Osama Mosaad@omosaad

ResearchWriterGPT: An Advanced Multimodal Research Paper Writing Assistant" is an innovative project designed to revolutionize academic writing. It combines the language and vision capabilities of GPT-4 with Clarifai's advanced AI tools to assist in drafting research papers. The tool is adept at processing both textual and visual data, ensuring comprehensive coverage from abstract creation to conclusion formulation, all in APA format. The project stands out for its integration of multimodal capabilities, including image and chart scanning, and analysis. It provides direct access to prominent academic databases like Google Scholar and Arxiv, streamlining the literature review process by aggregating and filtering relevant information. Furthermore, the tool enhances user experience by supporting interactive dialogues, including audio interaction, and allows for PDF analysis and conversion. Its innovative GPT-4 Vision feature broadens the application scope by enabling detailed image analysis, including reading texts and interpreting charts from various sources like research photos and medical images. In addition, the integration of Retrieval-Augmented Generation (RAG) with Clarifai creates a vast knowledge base, processing over 1.7 million STEM articles from ArXiv for quick, contextually relevant responses. This system not only supports efficient document management but also advances AI interaction for academic research. The technology backbone of ResearchWriterGPT includes GPT-4 Turbo for text generation, GPT-4 Vision for image processing, DALL-E API for image generation, and Clarifai’s RAG system for enriched data handling. Future expansions envision incorporating advanced Clarifai models for face sentiment analysis and object detection, predictive bibliography features, and comprehensive writing support covering all aspects of a research paper.

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ResearchWriterGPT is an Advanced Multimodal Research Paper Writing Assistant is a groundbreaking tool designed to transform academic writing. It harnesses the language and vision capabilities of GPT-4 to assist in crafting research papers, processing both textual and visual data to ensure thorough coverage from abstract to conclusion in APA format. The project showcases its multimodal capabilities, including image and chart scanning and analysis. It offers direct access to academic databases like Google Scholar, Semantic Scholar, Pubmed, etc., facilitating the literature review process by aggregating and filtering peer-reviewed information. Additionally, the tool enhances user experience through interactive dialogues, audio interaction, PDF analysis, and PPT downloading option. GPT-4 Vision expands the application's scope by enabling detailed image analysis, such as reading texts and interpreting charts from research photos and medical images. The integration of a Pinecone-based RAG system allows users to upload a collection of documents, which the system appends to for relevant response generation. This creates a vast knowledge base, potentially processing millions of articles for quick, contextually relevant responses, supporting efficient document management and advancing context-based AI LLM feed. TruLens further strengthens the tool by evaluating hallucinations in three key dimensions: context relevance, groundedness, and answer relevance. This ensures the LLM application is free from hallucinations, delivering accurate and relevant information. The Trulens leaderboard feature displays the relevancy score of the LLM responses giving realtime feedback. Future expansions aim to incorporate advanced models for face sentiment analysis and object detection, predictive bibliography features, and comprehensive writing support covering all aspects of a research paper.