Zero Shot Article Generation

Objective

This project scrapes the web and uses GPT-3 + LangChain to automatically parse and generate new articles.

Deployment

The application is deployed on a Flask server, a lightweight WSGI web application framework. This setup provides a balance between ease of use and powerful functionality, suitable for both simple and complex applications.

Technologies Used

• GPT-3.5 Turbo: For advanced natural language processing.
• Langchain: Enhances large language model capabilities.
• BeautifulSoup & Requests: Python libraries for web scraping.
• dotenv: Manages environment variables for secure configuration.

How It Works

1. Web Scraping: Scrape the latest tech funding news articles from a specified site, storing in 'Scraped Articles'.
2. Article Processing: alpha.ipynb processes this information for content generation.
3. Multi-Shot Prompting: This method is used for maintaining output quality.
4. Model Flexibility: Adaptable for use with various language models.

Possible Future Extensions

• Dynamic Information Retrieval with Retrieval-Augmented Generation: By incorporating RAG, the project can dynamically access and integrate external information. This would require combining a language model with an information retrieval system, which fetches the most relevant and up-to-date data from the Vector Database or other external sources.
• Automated Quality Control: Develop a system to automatically check the factual accuracy, grammar, and style of generated articles - potentially with 3rd party data sources like Pitchbook or Crunchbase. This will ensure consistently high-quality output and reduce the need for manual reviews.
• Multi-Language Support: Extend the system to generate articles in multiple languages. This will cater to a broader, international audience and increase the project's reach and applicability.

Implementation Details

• Utilizes Python's BeautifulSoup for web scraping and requests for web page fetching.
• Employs Langchain's ChatOpenAI for prompt-based GPT-3.5 Turbo interactions.
• Methods to filter data, convert HTML to text, and manage JSON files are implemented.

Workflow

1. Scrape articles.
2. Convert HTML content to text.
3. Process content with GPT-3.5 Turbo.
4. Use conversation chains for checks and context maintenance.
5. Refine the article list and compile the final text file.

Usage

Ensure all dependencies are installed and environment variables are set. Run alpha.ipynb to initiate the process. Follow the notebook's steps for complete article generation.

Conclusion

This project demonstrates the integration of LLM models and web scraping for automated, relevant, and current article generation. Its design allows for easy adaptation and scalability.