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.
- Document Ingestion: Efficiently processes and chunks large datasets for easy retrieval.
- Advanced Embeddings: Uses nomic-embed-text for high-quality vector embeddings of documents.
- Chroma Vector Store: A scalable solution for storing and querying document vectors.
- Dynamic Query Expansion: Enhances retrieval quality through context-aware query handling.
- LLM Integration: Leverages Ollama models to generate precise, contextually relevant answers.
- Evaluation Metrics: Includes recall, MAP (mean average precision), and exact match for rigorous system performance evaluation.
Python | Chroma DB | Ollama Models | Gradio Interface Embedding model: nomic-embed-text Generation model: Ollama Mistral, llama 3.1, phi3.5
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.