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Local Deep Research: An Open-Source AI Tool for Efficient In-Depth Research
What is Local Deep Research?
Local Deep Research is an open-source deep research tool based on AI. It helps users conduct complex-topic research quickly and efficiently. Through multi-round iterative analysis and intelligent question generation, it can automatically mine and organize information to produce detailed research reports. It can be applied across many scenarios, including academic research, market analysis, and product evaluation.
Core Features of Local Deep Research
Deep, Iterative Research
Automatically generates intelligent questions and progressively dives deeper to ensure full coverage of every aspect of the topic.
Supports multi-round analysis, progressively refining the research content.
Flexible AI Model Support
Supports local AI models (such as Ollama), ensuring data privacy.
Supports cloud LLMs (such as Claude, GPT), selectable as needed.
Rich Output Options
Provides detailed research reports, quick summaries, and complete citations.
Supports multiple output formats for easy further organization and sharing.
Privacy Protection
Can run entirely locally, ensuring data privacy and security.
Powerful Search Integration
Supports multiple search sources, including Wikipedia, arXiv, DuckDuckGo, and more.
Supports local document search, allowing users to upload custom document collections.
Versatile Scenario Applicability
Suitable for academic research, business analysis, personal learning, and many other scenarios.
Key Project Features of Local Deep Research
Automated Research Workflow: Uses AI to automatically generate questions and perform iterative analysis, reducing manual intervention.
Multi-Source Information Integration: Combines multiple data sources to provide a comprehensive research perspective.
Privacy-First: Supports local execution, ensuring user data is never leaked.
Flexible Configuration: Supports both local and cloud models, allowing users to choose based on their needs.
Use Cases for Local Deep Research
Academic Research: Quickly retrieve and analyze scientific papers and organize research ideas.
Business Analysis: Track industry developments and market trends to generate business reports.
Personal Learning: Organize and summarize knowledge to boost learning efficiency.
Local Deep Research Installation and Usage
1. Clone the repository and install dependencies:
git clone https://github.com/LearningCircuit/local-deep-research.git cd local-deep-research pip install -r requirements.txt
2. Install Ollama and configure the model:
ollama pull mistral
3. Configure environment variables (if using a cloud LLM):
cp .env.template .env
4. Fill in the corresponding API keys in the .env file.
GitHub Repository:https://github.com/LearningCircuit/local-deep-research
