RAG-Enhanced-Knowledge-Synthesizer

Overview

The RAG-Enhanced-Knowledge-Synthesizer project demonstrates the development of an advanced Retrieval-Augmented Generation (RAG) system. Utilizing cutting-edge technologies like Llama 2.0, Langchain, and ChromaDB, this project focuses on enhancing document retrieval efficiency and improving the relevancy of generated answers.

Features

• Llama 2.0 Integration: Leverages Llama 2.0 to handle complex language tasks and ensure high-quality text generation.
• Langchain Framework: Employs Langchain to facilitate seamless integration and management of language models.
• ChromaDB Vector Databases: Utilizes ChromaDB for efficient document retrieval through vector-based search and indexing.
• Addressed Hallucination Issues: Implements techniques to mitigate hallucination issues and ensure accurate and reliable results.

Project Components

1. Document Retrieval: Uses ChromaDB to enhance querying capabilities through vector embeddings.
2. Answer Generation: Utilizes Llama 2.0 and Langchain for generating accurate and contextually relevant responses.
3. Error Mitigation: Implements strategies to address and reduce hallucination issues in generated outputs.