This project demonstrates the implementation of a Retrieval-Augmented Generation (RAG) system using OpenAI GPT-3.5-turbo and LlamaIndex. The system is designed to enhance the accuracy and reliability of generative AI models by fetching relevant information from external sources and augmenting queries with contextual data.
- Retrieve and Augment Data: Fetches relevant data and augments queries with context for improved response quality.
- Generate Accurate Responses: Uses OpenAI GPT-3.5-turbo to generate context-aware answers.
- Agentic Property: Utilizes advanced agents to manage and direct complex queries to the appropriate tools and resources.
- OpenAI GPT-3.5-turbo
- LlamaIndex
- Python
- IPython Notebook
- Custom RAG Pipeline: Integrates retrieval and generation capabilities for accurate responses.
- Advanced Query Handling: Agents manage tools and direct queries efficiently.
This project showcases the potential of combining retrieval mechanisms with generative AI models to build powerful AI-driven research assistants. For detailed implementation, refer to the provided IPython notebook.