This project provides a command-line REPL for GPT-4 that integrates Google Search, enabling it to access and process real-time information. It's designed for developers and researchers interested in exploring LLM plugin architectures and building conversational agents with up-to-date knowledge. The core benefit is a functional demonstration of how LLMs can leverage external tools for enhanced capabilities.

How It Works

The system operates by presenting GPT-4 with a list of available plugins, including a web search tool. User prompts are augmented with this plugin information and sent to the LLM. GPT-4's response is then parsed to detect any plugin calls. If a call is identified, the corresponding plugin (e.g., Google Search) is executed, and its output is fed back into the LLM's context. This iterative process continues until GPT-4 generates a response without requiring further tool interactions, effectively allowing it to research and synthesize information dynamically.

Quick Start & Requirements

• Install: poetry install
• Run: poetry run python gpt-4-search.py
• Prerequisites: OpenAI API key with GPT-4 access, Google Search API key.
• Setup: Clone the repository, copy .env.template to .env and fill in API keys.
• Documentation: README

Highlighted Details

• Demonstrates a plugin system for LLMs.
• Integrates Google Search for real-time data retrieval.
• Operates as a command-line REPL.
• Showcases a concise implementation (around 200 lines of code).

Maintenance & Community

• The project appears to be a personal demonstration project with limited public community engagement or ongoing maintenance signals.

Licensing & Compatibility

• The README does not explicitly state a license. Users should assume all rights reserved or contact the author for clarification.

Limitations & Caveats

• Requires paid API access for OpenAI (GPT-4) and Google Search.
• The project's small codebase suggests it may be a proof-of-concept rather than a robust, production-ready application.
• No explicit mention of error handling for API failures or unexpected LLM outputs.