AI Projects
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.
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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.
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.
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.
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.
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.
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.
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.
HyDE-Powered Document Retrieval Using DeepSeek
Efficient document retrieval system using FAISS, DeepSeek and LangChain, generating accurate answers and quick access to relevant information.
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