TL;DS (Too Lazy; Didn't Search)

Overview

TL;DS (Too Lazy; Didn't Search) is a showcase project demonstrating my ability to create high-quality front-end and back-end AI applications using modern frameworks and packages. For demonstration purposes, this project aims to replicate OpenAI's SearchGPT, which is yet to be released to the public.

The application combines advanced AI capabilities with a SearchGPT inspired, user-friendly interface to provide an enhanced search experience. It leverages state-of-the-art language models and search technologies to deliver accurate, up-to-date information in response to user queries.

Implementation

TL;DS is built using a robust tech stack that includes:

• Frontend: HTML, CSS (Tailwind CSS), and JavaScript
• Backend: Python with Flask
• AI Model: OpenAI's GPT-4o-mini
• Search Functionality: LangChain for AI-driven search operations
• SERP API: Docker container running OpenSERP for Google search queries
• Additional Libraries: Requests and BeautifulSoup for web scraping

The application uses LangChain to orchestrate the AI model's interactions with the search functionality. This allows for dynamic, context-aware searches that can be chained together for complex queries.

Functionality

TL;DS offers a unique search experience:

1. User Query: Users can enter any query into the search box.

2. AI-Driven Search: The AI model analyzes the query and determines what information it needs to look up using Google.

3. Intelligent Information Gathering: For complex, multi-part queries, the AI can string together multiple individual search queries to gather all necessary information.

4. Up-to-Date Sources: The AI provides responses with up-to-date sources as hyperlinks, ensuring the information is current and verifiable.

5. Transparent Results: Users can switch to the "Google Search" tab to view the sources referenced by the AI during its search process.

6. Raw Data Access: For those interested in the underlying data, a "Raw Data" tab is available to view the JSON response from the server.

This functionality demonstrates the power of combining AI language models with real-time web search capabilities, providing users with a comprehensive and intelligent search tool.

Getting Started

To run TL;DS on your local machine, follow these steps:

1. Ensure you have Docker installed. If not, you can install it using:

   sudo apt-get install docker.io -y

2. Clone the repository:

   git clone https://github.com/andrewcampi/TLDS.git

3. Navigate to the project directory:

   cd TLDS

4. Install the required Python packages:

   pip3 install -r requirements.txt

5. Store your OpenAI API key on the first line of a new file named ".env", like this:

   OPENAI_API_KEY=copy-and-paste-your-key-here
   

6. Run the application:

   python3 main.py

7. Access the web interface at the URL provided in the output of the previous command.

Thoughts for Improvements

While a 10 second wait for results may not seem like a long time, it is, especially realtive to a direct Google search. The vast majority of this time is spent waiting for the SERP API to respond with the Google results. Paid-for SERP APIs would likely be significantly faster, making this project more usable.

Happy searching!