AI Projects
Graph-Enhanced Retrieval-Augmented Generation (GRAPH-RAG)
GraphRAG is a document retrieval system that combines vector search, knowledge graph traversal and LLMs for accurate, context-aware query responses.
Image Segmentation using Mask R CNN with PyTorch
Deep learning-based brain tumor detection using Mask R-CNN for accurate segmentation, aiding early diagnosis and assisting healthcare professionals.
Fusion Retrieval: Combining Vector Search and BM25 for Enhanced Document Retrieval
AI-driven document retrieval system using FAISS, BM25 and LLMs for fast, accurate search in legal, academic, corporate and research applications with citations.
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Corrective Retrieval-Augmented Generation (RAG) with Dynamic Adjustments
Corrective Retrieval-Augmented Generation (RAG) enhances response accuracy by dynamically adjusting the retrieval process, ensuring relevant, up-to-date information.
Enhancing Document Retrieval with Contextual Overlapping Windows
Improve document retrieval with contextual overlapping windows, PDF processing, text chunking, FAISS, and OpenAI embeddings for more coherent search results.
Context Enrichment Window Around Chunks Using LlamaIndex
Optimize document retrieval with AI using FAISS, OpenAI embeddings & context windows for smarter knowledge management & Q&A systems.
HyDE-Powered Document Retrieval Using DeepSeek
Efficient document retrieval system using FAISS, DeepSeek and LangChain, generating accurate answers and quick access to relevant information.
Optimizing Chunk Sizes for Efficient and Accurate Document Retrieval Using HyDE Evaluation
Optimize document retrieval with GPT-4, using vector indexing and chunk size tuning for fast, accurate real-time and real-world AI search insights.
Document Augmentation through Question Generation for Enhanced Retrieval
Improve document retrieval with OpenAI's GPT-4 and FAISS, generating context-based questions and accurate answers for efficient processing and information extraction from PDFs.
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