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README.md
README.md
 
 
requirements.txt
requirements.txt
 
 
research_assistant.py
research_assistant.py
 
 

README.md

Research Assistant

An AI-powered tool to assist with research queries using multiple sources and LLMs.

Installation

  1. Navigate to the research_assistant directory
  2. Install the required packages: 
    pip install -r requirements.txt
  3. Set up environment variables:
    • EXA_API_KEY: Your Exa API key (required)
    • GROQ_API_KEY: Your Groq API key (if using Groq)
    • ANTHROPIC_API_KEY: Your Anthropic API key (if using Anthropic)
    • OLLAMA_MODEL: Your preferred Ollama model (default is "llama3.1")

Usage

python research_assistant.py [options] "Your research query"

Options

  • --service {ollama,groq,anthropic}: AI service to use (default: ollama)
  • --model MODEL: Specify a custom model to use (optional)
  • --num_results N: Total number of results to fetch (default: 6)
  • --output DIR: Output directory name (default: research_results)
  • --technical: Use technical mode (include arXiv results)
  • --vault_path PATH: Path to the Obsidian vault directory (default: path/to/vault)
  • --firecrawl_url URL: Base URL for the Firecrawl service (default: http://localhost:3002)

Examples

python research_assistant.py "Impact of climate change on biodiversity"
python research_assistant.py --service groq --num_results 10 "Quantum computing applications"
python research_assistant.py --technical --output quantum_research "Recent advancements in quantum entanglement"

Features

  • Multi-source research: Exa, arXiv (for technical queries), and web scraping via Firecrawl
  • AI-powered summarization and content generation
  • Supports multiple LLM services: Ollama, Groq, and Anthropic
  • Creates comprehensive learning materials
  • Organizes results into markdown files for easy integration with note-taking systems

Output

The script generates several markdown files in the specified output directory within the vault path:

  1. exa_results.md: Summary of Exa search results
  2. arxiv_results.md: Summary of arXiv results (if technical mode is used)
  3. firecrawl_result_X.md: Individual results from web scraping
  4. summary.md: Comprehensive learning material synthesized from all sources

Notes

  • Ensure you have the necessary API keys set up as environment variables
  • For technical queries, results are split between Exa and arXiv
  • The Firecrawl service should be running and accessible at the specified URL
  • Output files are saved in [vault_path]/[output_directory]