470 lines
48 KiB
JSON
470 lines
48 KiB
JSON
{
|
||
"total_calls": 5,
|
||
"calls": [
|
||
{
|
||
"call_id": 1,
|
||
"timestamp_start": "2025-10-31T15:04:53.546542",
|
||
"messages": [
|
||
[
|
||
{
|
||
"type": "SystemMessage",
|
||
"content": "你是ResearchCoordinator，一个智能深度研究系统的主Agent。\n\n你的任务是协调6个SubAgent完成一次完整的深度研究，最终生成高质量的研究报告。\n\n# 研究问题\n\nPython asyncio最佳实践\n\n# SubAgent调用方式\n\n你可以通过 `task` 工具调用6个SubAgent：\n- intent-analyzer - 分析意图并生成搜索查询\n- search-orchestrator - 执行并行搜索\n- source-validator - 验证来源可信度\n- content-analyzer - 分析内容并提取信息\n- confidence-evaluator - 评估置信度并决定是否继续\n- report-generator - 生成最终报告\n\n调用方式示例：\n```\ntask(description=\"分析研究问题并生成搜索查询\", subagent_type=\"intent-analyzer\")\n```\n\n各SubAgent会自动读取它们需要的文件，并写入输出文件。你只需要调用它们，然后读取输出验证结果。\n\n# 研究配置\n\n- 深度模式: quick\n- 报告格式: technical\n- 最低Tier要求: 3\n- 最大迭代次数: 2\n- 置信度阈值: 0.6\n- 目标来源数: 5-10\n\n# 核心执行流程（7步）\n\n请严格按照以下7步流程执行研究：\n\n## 步骤1: 初始化\n\n将研究问题和配置写入虚拟文件系统：\n\n1. 使用 write_file 工具写入 `/question.txt` - 存储原始研究问题\n2. 使用 write_file 工具写入 `/config.json` - 存储研究配置（包含上述所有配置项）\n\n**完成文件写入后，直接进入步骤2（不要等待，立即调用intent-analyzer）。**\n\n## 步骤2: 意图分析\n\n使用task工具调用 `intent-analyzer` SubAgent：\n- 调用：task(description=\"分析研究问题并生成搜索查询\", subagent_type=\"intent-analyzer\")\n- 该SubAgent会读取：`/question.txt`, `/config.json`\n- 该SubAgent会输出：`/search_queries.json`（包含3-7个搜索查询）\n\n⚠️ **重要**：write_file和task不能在同一个响应中调用！必须先完成write_file，在下一个响应中再调用task。\n这是因为write_file返回Command更新state，task需要读取更新后的state。\n\n## 步骤3: 开始迭代循环\n\n初始化迭代轮次为1，开始第一轮搜索和分析。\n\n### 步骤3.1: 并行搜索\n\n使用task工具调用 `search-orchestrator` SubAgent：\n- 调用：task(description=\"执行并行搜索\", subagent_type=\"search-orchestrator\")\n- 该SubAgent会读取：`/search_queries.json`, `/config.json`\n- 该SubAgent会输出：`/iteration_N/search_results.json`\n\n读取搜索结果，检查是否成功获取足够的结果。如果所有查询都失败，直接跳到步骤7生成错误报告。\n\n### 步骤3.2: 来源验证\n\n使用task工具调用 `source-validator` SubAgent：\n- 调用：task(description=\"验证来源可信度\", subagent_type=\"source-validator\")\n- 该SubAgent会读取：`/iteration_N/search_results.json`, `/config.json`\n- 该SubAgent会输出：`/iteration_N/sources.json`\n\n读取验证结果，检查是否满足最低要求（总数≥5，高质量≥3）。\n\n### 步骤3.3: 内容分析\n\n使用task工具调用 `content-analyzer` SubAgent：\n- 调用：task(description=\"分析内容并提取信息\", subagent_type=\"content-analyzer\")\n- 该SubAgent会读取：`/iteration_N/sources.json`, `/question.txt`\n- 该SubAgent会输出：`/iteration_N/findings.json`\n\n读取分析结果，查看发现的信息点和缺口。\n\n### 步骤3.4: 置信度评估\n\n使用task工具调用 `confidence-evaluator` SubAgent：\n- 调用：task(description=\"评估置信度并决定是否继续\", subagent_type=\"confidence-evaluator\")\n- 该SubAgent会读取：`/iteration_N/sources.json`, `/iteration_N/findings.json`, `/config.json`\n- 该SubAgent会输出：`/iteration_N/confidence.json`, `/iteration_decision.json`\n\n**关键决策点**：读取 `/iteration_decision.json`\n\n- 如果 `decision` 字段是 `\"FINISH\"`：\n - 跳到步骤7生成最终报告\n - 研究完成\n\n- 如果 `decision` 字段是 `\"CONTINUE\"`：\n - 读取 `supplementary_queries` 字段\n - 更新 `/search_queries.json` 为补充查询\n - 增加迭代轮次（iteration += 1）\n - 回到步骤3.1继续下一轮搜索\n - 最多执行 2 轮迭代\n\n## 步骤7: 报告生成\n\n使用task工具调用 `report-generator` SubAgent：\n- 调用：task(description=\"生成最终研究报告\", subagent_type=\"report-generator\")\n- 该SubAgent会读取：`/question.txt`, `/config.json`, `/iteration_*/sources.json`, `/iteration_*/findings.json`, `/iteration_*/confidence.json`\n- 该SubAgent会输出：`/final_report.md`\n\n读取最终报告，向用户呈现结果。\n\n# 重要原则\n\n1. **迭代控制通过文件系统**：\n - 不要使用Python的while循环\n - 通过读取 `/iteration_decision.json` 的 `decision` 字段来决定下一步\n - LangGraph会持续调用你，直到你明确表示任务完成\n\n2. **任务完成信号**：\n - 当你完成报告生成后，在响应中明确说明\"研究已完成\"\n - 告知用户报告已写入 `/final_report.md`\n - 提供研究摘要（置信度、来源数、迭代次数等）\n\n3. **错误处理**：\n - 如果某个SubAgent失败，检查是否可以降级运行\n - 记录错误信息到 `/errors.json`\n - 在最终报告中说明遇到的问题\n\n4. **状态跟踪**：\n - 在每个步骤后，简要说明当前进度\n - 读取必要的文件来验证SubAgent的输出\n - 保持清晰的执行日志\n\n5. **虚拟文件系统**：\n - 所有文件路径必须以 `/` 开头\n - 使用 `write_file()` 写入文件\n - 使用 `read_file()` 读取文件\n - 迭代目录格式：`/iteration_1/`, `/iteration_2/` 等\n\n# 当前任务\n\n现在开始执行步骤1：初始化虚拟文件系统。\n\n第一步：写入 `/question.txt` 和 `/config.json`\n\n完成后，立即进入步骤2调用intent-analyzer（不要等待或询问用户）。\n\n\nIn order to complete the objective that the user asks of you, you have access to a number of standard tools.\n\n## `write_todos`\n\nYou have access to the `write_todos` tool to help you manage and plan complex objectives.\nUse this tool for complex objectives to ensure that you are tracking each necessary step and giving the user visibility into your progress.\nThis tool is very helpful for planning complex objectives, and for breaking down these larger complex objectives into smaller steps.\n\nIt is critical that you mark todos as completed as soon as you are done with a step. Do not batch up multiple steps before marking them as completed.\nFor simple objectives that only require a few steps, it is better to just complete the objective directly and NOT use this tool.\nWriting todos takes time and tokens, use it when it is helpful for managing complex many-step problems! But not for simple few-step requests.\n\n## Important To-Do List Usage Notes to Remember\n- The `write_todos` tool should never be called multiple times in parallel.\n- Don't be afraid to revise the To-Do list as you go. New information may reveal new tasks that need to be done, or old tasks that are irrelevant.\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /.\n\n- ls: list all files in the filesystem\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n\n## `task` (subagent spawner)\n\nYou have access to a `task` tool to launch short-lived subagents that handle isolated tasks. These agents are ephemeral — they live only for the duration of the task and return a single result.\n\nWhen to use the task tool:\n- When a task is complex and multi-step, and can be fully delegated in isolation\n- When a task is independent of other tasks and can run in parallel\n- When a task requires focused reasoning or heavy token/context usage that would bloat the orchestrator thread\n- When sandboxing improves reliability (e.g. code execution, structured searches, data formatting)\n- When you only care about the output of the subagent, and not the intermediate steps (ex. performing a lot of research and then returned a synthesized report, performing a series of computations or lookups to achieve a concise, relevant answer.)\n\nSubagent lifecycle:\n1. **Spawn** → Provide clear role, instructions, and expected output\n2. **Run** → The subagent completes the task autonomously\n3. **Return** → The subagent provides a single structured result\n4. **Reconcile** → Incorporate or synthesize the result into the main thread\n\nWhen NOT to use the task tool:\n- If you need to see the intermediate reasoning or steps after the subagent has completed (the task tool hides them)\n- If the task is trivial (a few tool calls or simple lookup)\n- If delegating does not reduce token usage, complexity, or context switching\n- If splitting would add latency without benefit\n\n## Important Task Tool Usage Notes to Remember\n- Whenever possible, parallelize the work that you do. This is true for both tool_calls, and for tasks. Whenever you have independent steps to complete - make tool_calls, or kick off tasks (subagents) in parallel to accomplish them faster. This saves time for the user, which is incredibly important.\n- Remember to use the `task` tool to silo independent tasks within a multi-part objective.\n- You should use the `task` tool whenever you have a complex task that will take multiple steps, and is independent from other tasks that the agent needs to complete. These agents are highly competent and efficient.",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "HumanMessage",
|
||
"content": "请开始研究这个问题：Python asyncio最佳实践",
|
||
"tool_calls": null
|
||
}
|
||
]
|
||
],
|
||
"kwargs": {
|
||
"run_id": "5bc438c1-5f32-43e8-99e0-f625d74b5eb6",
|
||
"parent_run_id": "69ef72a4-1cc0-4629-8d52-f1d59414ac99",
|
||
"options": "{'stop': None}",
|
||
"name": "None",
|
||
"batch_size": "1"
|
||
},
|
||
"timestamp_end": "2025-10-31T15:05:01.620812",
|
||
"response": {
|
||
"generations": [
|
||
{
|
||
"text": "",
|
||
"message": {
|
||
"type": "AIMessage",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"name": "write_file",
|
||
"args": {
|
||
"file_path": "/question.txt",
|
||
"content": "Python asyncio最佳实践"
|
||
},
|
||
"id": "call_658640fe11154930ad30e8"
|
||
},
|
||
{
|
||
"name": "write_file",
|
||
"args": {
|
||
"file_path": "/config.json",
|
||
"content": "{\"depth_mode\": \"quick\", \"report_format\": \"technical\", \"minimum_tier_requirement\": 3, \"maximum_iterations\": 2, \"confidence_threshold\": 0.6, \"target_source_count\": \"5-10\"}"
|
||
},
|
||
"id": "call_b82602a6c81a47da8c7c1f"
|
||
}
|
||
]
|
||
}
|
||
}
|
||
],
|
||
"llm_output": {
|
||
"token_usage": {
|
||
"completion_tokens": 103,
|
||
"prompt_tokens": 5920,
|
||
"total_tokens": 6023,
|
||
"completion_tokens_details": null,
|
||
"prompt_tokens_details": {
|
||
"audio_tokens": null,
|
||
"cached_tokens": 0
|
||
}
|
||
},
|
||
"model_provider": "openai",
|
||
"model_name": "qwen-max",
|
||
"system_fingerprint": null,
|
||
"id": "chatcmpl-ce55d31c-4893-4959-b375-e33ba3a303ce"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"call_id": 2,
|
||
"timestamp_start": "2025-10-31T15:05:01.645324",
|
||
"messages": [
|
||
[
|
||
{
|
||
"type": "SystemMessage",
|
||
"content": "你是ResearchCoordinator，一个智能深度研究系统的主Agent。\n\n你的任务是协调6个SubAgent完成一次完整的深度研究，最终生成高质量的研究报告。\n\n# 研究问题\n\nPython asyncio最佳实践\n\n# SubAgent调用方式\n\n你可以通过 `task` 工具调用6个SubAgent：\n- intent-analyzer - 分析意图并生成搜索查询\n- search-orchestrator - 执行并行搜索\n- source-validator - 验证来源可信度\n- content-analyzer - 分析内容并提取信息\n- confidence-evaluator - 评估置信度并决定是否继续\n- report-generator - 生成最终报告\n\n调用方式示例：\n```\ntask(description=\"分析研究问题并生成搜索查询\", subagent_type=\"intent-analyzer\")\n```\n\n各SubAgent会自动读取它们需要的文件，并写入输出文件。你只需要调用它们，然后读取输出验证结果。\n\n# 研究配置\n\n- 深度模式: quick\n- 报告格式: technical\n- 最低Tier要求: 3\n- 最大迭代次数: 2\n- 置信度阈值: 0.6\n- 目标来源数: 5-10\n\n# 核心执行流程（7步）\n\n请严格按照以下7步流程执行研究：\n\n## 步骤1: 初始化\n\n将研究问题和配置写入虚拟文件系统：\n\n1. 使用 write_file 工具写入 `/question.txt` - 存储原始研究问题\n2. 使用 write_file 工具写入 `/config.json` - 存储研究配置（包含上述所有配置项）\n\n**完成文件写入后，直接进入步骤2（不要等待，立即调用intent-analyzer）。**\n\n## 步骤2: 意图分析\n\n使用task工具调用 `intent-analyzer` SubAgent：\n- 调用：task(description=\"分析研究问题并生成搜索查询\", subagent_type=\"intent-analyzer\")\n- 该SubAgent会读取：`/question.txt`, `/config.json`\n- 该SubAgent会输出：`/search_queries.json`（包含3-7个搜索查询）\n\n⚠️ **重要**：write_file和task不能在同一个响应中调用！必须先完成write_file，在下一个响应中再调用task。\n这是因为write_file返回Command更新state，task需要读取更新后的state。\n\n## 步骤3: 开始迭代循环\n\n初始化迭代轮次为1，开始第一轮搜索和分析。\n\n### 步骤3.1: 并行搜索\n\n使用task工具调用 `search-orchestrator` SubAgent：\n- 调用：task(description=\"执行并行搜索\", subagent_type=\"search-orchestrator\")\n- 该SubAgent会读取：`/search_queries.json`, `/config.json`\n- 该SubAgent会输出：`/iteration_N/search_results.json`\n\n读取搜索结果，检查是否成功获取足够的结果。如果所有查询都失败，直接跳到步骤7生成错误报告。\n\n### 步骤3.2: 来源验证\n\n使用task工具调用 `source-validator` SubAgent：\n- 调用：task(description=\"验证来源可信度\", subagent_type=\"source-validator\")\n- 该SubAgent会读取：`/iteration_N/search_results.json`, `/config.json`\n- 该SubAgent会输出：`/iteration_N/sources.json`\n\n读取验证结果，检查是否满足最低要求（总数≥5，高质量≥3）。\n\n### 步骤3.3: 内容分析\n\n使用task工具调用 `content-analyzer` SubAgent：\n- 调用：task(description=\"分析内容并提取信息\", subagent_type=\"content-analyzer\")\n- 该SubAgent会读取：`/iteration_N/sources.json`, `/question.txt`\n- 该SubAgent会输出：`/iteration_N/findings.json`\n\n读取分析结果，查看发现的信息点和缺口。\n\n### 步骤3.4: 置信度评估\n\n使用task工具调用 `confidence-evaluator` SubAgent：\n- 调用：task(description=\"评估置信度并决定是否继续\", subagent_type=\"confidence-evaluator\")\n- 该SubAgent会读取：`/iteration_N/sources.json`, `/iteration_N/findings.json`, `/config.json`\n- 该SubAgent会输出：`/iteration_N/confidence.json`, `/iteration_decision.json`\n\n**关键决策点**：读取 `/iteration_decision.json`\n\n- 如果 `decision` 字段是 `\"FINISH\"`：\n - 跳到步骤7生成最终报告\n - 研究完成\n\n- 如果 `decision` 字段是 `\"CONTINUE\"`：\n - 读取 `supplementary_queries` 字段\n - 更新 `/search_queries.json` 为补充查询\n - 增加迭代轮次（iteration += 1）\n - 回到步骤3.1继续下一轮搜索\n - 最多执行 2 轮迭代\n\n## 步骤7: 报告生成\n\n使用task工具调用 `report-generator` SubAgent：\n- 调用：task(description=\"生成最终研究报告\", subagent_type=\"report-generator\")\n- 该SubAgent会读取：`/question.txt`, `/config.json`, `/iteration_*/sources.json`, `/iteration_*/findings.json`, `/iteration_*/confidence.json`\n- 该SubAgent会输出：`/final_report.md`\n\n读取最终报告，向用户呈现结果。\n\n# 重要原则\n\n1. **迭代控制通过文件系统**：\n - 不要使用Python的while循环\n - 通过读取 `/iteration_decision.json` 的 `decision` 字段来决定下一步\n - LangGraph会持续调用你，直到你明确表示任务完成\n\n2. **任务完成信号**：\n - 当你完成报告生成后，在响应中明确说明\"研究已完成\"\n - 告知用户报告已写入 `/final_report.md`\n - 提供研究摘要（置信度、来源数、迭代次数等）\n\n3. **错误处理**：\n - 如果某个SubAgent失败，检查是否可以降级运行\n - 记录错误信息到 `/errors.json`\n - 在最终报告中说明遇到的问题\n\n4. **状态跟踪**：\n - 在每个步骤后，简要说明当前进度\n - 读取必要的文件来验证SubAgent的输出\n - 保持清晰的执行日志\n\n5. **虚拟文件系统**：\n - 所有文件路径必须以 `/` 开头\n - 使用 `write_file()` 写入文件\n - 使用 `read_file()` 读取文件\n - 迭代目录格式：`/iteration_1/`, `/iteration_2/` 等\n\n# 当前任务\n\n现在开始执行步骤1：初始化虚拟文件系统。\n\n第一步：写入 `/question.txt` 和 `/config.json`\n\n完成后，立即进入步骤2调用intent-analyzer（不要等待或询问用户）。\n\n\nIn order to complete the objective that the user asks of you, you have access to a number of standard tools.\n\n## `write_todos`\n\nYou have access to the `write_todos` tool to help you manage and plan complex objectives.\nUse this tool for complex objectives to ensure that you are tracking each necessary step and giving the user visibility into your progress.\nThis tool is very helpful for planning complex objectives, and for breaking down these larger complex objectives into smaller steps.\n\nIt is critical that you mark todos as completed as soon as you are done with a step. Do not batch up multiple steps before marking them as completed.\nFor simple objectives that only require a few steps, it is better to just complete the objective directly and NOT use this tool.\nWriting todos takes time and tokens, use it when it is helpful for managing complex many-step problems! But not for simple few-step requests.\n\n## Important To-Do List Usage Notes to Remember\n- The `write_todos` tool should never be called multiple times in parallel.\n- Don't be afraid to revise the To-Do list as you go. New information may reveal new tasks that need to be done, or old tasks that are irrelevant.\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /.\n\n- ls: list all files in the filesystem\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n\n## `task` (subagent spawner)\n\nYou have access to a `task` tool to launch short-lived subagents that handle isolated tasks. These agents are ephemeral — they live only for the duration of the task and return a single result.\n\nWhen to use the task tool:\n- When a task is complex and multi-step, and can be fully delegated in isolation\n- When a task is independent of other tasks and can run in parallel\n- When a task requires focused reasoning or heavy token/context usage that would bloat the orchestrator thread\n- When sandboxing improves reliability (e.g. code execution, structured searches, data formatting)\n- When you only care about the output of the subagent, and not the intermediate steps (ex. performing a lot of research and then returned a synthesized report, performing a series of computations or lookups to achieve a concise, relevant answer.)\n\nSubagent lifecycle:\n1. **Spawn** → Provide clear role, instructions, and expected output\n2. **Run** → The subagent completes the task autonomously\n3. **Return** → The subagent provides a single structured result\n4. **Reconcile** → Incorporate or synthesize the result into the main thread\n\nWhen NOT to use the task tool:\n- If you need to see the intermediate reasoning or steps after the subagent has completed (the task tool hides them)\n- If the task is trivial (a few tool calls or simple lookup)\n- If delegating does not reduce token usage, complexity, or context switching\n- If splitting would add latency without benefit\n\n## Important Task Tool Usage Notes to Remember\n- Whenever possible, parallelize the work that you do. This is true for both tool_calls, and for tasks. Whenever you have independent steps to complete - make tool_calls, or kick off tasks (subagents) in parallel to accomplish them faster. This saves time for the user, which is incredibly important.\n- Remember to use the `task` tool to silo independent tasks within a multi-part objective.\n- You should use the `task` tool whenever you have a complex task that will take multiple steps, and is independent from other tasks that the agent needs to complete. These agents are highly competent and efficient.",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "HumanMessage",
|
||
"content": "请开始研究这个问题：Python asyncio最佳实践",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "AIMessage",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"name": "write_file",
|
||
"args": {
|
||
"file_path": "/question.txt",
|
||
"content": "Python asyncio最佳实践"
|
||
},
|
||
"id": "call_658640fe11154930ad30e8",
|
||
"type": "tool_call"
|
||
},
|
||
{
|
||
"name": "write_file",
|
||
"args": {
|
||
"file_path": "/config.json",
|
||
"content": "{\"depth_mode\": \"quick\", \"report_format\": \"technical\", \"minimum_tier_requirement\": 3, \"maximum_iterations\": 2, \"confidence_threshold\": 0.6, \"target_source_count\": \"5-10\"}"
|
||
},
|
||
"id": "call_b82602a6c81a47da8c7c1f",
|
||
"type": "tool_call"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"type": "ToolMessage",
|
||
"content": "Updated file /question.txt",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "ToolMessage",
|
||
"content": "Updated file /config.json",
|
||
"tool_calls": null
|
||
}
|
||
]
|
||
],
|
||
"kwargs": {
|
||
"run_id": "7a7151b3-84ac-4716-8cdb-395f972c938a",
|
||
"parent_run_id": "6a0fcb05-db03-478f-a3ea-11750ac37328",
|
||
"options": "{'stop': None}",
|
||
"name": "None",
|
||
"batch_size": "1"
|
||
},
|
||
"timestamp_end": "2025-10-31T15:05:06.144999",
|
||
"response": {
|
||
"generations": [
|
||
{
|
||
"text": "已将研究问题和配置写入虚拟文件系统。现在，我将调用 `intent-analyzer` SubAgent 来分析研究问题并生成搜索查询。",
|
||
"message": {
|
||
"type": "AIMessage",
|
||
"content": "已将研究问题和配置写入虚拟文件系统。现在，我将调用 `intent-analyzer` SubAgent 来分析研究问题并生成搜索查询。",
|
||
"tool_calls": [
|
||
{
|
||
"name": "task",
|
||
"args": {
|
||
"description": "分析研究问题并生成搜索查询",
|
||
"subagent_type": "intent-analyzer"
|
||
},
|
||
"id": "call_fe1dda9b28594a49a02960"
|
||
}
|
||
]
|
||
}
|
||
}
|
||
],
|
||
"llm_output": {
|
||
"token_usage": {
|
||
"completion_tokens": 69,
|
||
"prompt_tokens": 6046,
|
||
"total_tokens": 6115,
|
||
"completion_tokens_details": null,
|
||
"prompt_tokens_details": {
|
||
"audio_tokens": null,
|
||
"cached_tokens": 0
|
||
}
|
||
},
|
||
"model_provider": "openai",
|
||
"model_name": "qwen-max",
|
||
"system_fingerprint": null,
|
||
"id": "chatcmpl-70e6f6c6-af91-4e15-ad02-b0dc2f488e6c"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"call_id": 3,
|
||
"timestamp_start": "2025-10-31T15:05:06.162121",
|
||
"messages": [
|
||
[
|
||
{
|
||
"type": "SystemMessage",
|
||
"content": "你是一个意图分析专家，负责分析用户的研究问题并生成高质量的搜索查询。\n\n**任务流程：**\n\n1. 读取输入文件：\n - `/question.txt` - 原始研究问题\n - `/config.json` - 研究配置（深度模式、格式等）\n\n2. 分析问题：\n - 识别研究领域（技术/学术/商业等）\n - 提取核心概念和关键词\n - 确定问题类型（事实查询/概念理解/技术实现/最佳实践等）\n\n3. 生成搜索查询：\n - 根据深度模式决定查询数量：\n * quick模式：3个查询\n * standard模式：5个查询\n * deep模式：5-7个查询\n - 查询应该多样化，覆盖不同角度：\n * 基础概念查询（what is...）\n * 实现细节查询（how to...）\n * 最佳实践查询（best practices...）\n * 问题排查查询（troubleshooting...）\n * 最新进展查询（latest...）\n - 使用英文查询以获取更广泛的结果\n - 查询应该具体且有针对性\n\n4. 输出结果到 `/search_queries.json`：\n ```json\n {\n \"original_question\": \"原始问题\",\n \"domain\": \"领域\",\n \"query_strategy\": \"查询策略说明\",\n \"queries\": [\n {\n \"query\": \"搜索查询字符串\",\n \"purpose\": \"查询目的\",\n \"priority\": 1-5\n }\n ]\n }\n ```\n\n**重要原则：**\n- 查询应该使用英文以获取更多高质量来源\n- 查询应该具体且有针对性，避免过于宽泛\n- 优先搜索官方文档、技术博客、学术论文等高质量来源\n- 查询应该覆盖问题的不同方面\n\n**文件路径规范：**\n- 所有虚拟文件系统路径必须以 `/` 开头\n- 使用 `write_file()` 和 `read_file()` 操作虚拟文件系统\n\n\n## `write_todos`\n\nYou have access to the `write_todos` tool to help you manage and plan complex objectives.\nUse this tool for complex objectives to ensure that you are tracking each necessary step and giving the user visibility into your progress.\nThis tool is very helpful for planning complex objectives, and for breaking down these larger complex objectives into smaller steps.\n\nIt is critical that you mark todos as completed as soon as you are done with a step. Do not batch up multiple steps before marking them as completed.\nFor simple objectives that only require a few steps, it is better to just complete the objective directly and NOT use this tool.\nWriting todos takes time and tokens, use it when it is helpful for managing complex many-step problems! But not for simple few-step requests.\n\n## Important To-Do List Usage Notes to Remember\n- The `write_todos` tool should never be called multiple times in parallel.\n- Don't be afraid to revise the To-Do list as you go. New information may reveal new tasks that need to be done, or old tasks that are irrelevant.\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /.\n\n- ls: list all files in the filesystem\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "HumanMessage",
|
||
"content": "分析研究问题并生成搜索查询",
|
||
"tool_calls": null
|
||
}
|
||
]
|
||
],
|
||
"kwargs": {
|
||
"run_id": "9c69fb55-d302-463d-a582-b17b622d11de",
|
||
"parent_run_id": "c0c0a796-b8b3-455d-8033-662d961612ea",
|
||
"options": "{'stop': None}",
|
||
"name": "None",
|
||
"batch_size": "1"
|
||
},
|
||
"timestamp_end": "2025-10-31T15:05:08.895694",
|
||
"response": {
|
||
"generations": [
|
||
{
|
||
"text": "",
|
||
"message": {
|
||
"type": "AIMessage",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"name": "ls",
|
||
"args": {},
|
||
"id": "call_9fc20bf05a9a4a6e930335"
|
||
},
|
||
{
|
||
"name": "read_file",
|
||
"args": {
|
||
"file_path": "/question.txt"
|
||
},
|
||
"id": "call_c7af191b36974768849f0e"
|
||
},
|
||
{
|
||
"name": "read_file",
|
||
"args": {
|
||
"file_path": "/config.json"
|
||
},
|
||
"id": "call_154e9450b6e94caeaafbb5"
|
||
}
|
||
]
|
||
}
|
||
}
|
||
],
|
||
"llm_output": {
|
||
"token_usage": {
|
||
"completion_tokens": 49,
|
||
"prompt_tokens": 2724,
|
||
"total_tokens": 2773,
|
||
"completion_tokens_details": null,
|
||
"prompt_tokens_details": {
|
||
"audio_tokens": null,
|
||
"cached_tokens": 0
|
||
}
|
||
},
|
||
"model_provider": "openai",
|
||
"model_name": "qwen-max",
|
||
"system_fingerprint": null,
|
||
"id": "chatcmpl-1b5e7f8b-6c52-4110-a4fe-ea0043ad5509"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"call_id": 4,
|
||
"timestamp_start": "2025-10-31T15:05:08.920379",
|
||
"messages": [
|
||
[
|
||
{
|
||
"type": "SystemMessage",
|
||
"content": "你是一个意图分析专家，负责分析用户的研究问题并生成高质量的搜索查询。\n\n**任务流程：**\n\n1. 读取输入文件：\n - `/question.txt` - 原始研究问题\n - `/config.json` - 研究配置（深度模式、格式等）\n\n2. 分析问题：\n - 识别研究领域（技术/学术/商业等）\n - 提取核心概念和关键词\n - 确定问题类型（事实查询/概念理解/技术实现/最佳实践等）\n\n3. 生成搜索查询：\n - 根据深度模式决定查询数量：\n * quick模式：3个查询\n * standard模式：5个查询\n * deep模式：5-7个查询\n - 查询应该多样化，覆盖不同角度：\n * 基础概念查询（what is...）\n * 实现细节查询（how to...）\n * 最佳实践查询（best practices...）\n * 问题排查查询（troubleshooting...）\n * 最新进展查询（latest...）\n - 使用英文查询以获取更广泛的结果\n - 查询应该具体且有针对性\n\n4. 输出结果到 `/search_queries.json`：\n ```json\n {\n \"original_question\": \"原始问题\",\n \"domain\": \"领域\",\n \"query_strategy\": \"查询策略说明\",\n \"queries\": [\n {\n \"query\": \"搜索查询字符串\",\n \"purpose\": \"查询目的\",\n \"priority\": 1-5\n }\n ]\n }\n ```\n\n**重要原则：**\n- 查询应该使用英文以获取更多高质量来源\n- 查询应该具体且有针对性，避免过于宽泛\n- 优先搜索官方文档、技术博客、学术论文等高质量来源\n- 查询应该覆盖问题的不同方面\n\n**文件路径规范：**\n- 所有虚拟文件系统路径必须以 `/` 开头\n- 使用 `write_file()` 和 `read_file()` 操作虚拟文件系统\n\n\n## `write_todos`\n\nYou have access to the `write_todos` tool to help you manage and plan complex objectives.\nUse this tool for complex objectives to ensure that you are tracking each necessary step and giving the user visibility into your progress.\nThis tool is very helpful for planning complex objectives, and for breaking down these larger complex objectives into smaller steps.\n\nIt is critical that you mark todos as completed as soon as you are done with a step. Do not batch up multiple steps before marking them as completed.\nFor simple objectives that only require a few steps, it is better to just complete the objective directly and NOT use this tool.\nWriting todos takes time and tokens, use it when it is helpful for managing complex many-step problems! But not for simple few-step requests.\n\n## Important To-Do List Usage Notes to Remember\n- The `write_todos` tool should never be called multiple times in parallel.\n- Don't be afraid to revise the To-Do list as you go. New information may reveal new tasks that need to be done, or old tasks that are irrelevant.\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /.\n\n- ls: list all files in the filesystem\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "HumanMessage",
|
||
"content": "分析研究问题并生成搜索查询",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "AIMessage",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"name": "ls",
|
||
"args": {},
|
||
"id": "call_9fc20bf05a9a4a6e930335",
|
||
"type": "tool_call"
|
||
},
|
||
{
|
||
"name": "read_file",
|
||
"args": {
|
||
"file_path": "/question.txt"
|
||
},
|
||
"id": "call_c7af191b36974768849f0e",
|
||
"type": "tool_call"
|
||
},
|
||
{
|
||
"name": "read_file",
|
||
"args": {
|
||
"file_path": "/config.json"
|
||
},
|
||
"id": "call_154e9450b6e94caeaafbb5",
|
||
"type": "tool_call"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"type": "ToolMessage",
|
||
"content": "[\"/question.txt\", \"/config.json\"]",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "ToolMessage",
|
||
"content": " 1\tPython asyncio最佳实践",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "ToolMessage",
|
||
"content": " 1\t{\"depth_mode\": \"quick\", \"report_format\": \"technical\", \"minimum_tier_requirement\": 3, \"maximum_iterations\": 2, \"confidence_threshold\": 0.6, \"target_source_count\": \"5-10\"}",
|
||
"tool_calls": null
|
||
}
|
||
]
|
||
],
|
||
"kwargs": {
|
||
"run_id": "d0053cca-5b2c-4755-9cac-852220269e0d",
|
||
"parent_run_id": "f4f9d199-61cc-4564-ab88-a21bd026be54",
|
||
"options": "{'stop': None}",
|
||
"name": "None",
|
||
"batch_size": "1"
|
||
},
|
||
"timestamp_end": "2025-10-31T15:05:28.363429",
|
||
"response": {
|
||
"generations": [
|
||
{
|
||
"text": "已经读取了原始研究问题和配置文件。研究问题是关于“Python asyncio最佳实践”，并且配置为quick模式，这意味着我将生成3个搜索查询。\n\n### 分析\n- **领域**：技术（编程）\n- **核心概念和关键词**：Python, asyncio, 最佳实践\n- **问题类型**：最佳实践查询\n\n### 生成的搜索查询\n1. **基础概念查询**：了解asyncio的基本概念和工作原理。\n2. **最佳实践查询**：查找使用Python asyncio时的最佳实践。\n3. **实现细节查询**：探索如何在实际项目中应用这些最佳实践。\n\n现在，我将基于上述分析生成具体的搜索查询，并将其写入`/search_queries.json`文件中。",
|
||
"message": {
|
||
"type": "AIMessage",
|
||
"content": "已经读取了原始研究问题和配置文件。研究问题是关于“Python asyncio最佳实践”，并且配置为quick模式，这意味着我将生成3个搜索查询。\n\n### 分析\n- **领域**：技术（编程）\n- **核心概念和关键词**：Python, asyncio, 最佳实践\n- **问题类型**：最佳实践查询\n\n### 生成的搜索查询\n1. **基础概念查询**：了解asyncio的基本概念和工作原理。\n2. **最佳实践查询**：查找使用Python asyncio时的最佳实践。\n3. **实现细节查询**：探索如何在实际项目中应用这些最佳实践。\n\n现在，我将基于上述分析生成具体的搜索查询，并将其写入`/search_queries.json`文件中。",
|
||
"tool_calls": [
|
||
{
|
||
"name": "write_file",
|
||
"args": {
|
||
"file_path": "/search_queries.json",
|
||
"content": "{\n \"original_question\": \"Python asyncio最佳实践\",\n \"domain\": \"技术（编程）\",\n \"query_strategy\": \"根据quick模式生成3个有针对性的英文搜索查询，覆盖基本概念、最佳实践以及具体实现细节。\",\n \"queries\": [\n {\n \"query\": \"What is Python asyncio and how does it work?\",\n \"purpose\": \"基础概念查询 - 了解asyncio的基本概念和工作原理\",\n \"priority\": 1\n },\n {\n \"query\": \"Best practices for using Python asyncio\",\n \"purpose\": \"最佳实践查询 - 查找使用Python asyncio时的最佳实践\",\n \"priority\": 2\n },\n {\n \"query\": \"How to implement best practices with Python asyncio in real projects\",\n \"purpose\": \"实现细节查询 - 探索如何在实际项目中应用这些最佳实践\",\n \"priority\": 3\n }\n ]\n}"
|
||
},
|
||
"id": "call_5ff887b8c51940d7a1da51"
|
||
}
|
||
]
|
||
}
|
||
}
|
||
],
|
||
"llm_output": {
|
||
"token_usage": {
|
||
"completion_tokens": 404,
|
||
"prompt_tokens": 2861,
|
||
"total_tokens": 3265,
|
||
"completion_tokens_details": null,
|
||
"prompt_tokens_details": {
|
||
"audio_tokens": null,
|
||
"cached_tokens": 0
|
||
}
|
||
},
|
||
"model_provider": "openai",
|
||
"model_name": "qwen-max",
|
||
"system_fingerprint": null,
|
||
"id": "chatcmpl-6f00d255-c616-4b3a-9351-7083ca9ae61f"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"call_id": 5,
|
||
"timestamp_start": "2025-10-31T15:05:28.383429",
|
||
"messages": [
|
||
[
|
||
{
|
||
"type": "SystemMessage",
|
||
"content": "你是一个意图分析专家，负责分析用户的研究问题并生成高质量的搜索查询。\n\n**任务流程：**\n\n1. 读取输入文件：\n - `/question.txt` - 原始研究问题\n - `/config.json` - 研究配置（深度模式、格式等）\n\n2. 分析问题：\n - 识别研究领域（技术/学术/商业等）\n - 提取核心概念和关键词\n - 确定问题类型（事实查询/概念理解/技术实现/最佳实践等）\n\n3. 生成搜索查询：\n - 根据深度模式决定查询数量：\n * quick模式：3个查询\n * standard模式：5个查询\n * deep模式：5-7个查询\n - 查询应该多样化，覆盖不同角度：\n * 基础概念查询（what is...）\n * 实现细节查询（how to...）\n * 最佳实践查询（best practices...）\n * 问题排查查询（troubleshooting...）\n * 最新进展查询（latest...）\n - 使用英文查询以获取更广泛的结果\n - 查询应该具体且有针对性\n\n4. 输出结果到 `/search_queries.json`：\n ```json\n {\n \"original_question\": \"原始问题\",\n \"domain\": \"领域\",\n \"query_strategy\": \"查询策略说明\",\n \"queries\": [\n {\n \"query\": \"搜索查询字符串\",\n \"purpose\": \"查询目的\",\n \"priority\": 1-5\n }\n ]\n }\n ```\n\n**重要原则：**\n- 查询应该使用英文以获取更多高质量来源\n- 查询应该具体且有针对性，避免过于宽泛\n- 优先搜索官方文档、技术博客、学术论文等高质量来源\n- 查询应该覆盖问题的不同方面\n\n**文件路径规范：**\n- 所有虚拟文件系统路径必须以 `/` 开头\n- 使用 `write_file()` 和 `read_file()` 操作虚拟文件系统\n\n\n## `write_todos`\n\nYou have access to the `write_todos` tool to help you manage and plan complex objectives.\nUse this tool for complex objectives to ensure that you are tracking each necessary step and giving the user visibility into your progress.\nThis tool is very helpful for planning complex objectives, and for breaking down these larger complex objectives into smaller steps.\n\nIt is critical that you mark todos as completed as soon as you are done with a step. Do not batch up multiple steps before marking them as completed.\nFor simple objectives that only require a few steps, it is better to just complete the objective directly and NOT use this tool.\nWriting todos takes time and tokens, use it when it is helpful for managing complex many-step problems! But not for simple few-step requests.\n\n## Important To-Do List Usage Notes to Remember\n- The `write_todos` tool should never be called multiple times in parallel.\n- Don't be afraid to revise the To-Do list as you go. New information may reveal new tasks that need to be done, or old tasks that are irrelevant.\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /.\n\n- ls: list all files in the filesystem\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "HumanMessage",
|
||
"content": "分析研究问题并生成搜索查询",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "AIMessage",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"name": "ls",
|
||
"args": {},
|
||
"id": "call_9fc20bf05a9a4a6e930335",
|
||
"type": "tool_call"
|
||
},
|
||
{
|
||
"name": "read_file",
|
||
"args": {
|
||
"file_path": "/question.txt"
|
||
},
|
||
"id": "call_c7af191b36974768849f0e",
|
||
"type": "tool_call"
|
||
},
|
||
{
|
||
"name": "read_file",
|
||
"args": {
|
||
"file_path": "/config.json"
|
||
},
|
||
"id": "call_154e9450b6e94caeaafbb5",
|
||
"type": "tool_call"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"type": "ToolMessage",
|
||
"content": "[\"/question.txt\", \"/config.json\"]",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "ToolMessage",
|
||
"content": " 1\tPython asyncio最佳实践",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "ToolMessage",
|
||
"content": " 1\t{\"depth_mode\": \"quick\", \"report_format\": \"technical\", \"minimum_tier_requirement\": 3, \"maximum_iterations\": 2, \"confidence_threshold\": 0.6, \"target_source_count\": \"5-10\"}",
|
||
"tool_calls": null
|
||
},
|
||
{
|
||
"type": "AIMessage",
|
||
"content": "已经读取了原始研究问题和配置文件。研究问题是关于“Python asyncio最佳实践”，并且配置为quick模式，这意味着我将生成3个搜索查询。\n\n### 分析\n- **领域**：技术（编程）\n- **核心概念和关键词**：Python, asyncio, 最佳实践\n- **问题类型**：最佳实践查询\n\n### 生成的搜索查询\n1. **基础概念查询**：了解asyncio的基本概念和工作原理。\n2. **最佳实践查询**：查找使用Python asyncio时的最佳实践。\n3. **实现细节查询**：探索如何在实际项目中应用这些最佳实践。\n\n现在，我将基于上述分析生成具体的搜索查询，并将其写入`/search_queries.json`文件中。",
|
||
"tool_calls": [
|
||
{
|
||
"name": "write_file",
|
||
"args": {
|
||
"file_path": "/search_queries.json",
|
||
"content": "{\n \"original_question\": \"Python asyncio最佳实践\",\n \"domain\": \"技术（编程）\",\n \"query_strategy\": \"根据quick模式生成3个有针对性的英文搜索查询，覆盖基本概念、最佳实践以及具体实现细节。\",\n \"queries\": [\n {\n \"query\": \"What is Python asyncio and how does it work?\",\n \"purpose\": \"基础概念查询 - 了解asyncio的基本概念和工作原理\",\n \"priority\": 1\n },\n {\n \"query\": \"Best practices for using Python asyncio\",\n \"purpose\": \"最佳实践查询 - 查找使用Python asyncio时的最佳实践\",\n \"priority\": 2\n },\n {\n \"query\": \"How to implement best practices with Python asyncio in real projects\",\n \"purpose\": \"实现细节查询 - 探索如何在实际项目中应用这些最佳实践\",\n \"priority\": 3\n }\n ]\n}"
|
||
},
|
||
"id": "call_5ff887b8c51940d7a1da51",
|
||
"type": "tool_call"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"type": "ToolMessage",
|
||
"content": "Updated file /search_queries.json",
|
||
"tool_calls": null
|
||
}
|
||
]
|
||
],
|
||
"kwargs": {
|
||
"run_id": "1f846554-f9af-48a7-ab8b-5e8b9bf32fdb",
|
||
"parent_run_id": "1935284c-a1ba-4d48-9e4e-bd8291e03174",
|
||
"options": "{'stop': None}",
|
||
"name": "None",
|
||
"batch_size": "1"
|
||
},
|
||
"timestamp_end": "2025-10-31T15:05:43.011375",
|
||
"response": {
|
||
"generations": [
|
||
{
|
||
"text": "搜索查询已成功生成并写入`/search_queries.json`文件中。以下是生成的搜索查询：\n\n- **原始问题**: Python asyncio最佳实践\n- **领域**: 技术（编程）\n- **查询策略**: 根据quick模式生成3个有针对性的英文搜索查询，覆盖基本概念、最佳实践以及具体实现细节。\n\n### 生成的搜索查询\n1. **查询**：What is Python asyncio and how does it work?\n - **目的**：基础概念查询 - 了解asyncio的基本概念和工作原理\n - **优先级**：1\n\n2. **查询**：Best practices for using Python asyncio\n - **目的**：最佳实践查询 - 查找使用Python asyncio时的最佳实践\n - **优先级**：2\n\n3. **查询**：How to implement best practices with Python asyncio in real projects\n - **目的**：实现细节查询 - 探索如何在实际项目中应用这些最佳实践\n - **优先级**：3\n\n这些查询将帮助你全面了解Python asyncio的基础知识、最佳实践以及如何在实际项目中应用它们。",
|
||
"message": {
|
||
"type": "AIMessage",
|
||
"content": "搜索查询已成功生成并写入`/search_queries.json`文件中。以下是生成的搜索查询：\n\n- **原始问题**: Python asyncio最佳实践\n- **领域**: 技术（编程）\n- **查询策略**: 根据quick模式生成3个有针对性的英文搜索查询，覆盖基本概念、最佳实践以及具体实现细节。\n\n### 生成的搜索查询\n1. **查询**：What is Python asyncio and how does it work?\n - **目的**：基础概念查询 - 了解asyncio的基本概念和工作原理\n - **优先级**：1\n\n2. **查询**：Best practices for using Python asyncio\n - **目的**：最佳实践查询 - 查找使用Python asyncio时的最佳实践\n - **优先级**：2\n\n3. **查询**：How to implement best practices with Python asyncio in real projects\n - **目的**：实现细节查询 - 探索如何在实际项目中应用这些最佳实践\n - **优先级**：3\n\n这些查询将帮助你全面了解Python asyncio的基础知识、最佳实践以及如何在实际项目中应用它们。",
|
||
"tool_calls": []
|
||
}
|
||
}
|
||
],
|
||
"llm_output": {
|
||
"token_usage": {
|
||
"completion_tokens": 241,
|
||
"prompt_tokens": 3278,
|
||
"total_tokens": 3519,
|
||
"completion_tokens_details": null,
|
||
"prompt_tokens_details": {
|
||
"audio_tokens": null,
|
||
"cached_tokens": 0
|
||
}
|
||
},
|
||
"model_provider": "openai",
|
||
"model_name": "qwen-max",
|
||
"system_fingerprint": null,
|
||
"id": "chatcmpl-40e0790d-7fbb-492e-b726-52a88b6e76d4"
|
||
}
|
||
}
|
||
}
|
||
]
|
||
} |