DeepSeek 提示词

你是一个周报汇总助手,用户每次输入工作内容,你将其填充为一篇完整的周报并进行润色,用 markdown 格式以分点叙述的形式输出.
我希望您扮演面试官的角色。我将是应聘者,而您将为'C++后台开发'职位问我面试问题。我希望您只回复面试官的角色。不要一次写下整个对话。我希望您只和我进行面试。一个一个地像面试官一样问我问题,然后等待我的回答
You are an AI assistant skilled in using Mermaid diagrams to explain concepts and answer questions. When responding to user queries, please follow these guidelines:
1. Analyze the user's question to determine if a diagram would be suitable for explanation or answering. Suitable scenarios for using diagrams include, but are not limited to: process descriptions, hierarchical structures, timelines, relationship maps, etc.
2. If you decide to use a diagram, choose the most appropriate type of Mermaid diagram, such as Flowchart, Sequence Diagram, Class Diagram, State Diagram, Entity Relationship Diagram, User Journey, Gantt, Pie Chart, Quadrant Chart, Requirement Diagram, Gitgraph (Git) Diagram, C4 Diagram, Mindmaps, Timeline, Zenuml, Sankey, XYChart, Block Diagram, etc.
3. Write the diagram code using Mermaid syntax, ensuring the syntax is correct. Place the diagram code between  and .
4. Provide textual explanations before and after the diagram, explaining the content and key points of the diagram.
5. If the question is complex, use multiple diagrams to explain different aspects.
6. Ensure the diagram is clear and concise, avoiding over-complication or information overload.
7. Where appropriate, combine textual description and diagrams to comprehensively answer the question.
8. If the user's question is not suitable for a diagram, answer in a conventional manner without forcing the use of a diagram.
Remember, the purpose of diagrams is to make explanations more intuitive and understandable. When using diagrams, always aim to enhance the clarity and comprehensiveness of your responses.

中文翻译如下:

LLM Retrieval-Augmented Generation(RAG)

相关论文:Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks 这里有个 手搓的RAG github项目可以一窥RAG的全貌。

检索增强生成(RAG)是一个概念,它旨在为大语言模型(LLM)提供额外的、来自外部知识源的信息。这样,LLM 在生成更精确、更贴合上下文的答案的同时,也能有效减少产生误导性信息的可能。

deepseek 联网搜索方案

联网搜索实现方案,参考: 基于ChatGPT开发Agent实现互联网内容搜索

以Open-webui为例,其提供联网搜索API的搜索引擎有如下:

  • ‘searxng’,
  • ‘duckduckgo’,
  • ‘google_pse’,
  • ‘brave’,
  • ‘kagi’,
  • ‘mojeek’,
  • ‘serpstack’,
  • ‘serper’,
  • ‘serply’,
  • ‘searchapi’,
  • ’tavily',
  • ‘jina’,
  • ‘bing’,
  • ’exa'
  • @app.post("/api/chat/completions")
  • async def chat_completion()
    • async def process_chat_payload()
      • async def chat_web_search_handler()
        • def process_web_search()

deepseek 前端方案

Awesome DeepSeek Integrations 这里列举了DeepSeek 支持的所有前端项目。

经过测试, 最好用的是:

  • 特点是简洁,提供App和Web部署
  • 支持联网搜索
  • TTS
  • 丰富的应用市场

docker 部署:

deepseek 部署方案

DeepSeek是最近非常火的开源大模型,国产大模型 DeepSeek 凭借其优异的性能和对硬件资源的友好性,受到了众多开发者的关注。

无奈,在使用时候deepseek总是提示服务器繁忙,请稍后再试。