agent-Specialization/data/conversations/conv_20251008_195110_979.json

{
  "id": "conv_20251008_195110_979",
  "title": "搜索deepseekv3.1，qwen2.5-max，kimi-k2，gemini2.5pro的模型...",
  "created_at": "2025-10-08T19:51:10.979871",
  "updated_at": "2025-10-08T19:54:16.471919",
  "messages": [
    {
      "role": "user",
      "content": "搜索deepseekv3.1，qwen2.5-max，kimi-k2，gemini2.5pro的模型相关信息，保证数据的准确性，主要了解其性能，做成一个精美的，设计高级的，带有可互动和方便对比的各种图表，动画和互动效果精致高级的html页面",
      "timestamp": "2025-10-08T19:51:13.840487"
    },
    {
      "role": "assistant",
      "content": "我来帮您搜索这些AI模型的相关信息，然后创建一个精美的对比展示页面。",
      "timestamp": "2025-10-08T19:51:16.920712"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:51:16.927006",
      "tool_calls": [
        {
          "id": "web_search:0",
          "index": 0,
          "type": "function",
          "function": {
            "name": "web_search",
            "arguments": "{\"query\": \"DeepSeek V3.1 AI model performance benchmarks specifications\", \"max_results\": 5}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"summary\": \"🔍 搜索查询: DeepSeek V3.1 AI model performance benchmarks specifications\\n📅 搜索时间: 2025-10-08T19:51:22.879696\\n\\n📝 AI摘要:\\nDeepSeek V3.1 is a hybrid AI model with fast reasoning and 128K context. It supports both thinking and non-thinking modes. It is known for strong tool use capabilities.\\n\\n---\\n\\n📊 搜索结果:\\n\\n1. DeepSeek V3.1 - Intelligence, Performance & Price Analysis\\n   🔗 https://artificialanalysis.ai/models/deepseek-v3-1-reasoning\\n   📄 | Llama 3.3 Nemotron Super 49B v1 (Reasoning) | NVIDIA | Open | 128k |  | | Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) | NVIDIA | Open | 128k |  | | Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) | NVI...\\n\\n2. DeepSeek-V3.1-Terminus\\n   🔗 https://api-docs.deepseek.com/news/news250922\\n   📄 [Skip to main content](https://api-docs.deepseek.com/news/news250922#__docusaurus_skipToContent_fallback) [English](https://api-docs.deepseek.com/news/news250922#) *   [English](https://api-docs.deeps...\\n\\n3. DeepSeek V3.1 Complete Evaluation Analysis: The New AI ...\\n   🔗 https://dev.to/czmilo/deepseek-v31-complete-evaluation-analysis-the-new-ai-programming-benchmark-for-2025-58jc\\n   📄 [![Image 1: Forem Logo](https://media2.dev.to/dynamic/image/width=65,height=,fit=scale-down,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fwalhydbusoe2o1...\\n\\n4. deepseek-ai/DeepSeek-V3.1 - Hugging Face\\n   🔗 https://huggingface.co/deepseek-ai/DeepSeek-V3.1\\n   📄 [DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) [Model card](https://huggingface.co/deepseek-ai/DeepSeek-V3.1)[Files Files and versions xet](https://huggingface.co/deepseek-ai/DeepSe...\\n\\n5. deepseek-v3.1 Model by Deepseek-ai - NVIDIA NIM APIs\\n   🔗 https://build.nvidia.com/deepseek-ai/deepseek-v3_1/modelcard\\n   📄 DeepSeek V3.1 Instruct is a hybrid AI model with fast reasoning, 128K context, and strong tool use. DeepSeek V3.1 Instruct is a hybrid AI model with fast reasoning, 128K context, and strong tool use. ...\"}",
      "timestamp": "2025-10-08T19:51:22.888530",
      "tool_call_id": "web_search:0",
      "name": "web_search"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:51:26.267226",
      "tool_calls": [
        {
          "id": "web_search:1",
          "index": 0,
          "type": "function",
          "function": {
            "name": "web_search",
            "arguments": "{\"max_results\": 5, \"query\": \"Qwen 2.5 Max AI model performance benchmarks specifications\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"summary\": \"搜索失败: 搜索失败: \"}",
      "timestamp": "2025-10-08T19:51:30.689980",
      "tool_call_id": "web_search:1",
      "name": "web_search"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:51:32.542605",
      "tool_calls": [
        {
          "id": "web_search:2",
          "index": 0,
          "type": "function",
          "function": {
            "name": "web_search",
            "arguments": "{\"max_results\": 5, \"query\": \"Qwen 2.5 Max AI model specifications performance\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"summary\": \"🔍 搜索查询: Qwen 2.5 Max AI model specifications performance\\n📅 搜索时间: 2025-10-08T19:51:38.276869\\n\\n📝 AI摘要:\\nQwen 2.5 Max is a high-performance AI model developed by Alibaba. It competes with other leading models like GPT-4o and DeepSeek V3. It is available via API and chat interfaces.\\n\\n---\\n\\n📊 搜索结果:\\n\\n1. Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE ...\\n   🔗 https://qwenlm.github.io/blog/qwen2.5-max/\\n   📄 Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model | Qwen Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model *   [简体中文](https://qwenlm.github.io/zh/blog/qwen2.5-max/) ![Ima...\\n\\n2. Qwen 2.5-Max: Features, DeepSeek V3 Comparison & More\\n   🔗 https://www.datacamp.com/blog/qwen-2-5-max\\n   📄 Learn about Alibaba's Qwen2.5-Max, a model that competes with GPT-4o, Claude 3.5 Sonnet, and DeepSeek V3. Qwen2.5-Max is Alibaba’s most powerful AI model to date, designed to compete with top-tier mod...\\n\\n3. Qwen2.5-Max Surpasses DeepSeek-V3 | by Cobus Greyling | Medium\\n   🔗 https://cobusgreyling.medium.com/qwen2-5-max-surpasses-deepseek-v3-c1f8dbec2508\\n   📄 The **Qwen2.5-Max** language model is the latest and most advanced iteration in the Qwen series, pushing the boundaries of AI performance. When it comes to **instruct models**, which are optimised for...\\n\\n4. Qwen 2.5: What It Is, How to Use It, and Key Features\\n   🔗 https://www.prismetric.com/qwen-2-5-what-it-is-and-how-to-use-it/\\n   📄 This allows businesses and developers to integrate Qwen’s capabilities into apps, websites, chatbots, and enterprise AI software development. ✅ **Real-Time Adaptability & Fine-Tuning** – Developers ma...\\n\\n5. Qwen2.5 & Comparison with Deepseek and ChatGPT - OpenCV\\n   🔗 https://opencv.org/blog/qwen/\\n   📄 *   [Mastering Generative AI for Art](https://opencv.org/university/course/mastering-generative-ai-for-art/?utm_source=opcv&utm_medium=menu) *   [Free Courses](https://opencv.org/university/free-cours...\"}",
      "timestamp": "2025-10-08T19:51:38.284095",
      "tool_call_id": "web_search:2",
      "name": "web_search"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:51:40.960099",
      "tool_calls": [
        {
          "id": "web_search:3",
          "index": 0,
          "type": "function",
          "function": {
            "name": "web_search",
            "arguments": "{\"max_results\": 5, \"query\": \"Kimi K2 AI model performance benchmarks specifications\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"summary\": \"🔍 搜索查询: Kimi K2 AI model performance benchmarks specifications\\n📅 搜索时间: 2025-10-08T19:51:46.394419\\n\\n📝 AI摘要:\\nKimi K2 is an open-source model excelling in advanced reasoning and coding tasks. It outperforms proprietary models on high-difficulty benchmarks. It scores 65.8% on SWE-bench and is available in Cline.\\n\\n---\\n\\n📊 搜索结果:\\n\\n1. Kimi-k2 Benchmarks explained\\n   🔗 https://medium.com/data-science-in-your-pocket/kimi-k2-benchmarks-explained-5b25dd6d3a3e\\n   📄 **Tau-2 Bench** focuses on advanced reasoning tasks like planning, multi-step problem solving, and tool use. ### **AIME 2025** and **GPQA-Diamond** are high-difficulty benchmarks aimed at testing the ...\\n\\n2. Kimi K2: Open Agentic Intelligence - Moonshot\\n   🔗 https://moonshotai.github.io/Kimi-K2/\\n   📄 Imagine using Kimi K2 to explore remote-work salaries with the Salary Data Analysis example, where 16 IPython calls generate stats, visualizations, and an interactive webpage of insights. For pre/post...\\n\\n3. Kimi K2 is the large language model series developed by Moonshot ...\\n   🔗 https://github.com/MoonshotAI/Kimi-K2\\n   📄 [Skip to content](https://github.com/MoonshotAI/Kimi-K2#start-of-content) [View license](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) *   [Code](https://github.com/MoonshotAI/Kimi-K2) *   ...\\n\\n4. Moonshot's Kimi K2 for Coding: Our First Impressions in Cline\\n   🔗 https://cline.bot/blog/moonshots-kimi-k2-for-coding-our-first-impressions-in-cline\\n   📄 # Moonshot's Kimi K2 for Coding: Our First Impressions in Cline Test Kimi K2, the open-source model achieving 65.8% on SWE-bench. Moonshot AI's Kimi K2 is now available in Cline. Kimi K2's coding perf...\\n\\n5. Kimi K2 - Intelligence, Performance & Price Analysis\\n   🔗 https://artificialanalysis.ai/models/kimi-k2\\n   📄 ![Image 4: Moonshot AI logo](https://artificialanalysis.ai/img/logos/moonshotai_small.svg)Kimi K2 Model Details *   [![Image 29: Alibaba logo](https://artificialanalysis.ai/img/logos/alibaba_small.svg...\"}",
      "timestamp": "2025-10-08T19:51:46.401036",
      "tool_call_id": "web_search:3",
      "name": "web_search"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:51:49.560042",
      "tool_calls": [
        {
          "id": "web_search:4",
          "index": 0,
          "type": "function",
          "function": {
            "name": "web_search",
            "arguments": "{\"max_results\": 5, \"query\": \"Gemini 2.5 Pro AI model performance benchmarks specifications\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"summary\": \"🔍 搜索查询: Gemini 2.5 Pro AI model performance benchmarks specifications\\n📅 搜索时间: 2025-10-08T19:51:56.752600\\n\\n📝 AI摘要:\\nGemini 2.5 Pro excels in advanced coding and reasoning. It supports multimodal inputs and has a large context window. It's available via Google AI Studio and Gemini API.\\n\\n---\\n\\n📊 搜索结果:\\n\\n1. Gemini 2.5 Pro | Generative AI on Vertex AI\\n   🔗 https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-pro\\n   📄 *   [English](https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-pro) *   [Deutsch](https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-pro?hl=de) *   [Italian...\\n\\n2. Gemini 2.5: Our most intelligent AI model - Google Blog\\n   🔗 https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/\\n   📄 [Skip to main content](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/#jump-content) [Share](https://blog.google/technology/google-deepmind/gemini-model-thinki...\\n\\n3. Gemini 2.5 Pro - Google DeepMind\\n   🔗 https://deepmind.google/models/gemini/pro/\\n   📄 [![Image 91](https://lh3.googleusercontent.com/6sGpsoYb7GRGRu_aWHAFl6z6tE2ArllKaSHAkGYQKemKl_oM5wtjJEm1pbA6VlGardymb3nTyA0UIY2gWxASl95FTo0LzyPo3z_aa_I4jTmhZhDmaA=w200-h115-n-nu) Models Advanced audio ...\\n\\n4. Gemini 2.5 Pro - Intelligence, Performance & Price Analysis\\n   🔗 https://artificialanalysis.ai/models/gemini-2-5-pro\\n   📄 Analysis of Google's Gemini 2.5 Pro and comparison to other AI models across key metrics including quality, price, performance (tokens per second & time to first token), context window & more. ## Arti...\\n\\n5. Google AI Plans and Features\\n   🔗 https://one.google.com/intl/en/about/google-ai-plans/\\n   📄 *   **Gemini app:** Get higher access to our most capable model 2.5 Pro and Deep Research on 2.5 Pro, plus unlock video generation with limited access to Veo 3 Fast[3](https://one.google.com/intl/en/a...\"}",
      "timestamp": "2025-10-08T19:51:56.761995",
      "tool_call_id": "web_search:4",
      "name": "web_search"
    },
    {
      "role": "user",
      "content": "等一下，不用创建html了，创建一个txt文件，把你搜集到的关键数据信息，注意只要数据信息，不要太多文字描述写进去就行了",
      "timestamp": "2025-10-08T19:52:55.358197"
    },
    {
      "role": "assistant",
      "content": "好的，我来创建一个包含关键数据信息的txt文件。",
      "timestamp": "2025-10-08T19:53:00.296041"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:53:00.302060",
      "tool_calls": [
        {
          "id": "create_file:5",
          "index": 0,
          "type": "function",
          "function": {
            "name": "create_file",
            "arguments": "{\"path\": \"ai_models_data.txt\", \"file_type\": \"txt\", \"annotation\": \"AI模型关键数据信息汇总\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"path\": \"ai_models_data.txt\", \"size\": 0, \"message\": \"已创建空文件: ai_models_data.txt。请使用 append_to_file 追加正文内容，或使用 modify_file 进行小范围替换。\"}",
      "timestamp": "2025-10-08T19:53:02.133813",
      "tool_call_id": "create_file:5",
      "name": "create_file"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:53:05.803426",
      "tool_calls": [
        {
          "id": "append_to_file:6",
          "index": 0,
          "type": "function",
          "function": {
            "name": "append_to_file",
            "arguments": "{\"path\": \"ai_models_data.txt\", \"reason\": \"添加AI模型关键数据信息\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"awaiting_content\": true, \"path\": \"ai_models_data.txt\", \"message\": \"\\n请按照以下格式输出需要追加到文件的完整内容，禁止输出任何解释性文字：\\n<<<APPEND:ai_models_data.txt>>>\\n(在此行之后紧接着写入要追加的全部内容，可包含多行代码)\\n<<<END_APPEND>>>\\n若追加完成后仍需继续写入，请再次调用 append_to_file。\"}",
      "timestamp": "2025-10-08T19:53:07.617147",
      "tool_call_id": "append_to_file:6",
      "name": "append_to_file"
    },
    {
      "role": "assistant",
      "content": "已向 ai_models_data.txt 追加 27 行（666 字节）",
      "timestamp": "2025-10-08T19:53:20.780744"
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"path\": \"ai_models_data.txt\", \"lines\": 27, \"bytes\": 666, \"forced\": false, \"message\": \"已向 ai_models_data.txt 追加 27 行（666 字节）\"}",
      "timestamp": "2025-10-08T19:53:20.790316",
      "tool_call_id": "append_to_file:6",
      "name": "append_to_file"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:53:22.884045",
      "tool_calls": [
        {
          "id": "append_to_file:7",
          "index": 0,
          "type": "function",
          "function": {
            "name": "append_to_file",
            "arguments": "{\"path\": \"ai_models_data.txt\", \"reason\": \"添加Qwen 2.5 Max数据信息\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"awaiting_content\": true, \"path\": \"ai_models_data.txt\", \"message\": \"\\n请按照以下格式输出需要追加到文件的完整内容，禁止输出任何解释性文字：\\n<<<APPEND:ai_models_data.txt>>>\\n(在此行之后紧接着写入要追加的全部内容，可包含多行代码)\\n<<<END_APPEND>>>\\n若追加完成后仍需继续写入，请再次调用 append_to_file。\"}",
      "timestamp": "2025-10-08T19:53:24.698526",
      "tool_call_id": "append_to_file:7",
      "name": "append_to_file"
    },
    {
      "role": "assistant",
      "content": "已向 ai_models_data.txt 追加 6 行（203 字节）",
      "timestamp": "2025-10-08T19:53:29.729042"
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"path\": \"ai_models_data.txt\", \"lines\": 6, \"bytes\": 203, \"forced\": false, \"message\": \"已向 ai_models_data.txt 追加 6 行（203 字节）\"}",
      "timestamp": "2025-10-08T19:53:29.736100",
      "tool_call_id": "append_to_file:7",
      "name": "append_to_file"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:53:31.444737",
      "tool_calls": [
        {
          "id": "append_to_file:8",
          "index": 0,
          "type": "function",
          "function": {
            "name": "append_to_file",
            "arguments": "{\"path\": \"ai_models_data.txt\", \"reason\": \"添加Kimi K2数据信息\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"awaiting_content\": true, \"path\": \"ai_models_data.txt\", \"message\": \"\\n请按照以下格式输出需要追加到文件的完整内容，禁止输出任何解释性文字：\\n<<<APPEND:ai_models_data.txt>>>\\n(在此行之后紧接着写入要追加的全部内容，可包含多行代码)\\n<<<END_APPEND>>>\\n若追加完成后仍需继续写入，请再次调用 append_to_file。\"}",
      "timestamp": "2025-10-08T19:53:33.259553",
      "tool_call_id": "append_to_file:8",
      "name": "append_to_file"
    },
    {
      "role": "assistant",
      "content": "已向 ai_models_data.txt 追加 10 行（301 字节）",
      "timestamp": "2025-10-08T19:53:37.298829"
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"path\": \"ai_models_data.txt\", \"lines\": 10, \"bytes\": 301, \"forced\": false, \"message\": \"已向 ai_models_data.txt 追加 10 行（301 字节）\"}",
      "timestamp": "2025-10-08T19:53:37.304428",
      "tool_call_id": "append_to_file:8",
      "name": "append_to_file"
    },
    {
      "role": "assistant",
      "content": "",
      "timestamp": "2025-10-08T19:53:41.064603",
      "tool_calls": [
        {
          "id": "append_to_file:9",
          "index": 0,
          "type": "function",
          "function": {
            "name": "append_to_file",
            "arguments": "{\"path\": \"ai_models_data.txt\", \"reason\": \"添加Gemini 2.5 Pro数据信息\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"awaiting_content\": true, \"path\": \"ai_models_data.txt\", \"message\": \"\\n请按照以下格式输出需要追加到文件的完整内容，禁止输出任何解释性文字：\\n<<<APPEND:ai_models_data.txt>>>\\n(在此行之后紧接着写入要追加的全部内容，可包含多行代码)\\n<<<END_APPEND>>>\\n若追加完成后仍需继续写入，请再次调用 append_to_file。\"}",
      "timestamp": "2025-10-08T19:53:42.880307",
      "tool_call_id": "append_to_file:9",
      "name": "append_to_file"
    },
    {
      "role": "assistant",
      "content": "已向 ai_models_data.txt 追加 12 行（392 字节）",
      "timestamp": "2025-10-08T19:53:46.549814"
    },
    {
      "role": "tool",
      "content": "{\"success\": true, \"path\": \"ai_models_data.txt\", \"lines\": 12, \"bytes\": 392, \"forced\": false, \"message\": \"已向 ai_models_data.txt 追加 12 行（392 字节）\"}",
      "timestamp": "2025-10-08T19:53:46.558688",
      "tool_call_id": "append_to_file:9",
      "name": "append_to_file"
    },
    {
      "role": "assistant",
      "content": "<<<APPEND:ai_models_data.txt>>>\nGemini 2.5 Pro:\n- Developer: Google DeepMind\n- Model Type: Multimodal AI\n- Context Window: Large (exact size not specified)\n- Special Features: Advanced coding, reasoning, multimodal inputs\n- Availability: Google AI Studio, Gemini API\n- Performance Focus: Advanced coding and reasoning tasks\n- Key Strengths: Multimodal capabilities, large context understanding\n<<<END_APPEND>>>",
      "timestamp": "2025-10-08T19:53:50.253995"
    }
  ],
  "metadata": {
    "project_path": "/Users/jojo/Desktop/agents/正在修复中/agent_kimi_源码备份/project",
    "thinking_mode": true,
    "total_messages": 33,
    "total_tools": 24,
    "status": "active"
  },
  "token_statistics": {
    "total_input_tokens": 233192,
    "total_output_tokens": 766,
    "updated_at": "2025-10-08T19:54:16.471988"
  }
}