{"id":53713,"date":"2025-02-16T10:01:12","date_gmt":"2025-02-16T02:01:12","guid":{"rendered":"https:\/\/fwq.ai\/blog\/53713\/"},"modified":"2025-02-16T10:01:12","modified_gmt":"2025-02-16T02:01:12","slug":"deepseek-r1%e8%81%8a%e5%a4%a9%e6%9c%ba%e5%99%a8%e4%ba%ba%e5%bc%80%e5%8f%91%e6%95%99%e7%a8%8b","status":"publish","type":"post","link":"https:\/\/fwq.ai\/blog\/53713\/","title":{"rendered":"DeepSeek-R1\u804a\u5929\u673a\u5668\u4eba\u5f00\u53d1\u6559\u7a0b"},"content":{"rendered":"<p>AI \u9886\u57df\u7684\u8fdb\u6b65\u901f\u5ea6\u786e\u5b9e\u5728\u4ee5\u524d\u6240\u672a\u6709\u7684\u901f\u5ea6\u524d\u8fdb\u3002\u77ed\u77ed\u4e00\u5468\u591a\u7684\u65f6\u95f4\uff0cDeepSeek-R1 LLM \u6a21\u578b\u7684\u53d1\u5e03\u5c31\u4ee5\u5176\u4ee4\u4eba\u5370\u8c61\u6df1\u523b\u7684\u51c6\u786e\u6027\u9707\u64bc\u4e86 AI \u4e16\u754c\uff0c\u5176\u51c6\u786e\u6027\u4e0e\u73b0\u6709\u6a21\u578b\u76f8\u5f53\uff0c\u4f46\u521b\u5efa\u6210\u672c\u4ec5\u4e3a\u5178\u578b\u6210\u672c\u7684\u4e00\u5c0f\u90e8\u5206\u3002<\/p>\n<p>DeepSeek \u56e2\u961f\u6210\u529f\u5730\u5c06\u5176\u5e9e\u5927\u7684 671B \u53c2\u6570\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\u63d0\u70bc\u4e3a\u57fa\u4e8e Qwen\uff08DeepSeek-R1-Distill-Qwen-1.5B\u30017B\u300114B \u548c 32B\uff09\u548c Llama\uff08DeepSeek-R1-Distill-Llama-8B \u548c 70B\uff09\u7684 6 \u4e2a\u5c0f\u6a21\u578b\u3002\u8fd9\u5b9e\u9645\u4e0a\u610f\u5473\u7740\u4f60\u53ef\u4ee5\u4f7f\u7528\u81ea\u5df1\u7684\u6a21\u578b\u526f\u672c &#8211; \u81ea\u5b9a\u4e49\u5b83\u3001\u8fdb\u884c\u66f4\u6539\u3001\u5728\u672c\u5730\u8fd0\u884c\u5b83\u6216\u5c06\u5176\u6258\u7ba1\u5728\u4e91\u5e73\u53f0\u4e0a\u3002<\/p>\n<p>\u5728\u672c\u535a\u5ba2\u4e2d\uff0c\u6211\u4eec\u7528 Python \u6784\u5efa\u4e00\u4e2a DeepSeek-R1 \u804a\u5929\u673a\u5668\u4eba\u3002\u7b80\u800c\u8a00\u4e4b\uff0cStreamlit \u7528\u4e8e\u524d\u7aef\uff0c\u800c\u5728\u540e\u7aef\uff0c\u901a\u8fc7\u5bf9\u6258\u7ba1\u5728 Snowflake \u4e2d\u7684 DeepSeek-R1 \u6a21\u578b\u7684 API \u8c03\u7528\uff0c\u53ef\u4ee5\u5b9e\u73b0\u4e3a\u5e94\u7528\u7a0b\u5e8f\u7684\u54cd\u5e94\u63d0\u4f9b\u652f\u6301\u7684 LLM \u6a21\u578b\u3002<\/p>\n<p>\u70b9\u51fb\u67e5\u770b\u672c\u6559\u7a0b\u7684 GitHub \u5b58\u50a8\u5e93\u3002<\/p>\n<h2>1\u3001\u4ec0\u4e48\u662f DeepSeek-R1\uff1f<\/h2>\n<p>\u7b80\u800c\u8a00\u4e4b\uff0cDeepSeek-R1 \u662f\u4e00\u79cd\u63a8\u7406\u6a21\u578b\uff0c\u5b83\u5229\u7528\u5f3a\u5316\u5b66\u4e60\u6765\u6559\u6388\u57fa\u672c\u8bed\u8a00\u6a21\u578b DeepSeek-V3 \u8fdb\u884c\u63a8\u7406\uff0c\u800c\u65e0\u9700\u4eba\u5de5\u76d1\u7763\u3002<\/p>\n<p>\u4ee5\u4e0b\u662f DeepSeek-R1 \u7684 5 \u4e2a\u4e3b\u8981\u529f\u80fd\uff1a<\/p>\n<ul>\n<li>\u6700\u5148\u8fdb\u7684\u63a8\u7406\uff1aDeepSeek-R1 \u5728\u9ad8\u7ea7\u6570\u5b66\u4efb\u52a1\u4e0a\u53ef\u4ee5\u8fbe\u5230 97.3% \u7684\u51c6\u786e\u7387\uff0c\u4f18\u4e8e\u65e9\u671f\u7684\u57fa\u51c6\u3002\u540c\u6837\uff0c\u5b83\u5728 AIME 2024 \u4e0a\u7684\u5f97\u5206\u4e3a 79.8%\uff0c\u5728 SWE-bench Verified \u4e0a\u7684\u5f97\u5206\u4e3a 49.2%\uff0c\u4e5f\u4f18\u4e8e\u5176\u4ed6\u6a21\u578b\u3002<\/li>\n<li>\u6210\u672c\u6548\u7387\uff1a\u4e0e\u884c\u4e1a\u6807\u51c6\u76f8\u6bd4\uff0cDeepSeek \u6a21\u578b\u7684\u8bad\u7ec3\u6210\u672c\u663e\u8457\u964d\u4f4e<\/li>\n<li>\u5e94\u7528\u5e7f\u6cdb\uff1a\u5728\u521b\u610f\u5199\u4f5c\u3001\u957f\u4e0a\u4e0b\u6587\u7406\u89e3\u548c\u4e8b\u5b9e\u95ee\u7b54\u65b9\u9762\u8868\u73b0\u51fa\u8272\u3002<\/li>\n<li>\u53ef\u6269\u5c55\u6027\uff1a\u6a21\u578b\u6709\u591a\u4e2a\u7cbe\u7b80\u7248\u672c\uff081.5B \u5230 70B \u4e2a\u53c2\u6570\uff09\uff0c\u4ece\u800c\u4f18\u5316\u4e86\u80fd\u529b\u548c\u8d44\u6e90\u4f7f\u7528\u4e4b\u95f4\u7684\u5e73\u8861\u3002<\/li>\n<li>\u53ef\u8bbf\u95ee\u6027\uff1a\u5f00\u6e90\u53ef\u7528\u6027\u4f7f\u5f00\u53d1\u4eba\u5458\u548c\u7814\u7a76\u4eba\u5458\u80fd\u591f\u5c1d\u8bd5\u8be5\u6a21\u578b\uff0c\u4ee5\u5c06\u9ad8\u7ea7 AI \u5de5\u5177\u5e94\u7528\u4e8e\u5b9e\u9645\u9879\u76ee\u3002<\/li>\n<\/ul>\n<p>\u5c3d\u7ba1\u5176\u6027\u80fd\u51fa\u8272\uff0c\u4f46\u4eba\u4eec\u4ecd\u53ef\u80fd\u5bf9\u5176\u4f7f\u7528\u65b9\u9762\u7684\u5b89\u5168\u95ee\u9898\u611f\u5230\u62c5\u5fe7\u3002\u9274\u4e8e\u6b64\uff0c\u7531\u4e8e\u6a21\u578b\u662f\u5f00\u6e90\u7684\uff0c\u56e0\u6b64\u53ef\u4ee5\u68c0\u67e5\u5e95\u5c42\u4ee3\u7801\uff0c\u800c\u6a21\u578b\u672c\u8eab\u53ef\u4ee5\u5728\u7528\u6237\u81ea\u5df1\u7684\u8ba1\u7b97\u8d44\u6e90\u4e0a\u81ea\u884c\u90e8\u7f72\u3002<\/p>\n<p>\u5728\u672c\u6559\u7a0b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u6258\u7ba1\u5728\u3002<\/p>\n<h2>2\u3001\u5e94\u7528\u7a0b\u5e8f\u6982\u8ff0<\/h2>\n<p>\u4ee5\u4e0b\u662f DeepSeek-R1 \u804a\u5929\u673a\u5668\u4eba\u5e94\u7528\u7a0b\u5e8f\u7684\u9ad8\u7ea7\u6982\u8ff0\uff1a<\/p>\n<ul>\n<li>\u7528\u6237\u63d0\u4f9b\u63d0\u793a\u8f93\u5165\uff08\u5373\u63d0\u51fa\u95ee\u9898\uff09\u3002<\/li>\n<li>\u901a\u8fc7\u4f7f\u7528 <code>SNOWFLAKE.CORTEX.COMPLETE()<\/code> \u8fdb\u884c LLM \u8c03\u7528\uff0c\u5176\u4e2d\u63d0\u4ea4\u63d0\u793a\u8f93\u5165\uff0c\u5e76\u83b7\u53d6 LLM \u751f\u6210\u7684\u54cd\u5e94\uff0c\u5e76\u5c06\u5176\u663e\u793a\u5728\u5e94\u7528\u7a0b\u5e8f\u4e2d\u3002<\/li>\n<\/ul>\n<h2>3\u3001\u804a\u5929\u673a\u5668\u4eba\u7684\u8fd0\u884c<\/h2>\n<p>\u8ba9\u6211\u4eec\u770b\u770b\u804a\u5929\u673a\u5668\u4eba\u7684\u5b9e\u9645\u8fd0\u884c\uff0c\u542f\u52a8\u804a\u5929\u673a\u5668\u4eba\u5e76\u5728\u804a\u5929\u8f93\u5165\u4e2d\u8f93\u5165\u95ee\u9898\uff1a<\/p>\n<p>\u4f5c\u4e3a\u63a8\u7406\u6a21\u578b\uff0cLLM \u9996\u5148\u8fdb\u5165\u601d\u8003\u9636\u6bb5\uff1a<\/p>\n<p>\u601d\u8003\u8fc7\u7a0b\u5b8c\u6210\u540e\uff0c\u6700\u7ec8\u7b54\u6848\u5c06\u5728\u4e0b\u9762\u663e\u793a\uff1a<\/p>\n<p>\u5e94\u8be5\u6ce8\u610f\u7684\u662f\uff0c\u601d\u8003\u548c\u7b54\u6848\u7684\u6df1\u5ea6\u76f4\u63a5\u53d7\u5230\u6700\u5927\u4ee4\u724c\u53c2\u6570\u7684\u5f71\u54cd\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u6700\u5927\u4ee4\u724c\u9650\u5236\u4e3a 800 \u4e2a\u4ee5\u8fdb\u884c\u6d4b\u8bd5\uff0c\u4f46\u4f60\u53ef\u4ee5\u5c06\u5176\u589e\u52a0\u5230 20,480\u3002<\/p>\n<h2>4\u3001\u6784\u5efa DeepSeek-R1 \u804a\u5929\u673a\u5668\u4eba<\/h2>\n<p>\u73b0\u5728\u8ba9\u6211\u4eec\u7ee7\u7eed\u5728 Snowflake \u5e73\u53f0\u4e0a\u6784\u5efa DeepSeek-R1 \u804a\u5929\u673a\u5668\u4eba\u3002<\/p>\n<h3>4.1 \u8bbe\u7f6e\u5f00\u53d1\u73af\u5883<\/h3>\n<p>\u8981\u8bbf\u95ee\u5fc5\u8981\u7684\u5de5\u5177\uff0c\u8bf7\u786e\u4fdd\u4f60\u53ef\u4ee5\u8bbf\u95ee\u3002<\/p>\n<p>\u63a5\u4e0b\u6765\uff0c\u5bfc\u822a\u5230 \uff0c\u8f6c\u5230\u9879\u76ee\u2192Streamlit\uff0c\u7136\u540e\u5355\u51fb + Streamlit App \u4ee5\u521b\u5efa\u5e94\u7528\u7a0b\u5e8f\uff08\u7136\u540e\u4f60\u5c06\u6307\u5b9a\u5e94\u7528\u7a0b\u5e8f\u4f4d\u7f6e\u548c\u4ed3\u5e93\uff09\uff1a<\/p>\n<p>\u63a5\u4e0b\u6765\uff0c\u4f60\u5c06\u770b\u5230\u4e00\u4e2a\u793a\u4f8b\u5165\u95e8\u5e94\u7528\u7a0b\u5e8f\u6765\u5e2e\u52a9\u5165\u95e8\uff1a<\/p>\n<p>Snowflake \u4e2d\u7684 Streamlit \u754c\u9762\u7c7b\u4f3c\u4e8e\u5728\u7ebf\u4ee3\u7801\u7f16\u8f91\u5668\uff0c\u60a8\u53ef\u4ee5\u5728\u5de6\u4fa7\u7f16\u8f91\u4ee3\u7801\u5e76\u5728\u53f3\u4fa7\u67e5\u770b\u5448\u73b0\u7684\u5e94\u7528\u7a0b\u5e8f\u3002<\/p>\n<p>\u7ee7\u7eed\u5c06\u5176\u66ff\u6362\u4e3a\u6211\u4eec\u4eca\u5929\u8981\u6784\u5efa\u7684\u5e94\u7528\u7a0b\u5e8f\u3002<\/p>\n<h3>4.2 \u68c0\u7d22\u4ee3\u7801<\/h3>\n<p>\u6211\u4eec\u4eca\u5929\u8981\u6784\u5efa\u7684 DeepSeek-R1 \u804a\u5929\u673a\u5668\u4eba\u5e94\u7528\u7a0b\u5e8f\u7531\u4ee5\u4e0b\u90e8\u5206\u7ec4\u6210\uff1a<\/p>\n<ul>\n<li>environment.yml \u2014 \u5e94\u7528\u7a0b\u5e8f\u7684\u73af\u5883\u4f9d\u8d56\u9879<\/li>\n<li>streamlit_app.py \u2014 Streamlit \u5e94\u7528\u7a0b\u5e8f\u6587\u4ef6<\/li>\n<\/ul>\n<p>\u9996\u5148\uff0cenvironment.yml \u6587\u4ef6\u7684\u5185\u5bb9\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n<pre><code>name: app_environment\nchannels:\n  - snowflake\ndependencies:\n  - python=3.11.*\n  - snowflake-ml-python\n  - snowflake-snowpark-python\n  - streamlit<\/code><\/pre>\n<p>\u5176\u6b21\uff0csis_app.py \u6587\u4ef6\u7684\u5185\u5bb9\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n<pre><code>import streamlit as st\nfrom snowflake.snowpark.context import get_active_session\nimport pandas as pd\nimport json\nimport re\n\n# App configuration\nst.set_page_config(page_title=\" DeepSeek R1 Chatbot\", initial_sidebar_state=\"expanded\")\nsession = get_active_session()\n\n# Helper functions\ndef clear_chat_history():\n    st.session_state.messages = [{\"role\": \"assistant\", \"content\": \"How may I assist you today?\"}]\n\ndef escape_sql_string(s):\n    return s.replace(\"'\", \"''\")\n\ndef extract_think_content(response):\n    think_pattern = r'&lt;think&gt;(.*?)&lt;\/think&gt;'\n    think_match = re.search(think_pattern, response, re.DOTALL)\n    \n    if think_match:\n        think_content = think_match.group(1).strip()\n        main_response = re.sub(think_pattern, '', response, flags=re.DOTALL).strip()\n        return think_content, main_response\n    return None, response\n\ndef generate_deepseek_response(prompt, **params):\n    string_dialogue = \"\".join(\n        f\"{msg['content']}\\n\\n\" \n        for msg in st.session_state.messages\n    )\n    \n    cortex_prompt = f\"'[INST] {string_dialogue}{prompt} [\/INST]'\"\n    prompt_data = [{'role': 'user', 'content': cortex_prompt}], params\n    prompt_json = escape_sql_string(json.dumps(prompt_data))\n    response = session.sql(\n        \"select snowflake.cortex.complete(?, ?)\", \n        params=['deepseek-r1', prompt_json]\n    ).collect()[0][0]\n    \n    return response\n\n# Model parameters configuration\nMODEL_PARAMS = {\n    'temperature': {'min': 0.01, 'max': 1.0, 'default': 0.7, 'step': 0.01},\n    'top_p': {'min': 0.01, 'max': 1.0, 'default': 1.0, 'step': 0.01},\n    'max_tokens': {'min': 10, 'max': 100, 'default': 20, 'step': 10},\n    'presence_penalty': {'min': -1.0, 'max': 1.0, 'default': 0.0, 'step': 0.1},\n    'frequency_penalty': {'min': -1.0, 'max': 1.0, 'default': 0.0, 'step': 0.1}\n}\n\n# Sidebar UI\nwith st.sidebar:\n    st.title(' DeepSeek R1 Chatbot')\n    st.write('This chatbot is created using the DeepSeek R1 LLM model via Snowflake Cortex.')\n    \n    st.subheader('\ufe0f Model parameters')\n    params = {\n        param: st.sidebar.slider(\n            param.replace('_', ' ').title(),\n            min_value=settings['min'],\n            max_value=settings['max'],\n            value=settings['default'],\n            step=settings['step']\n        )\n        for param, settings in MODEL_PARAMS.items()\n    }\n    \n    st.button('Clear Chat History', on_click=clear_chat_history)\n\n# Initialize chat history\nif \"messages\" not in st.session_state:\n    st.session_state.messages = [{\"role\": \"assistant\", \"content\": \"How may I assist you today?\"}]\n\n# Display chat messages\nfor message in st.session_state.messages:\n    with st.chat_message(message[\"role\"]):\n        st.write(message[\"content\"])\n\n# Handle user input\nif prompt := st.chat_input():\n    with st.chat_message(\"user\"):\n        st.write(prompt)\n    st.session_state.messages.append({\"role\": \"user\", \"content\": prompt})\n    \n    if st.session_state.messages[-1][\"role\"] != \"assistant\":\n        with st.chat_message(\"assistant\"):\n            status_container = st.status(\"Thinking ...\", expanded=True)\n            \n            with status_container:\n                response = generate_deepseek_response(prompt, **params)\n                think_content, main_response = extract_think_content(response)\n                if think_content:\n                    st.write(think_content)\n            \n            status_container.update(label=\"Thoughts\", state=\"complete\", expanded=False)\n            st.markdown(main_response)\n            st.session_state.messages.append({\"role\": \"assistant\", \"content\": main_response})<\/code><\/pre>\n<p>\u4f60\u8fd8\u53ef\u4ee5\u4ece\u6b64 DeepSeek-R1 \u804a\u5929\u673a\u5668\u4eba\u4e0b\u8f7d\u5fc5\u8981\u7684\u5e94\u7528\u6587\u4ef6\u3002<\/p>\n<h3>4.3 \u8fd0\u884c\u5e94\u7528<\/h3>\n<p>\u8981\u8fd0\u884c\u5e94\u7528\uff0c\u8bf7\u590d\u5236\u7c98\u8d34\u4e0a\u8ff0\u4ee3\u7801\uff0c\u7136\u540e\u5355\u51fb\u201c\u8fd0\u884c\u201d\u6309\u94ae\u3002<\/p>\n<p>\u4f60\u53ef\u4ee5\u5c06\u9f20\u6807\u5149\u6807\u653e\u5728\u4ee3\u7801\/\u5e94\u7528\u7a0b\u5e8f\u9762\u677f\u7684\u5206\u9694\u7ebf\u4e0a\uff0c\u7136\u540e\u5c06\u5176\u5411\u5de6\u79fb\u52a8\uff0c\u76f4\u5230\u4ee3\u7801\u9762\u677f\u6d88\u5931\uff08\u89c1\u4e0b\u6587\uff09\u3002\u8fd9\u4f1a\u5c06\u5e94\u7528\u7a0b\u5e8f\u9762\u677f\u6269\u5c55\u81f3\u5168\u5c4f\u3002<\/p>\n<p>\u7ee7\u7eed\u5e76\u8f93\u5165\u63d0\u793a\u4ee5\u5f00\u59cb\u4f60\u7684\u804a\u5929\u4f1a\u8bdd\uff1a<\/p>\n<p>\u6b64\u5916\uff0c\u8bf7\u53c2\u9605\u4e0a\u9762\u7684 Chatbot \u5b9e\u9645\u64cd\u4f5c\u90e8\u5206\uff0c\u4e86\u89e3 LLM \u751f\u6210\u7684\u601d\u7ef4\u8fc7\u7a0b\u548c\u7b54\u6848\u3002<\/p>\n<h2>5\u3001\u4ee3\u7801\u8bf4\u660e<\/h2>\n<p>\u8ba9\u6211\u4eec\u63a2\u7d22\u6bcf\u4e2a\u4ee3\u7801\u5757\u5728\u505a\u4ec0\u4e48\u2026\u2026<\/p>\n<h3>5.1 \u5bfc\u5165\u5e93<\/h3>\n<p>\u6211\u4eec\u9996\u5148\u4ece\u5bfc\u5165\u5148\u51b3\u6761\u4ef6\u5e93\u5f00\u59cb\u3002<\/p>\n<pre><code>import streamlit as st\nfrom snowflake.snowpark.context import get_active_session\nimport pandas as pd\nimport json\nimport re<\/code><\/pre>\n<h3>5.2 \u5e94\u7528\u7a0b\u5e8f\u914d\u7f6e<\/h3>\n<p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528 st.set_page_config() \u5b9a\u4e49\u5e94\u7528\u7a0b\u5e8f\u9875\u9762\u5e76\u8bbe\u7f6e\u5176\u521d\u59cb\u9875\u9762\u53c2\u6570\uff0c\u6211\u4eec\u5c06\u8bbe\u7f6e initial_sidebar_state=&#8221;expanded&#8221;\uff0c\u4ee5\u4fbf\u5b83\u4eec\u5b8c\u5168\u6309\u7167\u90a3\u6837\u505a\uff0c\u5c55\u5f00\u4fa7\u8fb9\u680f\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u8fd8\u8bbe\u7f6e\u4e86\u4f1a\u8bdd\u53d8\u91cf\uff0c\u6211\u4eec\u7a0d\u540e\u4f1a\u7528\u5230\u5b83\u3002<\/p>\n<pre><code># App configuration\nst.set_page_config(page_title=\" DeepSeek R1 Chatbot\", initial_sidebar_state=\"expanded\")\nsession = get_active_session()<\/code><\/pre>\n<h3>5.3 \u8f85\u52a9\u51fd\u6570<\/h3>\n<p>\u5728\u6b64\u90e8\u5206\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u51e0\u4e2a\u5c06\u5728\u5e94\u7528\u7a0b\u5e8f\u7684\u540e\u9762\u90e8\u5206\u4f7f\u7528\u7684\u8f85\u52a9\u51fd\u6570\uff1a<\/p>\n<ul>\n<li><code>clear_chat_history()<\/code> \u2014 \u8fd9\u5141\u8bb8\u6211\u4eec\u5c06\u804a\u5929\u8bb0\u5f55\u6e05\u9664\u5230\u521d\u59cb\u72b6\u6001<\/li>\n<li><code>escape_sql_string()<\/code> \u2014 \u5728\u6267\u884c\u4e00\u4e9b\u6587\u672c\u683c\u5f0f\u5316\u65f6\u66ff\u6362 SQL \u5b57\u7b26\u4e32<\/li>\n<li><code>extract_think_content()<\/code> \u2014 \u89e3\u6790\u5e76\u5206\u79bb\u5305\u542b\u5728 XML \u6837\u5f0f\u201cthink\u201d\u6807\u7b7e\uff08 \u548c \uff09\u4e2d\u7684\u5185\u5bb9\uff0c\u5e76\u5c06\u5176\u4e0e\u6700\u7ec8\u54cd\u5e94\u5206\u5f00\u3002<\/li>\n<\/ul>\n<pre><code># Helper functions\ndef clear_chat_history():\n    st.session_state.messages = [{\"role\": \"assistant\", \"content\": \"How may I assist you today?\"}]\n\ndef escape_sql_string(s):\n    return s.replace(\"'\", \"''\")\n\ndef extract_think_content(response):\n    think_pattern = r'&lt;think&gt;(.*?)&lt;\/think&gt;'\n    think_match = re.search(think_pattern, response, re.DOTALL)\n    \n    if think_match:\n        think_content = think_match.group(1).strip()\n        main_response = re.sub(think_pattern, '', response, flags=re.DOTALL).strip()\n        return think_content, main_response\n    return None, response<\/code><\/pre>\n<p>\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u751f\u6210\u7684\u54cd\u5e94\uff1a<\/p>\n<pre><code>&lt;think&gt;Let me analyze this problem step by step...&lt;\/think&gt;\nHere's the solution you're looking for...<\/code><\/pre>\n<p>\u5b83\u5c06\u89e3\u6790\u5e76\u5206\u79bb\u4e3a\uff1a<\/p>\n<ul>\n<li><code>think_content<\/code>\uff1a\u201c\u8ba9\u6211\u4e00\u6b65\u4e00\u6b65\u5206\u6790\u8fd9\u4e2a\u95ee\u9898\u2026\u2026\u201d<\/li>\n<li><code>main_response<\/code>\uff1a\u201c\u8fd9\u662f\u60a8\u6b63\u5728\u5bfb\u627e\u7684\u89e3\u51b3\u65b9\u6848\u2026\u2026\u201d<\/li>\n<\/ul>\n<p>\u8ba9\u6211\u4eec\u7ee7\u7eed\u4f7f\u7528\u6700\u540e\u4e00\u4e2a\u8f85\u52a9\u51fd\u6570\uff1a<\/p>\n<ul>\n<li><code>generate_deepseek_response()<\/code> \u2014 \u4f7f\u7528 Snowflake \u7684 Cortex \u670d\u52a1\u548c DeepSeek R1 \u6a21\u578b\u751f\u6210 LLM \u54cd\u5e94<\/li>\n<\/ul>\n<pre><code>def generate_deepseek_response(prompt, **params):\n    string_dialogue = \"\".join(\n        f\"{msg['content']}\\n\\n\" \n        for msg in st.session_state.messages\n    )\n    \n    cortex_prompt = f\"'[INST] {string_dialogue}{prompt} [\/INST]'\"\n    prompt_data = [{'role': 'user', 'content': cortex_prompt}], params\n    prompt_json = escape_sql_string(json.dumps(prompt_data))\n    response = session.sql(\n        \"select snowflake.cortex.complete(?, ?)\", \n        params=['deepseek-r1', prompt_json]\n    ).collect()[0][0]\n    \n    return response<\/code><\/pre>\n<h3>5.4 \u4fa7\u8fb9\u680f UI<\/h3>\n<p>\u6211\u4eec\u9996\u5148\u5c06 <code>MODEL_PARAMS<\/code> \u53d8\u91cf\u5b9a\u4e49\u4e3a\u5b57\u5178\u683c\u5f0f\uff0c\u5176\u4e2d\u5305\u542b\u6a21\u578b\u53c2\u6570\u4ee5\u53ca\u76f8\u5173\u7684\u6700\u5c0f\u503c\u3001\u6700\u5927\u503c\u3001\u9ed8\u8ba4\u503c\u548c\u6b65\u957f\u503c\u3002<\/p>\n<p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u5b9a\u4e49\u4ee5\u5e94\u7528\u7a0b\u5e8f\u6807\u9898\u548c\u5e94\u7528\u7a0b\u5e8f\u63cf\u8ff0\u5f00\u5934\u7684\u4fa7\u8fb9\u680f\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u8fd8\u5305\u62ec\u51e0\u4e2a\u901a\u8fc7 for \u5faa\u73af\u8fed\u4ee3\u521b\u5efa\u7684\u6ed1\u5757\u5c0f\u90e8\u4ef6\u3002\u6700\u540e\uff0c\u6211\u4eec\u6709\u4e00\u4e2a\u6e05\u9664\u804a\u5929\u5386\u53f2\u8bb0\u5f55\u6309\u94ae\uff0c\u5b83\u8c03\u7528 <code>clear_chat_history<\/code> \u56de\u8c03\u51fd\u6570\u5c06\u5386\u53f2\u8bb0\u5f55\u91cd\u7f6e\u4e3a\u521d\u59cb\u72b6\u6001\u3002<\/p>\n<pre><code># Model parameters configuration\nMODEL_PARAMS = {\n    'temperature': {'min': 0.01, 'max': 1.0, 'default': 0.7, 'step': 0.01},\n    'top_p': {'min': 0.01, 'max': 1.0, 'default': 1.0, 'step': 0.01},\n    'max_tokens': {'min': 10, 'max': 100, 'default': 20, 'step': 10},\n    'presence_penalty': {'min': -1.0, 'max': 1.0, 'default': 0.0, 'step': 0.1},\n    'frequency_penalty': {'min': -1.0, 'max': 1.0, 'default': 0.0, 'step': 0.1}\n}\n\n# Sidebar UI\nwith st.sidebar:\n    st.title(' DeepSeek R1 Chatbot')\n    st.write('This chatbot is created using the DeepSeek R1 LLM model via Snowflake Cortex.')\n    \n    st.subheader('\ufe0f Model parameters')\n    params = {\n        param: st.sidebar.slider(\n            param.replace('_', ' ').title(),\n            min_value=settings['min'],\n            max_value=settings['max'],\n            value=settings['default'],\n            step=settings['step']\n        )\n        for param, settings in MODEL_PARAMS.items()\n    }\n    \n    st.button('Clear Chat History', on_click=clear_chat_history)<\/code><\/pre>\n<h3>5.5 \u804a\u5929\u5143\u7d20<\/h3>\n<p>\u5728\u5e94\u7528\u7a0b\u5e8f\u7684\u6700\u540e\u90e8\u5206\uff0c\u6211\u4eec\u5c06\u521d\u59cb\u5316\u804a\u5929\u5386\u53f2\u8bb0\u5f55\u7684\u4f1a\u8bdd\u72b6\u6001\u53d8\u91cf\uff0c\u8fed\u4ee3\u663e\u793a\u4f20\u5165\u7684\u804a\u5929\u6d88\u606f\uff0c\u5e76\u6700\u7ec8\u5b9a\u4e49\u5904\u7406\u7528\u6237\/\u5e94\u7528\u7a0b\u5e8f\u804a\u5929\u903b\u8f91\u7684\u6761\u4ef6\u6d41\u3002\u540e\u4e00\u90e8\u5206\u5229\u7528\u5148\u524d\u5b9a\u4e49\u7684\u8f85\u52a9\u51fd\u6570\u6765\u5904\u7406 LLM \u751f\u6210\u7684\u54cd\u5e94\u3002<\/p>\n<pre><code># Initialize chat history\nif \"messages\" not in st.session_state:\n    st.session_state.messages = [{\"role\": \"assistant\", \"content\": \"How may I assist you today?\"}]\n\n# Display chat messages\nfor message in st.session_state.messages:\n    with st.chat_message(message[\"role\"]):\n        st.write(message[\"content\"])\n\n# Handle user input\nif prompt := st.chat_input():\n    with st.chat_message(\"user\"):\n        st.write(prompt)\n    st.session_state.messages.append({\"role\": \"user\", \"content\": prompt})\n    \n    if st.session_state.messages[-1][\"role\"] != \"assistant\":\n        with st.chat_message(\"assistant\"):\n            status_container = st.status(\"Thinking ...\", expanded=True)\n            \n            with status_container:\n                response = generate_deepseek_response(prompt, **params)\n                think_content, main_response = extract_think_content(response)\n                if think_content:\n                    st.write(think_content)\n            \n            status_container.update(label=\"Thoughts\", state=\"complete\", expanded=False)\n            st.markdown(main_response)\n            st.session_state.messages.append({\"role\": \"assistant\", \"content\": main_response})<\/code><\/pre>\n<p>\u5c06\u8fd9\u4e9b\u4ee3\u7801\u5757\u62fc\u51d1\u5728\u4e00\u8d77\uff0c\u6211\u4eec\u5c31\u5f97\u5230\u4e86 DeepSeek-R1 \u804a\u5929\u673a\u5668\u4eba\uff01<\/p>\n<h2>6\u3001\u7ed3\u675f\u8bed<\/h2>\n<p>\u6784\u5efa\u81ea\u5df1\u7684\u804a\u5929\u673a\u5668\u4eba\uff0c\u8be5\u804a\u5929\u673a\u5668\u4eba\u7531\u5f3a\u5927\u7684 DeepSeek-R1 \u6a21\u578b\u63d0\u4f9b\u652f\u6301\u3002\u8fd9\u5728\u4e0d\u5230 100 \u884c\u4ee3\u7801\u4e2d\u4ee4\u4eba\u5370\u8c61\u6df1\u523b\u5730\u5b9e\u73b0\u3002<\/p>\n<p>\u4f60\u53ef\u80fd\u4f1a\u6ce8\u610f\u5230\uff0c\u4ee3\u7801\u7684\u5f88\u5927\u4e00\u90e8\u5206\u6d89\u53ca\u201c\u601d\u8003\u201d\u6807\u7b7e\u548c\u5927\u91cf\u5185\u8054\u6ce8\u91ca\u7684\u5904\u7406\uff0c\u5982\u679c\u5220\u9664\u8fd9\u4e9b\u6ce8\u91ca\uff0c\u5e94\u7528\u7a0b\u5e8f\u4f1a\u5927\u5927\u7f29\u5c0f\u3002<\/p>\n<hr>\n","protected":false},"excerpt":{"rendered":"<p>AI \u9886\u57df\u7684\u8fdb\u6b65\u901f\u5ea6\u786e\u5b9e\u5728\u4ee5\u524d\u6240\u672a\u6709\u7684\u901f\u5ea6\u524d\u8fdb\u3002\u77ed\u77ed\u4e00\u5468\u591a\u7684\u65f6\u95f4\uff0cDeepSeek-R1 LLM \u6a21\u578b\u7684\u53d1\u5e03\u5c31\u4ee5\u5176\u4ee4\u4eba\u5370\u8c61\u6df1\u523b\u7684\u51c6\u786e\u6027\u9707\u64bc\u4e86 AI \u4e16\u754c\uff0c\u5176\u51c6\u786e\u6027\u4e0e\u73b0\u6709\u6a21\u578b\u76f8\u5f53\uff0c\u4f46\u521b\u5efa\u6210\u672c\u4ec5\u4e3a\u5178\u578b\u6210\u672c\u7684\u4e00\u5c0f\u90e8\u5206\u3002 DeepSeek \u56e2\u961f\u6210\u529f\u5730\u5c06\u5176\u5e9e\u5927\u7684 671B \u53c2\u6570\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\u63d0\u70bc\u4e3a\u57fa\u4e8e Qwen\uff08DeepSeek-R1-Distill-Qwen-1.5B\u30017B\u300114B \u548c 32B\uff09\u548c Llama\uff08DeepSeek-R1-Distill-Llama-8B \u548c 70B\uff09\u7684 6 \u4e2a\u5c0f\u6a21\u578b\u3002\u8fd9\u5b9e\u9645\u4e0a\u610f\u5473\u7740\u4f60\u53ef\u4ee5\u4f7f\u7528\u81ea\u5df1\u7684\u6a21\u578b\u526f\u672c &#8211; \u81ea\u5b9a\u4e49\u5b83\u3001\u8fdb\u884c\u66f4\u6539\u3001\u5728\u672c\u5730\u8fd0\u884c\u5b83\u6216\u5c06\u5176\u6258\u7ba1\u5728\u4e91\u5e73\u53f0\u4e0a\u3002 \u5728\u672c\u535a\u5ba2\u4e2d\uff0c\u6211\u4eec\u7528 Python \u6784\u5efa\u4e00\u4e2a DeepSeek-R1 \u804a\u5929\u673a\u5668\u4eba\u3002\u7b80\u800c\u8a00\u4e4b\uff0cStreamlit \u7528\u4e8e\u524d\u7aef\uff0c\u800c\u5728\u540e\u7aef\uff0c\u901a\u8fc7\u5bf9\u6258\u7ba1\u5728 Snowflake \u4e2d\u7684 DeepSeek-R1 \u6a21\u578b\u7684 API \u8c03\u7528\uff0c\u53ef\u4ee5\u5b9e\u73b0\u4e3a\u5e94\u7528\u7a0b\u5e8f\u7684\u54cd\u5e94\u63d0\u4f9b\u652f\u6301\u7684 LLM \u6a21\u578b\u3002 \u70b9\u51fb\u67e5\u770b\u672c\u6559\u7a0b\u7684 GitHub \u5b58\u50a8\u5e93\u3002 1\u3001\u4ec0\u4e48\u662f DeepSeek-R1\uff1f \u7b80\u800c\u8a00\u4e4b\uff0cDeepSeek-R1 \u662f\u4e00\u79cd\u63a8\u7406\u6a21\u578b\uff0c\u5b83\u5229\u7528\u5f3a\u5316\u5b66\u4e60\u6765\u6559\u6388\u57fa\u672c\u8bed\u8a00\u6a21\u578b DeepSeek-V3 \u8fdb\u884c\u63a8\u7406\uff0c\u800c\u65e0\u9700\u4eba\u5de5\u76d1\u7763\u3002 \u4ee5\u4e0b\u662f DeepSeek-R1 \u7684 5 \u4e2a\u4e3b\u8981\u529f\u80fd\uff1a \u6700\u5148\u8fdb\u7684\u63a8\u7406\uff1aDeepSeek-R1 \u5728\u9ad8\u7ea7\u6570\u5b66\u4efb\u52a1\u4e0a\u53ef\u4ee5\u8fbe\u5230 97.3% \u7684\u51c6\u786e\u7387\uff0c\u4f18\u4e8e\u65e9\u671f\u7684\u57fa\u51c6\u3002\u540c\u6837\uff0c\u5b83\u5728 AIME 2024 \u4e0a\u7684\u5f97\u5206\u4e3a [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"class_list":["post-53713","post","type-post","status-publish","format-standard","hentry","category-ai"],"_links":{"self":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/posts\/53713","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/comments?post=53713"}],"version-history":[{"count":0,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/posts\/53713\/revisions"}],"wp:attachment":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/media?parent=53713"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/categories?post=53713"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/tags?post=53713"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}