Open Deep Research with DeepSeek

这是dzhng/deep-research的DeepSeek中文化版本，一款由人工智能驱动的研究助手，它通过结合搜索引擎、网络抓取和大型语言模型，对任何主题进行迭代式、深入的研究。

和原项目相比，主要就是两个修改，一个是将模型由o3-mini改成deepseek r1（爬虫依然依赖firecrawl），二是将prompts全部改成中文，这样对于中文的查询更加友好。这是一个用来验证的快速原型，代码比较乱。

如果使用火山方舟的DeepSeek，运行办法如下：

export DEEPSEEK_API_KEY=xxx        (API key)
export DEEPSEEK_MODEL_R1=ep-xxx    (模型实例名)
export DEEPSEEK_API_BASE=https://ark.cn-beijing.volces.com/api/v3

export FIRECRAWL_KEY=fc-xxx        (Firecrawl API key)
npm start

输出结果例子。

Feng Zhou

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原作者David Zhang的README:

Open Deep Research

An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. If you like this project, please consider starring it and giving me a follow on X.

The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction overtime and deep dive into a topic. It'll always be kept at <500 LoC so it is easy to understand and build on top of.

How It Works

flowchart TB
    subgraph Input
        Q[User Query]
        B[Breadth Parameter]
        D[Depth Parameter]
    end

    DR[Deep Research] -->
    SQ[SERP Queries] -->
    PR[Process Results]

    subgraph Results[Results]
        direction TB
        NL((Learnings))
        ND((Directions))
    end

    PR --> NL
    PR --> ND

    DP{depth > 0?}

    RD["Next Direction:
    - Prior Goals
    - New Questions
    - Learnings"]

    MR[Markdown Report]

    %% Main Flow
    Q & B & D --> DR

    %% Results to Decision
    NL & ND --> DP

    %% Circular Flow
    DP -->|Yes| RD
    RD -->|New Context| DR

    %% Final Output
    DP -->|No| MR

    %% Styling
    classDef input fill:#7bed9f,stroke:#2ed573,color:black
    classDef process fill:#70a1ff,stroke:#1e90ff,color:black
    classDef recursive fill:#ffa502,stroke:#ff7f50,color:black
    classDef output fill:#ff4757,stroke:#ff6b81,color:black
    classDef results fill:#a8e6cf,stroke:#3b7a57,color:black

    class Q,B,D input
    class DR,SQ,PR process
    class DP,RD recursive
    class MR output
    class NL,ND results

Loading

Features

• Iterative Research: Performs deep research by iteratively generating search queries, processing results, and diving deeper based on findings
• Intelligent Query Generation: Uses LLMs to generate targeted search queries based on research goals and previous findings
• Depth & Breadth Control: Configurable parameters to control how wide (breadth) and deep (depth) the research goes
• Smart Follow-up: Generates follow-up questions to better understand research needs
• Comprehensive Reports: Produces detailed markdown reports with findings and sources
• Concurrent Processing: Handles multiple searches and result processing in parallel for efficiency

Requirements

• Node.js environment
• API keys for:
  • Firecrawl API (for web search and content extraction)
  • OpenAI API (for o3 mini model)

Setup

1. Clone the repository
2. Install dependencies:

npm install

3. Set up environment variables in a .env.local file:

FIRECRAWL_KEY="your_firecrawl_key"
# If you want to use your self-hosted Firecrawl, add the following below:
# FIRECRAWL_BASE_URL="http://localhost:3002"

OPENAI_KEY="your_openai_key"

Usage

Run the research assistant:

npm start

You'll be prompted to:

1. Enter your research query
2. Specify research breadth (recommended: 3-10, default: 6)
3. Specify research depth (recommended: 1-5, default: 3)
4. Answer follow-up questions to refine the research direction

The system will then:

1. Generate and execute search queries
2. Process and analyze search results
3. Recursively explore deeper based on findings
4. Generate a comprehensive markdown report

The final report will be saved as output.md in your working directory.

How It Works

1. Initial Setup

   • Takes user query and research parameters (breadth & depth)
   • Generates follow-up questions to understand research needs better

2. Deep Research Process

   • Generates multiple SERP queries based on research goals
   • Processes search results to extract key learnings
   • Generates follow-up research directions

3. Recursive Exploration

   • If depth > 0, takes new research directions and continues exploration
   • Each iteration builds on previous learnings
   • Maintains context of research goals and findings

4. Report Generation

   • Compiles all findings into a comprehensive markdown report
   • Includes all sources and references
   • Organizes information in a clear, readable format

License

MIT License - feel free to use and modify as needed.