🧠 Personalized AI Research Assistant: Let AI Read & Explain Research Papers for You12 min read·Just now--Imagine never having to struggle through 20-page academic papers again. What if you had an AI assistant that could read, summarize, and answer your questions — instantly?In today’s world of information overload, the ability to process academic research quickly and effectively is more valuable than ever. As part of my Kaggle Capstone Project, I created the Personalized AI Research Assistant, a smart tool designed to help users interact with research papers effortlessly.Whether you’re a student, researcher, or just someone curious about AI, this project brings together the power of LLMs (Large Language Models), semantic search, and PDF parsing to deliver concise answers from complex papers — just like a personal research buddy.🔍 Problem StatementReading academic papers can be overwhelming. They’re often long, filled with domain-specific jargon, and not beginner-friendly. Manually summarizing and understanding their content takes hours.So I asked myself:Can I build an AI that reads and explains research papers — just like a human assistant would?The rapid growth of academic literature and the increasing complexity of research processes pose significant challenges for researchers, students, and interdisciplinary scholars. These challenges include:Inefficient Literature Discovery: Traditional keyword-based search methods often fail to identify contextually relevant papers, missing nuanced or semantically related content. Navigating multiple academic databases (e.g., Semantic Scholar, arXiv) is time-consuming and fragmented, hindering comprehensive literature reviews.Labor-Intensive Paper Analysis: Manually summarizing, evaluating, and extracting insights from research papers is a resource-intensive task. Researchers struggle to quickly assess a paper’s relevance, quality, or methodological rigor, particularly when dealing with large volumes of literature.Difficulty in Refining Manuscripts: Identifying weaknesses in research drafts, such as unclear problem statements, weak methodologies, or poorly structured conclusions, requires significant expertise. Improving grammar, academic style, and technical depth is challenging, especially for novice researchers or non-native English speakersKeeping Up with Trends and Methods: Staying informed about current trends, emerging methodologies, and relevant models in a field is daunting, particularly for interdisciplinary researchers or those entering new domains. This knowledge gap limits the ability to align research with cutting-edge developments.These challenges create bottlenecks in the research process, reducing efficiency and accessibility for diverse users. The Personalized AI Research Assistant addresses these issues by leveraging Retrieval-Augmented Generation (RAG), generative AI (Google Gemini), and advanced text analysis to deliver an integrated solution. By automating literature discovery, paper analysis, manuscript refinement, and trend identification, the project streamlines the research workflow, empowers users to produce high-quality work, and fosters inclusivity across academic levels and disciplines.Introducing the Personalized AI Research Assistant — a smart, dual-phased tool designed to help you search, understand, analyze,rewrite & Visualize sections of academic papers using RAG (Retrieval-Augmented Generation), FAISS, and Google Gemini.InnovationsRAG-Powered Search: Uses FAISS and Gemini embeddings to retrieve and rank papers from multiple databases, generating context-aware summaries and explanations.Comprehensive PDF Analysis: Extracts sections, summarizes content, scores quality, and visualizes insights (word clouds, sentiment, readability).AI Agentic-Driven Rewriting: Rewrites sections to enhance academic style, a novel feature for draft improvement.Graphical Insights: Visualizes section lengths, sentiment, and readability, aiding manuscript evaluation.Interactive Q&A with RAG: Retrieves relevant chunks for uploaded papers and papers for external queries, generating tailored responses.Beginner-Friendly Design: Simplifies complex concepts and recommends accessible papers, supporting students and interdisciplinary scholars.Use CaseThe Personalized AI Research Assistant is a versatile tool designed to streamline academic research and manuscript preparation for a wide range of users, including students, researchers, professors, and interdisciplinary scholars. By leveraging Retrieval-Augmented Generation (RAG), generative AI (Google Gemini), and advanced text analysis, the assistant addresses diverse research needs through targeted functionalities. Below are the primary use cases for each user group:Students:Simplifying Literature Reviews: Students can input research topics to retrieve and rank relevant papers from multiple databases (e.g., Semantic Scholar, arXiv), with AI-generated summaries and beginner-friendly explanations, reducing the complexity of navigating academic literature.Understanding Papers: The assistant provides structured summaries and answers specific questions about uploaded papers, helping students grasp key concepts, methodologies, and findings.Improving Drafts: Students can upload their manuscripts to receive section-specific analyses, quality scores, and AI-rewritten sections, enhancing clarity, academic style, and technical depth for assignments or theses.2.Researchers:Discovering Papers: Researchers can explore cutting-edge literature by querying the assistant, which uses RAG to retrieve semantically relevant papers and highlight their relevance, saving time in literature searches.Analyzing Trends: The interactive Q&A mode identifies current trends and commonly used methods/models in a field, enabling researchers to align their work with the latest advancements.Refining Manuscripts: By analyzing uploaded papers, scoring quality (e.g., clarity, rigor), and rewriting sections, the assistant helps researchers polish drafts for publication, addressing weaknesses like unclear methodologies or redundant content.3.Professors:Evaluating Student Papers: Professors can upload student submissions to receive automated analyses, rubric-based scores, and graphical insights (e.g., section lengths, readability), streamlining grading and feedback processes.Curating Teaching Materials: The assistant retrieves and summarizes recent papers on a topic, helping professors compile relevant, up-to-date resources for lectures or course readings.4.Interdisciplinary Scholars:Exploring Unfamiliar Fields: Scholars entering new domains can query the assistant to retrieve accessible papers, with summaries and visualizations (e.g., word clouds, sentiment analysis) that clarify key themes and terminology.Cross-Disciplinary Insights: The assistant’s ability to analyze trends and methods across fields supports scholars in identifying connections and integrating diverse perspectives into their research.Phase 1: Discover and Understand Research EffortlesslyIn the first phase, the assistant works like your personal research librarian, enabling:✅ Vector-based search across scientific databases ✅ RAG-powered explanations of top-ranked papers ✅ Conversational Q&A about paper content🔍 How it Works:Enter a query like “Generative AI in medical imaging.”The assistant fetches scholarly articles using APIs like Semantic Scholar and CORE.FAISS is used to semantically rank these papers.Google Gemini is prompted to explain key concepts in simplified, academic tone.You can interact with the bot and ask detailed questions.💬 Sample question:“Summarize the methods used in the paper on Federated Learning for Healthcare.”Interactive Main Function for Paper SearchThis section explains the main function, which drives the External Paper Search mode of the Personalized AI Research Assistant.def main(): print("👋 Hey, hi there! I'm your friendly Research Assistant! 📚✨") while True: user_query = input("\n💬 Please enter your research topic or question (or type 'exit' to quit): ") if user_query.lower() == 'exit': print("👋 Goodbye! Happy researching!") break print("\n🔎 Searching for the best matching research papers across multiple databases...\n") ranked_papers = get_ranked_papers(user_query) if not ranked_papers: print("❌ Sorry, no papers found. Try a different topic!") continue for idx, (paper, similarity) in enumerate(ranked_papers, 1): title = paper.get('title', 'No Title') authors = ', '.join([author.get('name', '') for author in paper.get('authors', [])]) or "Unknown Authors" year = paper.get('year', 'Unknown Year') url = paper.get('url', '#') print(f"\n📄 Paper {idx}: {title}") print(f"👨‍🔬 Authors: {authors}") print(f"📅 Year: {year}") print(f"🔗 Link: {url}") print(f"📈 Match Score: {similarity:.2f}%\n") # Explain and display in Markdown abstract = paper.get('abstract', '') explanation = explain_relevance(title, abstract, user_query) display(Markdown(explanation)) interactive_qna_session(ranked_papers, user_query) # -----------------------------------# Run the Assistant# -----------------------------------if __name__ == "__main__": main()📄 Phase 2: Upload Your Own PDFs for Deep AnalysisNow comes the powerful second phase — Upload your own research paper, and the AI takes it from there.Once uploaded, the assistant:📑 Extracts text by section (abstract, intro, methodology, etc.)🧠 Performs AI-powered summarization with formal academic tone🧾 Analyzes sentiment, readability, and structure✍️ Suggests section-wise rewrites💬 Enables question-answering based on your uploaded contentMain Driver Code for Uploaded Paper AnalysisThis section explains the main driver code for analyzing and rewriting an uploaded research paper PDF, including example questions for interactive Q&A.Code Explanation :Purpose:This code drives the Uploaded Paper Analysis mode, processing a research paper PDF to extract text, analyze content, score quality, and rewrite sections. It uses RAG for interactive Q&A, retrieving relevant chunks with FAISS and generating answers with Google Gemini, while offering AI-driven rewriting to enhance the manuscript.Implementation:Extracts and chunks PDF text, embeds chunks with Gemini, and builds a FAISS index for RAG-based Q&A. Analyzes the full paper with analyze_uploaded_paper, scores it via score_paper_rubric, and displays results in Markdown. Lists example questions to guide Q&A, then enters a loop for user questions using chat_about_paper. Splits text into sections, analyzes each with analyze_section, and allows iterative rewriting with rewrite_section, updating sections if desired.Role in the Project:Central to the project’s goal of aiding manuscript refinement, it complements the external paper search mode by providing deep analysis and rewriting for uploaded papers. The RAG-powered Q&A and Gemini-driven analyses/rewrites enhance user productivity, making it invaluable for students and researchers improving their drafts.example_questions = [ """ 🔹 Critique the clarity and focus of my research problem statement. 🔹 Identify gaps or weaknesses in my literature review section. 🔹 Analyze the strength of my methodology — any flaws or improvements? 🔹 Evaluate if my results and analysis are convincing and well-supported. 🔹 Suggest improvements for the conclusion to make it more powerful. 🔹 Recommend recent papers or citations I should add. 🔹 Check if my paper aligns with current trends in the field. 🔹 Advise on making my abstract more concise and engaging. 🔹 Point out any redundancy, irrelevant sections, or off-topic parts. 🔹 Detect possible ethical concerns or biases in my study. 🔹 Suggest formatting or structure improvements for better flow. 🔹 Help me prepare potential reviewer questions for peer-review. 🔹 Check if my research contributions are stated clearly enough. 🔹 Critique the originality and innovation level of my work. """]# -----------------------------------# Step 9: Main Driver Code# -----------------------------------# Example Usageuploaded_pdf_path = "/kaggle/input/research-papers/pdf4.pdf" # <-- Change this!# Extract + Chunkfull_text = extract_text_from_pdf(uploaded_pdf_path)chunks = chunk_text(full_text)# Embed Chunkschunk_embeddings = np.vstack([get_gemini_embedding(chunk) for chunk in chunks])# Build FAISSindex = build_faiss_index(chunk_embeddings)# Analyzefull_analysis = analyze_uploaded_paper(full_text)print("📄 Full Analysis:\n")display(Markdown(full_analysis))rubric = score_paper_rubric(full_text)print("Paper Score:\n")display(Markdown(rubric))# Show example questionsprint("\n💬 Example Questions You Can Ask About Your Paper:")for q in example_questions: print(f"- {q}")# QnA Modeprint("\n🚀 Entering Chat Mode: Discuss Your Paper")while True: user_question = input("\nAsk a question (or type 'exit' to stop): ") if user_question.lower() == 'exit': break answer = chat_about_paper(user_question, chunks, chunk_embeddings, index) print("\n🤖 Bot's Answer:\n") display(Markdown(answer))uploaded_text = extract_text_from_pdf(uploaded_pdf_path)# Step 2: Split the full text into sectionssections = split_into_sections(uploaded_text)# Step 3: Ask user if they want to enter AI Rewriter Modeuser_choice = input("\n🤖 Would you like to enter AI Rewriter Mode to enhance your paper? (yes/no): ").strip().lower()if user_choice == "yes": # Step 4: Analyze each section first print("\n📊 Analyzing sections of your paper...\n") for section_name, section_content in sections.items(): if section_content.strip(): analysis = analyze_section(section_name, section_content) print(f"\n--- 📚 Analysis for {section_name} ---") print(analysis) print("\n--------------------------------------\n") else: print(f"\n⚠️ {section_name} section appears empty. Skipping analysis.\n") # Step 5: Allow user to interactively choose and rewrite sections while True: print("\n📚 Sections found in your paper:") for idx, section_name in enumerate(sections.keys(), start=1): print(f"{idx}. {section_name}") selected_section = input("\n✍️ Which section would you like to rewrite? (Type section name exactly or type 'exit' to quit): ").strip() if selected_section.lower() == "exit": print("\n👋 Ending AI Rewriter Session. Happy Writing!") break if selected_section in sections: original_content = sections[selected_section] if not original_content.strip(): print(f"\n⚠️ The {selected_section} section is empty. Please choose another section.") continue rewritten = rewrite_section(selected_section, original_content) print(f"\n📝 Here is the rewritten {selected_section} section:\n") print(rewritten) # Optionally update the section with rewritten text update_choice = input("\n✅ Do you want to update this section with the rewritten version? (yes/no): ").strip().lower() if update_choice == "yes": sections[selected_section] = rewritten print(f"\n💾 {selected_section} section updated successfully!") else: print("\n⚠️ Invalid section name. Please type the section exactly as shown.")else: print("\n👋 Okay! Ending session. You can always come back for rewriting later!")📝 📚 Generate High-Quality Paper SummariesThis feature transforms your entire paper into a professional summary between 250–350 words with an academic tone, structured logically:✅ Summary Covers:Research objective and backgroundMethodologies usedMajor resultsDiscussions and implicationsConclusion and future workimport os# 📚 Paper Summary Generatordef generate_summary(full_text): prompt = f"""You are a professional academic editor and scientific writer.Your task is to generate a high-quality structured summary of the following research paper. The summary should be:- Precise, clear, and highly professional- Formal academic tone (no casual language)- Between 250 and 350 words- Well-organized into logical flow- Emphasizing key points without unnecessary detailFocus on summarizing:1. The research objective and background2. Methodologies and techniques used3. Major findings and results4. Important discussions and implications5. Final conclusions and potential future workStrictly avoid personal opinions or assumptions not grounded in the text.Here is the paper content:{full_text}""" model = genai.GenerativeModel('gemini-2.0-flash') response = model.generate_content(prompt) return response.text# 📥 Save Summary Functiondef save_summary(summary_text, filename="summary1.txt"): with open(filename, "w", encoding="utf-8") as f: f.write(summary_text) print(f"\n✅ Summary saved as '{filename}' successfully!")uploaded_text = extract_text_from_pdf(uploaded_pdf_path)# Ask user if they want a summarysummarize_choice = input("\n🧠 Would you like to generate a summary of your paper? (yes/no): ").strip().lower()if summarize_choice == "yes": summary = generate_summary(uploaded_text) print("\n📄 Here is the summary of your paper:\n") display(Markdown(summary)) # Ask user if they want to download it download_choice = input("\n💾 Would you like to download the summary? (yes/no): ").strip().lower() if download_choice == "yes": save_summary(summary) else: print("\n👍 No problem! Summary not downloaded.")else: print("\n👍 Skipping summary generation!")📊 Analyze, Visualize & Score Your ResearchThis Python script is designed to analyze the structure and content of a research paper in PDF format. It provides various graphical insights to help visualize key aspects of the paper, making it easier to understand its readability, sentiment, and word distribution. The process is broken down into the following key functions:Word Cloud Generation (plot_word_cloud): This function creates a visual representation of the most frequent words in the entire text. It uses the WordCloud library to generate a word cloud, where the size of each word is proportional to its frequency in the document.Section Lengths Distribution (plot_section_lengths): This function calculates and plots the word count of each section in the paper. It helps in understanding the length distribution across various sections, such as Introduction, Methodology, Results, etc.Sentiment Analysis (plot_sentiment_analysis): By using TextBlob, this function performs sentiment analysis on each section and plots the sentiment polarity score. The sentiment score ranges from -1 (negative) to 1 (positive), providing insights into the tone of the sections.Readability Scores (plot_readability_scores): This function evaluates the readability of each section using the Flesch Reading Ease score, which indicates how easy or difficult the text is to read. A higher score suggests easier readability.Graphical Analysis (perform_graphical_analysis): This function orchestrates the entire graphical analysis process, calling all the above functions to generate the corresponding plots.The main function, ai_research_paper_helper_with_graphics, serves as the entry point. It extracts the text from a given PDF, splits it into sections, and then prompts the user to perform the graphical analysis. If the user agrees, it displays the word cloud, section lengths, sentiment analysis, and readability scores of the paper.This script is a helpful tool for anyone looking to analyze the structure and quality of a research paper, offering visual insights into the content’s distribution, sentiment, and readability.word frequency analysis in a research paperSentiment Analysis in a research paperDemo Video🎥 Add a short screen recording of:1.User typing a query and getting relevant papers from the external sources using Vector search/RAG2.Uploading a paper and generating summary/analytics.3.Asking the AI a question based on the paper.🧠 Final ThoughtsThis project represents a leap in making research intelligent, accessible, and interactive. Whether you’re diving into a new field or polishing your own manuscript, the Personalized AI Research Assistant is designed to empower you.✨ It’s not just an assistant — it’s your AI-powered research companion.KAGGLE NOTEBOOK & THE APP LINKHere i have given the links for the project’s kaggle notebook & it’s demo UI app for the project feel free to run and test out the appKaggle Notebook : Personalized AI Research AssistantStreamlit UI App: Personalized AI Research Assistant