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AutoResearcher 🤖

Automated Data Collection with LLM, Google Custom Search, Video Transcription, Web Scraping, Instagram API, OpenAI Vision, and OpenAI Whisper.

Frontend for using the backend AI tools.

This repo has kind of turned into a playground for me to use OpenAI LLMs.

This Python project automates data collection on user provided topics.

Features

  1. Query Generation: LLM creates relevant search queries.
  2. Data Collection: NewsApi, YouTube, and Google Search to collect data on specific topics.
  3. Data Scraping: Uses Playwright and BeautifulSoup to fetch and extract web content. Uses MoviePy to extract audio data from videos. Uses OpenAI Whisper Model to create audio transcriptions.
  4. Summarization: LLM summarizes scraped data into concise reports.
  5. End-to-End Automation: Fully automated from input to summarized output.
  6. Frontend: Auto Researcher front end for viewing collected data.
  7. Instagram Integration:
    • Read a user's Instagram.
    • Process pictures using OpenAI vision.
    • Create an object-weighted graph based on object occurrences in the pictures.
    • Summarize picture settings and subjects.
    • Summarize the user's Instagram in total.
    • Weighted graph build off of Instagram profile
    • Chat functionality to chat with character AI built off of instagram
  8. Chat Functionality:
    • Chat with LLM
    • Build character from instagram images
    • Chat with instagram character
    • Frontend for chat integration
  9. Chat Assistant Timeline Builder:
    • Timeline builder assistant
    • Saves, updates, suggests, finds milestones or memories for a persons life.
    • Uses GPT functions to modify, add, update, delete, find user milestones from milestone store.
    • Allows users to upload images to associate with milestones.
    • Provies output for visualizing on a timeline in the front end.
    • Frontend for chat integration
  10. p5.js Milestone, Event, Character Visualization
    • p5.js for creating timeline, force directed graph, and more

Technologies

  • Python, OpenAI API, Google Custom Search API
  • Playwright, BeautifulSoup, MoviePy, NewsAPI
  • InstagramAPI, NetworkX, p5.js

Setup

  1. Clone the repo:

    git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name

  2. Install dependencies:

    pip install -r requirements.txt

  3. Set up API keys:
    Create a .env file with:

    OPENAI_API_KEY=your_openai_api_key
GOOGLE_CUSTOM_SEARCH_API_KEY=your_google_custom_search_api_key
GOOGLE_CX=your_google_cse_id
NEWS_API_KEY=newsapi_key
INSTAGRAM_CLIENT_ID=insagram_client_id
INSTAGRAM_CLIENT_SECRET=insagram_client_secret
INSTAGRAM_REDIRECT_URI=instagram_redirect_uri
INSTAGRAM_ACCESS_CODE=insagram_access_code

  4. Run the script: Currently only the "news" source is supported.

    python src/researcher/main.py --topic "Your topic here" --source "[news, google, youtube, all]"

  5. Check Results Folder: Find result json file in results folder

Usage

  1. Input a topic.
  2. LLM generates search queries.
  3. Data source retrieves results.
  4. MoviePY transcribes video or Playwright & BeautifulSoup scrapes web pages.
  5. LLM summarizes the scraped content.

Future Enhancements

  • Support for more search engines.
  • Advanced query generation and filtering.
  • Customizable summarization options.
  • Add UI for visualizing results
  • Add subscription features for users to get updates on a schedule

Needed Improvements

  • Web page scraping in web_page_reader.py
  • More abstract data_gatherer.py class
  • Chunking in openAI requests for large text especially in video summarization
  • Potentially increase performance by decreasing run time?
  • Add SerpApi
  • Reduce number of LLM calls and increase relevancy of sumary data