The advent of Large Language Models (LLMs) has significantly impacted the field of natural language processing, offering solutions to a wide array of general problems. However, these models often struggle with domain-specific tasks, emerging knowledge, and especially with issues like polysemy, homonyms, and entity ambiguity, leading to inaccurate or hallucinated responses. This project presents the enhanced Retrieval-Augmented Generation (RAG) systems using GLiNER, a state-of-the-art Named Entity Recognition (NER) tool, in combination with Llama-Index and Mistral, to address these challenges effectively.
Before you start, ensure your system meets the following requirements:
Python 3.8 or higher pip for Python 3
This project involves two significant steps to enhance the RAG system: entity recognition, query refinement, and leveraging LLMs for improved performance. Follow these steps to set up and use the system:
Step 1: Setup GLiNER Clone the GLiNER repository or install it via HuggingFace:
Step 2: Run Clone my repository and then run RAG_NER.py:
We welcome contributions from the community. If you'd like to improve the project or suggest enhancements, please follow reach my email: [email protected]
The advent of Large Language Models (LLMs) has significantly impacted the field of natural language processing, offering solutions to a wide array of general problems. However, these models often struggle with domain-specific tasks, emerging knowledge, and especially with issues like polysemy, homonyms, and entity ambiguity, leading to inaccurate or hallucinated responses. This project presents the enhanced Retrieval-Augmented Generation (RAG) systems using GLiNER, a state-of-the-art Named Entity Recognition (NER) tool, in combination with Llama-Index and Mistral, to address these challenges effectively.
Before you start, ensure your system meets the following requirements:
Python 3.8 or higher pip for Python 3
This project involves two significant steps to enhance the RAG system: entity recognition, query refinement, and leveraging LLMs for improved performance. Follow these steps to set up and use the system:
Step 1: Setup GLiNER Clone the GLiNER repository or install it via HuggingFace:
Step 2: Run Clone my repository and then run RAG_NER.py:
We welcome contributions from the community. If you'd like to improve the project or suggest enhancements, please follow reach my email: [email protected]
