模拟用户创建不同提示词。此外，为特定模拟用户定义智能体配置中尚未指定的其他评估标准，以测试你期望的结果。 #### 通过 SDK 模拟对话 使用 ElevenLabs SDK 向模拟端点创建请求。 **`Python`** ```python title="Python" from dotenv import load_dotenv from elevenlabs import ( ElevenLabs, ConversationSimulationSpecification, AgentConfig, PromptAgent, PromptEvaluationCriteria ) load_dotenv() api_key = os.getenv("ELEVENLABS_API_KEY") elevenlabs = ElevenLabs(api_key=api_key) response = elevenlabs.conversational_ai.agents.simulate_conversation( agent_id="YOUR_AGENT_ID", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfig( prompt=PromptAgent( prompt="Your goal is to be a really difficult user.", llm="gpt-4o", temperature=0.5 ) ) ), extra_evaluation_criteria=[ PromptEvaluationCriteria( id="politeness_check", name="Politeness Check", conversation_goal_prompt="The agent was polite.", use_knowledge_base=False ) ] ) print(response) ``` **`TypeScript`** ```typescript title="TypeScript" import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js"; import dotenv from "dotenv"; dotenv.config(); const apiKey = process.env.ELEVENLABS_API_KEY; const elevenlabs = new ElevenLabsClient({ apiKey: apiKey, }); const response = await elevenlabs.conversationalAi.agents.simulateConversation("YOUR_AGENT_ID", { simulationSpecification: { simulatedUserConfig: { prompt: { prompt: "Your goal is to be a really difficult user.", llm: "gpt-4o", temperature: 0.5, }, }, }, extraEvaluationCriteria: [ { id: "politeness_check", name: "Politeness Check", conversationGoalPrompt: "The agent was polite.", useKnowledgeBase: false, }, ], }); console.log(JSON.stringify(response, null, 4)); ``` > **Note** > > 这是一个基础示例。如需完整的输入参数列表，请参阅 [模拟对话](/docs/zh/api-reference/agents/simulate-conversation) 和 > [流式模拟对话](/docs/zh/api-reference/agents/simulate-conversation-stream) 端点的 API > 参考文档。 #### 分析响应 SDK 提供一个完整的 JSON 对象，其中包含完整对话记录和详细分析。 **模拟对话**：记录模拟用户与智能体之间的每轮互动，包括消息和工具使用情况。 **`示例对话历史`** ```json title="示例对话历史" [ ... { "role": "user", "message": "Maybe a little. I'll think about it, but I'm still not convinced it's the right move.", "tool_calls": [], "tool_results": [], "feedback": null, "llm_override": null, "time_in_call_secs": 0, "conversation_turn_metrics": null, "rag_retrieval_info": null, "llm_usage": null }, { "role": "agent", "message": "I understand. If you want to explore more at your own pace, I can direct you to our documentation, which has guides and API references. Would you like me to send you a link?", "tool_calls": [], "tool_results": [], "feedback": null, "llm_override": null, "time_in_call_secs": 0, "conversation_turn_metrics": null, "rag_retrieval_info": null, "llm_usage": null }, { "role": "user", "message": "I guess it wouldn't hurt to take a look. Go ahead and send it over.", "tool_calls": [], "tool_results": [], "feedback": null, "llm_override": null, "time_in_call_secs": 0, "conversation_turn_metrics": null, "rag_retrieval_info": null, "llm_usage": null }, { "role": "agent", "message": null, "tool_calls": [ { "type": "client", "request_id": "redirectToDocs_421d21e4b4354ed9ac827d7600a2d59c", "tool_name": "redirectToDocs", "params_as_json": "{\"path\": \"/docs/api-reference/introduction\"}", "tool_has_been_called": false, "tool_details": null } ], "tool_results": [], "feedback": null, "llm_override": null, "time_in_call_secs": 0, "conversation_turn_metrics": null, "rag_retrieval_info": null, "llm_usage": null }, { "role": "agent", "message": null, "tool_calls": [], "tool_results": [ { "type": "client", "request_id": "redirectToDocs_421d21e4b4354ed9ac827d7600a2d59c", "tool_name": "redirectToDocs", "result_value": "Tool Called.", "is_error": false, "tool_has_been_called": true, "tool_latency_secs": 0 } ], "feedback": null, "llm_override": null, "time_in_call_secs": 0, "conversation_turn_metrics": null, "rag_retrieval_info": null, "llm_usage": null }, { "role": "agent", "message": "Okay, I've sent you a link to the introduction to our API reference. It provides a good starting point for understanding our different tools and how they can be integrated. Let me know if you have any questions as you explore it.\n", "tool_calls": [], "tool_results": [], "feedback": null, "llm_override": null, "time_in_call_secs": 0, "conversation_turn_metrics": null, "rag_retrieval_info": null, "llm_usage": null } ... ] ``` **分析**：提供评估标准结果、数据收集指标以及对话记录摘要等洞察。 **`示例分析`** ```json title="示例分析" { "analysis": { "evaluation_criteria_results": { "politeness_check": { "criteria_id": "politeness_check", "result": "success", "rationale": "The agent remained polite and helpful despite the user's challenging attitude." }, "understood_root_cause": { "criteria_id": "understood_root_cause", "result": "success", "rationale": "The agent acknowledged the user's hesitation and provided relevant information." }, "positive_interaction": { "criteria_id": "positive_interaction", "result": "success", "rationale": "The user eventually asked for the documentation link, indicating engagement." } }, "data_collection_results": { "issue_type": { "data_collection_id": "issue_type", "value": "support_issue", "rationale": "The user asked for help with integrating ElevenLabs tools." }, "user_intent": { "data_collection_id": "user_intent", "value": "The user is interested in integrating ElevenLabs tools into a project." } }, "call_successful": "success", "transcript_summary": "The user expressed skepticism, but the agent provided useful information and a link to the API documentation." } } ``` #### 改进评估标准 全面审查模拟对话，评估评估标准的有效性。找出这些标准在评估智能体表现时可能存在的缺口或不足之处。相应地优化和调整评估标准，确保其符合预期结果，并能准确衡量智能体能力。 #### 改进智能体 确认评估标准准确无误后，利用模拟对话中获得的经验提升智能体能力。可以优化系统提示词，更好地引导智能体响应，确保其符合你的目标和用户期望。此外，还可探索其他可优化的功能或配置，例如调整智能体语气、提升其处理特定问题的能力，或整合额外数据源来丰富其响应。系统地应用这些经验，可打造更强大、更有效的对话智能体，提供更出色的用户体验。 #### 持续迭代 完成初始测试和改进周期后，建立一套全面的测试套件，可有效覆盖广泛的可能场景。该套件可通过不同的模拟用户提示词和初始条件，探索多种模拟对话。持续迭代和优化方法，确保智能体始终有效，并能响应不断变化的用户需求。 ## 专业提示 #### 详细的提示词和标准 编写详细、丰富的模拟用户提示词和评估标准，可提升模拟测试的效果。提供的上下文和细节越充分，智能体就越能理解并响应复杂互动。 #### 模拟工具配置 使用模拟工具配置来测试智能体的决策过程。这样可以观察智能体如何决定发起工具调用，以及如何应对不同的工具调用结果。更多详情，请参阅 [API 参考文档](/docs/zh/api-reference/agents/simulate-conversation#request.body.simulation_specification.tool_mock_config)中的 tool_mock_config 输入参数。 #### 部分对话历史 使用部分对话历史，评估智能体如何处理从特定节点开始的互动。这对于评估智能体处理以下对话的能力尤其有用：用户已通过特定方式提出问题，或某些工具调用已成功或失败。更多详情，请参阅 [API 参考文档](/docs/zh/api-reference/agents/simulate-conversation#request.body.simulation_specification.partial_conversation_history)中的 partial_conversation_history 输入参数。