README.md

RAG-i1 is a state-of-the-art Retrieval-Augmented Generation (RAG) system designed for precise and efficient information retrieval and response generation. This project integrates advanced techniques for document chunking, embedding-based retrieval, and LLM-based response generation, creating a robust solution for knowledge-driven tasks.

Key Features:

1. Document Ingestion: Efficiently processes and chunks large datasets for easy retrieval.
2. Advanced Embeddings: Uses nomic-embed-text for high-quality vector embeddings of documents.
3. Chroma Vector Store: A scalable solution for storing and querying document vectors.
4. Dynamic Query Expansion: Enhances retrieval quality through context-aware query handling.
5. LLM Integration: Leverages Ollama models to generate precise, contextually relevant answers.
6. Evaluation Metrics: Includes recall, MAP (mean average precision), and exact match for rigorous system performance evaluation.

Technologies:

Python | Chroma DB | Ollama Models | Gradio Interface Embedding model: nomic-embed-text Generation model: Ollama Mistral, llama 3.1, phi3.5

Use Cases:

Enterprise knowledge management Intelligent document retrieval and summarization Dynamic question-answering systems

Cogniflow-i1 is perfect for organizations looking to leverage cutting-edge RAG systems for tasks like data analysis, report generation, and knowledge management. The project is modular, scalable, and designed with production-readiness in mind.

References (Research Papers)

• [Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering]url
• [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity]url
• [SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION]url