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Shandu
Overview :
Shandu is an AI-based research system capable of generating comprehensive research reports through multi-source information synthesis and deep iterative exploration. It leverages advanced language models and intelligent web crawling technology to automate the entire process from problem clarification to content analysis. Its main advantages include efficient information integration capabilities, flexible multi-source data processing, and powerful knowledge synthesis capabilities. This product is suitable for scenarios requiring the rapid generation of high-quality research reports, such as academic research, market intelligence analysis, and technological exploration. Currently, this product is an open-source project, and users can customize and extend it according to their needs.
Target Users :
Shandu is suitable for users who need to conduct in-depth research, such as scholars, market analysts, content creators, and technical researchers. It helps users quickly generate high-quality research reports, saving time and effort while ensuring accuracy and comprehensiveness.
Use Cases
Generate a global trend analysis report on renewable energy storage technology (2020-2025)
Analyze specific industry market trends and competitor strategies
Generate literature reviews and background information for academic papers
Features
Supports comprehensive research across multiple search engines and knowledge bases
Implements a structured research workflow through LangGraph
Recursively explores topics, dynamically adjusting research depth and breadth
Intelligently assesses the credibility and relevance of information sources
Generates structured reports and supports multiple citation formats
Supports multi-threaded processing to improve research efficiency
Allows customization of LLM models to meet different needs
Provides detailed user interaction and progress feedback
How to Use
1. Install Shandu: Install via PyPI or from source code.
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2. Configure API settings: Supports multiple LLM providers.
3. Run research commands: Specify the research question, depth, breadth, and output format.
4. View or save results: Output in Markdown, JSON, or plain text format.
5. Use the Python API for advanced customization: Call Shandu's functions through code.
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