/AI-Driven-Research-Assistant-Powered-by-Agentic-RAG

Primary LanguageJupyter Notebook

AI-Driven Research Assistant Powered by Agentic RAG

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.

Key Features

  • 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.

Frameworks and Tools

  • OpenAI GPT-3.5-turbo
  • LlamaIndex
  • Python
  • IPython Notebook

Highlights

  • Custom RAG Pipeline: Integrates retrieval and generation capabilities for accurate responses.
  • Advanced Query Handling: Agents manage tools and direct queries efficiently.

Conclusion

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.