AInalyst

Advanced AI-powered research and analysis assistant that transforms complex queries into comprehensive reports and professional presentations.

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AInalyst - AI Research & Analysis Assistant

Inspiration

The inspiration behind AInalyst stems from the growing need for efficient, automated research tools in an era of information overload. The project aims to bridge the gap between complex research requirements and the time-consuming nature of manual information gathering and synthesis. By leveraging gpt-oss-20b and gpt-oss-120b models, and web search capabilities, AInalyst was created to transform how professionals, students, and researchers conduct comprehensive investigations and create professional reports.

What it does

AInalyst is an advanced AI-powered research and analysis assistant that:

  1. Automates Research: Takes complex queries and breaks them down into specific sub-questions for thorough investigation
  2. Gathers Information: Utilizes multiple web search providers (Tavily and Exa) to collect comprehensive data
  3. Analyzes Content: Employs advanced language models (gpt-oss-20b and gpt-oss-120b) to synthesize information
  4. Generates Reports: Creates well-structured markdown reports with proper citations
  5. Creates Presentations: Automatically generates PowerPoint presentations from research findings
  6. Offers Multiple Interfaces: Provides both command-line and web interfaces for different user needs

How we built it

The project is built using a modular Node.js architecture with the following key components:

Core Technologies

  • Node.js (v14+) for the development
  • Express.js for the web server
  • Socket.IO for real-time updates in the interaction between the user and the platform
  • pptxgenjs (Node library) for PowerPoint generation
  • reveal.js for web presentations
  • gpt-oss-20b as the default model for AI analysis, searching, and results.

AI Integration

  • OpenAI-compatible API and SDK for accessing gpt-oss models (using HuggingFace, Ollama, or any other API).
  • Custom prompt engineering for research planning and content generation

Search Providers

  • Tavily API for general web search
  • Exa API for more specialized searches

Architecture

  • Modular design with separate components for research, reporting, and presentation
  • Clean separation of concerns between data retrieval, processing, and presentation layers
  • Environment-based configuration for easy deployment

Challenges we ran into

  1. API Integration: Managing different search provider APIs with varying response formats and rate limits.
  2. Content Quality: Ensuring the AI generates accurate, well-structured, and properly cited content (I had to test different prompts to get the one).
  3. Error Handling: Building robust error handling for network requests and API failures so users don't suffer from complex error messages.
  4. Performance Optimization: Managing long-running research tasks while maintaining a responsive user experience.
  5. Presentation Generation: Creating visually appealing PowerPoint slides programmatically with proper formatting.

Accomplishments that we're proud of

  • End-to-End Automation: Successfully creating a system that goes from research question to professional presentation with minimal user intervention, and tested in my situations and with other colleagues.
  • Modular Architecture: Building a flexible system that can easily integrate with different search providers and AI services with gpt-oss models.
  • User Experience: Developing both CLI and web interfaces to cater to different user preferences.
  • Quality Output: Generating well-structured, properly cited research reports that are ready for professional use.
  • Open Source Contribution: Creating a tool that can benefit researchers, students, and professionals worldwide (anyone can extend and improve).

What we learned

  1. AI Prompt Engineering: The importance of carefully crafting prompts to get high-quality, structured outputs from language models
  2. Asynchronous Programming: Managing complex asynchronous operations in Node.js for efficient research tasks
  3. API Design: Creating clean, maintainable interfaces between different system components
  4. Error Handling: Implementing comprehensive error handling for robust production applications
  5. Performance Optimization: Techniques for handling long-running tasks while maintaining system responsiveness

What's next for AInalyst

  1. Enhanced Search Capabilities:

    • Integration with academic databases and research papers
    • Support for more specialized search providers
    • Advanced filtering and source validation

  2. Improved AI Features:

    • Fine-tuned models for specific research domains
    • Multi-language support
    • Fact-checking and source verification

  3. User Experience:

    • Interactive web interface with real-time collaboration
    • Mobile application for on-the-go research
    • Browser extension for quick research from any webpage

  4. Advanced Analytics:

    • Sentiment analysis of research findings
    • Trend analysis and visualization
    • Comparative research capabilities

  5. Enterprise Features:

    • Team collaboration tools
    • Custom templates and branding
    • Advanced export options (PDF, Word, Google Slides)

  6. Community & Ecosystem:

    • Plugin system for custom integrations
    • Public API for developers
    • Community-contributed research templates and workflows

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