Context-Fusion: An Intelligent Retrieval-Augmented Conversational AI Framework with Multi-Model Support

Context-Fusion, an advanced Retrieval-Augmented Generation (RAG) framework, enhances AI responses by integrating multiple retrieval techniques with state-of-the-art language models. It combines Annoy (providing efficient approximate nearest neighbor search for high-dimensional semantic vectors), BM25 (offering robust probability ranking based on term frequency), TF-IDF (weighting term importance), Word2Vec (capturing word-level semantic meaning), and Sentence-BERT (enabling deep contextual understanding of text passages) to ensure precise, relevant, meaningful information retrieval. By leveraging deep semantic understanding alongside lexical matching, it provides accurate, contextually rich responses, improving performance and reliability. Supporting custom response styles like Detailed, Concise, Creative, and Technical, it allows users answers that match their needs. A web interface enables document uploads, parameter customization, and interactive querying, improving accessibility. With flexible deployment options, users choose between cloud APIs (OpenAI) for scalable processing or local models (Ollama) for privacy-focused, efficient performance. Evaluations show Context-Fusion outperforms traditional keyword-based retrieval, achieving 25% better precision in retrieving relevant data, making it effective for business intelligence, legal document analysis, and academic research. Its modular design guarantees scalability, adaptability, and future-proofness, allowing integration with new AI developments. Context-Fusion increases AI-driven discussions and context-aware responses by managing large-scale information retrieval with high accuracy and reliability, making it a tool for researchers and enterprises. It reimagines AI-based document retrieval with sophisticated search methods, profound comprehension, and customizable responses, making it more effective, adaptable, potent for practical uses. This guarantees long-term usefulness and ongoing advancements in information retrieval systems.