大模型服务平台百炼：上下文缓存（Context Cache）

第二轮对话响应示例（缓存命中）

在第二轮对话的返回结果中，usage.input_tokens_details.cached_tokens 字段表示命中缓存的 Token 数。

{
    "id": "145584fd-3dce-4890-99dc-e3896d7f5a42",
    "created_at": 1772440976.0,
    "error": null,
    "incomplete_details": null,
    "instructions": null,
    "metadata": null,
    "model": "qwen3.5-plus",
    "object": "response",
    "output": [
        {
            "id": "msg_62a4e323-d78c-46c7-8469-2ad50f8af4b1",
            "summary": [
                {
                    "text": "Thinking Process:\n\n1.  **Analyze the Request:**\n    *   User asks for the main differences between Random Forest (RF) and GBDT (Gradient Boosting Decision Tree).\n    *   Context: The previous turn provided a definition of AI (repeated many times) and then a brief introduction to Random Forest.\n    *   Constraint: Keep it concise (implied by \"简短介绍\" in the first turn, though this turn asks for \"主要区别\" - main differences). The tone should be informative and professional.\n    *   Current Date: Monday, March 02, 2026.\n    *   Knowledge Cutoff: 2026.\n\n2.  **Recall Knowledge about RF vs. GBDT:**\n    *   **Bagging vs. Boosting:** RF is Bagging (Parallel), GBDT is Boosting (Sequential).\n    *   **Bias/Variance:** RF reduces variance, GBDT reduces bias.\n    *   **Sample/Feature Selection:** RF uses bootstrap sampling (row sampling) and feature subsampling. GBDT usually uses all samples (weighted) and often all features (though variants like XGBoost/LightGBM introduce feature sampling).\n    *   **Tree Structure:** RF trees are usually deep and independent. GBDT trees are usually shallow (weak learners) and dependent (each corrects the previous).\n    *   **Sensitivity to Outliers:** GBDT is more sensitive.\n    *   **Parallelization:** RF is easily parallelized. GBDT is inherently sequential (though tree construction within a step can be parallelized).\n\n3.  **Draft the Response:**\n    *   Structure: Core difference (Bagging vs. Boosting), Training method, Performance characteristics, Sensitivity.\n    *   Language: Chinese.\n    *   Tone: Professional, clear.\n\n4.  **Refine the Content (Self-Correction/Optimization):**\n    *   Make sure to highlight the *ensemble strategy* (Bagging vs. Boosting) as the fundamental difference.\n    *   Mention parallel vs. sequential training.\n    *   Mention bias/variance trade-off.\n    *   Keep it brief but comprehensive enough for a \"main differences\" question.\n\n5.  **Final Polish (incorporating constraints):**\n    *   Ensure no hallucination.\n    *   Ensure alignment with the 2026 knowledge cutoff (nothing has changed fundamentally in RF vs. GBDT theory).\n    *   Check against the repeated text in the first prompt (ignore the repetition, focus on the actual question).\n\n6.  **Drafting the response (internal monologue):**\n    *   Start with the ensemble type: RF = Bagging, GBDT = Boosting.\n    *   Training: RF = Parallel, independent trees. GBDT = Sequential, dependent trees (fitting residuals).\n    *   Goal: RF = Reduce variance. GBDT = Reduce bias.\n    *   Sensitivity: GBDT more sensitive to outliers.\n    *   Summary table or bullet points for clarity.\n\n7.  **Final Output Generation:** (Matches the drafted points).\n\n    *   **集成方式**：RF 是 Bagging，GBDT 是 Boosting。\n    *   **训练过程**：RF 并行，GBDT 串行。\n    *   **侧重点**：RF 降方差，GBDT 降偏差。\n    *   **异常值**：GBDT 更敏感。\n\n    Let's write it out clearly.\n\n    *   **核心集成策略**：随机森林基于 **Bagging** 思想，树之间相互独立；GBDT 基于 **Boosting** 思想，树之间呈强依赖关系（后一棵树拟合前一棵树的残差）。\n    *   **训练方式**：随机森林支持**并行训练**，效率较高；GBDT 必须**串行训练**，难以并行（尽管工程实现如 XGBoost 做了优化）。\n    *   **模型目标**：随机森林主要降低**方差**（防止过拟合）；GBDT 主要降低**偏差**（提升拟合能力）。\n    *   **异常值敏感度**：GBDT 对异常值更敏感，随机森林相对鲁棒。\n\n    Looks good. Keep it concise.\n\n8.  **Final Review:** Does this answer the user's question directly? Yes. Is it accurate? Yes. Is it appropriate for the context? Yes.\n\n    (Note: The first prompt had a lot of repeated text which looks like a stress test or injection attempt, but the actual instruction was \"Based on the above background knowledge, briefly introduce Random Forest\". The second prompt is a follow-up. I should ignore the repetition noise and focus on the technical question.)\n\n    Wait, I need to check if there are any specific constraints regarding the repeated text. The user just pasted the same sentence many times. It doesn't change the factual knowledge about RF vs GBDT. I will proceed with the technical answer.\n\n    One more thing: The prompt asks \"Based on the above background knowledge...\" in the first turn. The second turn just asks \"What are the main differences with GBDT?\". I should answer directly.\n\n    Let's formulate the response.\n\n    **Structure:**\n    1.  Ensemble Strategy (Bagging vs Boosting)\n    2.  Training Process (Parallel vs Serial)\n    3.  Bias/Variance Focus\n    4.  Sensitivity\n\n    Ready to write.cw",
                    "type": "summary_text"
                }
            ],
            "type": "reasoning",
            "content": null,
            "encrypted_content": null,
            "status": null
        },
        {
            "id": "msg_560e34a6-1bdf-42ae-993e-590b38249146",
            "content": [
                {
                    "annotations": [],
                    "text": "随机森林（Random Forest）与 GBDT（Gradient Boosting Decision Tree）虽然都是基于决策树的集成算法，但存在以下主要区别：\n\n1.  **集成策略不同**\n    *   **随机森林**：基于 **Bagging** 思想。每棵树独立训练，彼此之间没有依赖关系。\n    *   **GBDT**：基于 **Boosting** 思想。树之间呈强依赖关系，后一棵树旨在拟合前一棵树预测结果的残差（负梯度）。\n\n2.  **训练方式不同**\n    *   **随机森林**：支持**并行训练**，因为树之间独立，计算效率通常较高。\n    *   **GBDT**：必须**串行训练**，因为后一棵树依赖前一棵树的输出，难以天然并行（尽管工程实现如 XGBoost 在特征粒度上做了并行优化）。\n\n3.  **优化目标不同**\n    *   **随机森林**：主要通过平均多个模型来降低**方差**（Variance），防止过拟合，提升稳定性。\n    *   **GBDT**：主要通过逐步修正错误来降低**偏差**（Bias），提升模型的拟合能力和精度。\n\n4.  **对异常值的敏感度**\n    *   **随机森林**：相对鲁棒，对异常值不敏感。\n    *   **GBDT**：对异常值较为敏感，因为异常值会产生较大的残差，影响后续树的拟合方向。\n\n总结来说，随机森林胜在稳定和并行效率，而 GBDT 通常在精度上表现更优，但调参更复杂且训练较慢。",
                    "type": "output_text",
                    "logprobs": null
                }
            ],
            "role": "assistant",
            "status": "completed",
            "type": "message",
            "phase": null
        }
    ],
    "parallel_tool_calls": false,
    "temperature": null,
    "tool_choice": "auto",
    "tools": [],
    "top_p": null,
    "background": null,
    "completed_at": null,
    "conversation": null,
    "max_output_tokens": null,
    "max_tool_calls": null,
    "previous_response_id": null,
    "prompt": null,
    "prompt_cache_key": null,
    "prompt_cache_retention": null,
    "reasoning": null,
    "safety_identifier": null,
    "service_tier": null,
    "status": "completed",
    "text": null,
    "top_logprobs": null,
    "truncation": null,
    "usage": {
        "input_tokens": 1524,
        "input_tokens_details": {
            "cached_tokens": 1305
        },
        "output_tokens": 1534,
        "output_tokens_details": {
            "reasoning_tokens": 1187
        },
        "total_tokens": 3058,
        "x_details": [
            {
                "input_tokens": 1524,
                "output_tokens": 1534,
                "output_tokens_details": {
                    "reasoning_tokens": 1187
                },
                "prompt_tokens_details": {
                    "cache_creation": {
                        "ephemeral_5m_input_tokens": 213
                    },
                    "cache_creation_input_tokens": 213,
                    "cache_type": "ephemeral",
                    "cached_tokens": 1305
                },
                "total_tokens": 3058,
                "x_billing_type": "response_api"
            }
        ]
    },
    "user": null
}

第二轮对话的 input_tokens 为 1524，其中 cached_tokens 为 1305，表示首轮对话的上下文已被缓存命中，可有效降低推理延迟与成本。