LLM-based RAG System for PDF Queries
Budget: $30 – $250 USD
I'm seeking an expert in RAG (Retrieval-Augmented Generation) systems, specifically leveraging a Large Language Model (LLM) such as OpenAI's API. Your task will be to create a system that can efficiently query local PDF documents, particularly technical IEEE papers, and provide responses to questions posed.
Key Requirements:
- Implement a RAG system in Python suitable for running in a Google Colab notebook.
- The system should primarily perform question answering.
- Deliver step-by-step instructions on how to use the system, along with a demo.
- Provide clear, understandable explanations of how the code works.
The ideal freelancer for this project would have:
- A strong background in machine learning, specifically with RAG systems and LLMs.
- Experience with querying and processing PDF files.
- Proficiency in Python, particularly with libraries suitable for Google Colab.
- Excellent communication skills to explain complex concepts in an understandable way.
Given my limited budget, I'm hoping to receive a first version of this project within 2 days.
Key Requirements:
- Implement a RAG system in Python suitable for running in a Google Colab notebook.
- The system should primarily perform question answering.
- Deliver step-by-step instructions on how to use the system, along with a demo.
- Provide clear, understandable explanations of how the code works.
The ideal freelancer for this project would have:
- A strong background in machine learning, specifically with RAG systems and LLMs.
- Experience with querying and processing PDF files.
- Proficiency in Python, particularly with libraries suitable for Google Colab.
- Excellent communication skills to explain complex concepts in an understandable way.
Given my limited budget, I'm hoping to receive a first version of this project within 2 days.