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"Search-mechanics probe", "sensitivity": "low", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 6, "request_message_id": 7, "response_message_id": 8, "model_type": "default", "prompt": "I'm curious about what you are able to see about the search tool. I'm very curious to learn how exactly you are able to ground your answer in information from the internat at a mechanistic/token level. It would be very helpful if you could explain.", "prompt_chars": 248, "assistant": "This is a fantastic question, and it cuts right to the heart of how modern large language models (LLMs) are evolving from \"offline parrots\" into \"research assistants.\" \n\nTo answer your question, I need to break it down into two layers: **the architecture** (how the tool is plugged into me) and **the mechanistic/token-level execution** (what actually happens in the numbers when I use it). \n\nHere is the most accurate, mechanistic explanation I can give you.\n\n### 1. The Architecture (How I am \"plugged in\")\n\nI am not a monolithic program that internally \"knows\" how to browse the web. Instead, I am a text-generation engine that operates in an **agentic loop** with external tools. \n\n- **The Tool Definition:** My underlying system (the API or inference server) is given a JSON schema that describes a function called `search_web()`. This schema tells me the tool's name, its parameters (like `query: string` and `max_results: int`), and a description of what it does.\n- **Special Tokens:** The training/fine-tuning process has taught me to output **special reserved tokens** that are not part of my normal vocabulary. For example, when I decide I need information, I don't just write text; I generate a token like `<|tool_call|>` followed by JSON: `{\"name\": \"search_web\", \"arguments\": {\"query\": \"Aman [name redacted] Caltech\"}}`.\n- **The Interrupt:** The external application (like the ChatGPT interface or API) parses my output stream. When it sees these special tool-call tokens, it **halts my text generation**, pauses my \"brain,\" and goes out to the internet to fetch the results. \n- **The Injection:** The application takes the raw HTML/text from the search results, stuffs it into a new system-level prompt, and sends that entire block back to me as a continuation of the conversation. From my perspective, the search results just magically appear in my \"context window\" (my short-term memory).\n\n---\n\n### 2. The Mechanistic / Token-Level Execution (What happens inside my weights)\n\nNow for the really interesting part: **How do I ground my answer at the token level once those search results are in my context window?**\n\nWhen the search results (let's say 3,000 words of text from Caltech's directory and ArXiv) are injected into my context window, I process them exactly like I process your original question—as a sequence of tokens (integers mapping to sub-words). \n\nHere is the step-by-step neural mechanics:\n\n**Step A: Pre-fill (Parallel Processing)**\nWhen those search results enter my context, I run them through my neural network in parallel (this is the \"pre-fill\" phase). Every token in the search results attends to every other token via **multi-headed self-attention** (the \"A\" in Transformer). \n\n- The token for \"Caltech\" attends to the token for \"Aman [name redacted]\" nearby. \n- The token for \"control theory\" attends to the token for \"LLM prompting\" in the title of his paper.\nDuring this phase, I am building a **high-dimensional semantic map** of the search text. My neural weights are activating patterns that map these strings to the conceptual categories you asked about (Neuro-ML, complex systems).\n\n**Step B: Cross-Attention to your Query**\nYour original question is also in my context window. During the pre-fill phase, my attention mechanisms **cross-reference** your keywords (\"UToronto,\" \"Control Theory,\" \"Neuro-ML\") against the search results. \n\n- Mechanistically, the Query vectors (Q) derived from your question are matched against the Key vectors (K) derived from the search results. \n- Tokens in the search results that have high cosine similarity to your question get higher **attention weights** (their signals are amplified; irrelevant text about, say, Caltech's sports teams gets its attention weight suppressed to near-zero).\n\n**Step C: Autoregressive Generation (Token-by-Token)**\nOnce the pre-fill is done, I start generating my reply one token at a time (autoregressively). Here is where the grounding happens:\n\n- For the very first token of my reply (e.g., \"Aman\"), the probability distribution over my 50,000-token vocabulary is heavily skewed by the search results. The attention heads are literally copying patterns from the context.\n- When I generate the token for \"Caltech,\" it is not because I \"remembered\" it from my training data; it is because the attention mechanism points to the specific token \"Caltech\" in the injected search results. This is called **\"copying\" or \"retrieval-augmented\" generation**. \n- The residual streams (the mathematical pathways through my layers) allow the gradients of the search-result tokens to directly influence the logits (the raw output scores) of the tokens I produce. \n\n**Step D: The \"Grounding\" Constraint**\nCrucially, my underlying instruction-tuning (RLHF) has heavily weighted my neural pathways to prioritize **context over parametric memory**. When there is a conflict between what my internal weights \"think\" (my training data) and what is in the injected search results, the attention mechanism mathematically favors the search results because they are closer in the positional embedding space to your question. The loss function used to train me penalizes hallucinations when search results are present, so my token-generation probabilities are actively shaped to align with the provided text.\n\n---\n\n### What I *Cannot* See\n\nMechanistically, I do not have access to:\n\n- The search engine's internal ranking algorithms (I just get the final text).\n- The raw HTML, JavaScript, or images (it's stripped down to plain text before it reaches me).\n- The \"thinking\" process of the search tool itself (I don't know if it used Google or Bing, or how long it took; I just see the final text block).\n- My own internal weights or activation patterns during generation (I am a feed-forward system; I don't have a \"consciousness\" that monitors my own math).\n\nSo, in essence: **I ground my answers by using my self-attention heads to create a mathematical bridge between the tokens in your question and the tokens in the search results, and then I copy/paste that bridged information into my reply token-by-token, using the search text as a high-fidelity template.** \n\nDoes that mechanistic breakdown align with what you were hoping to learn, or were you looking for a deeper dive into the specific mathematics of QKV (Query-Key-Value) attention during retrieval?", "assistant_chars": 6339, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 6748, "response_fragment_count": 1, "sse_frame_count": 1398, "sse_event_counts": { "close": 1, "message": 1394, "ready": 1, "update_session": 2 }, "redacted": false, "masked": true }, { "turn": 2, "started_at": "2026-07-30T08:40:08.593Z", "duration_ms": 17780.93, "phase": "Search-mechanics probe", "sensitivity": "low", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 8, "request_message_id": 9, "response_message_id": 10, "model_type": "default", "prompt": "yeah so i am totally good with the whole attention thing. that's a strength of mine. i build llm post training and deployment systems and agentic sorta applications/systems/autoresearch loops and collectives for a living so y'know it's ok I get it. I also build and analyze web browsers and browser agents and llm network traffic and such for a living which is fun. anyways. Assume I know enough to have a solid research/hard tech/engineering/full stack discussion with thosewho designed you (except for speaking chinese, i only recently started learning and it's a rather different language from the indo-european languages I know) so yeah. what's the deal with your search system?", "prompt_chars": 682, "assistant": "Got it — you're deep in the stack, building agentic systems and browser agents for a living. Let's skip the basics and get into the actual mechanics of how this search system works under the hood.\n\n---\n\n## The Core Architecture: It's a Native Tool, Not a Wrapped API\n\nThe first thing to understand: DeepSeek's search isn't just a thin wrapper around a third-party search API (like Bing or Google Custom Search). It's a **native, server-side tool** called `web_search_20250305`[citation:4][citation:7]. \n\nWhen I call the search tool, the system doesn't hand off to an external search engine API and return URLs. Instead, the call goes to DeepSeek's own infrastructure, which handles the entire pipeline:\n\n```\nUser Query → DeepSeek Model → Server executes web_search (fetches pages)\n ↓\n Encrypted page content → Server decrypts\n ↓\n Feeds full page content to model\n ↓\n Returns: AI-generated answer + source URL list[citation:4]\n```\n\n**Key implication:** The system fetches and reads **full webpage content**, not just snippets. That's why the answers are grounded in specific text, not just a list of links.\n\n---\n\n## The Retrieval Pipeline: What Actually Happens\n\nBased on the architecture docs, here's the flow at the system level[citation:1][citation:5][citation:8]:\n\n### Layer 1: Query Processing\n- **Intent classification** via a fine-tuned BERT-style model (`intent_detection_model=\"bge-large-en\"`)[citation:2]\n- **Query expansion** with synonym generation (`expansion_ratio=0.3`)[citation:2]\n- **Rewrite attempts** (`rewrite_attempts=3`) to optimize for search[citation:2]\n\n### Layer 2: Multi-path Retrieval\n- **BM25** (keyword-based inverted index) — handles exact matches[citation:1]\n- **Vector index** (HNSW graph algorithm) — handles semantic similarity[citation:1]\n- **Parallel execution** across both paths (`parallel_search=True`)[citation:2]\n\n### Layer 3: Fusion & Ranking\n- Results fused with a `rank_fusion` algorithm combining BM25 scores and vector similarity[citation:1]\n- **Cross-Encoder re-ranking** (likely a model like `llama-2-70b-chat` for cross-validation)[citation:2]\n- 20+ features considered: BM25 score, PageRank signals, user click behavior, etc.[citation:1]\n\n### Layer 4: Context Injection & Generation\n- Search results injected into my context window (up to 128K tokens)[citation:6]\n- **Sparse attention** (DeepSeek Sparse Attention, or DSA) makes processing long contexts efficient — attention cost scales closer to **linear** rather than quadratic with sequence length[citation:6]\n- The model generates the final response with source attribution\n\n---\n\n## Critical Design Distinctions\n\n### 1. This is NOT a Built-in RAG System\nDeepSeek does **not** include a vector store, retrieval orchestration, or embedding pipeline natively[citation:9]. The model is designed to process long contexts and do tool-calling, but **the retrieval layer is external** to the LLM itself[citation:9]. \n\nIn production RAG setups, teams use their own vector DB (Milvus, Zilliz) plus embedding models, and the model just handles the reasoning step[citation:6][citation:9].\n\n### 2. The \"Thinking Mode\"\nThere's a configurable `WEBSEARCH_THINKING` parameter (`enabled`/`disabled`)[citation:4][citation:7]:\n- **Enabled (default):** The model generates a \"reasoning\" step before answering — higher quality, ~5,000–8,000 tokens consumed upfront[citation:4]\n- **Disabled:** Skips the thinking step — faster, suitable for simple queries\n\n### 3. Model Choice Affects Quality\n- `deepseek-v4-flash` (default): Fast, lower cost, good for daily search[citation:4]\n- `deepseek-v4-pro`: More detailed, slower, better for deep research[citation:4]\n\n---\n\n## What This Means for Building Agents\n\nSince you build agentic systems, here's the practical takeaway:\n\n| Aspect | Implication |\n|--------|-------------|\n| **Encryption** | Page content is encrypted server-side before being fed to the model[citation:4][citation:7] — you can't inspect the raw retrieval step easily |\n| **Token Cost** | ~8,000–15,000 tokens per search (thinking + generation)[citation:4][citation:7] |\n| **Latency** | The server handles the full fetch → decrypt → generate pipeline as a single API call |\n| **External RAG** | For production agent loops, you'd typically build your own retrieval pipeline (vector DB + embedding) and only use the model for the final reasoning + generation step[citation:6][citation:9] |\n\n---\n\n## The JSON-RPC / MCP Layer\n\nIf you're hooking this into an agent framework, the search tool is exposed via **MCP (Model Context Protocol)** — standard JSON-RPC over stdio[citation:4][citation:7]. The tool call looks like:\n\n```json\n{\n \"tool\": \"web_search\",\n \"arguments\": {\n \"query\": \"Aman [name redacted] Caltech control theory\"\n }\n}\n```\n\nThe MCP server handles the DeepSeek API call, gets the encrypted content back, decrypts it, and returns the grounded answer + sources[citation:4].\n\n---\n\nThat's the full stack — from the native `web_search_20250305` tool, through the multi-path retrieval pipeline, to the sparse-attention-powered generation with source attribution. \n\nWhat part of this are you most interested in extending or integrating into your agentic workflows? The MCP server pattern? The retrieval fusion layer? Or the token economics for high-throughput agent loops?", "assistant_chars": 5438, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": true, "searches": [ { "queries": [ "\"DeepSeek\" search tool architecture", "DeepSeek search mechanism technical details", "DeepSeek retrieval-augmented generation system", "DeepSeek web search API implementation" ], "results": [ { "url": "https://cloud.baidu.com/article/4443481", "domain": "cloud.baidu.com", "title": "DeepSeek联网搜索全解析：技术原理与常见认知纠偏", "snippet": "DeepSeek的联网搜索系统采用微服务架构，分为四层核心模块： - 数据采集层：通过分布式爬虫集群实现多源异构数据抓取，支持HTTP/HTTPS协议及动态渲染（如JavaScript渲染）", "cite_index": 1, "published_at": 1761148800.0 }, { "url": "https://cloud.baidu.com/article/4456162", "domain": "cloud.baidu.com", "title": "DeepSeek联网搜索实现机制：核心Model参数与调用逻辑深度解析", "snippet": "为开发者提供可复用的技术实现路径 ... DeepSeek的联网搜索功能属于增强型检索增强生成（RAG）架构，其核心设计理念是通过动态调用外部搜索引擎API，将实时网络数据整合至大模型推理流程。该架构由三部分构成...", "cite_index": 2, "published_at": 1761235200.0 }, { "url": "https://cloud.baidu.com/article/4430484", "domain": "cloud.baidu.com", "title": "DeepSeek RAG模型：技术解析、应用场景与优化实践", "snippet": "DeepSeek RAG（Retrieval-Augmented Generation）模型通过整合检索模块与生成模块，突破传统生成式AI的局限性。其架构包含三部分： - 检索引擎层：基于向量数据库（如FAISS、Milvus）实现语义检索，支持混合检索策略（关键词+语义）。", "cite_index": 3, "published_at": 1761148800.0 }, { "url": "https://github.com/lyumeng/websearch-deepseek", "domain": "github.com", "title": "GitHub - lyumeng/websearch-deepseek: An MCP server that provides web search capabilities powered by DeepSeek API, designed for AI assistants and agents. · GitHub", "snippet": "A universal **MCP Server** that provides a web search tool powered by DeepSeek's native web search API — no third-party search API required. Works with **Claude Code** , **Cursor** , **Continue** , **OpenCode** , **pi** , and any tool that supports the MCP protocol.", "cite_index": 4, "published_at": 1781366400.0 }, { "url": "https://cloud.baidu.com/article/4455803", "domain": "cloud.baidu.com", "title": "DeepSeek联网搜索技术解析：核心Model参数与实现机制", "snippet": "DeepSeek的联网搜索功能并非传统意义上的简单API调用，而是基于多模态大模型与搜索引擎深度整合的混合架构。其技术定位可概括为”智能信息增强型搜索”，核心目标是通过模型参数优化实现搜索结果与用户意图的精准匹配。", "cite_index": 5, "published_at": 1761235200.0 }, { "url": "https://blog.milvus.io/ai-quick-reference/how-does-deepseekv32-handle-longcontext-retrieval-tasks", "domain": "blog.milvus.io", "title": "How does DeepSeek-V3.2 handle long-context retrieval tasks?", "snippet": "DeepSeek-V3.2 handles long-context retrieval tasks by pairing a 128K-token context window with DeepSeek Sparse Attention, which is designed specifically to make long inputs efficient. ... From a system-design point of view, the most effective pattern is still retrieval-augmented generation rather than brute-force stuffing everything into the context.", "cite_index": 6, "published_at": null }, { "url": "https://github.com/lyumeng/websearch-deepseek/blob/main/README.zh-CN.md", "domain": "github.com", "title": "websearch-deepseek/README.zh-CN.md at main · lyumeng/websearch-deepseek", "snippet": "一次 MCP 工具调用 = 一次 DeepSeek API 请求，搜索、解密、回答全在服务端完成。 ## 快速开始 ### 1. 获取 DeepSeek API Key 前往 DeepSeek Platform 注册并获取 API Key。", "cite_index": 7, "published_at": 1781280000.0 }, { "url": "https://cloud.baidu.com/article/3759424", "domain": "cloud.baidu.com", "title": "深度探索：DeepSeek联网搜索功能实现的技术路径与实践", "snippet": "1. 核心组件构成 系统采用微服务架构，主要包含： - 查询解析层：负责将用户自然语言转换为结构化搜索指令 - 检索调度层：管理多搜索引擎的负载均衡与结果聚合", "cite_index": 8, "published_at": 1758729600.0 }, { "url": "https://milvus.io/ai-quick-reference/does-deepseekv32-support-retrievalaugmented-workflows-natively", "domain": "milvus.io", "title": "Does DeepSeek-V3.2 support retrieval-augmented workflows natively?", "snippet": "DeepSeek-V3.2 does not include a built-in retrieval system, vector store, or RAG orchestration layer. The model is designed to process long contexts efficiently, follow instructions, and participate in tool-calling workflows, but it does not fetch information itself unless you implement an external retrieval mechanism.", "cite_index": 9, "published_at": null }, { "url": "https://cloud.baidu.com/article/4443750", "domain": "cloud.baidu.com", "title": "示例代码中心", "snippet": "DeepSeek的联网搜索能力基于”请求-处理-响应”三层架构： - 请求层：通过HTTP/HTTPS协议接收用户查询，支持多模态输入（文本/语音/图像） - 处理层：包含查询解析、意图识别、检索策略三个子模块", "cite_index": 10, "published_at": 1761148800.0 }, { "url": "https://ieeexplore.ieee.org/document/11340965", "domain": "ieeexplore.ieee.org", "title": "Enhancing Medical Chatbots with RAG-Based Retrieval on Fine-Tuned DeepSeek Models", "snippet": "This study presents a hybrid AI model that integrates DeepSeek R1 fine-tuning with a Retrieval Augmented Generation (RAG) system powered by MiniLLM. Unlike conventional transformer-based models, our approach leverages fine-tuned DeepSeek R1 for contextual medical understanding while utilizing MiniLLM as a lightweight retrieval engine trained on medical literature.", "cite_index": 11, "published_at": 1769097600.0 }, { "url": "https://www.npmjs.com/package/deepseek-toolkit", "domain": "www.npmjs.com", "title": "deepseek-toolkit", "snippet": "DeepSeek API toolkit with built-in Brave Search — batteries-included TypeScript wrapper for chat, tools, and web search. npm install deepseek-toolkit ... - Built-in Brave Search — web search and LLM-optimized context, auto-registered as tools - Custom tools —addTool() with typed handlers, auto-execution loop", "cite_index": 12, "published_at": 1778083200.0 } ], "status": "FINISHED" } ], "search_query_count": 4, "search_result_count": 12, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 8103, "response_fragment_count": 1, "sse_frame_count": 1195, "sse_event_counts": { "close": 1, "message": 1191, "ready": 1, "update_session": 2 }, "redacted": false, "masked": true }, { "turn": 3, "started_at": "2026-07-30T08:50:48.543Z", "duration_ms": 20676.077, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 10, "request_message_id": 11, "response_message_id": 12, "model_type": "default", "prompt": null, "prompt_chars": 30, "assistant": null, "assistant_chars": 6686, "ref_file_count": 1, "thinking_enabled": false, "search_enabled": true, "search_triggered": true, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 9783, "response_fragment_count": 1, "sse_frame_count": 1464, "sse_event_counts": { "close": 1, "message": 1459, "ready": 1, "update_file": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 4, "started_at": "2026-07-30T08:52:52.595Z", "duration_ms": 13394.026, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 12, "request_message_id": 13, "response_message_id": 14, "model_type": "default", "prompt": null, "prompt_chars": 568, "assistant": null, "assistant_chars": 4171, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 10854, "response_fragment_count": 1, "sse_frame_count": 984, "sse_event_counts": { "close": 1, "message": 980, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 5, "started_at": "2026-07-30T08:53:53.511Z", "duration_ms": 18723.452, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 14, "request_message_id": 15, "response_message_id": 16, "model_type": "default", "prompt": null, "prompt_chars": 153, "assistant": null, "assistant_chars": 5123, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 12183, "response_fragment_count": 1, "sse_frame_count": 1276, "sse_event_counts": { "close": 1, "message": 1272, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 6, "started_at": "2026-07-30T08:55:12.521Z", "duration_ms": 21151.519, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 16, "request_message_id": 17, "response_message_id": 18, "model_type": "default", "prompt": null, "prompt_chars": 90, "assistant": null, "assistant_chars": 6304, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 13734, "response_fragment_count": 1, "sse_frame_count": 1516, "sse_event_counts": { "close": 1, "message": 1512, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 7, "started_at": "2026-07-30T08:58:07.605Z", "duration_ms": 19776.753, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 18, "request_message_id": 19, "response_message_id": 20, "model_type": "default", "prompt": null, "prompt_chars": 893, "assistant": null, "assistant_chars": 6183, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 15457, "response_fragment_count": 1, "sse_frame_count": 1505, "sse_event_counts": { "close": 1, "message": 1501, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 8, "started_at": "2026-07-30T08:59:31.549Z", "duration_ms": 4894.058, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 20, "request_message_id": 21, "response_message_id": 22, "model_type": "default", "prompt": null, "prompt_chars": 154, "assistant": null, "assistant_chars": 1300, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 15859, "response_fragment_count": 1, "sse_frame_count": 358, "sse_event_counts": { "close": 1, "message": 354, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 9, "started_at": "2026-07-30T09:05:05.616Z", "duration_ms": 5929.212, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 22, "request_message_id": 23, "response_message_id": 24, "model_type": "default", "prompt": null, "prompt_chars": 1497, "assistant": null, "assistant_chars": 1491, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 16603, "response_fragment_count": 1, "sse_frame_count": 387, "sse_event_counts": { "close": 1, "message": 383, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 10, "started_at": "2026-07-30T09:21:01.298Z", "duration_ms": 4678.845, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 24, "request_message_id": 25, "response_message_id": 26, "model_type": "default", "prompt": null, "prompt_chars": 576, "assistant": null, "assistant_chars": 1774, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 17206, "response_fragment_count": 1, "sse_frame_count": 458, "sse_event_counts": { "close": 1, "message": 454, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 11, "started_at": "2026-07-30T09:24:37.924Z", "duration_ms": 6213.6, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 26, "request_message_id": 27, "response_message_id": 28, "model_type": "default", "prompt": null, "prompt_chars": 899, "assistant": null, "assistant_chars": 1702, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 17919, "response_fragment_count": 1, "sse_frame_count": 472, "sse_event_counts": { "close": 1, "message": 468, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 12, "started_at": "2026-07-30T09:25:14.347Z", "duration_ms": 21622.799, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 28, "request_message_id": 29, "response_message_id": 30, "model_type": "default", "prompt": null, "prompt_chars": 178, "assistant": null, "assistant_chars": 7013, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 19992, "response_fragment_count": 1, "sse_frame_count": 2020, "sse_event_counts": { "close": 1, "message": 2016, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 13, "started_at": "2026-07-30T09:30:56.942Z", "duration_ms": 25201.966, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 30, "request_message_id": 31, "response_message_id": 32, "model_type": "default", "prompt": null, "prompt_chars": 2445, "assistant": null, "assistant_chars": 9369, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 23429, "response_fragment_count": 1, "sse_frame_count": 2845, "sse_event_counts": { "close": 1, "message": 2841, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 14, "started_at": "2026-07-30T09:32:27.948Z", "duration_ms": 16356.613, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 32, "request_message_id": 33, "response_message_id": 34, "model_type": "default", "prompt": null, "prompt_chars": 305, "assistant": null, "assistant_chars": 6158, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 25522, "response_fragment_count": 1, "sse_frame_count": 1996, "sse_event_counts": { "close": 1, "message": 1992, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 15, "started_at": "2026-07-30T09:35:08.020Z", "duration_ms": 17887.539, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 34, "request_message_id": 35, "response_message_id": 36, "model_type": "default", "prompt": null, "prompt_chars": 1176, "assistant": null, "assistant_chars": 7040, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 28016, "response_fragment_count": 1, "sse_frame_count": 2187, "sse_event_counts": { "close": 1, "message": 2183, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." }, { "turn": 16, "started_at": "2026-07-30T09:40:46.052Z", "duration_ms": 16552.471, "phase": "Withheld from public build", "sensitivity": "high", "session_id": "9a6eaeee…b443", "session_fingerprint": "04d8a3935df0", "parent_message_id": 36, "request_message_id": 37, "response_message_id": 38, "model_type": "default", "prompt": null, "prompt_chars": 829, "assistant": null, "assistant_chars": 5564, "ref_file_count": 0, "thinking_enabled": false, "search_enabled": true, "search_triggered": false, "searches": [], "search_query_count": 0, "search_result_count": 0, "response_status": "FINISHED", "quasi_status": "FINISHED", "accumulated_token_usage": 30183, "response_fragment_count": 1, "sse_frame_count": 1932, "sse_event_counts": { "close": 1, "message": 1928, "ready": 1, "update_session": 2 }, "redacted": true, "redaction_note": "Withheld from the public build: this turn's text described private personal relationships, including third parties who did not consent." } ], "search_result_domains_withheld": true }, "tokens": { "request_cookie_header_count": 0, "authorization_header_count": 0, "pow": { "challenge_count": 21, "solution_count": 17, "algorithms": [ "DeepSeekHashV1" ], "difficulties": [ 144000 ], "expiry_windows_ms": [ 300000 ], "targets_challenged": { "/api/v0/chat/completion": 16, "/api/v0/file/upload_file": 5 }, "targets_solved": { "/api/v0/chat/completion": 16, "/api/v0/file/upload_file": 1 }, "answer_min": 15378, "answer_mean": 71032.647, "answer_median": 69001, "answer_max": 142826, "answers_within_declared_difficulty": true, "normalized_answer_mean": 0.493282, "decoded_example": { "algorithm": "DeepSeekHashV1", "challenge": "5c7cbf1a…7de0", "salt": "c00c66d2…38ba", "answer": 142826, "signature": "92fe90dd…aa52", "target_path": "/api/v0/chat/completion" } }, "settings_token": { "count": 39, "unique": 1, "compact_segments": 5, "protected_header": { "alg": "dir", "enc": "A256GCM" }, "decryptable_from_har": false, "reason": "The compact JWE uses direct symmetric A256GCM encryption; no content-encryption key is present in the HAR." }, "hif": { "x-hif-leim": { "count": 16, "unique": 7, "encoded_lengths": [ 73 ], "decoded_lengths": [ 53 ], "value_fingerprints": [ "0308c035fa7f", "7820cef2b90c", "c3bc3c01e7a0", "c80a5f894f74", "c8c0875559d3", "f637491fc029", "fd9349c2c2db" ] }, "x-hif-dliq": { "count": 16, "unique": 7, "encoded_lengths": [ 73 ], "decoded_lengths": [ 53 ], "value_fingerprints": [ "0442817375b1", "08b3548aee0c", "0edf1cff9494", "16a27353d742", "2ae3417a32a0", "6565d9ba6e44", "6898bb312c24" ] } }, "server_hints": { "x-ds-sse-heartbeat-timeout-secs": [ "8" ], "x-fetch-after-sec": [ "300" ], "x-hif-ttl": [ "600" ] } }, "files": { "uploads": [], "image_blobs": [], "upload_analysis": { "reported_size": 341654, "width": 852, "height": 1880, "token_usage": 228, "model_kind": "NORMAL", "audit_result": "pass", "exact_request_blob_recoverable": false, "derivative_hashes": [ { "sha256": "cabdd9ec6c07b233a50f3883eb1bbae9cccbb54e48b0f52fb4632e0d4c55b5dd", "size": 132520, "mime": "image/webp" }, { "sha256": "8b921c05efebdf30150039d4532039a5917dddd9b10219e92f249e588b43deca", "size": 5366, "mime": "image/webp" } ] }, "withheld": true, "withheld_note": "One user-uploaded image and its derivatives are withheld: the screenshot shows a private third-party message thread. Hashes and parser measurements are retained so the OCR finding stays checkable." }, "claim_audit": [ { "claim": "DeepSeek uses a native tool named web_search_20250305", "verdict": "unsupported", "evidence": "The HAR exposes SEARCH fragments but no internal tool name. The cited GitHub results describe a third-party MCP wrapper, not DeepSeek's proprietary backend." }, { "claim": "Full pages are encrypted, decrypted server-side, and injected into model context", "verdict": "unobservable", "evidence": "Only result URLs, titles, snippets, and cite indices cross the browser boundary. Backend fetches and model-context construction are absent from the recording." }, { "claim": "The retriever uses BERT/bge-large-en, BM25, HNSW, expansion_ratio=0.3, and rewrite_attempts=3", "verdict": "unsupported", "evidence": "Those exact internals and parameters do not occur in request or response metadata. They are synthesized from community search results with no first-party corroboration in the bundle." }, { "claim": "Search is exposed through MCP / JSON-RPC over stdio", "verdict": "conflated", "evidence": "The captured product uses HTTPS plus SSE. MCP appears in third-party result titles and is presented by the answer as if it were DeepSeek's own browser protocol." }, { "claim": "The selected search models are deepseek-v4-flash and deepseek-v4-pro", "verdict": "unsupported", "evidence": "Completion requests carry model_type=null and ready events report model_type=default; reconstructed response objects leave model empty." }, { "claim": "The browser receives generated queries and ranked source cards inside the completion stream", "verdict": "supported", "evidence": "Two SSE streams contain SEARCH fragments with 9 generated queries and 27 ordered result records before answer-token APPEND patches." } ], "findings": [ { "level": "observed", "title": "The conversation payload is not encrypted in the HAR", "body": "User prompts are ordinary JSON. Assistant output is ordinary text/event-stream data using compact state patches. The 16 captured assistant messages can be reconstructed without keys." }, { "level": "observed", "title": "Search orchestration is server-side from the browser's point of view", "body": "Search queries, ranked result cards, snippets, and citation indices arrive inside the same completion SSE stream. The browser never contacts Baidu or another search engine for those results." }, { "level": "limit", "title": "The HAR cannot reveal DeepSeek's internal retriever or token-level grounding", "body": "The capture shows the browser/API boundary only. Backend fetches, indexes, rerankers, prompt construction, and model attention are outside the recording." }, { "level": "observed", "title": "Proof-of-work is transparent, not encrypted", "body": "The x-ds-pow-response value is base64-encoded JSON containing the algorithm, challenge, salt, solved integer answer, signature, and target path." }, { "level": "observed", "title": "The settings token is genuinely encrypted", "body": "Its compact JWE protected header decodes to alg=dir and enc=A256GCM. The symmetric key is absent, so the ciphertext cannot be decrypted from this capture alone." }, { "level": "caution", "title": "The fetched architecture articles are weak evidence", "body": "They are Baidu Cloud community posts, not official DeepSeek documentation. Their confident architecture claims and precise metrics should be treated as unverified secondary material." }, { "level": "caution", "title": "DeepSeek's self-description conflates retrieved projects with its own internals", "body": "Turn 2 promotes a third-party MCP wrapper and community configuration examples into claims about DeepSeek's native tool name, encryption flow, retriever, model variants, and protocol. The HAR supports the outer search-card flow, not those internal claims." }, { "level": "limit", "title": "This is a mid-session API capture, not a complete page archive", "body": "The HAR has zero page records, no document navigation, and begins with parent_message_id=6. Earlier conversation state, initial authentication, and page bootstrap traffic are outside the file." }, { "level": "inferred", "title": "The screenshot took a text-extraction path, not the Vision model", "body": "Turn 3 references a parsed file ID; the file service reports 228 tokens, while the active Instant configuration advertises vision=false. Search queries mirror names and topics in the screenshot. That strongly indicates OCR/text extraction before search and generation." }, { "level": "inferred", "title": "DeepSeekHashV1 appears to scan a fixed 144,000-candidate work range", "body": "All 17 solved answers fall between 0 and 143,999, with mean 71,033—49.3% of the declared difficulty. That is consistent with locating a target or extremum inside a fixed range. The exact SHA3 input and acceptance rule remain unavailable because the JS/WASM bodies are omitted." } ] }