# Abstract / TL;DR > [KnowBot](https://github.com/stephanie0324/KnowBot) is ideal for researchers, students, and professionals navigating complex literature with a focus on rigor and clarity. ![knowbot.gif](knowbot.gif)

🌐 Live Demo

## Background and Motivation While RAG-based systems are increasingly used in general tasks like web search and chatbots, they often fall short in academic workflows. Below is a comparison of common limitations and how **KnowBot** addresses them: | Limitation in Existing Systems | How KnowBot Addresses It | |-------------------------------|---------------------------| | ❌ No source traceability | ✅ Explicit citations and document-level attribution in every answer | | ❌ Broad, unfocused retrieval | ✅ User-controlled, document-specific reference selection | | ❌ Poor document interaction | ✅ Markdown previews, LLM summaries, and metadata tags | | ❌ Opaque reasoning | ✅ Clear multi-source attribution for transparency | | ❌ Lack of academic workflow support | ✅ Designed for research: reference refinement, follow-up Q&A, and controlled context | **KnowBot** is built to support transparent, controllable, and document-grounded academic search and questioning—empowering users to explore literature with precision. ## Key Contributions ![KnowBots](https://i.pinimg.com/736x/bc/e5/ac/bce5ac5b71cc60c73b93286526d1d10a.jpg) KnowBot introduces several innovations: - 🎯 **Targeted Search**: Question specific documents rather than the full corpus. - 📚 **Reference Control**: Manually add/remove documents used for response generation. - 📝 **Summarization**: Preview each document chunk with LLM-generated markdown summaries. - 📎 **Traceable Output**: Citations and per-chunk attribution ensure transparency. - 🔧 **Modular Design**: Adaptable to new models, stores, or workflows. ## 🌟 Core Features Overview KnowBot helps users explore academic documents through explainable, reference-based semantic search. Below is a summary of its core capabilities: | Area | Description | |------|-------------| | 🔍 **Semantic Retrieval** | Context-aware search across academic JSON/PDFs using HuggingFace embeddings + MongoDB vector search. Results are scoped to user-selected references. | | 📄 **Document Interaction** | Markdown preview of full content with LLM-generated summaries. Real-time reference panel for adding/removing relevant documents. | | 💡 **Follow-up Suggestions** | Multi-turn questioning with suggested next questions tailored to the current topic and reference set. | | 🔎 **Explainable Answers** | Generated responses cite sources by document ID and explain multi-source contribution. | | 🛠️ **Modular System** | Backend supports custom embeddings and models. Frontend built with Streamlit. Integrated CI/CD via GitLab. | --DIVIDER--# System Overview KnowBot’s core system consists of modules for document ingestion, metadata tagging, chunk summarization, embedding and indexing in MongoDB, and retrieval-augmented generation powered by large language models. A responsive UI and automated CI/CD pipelines support efficient and transparent academic search and question answering. ## Components ```note 1️⃣ Ingest 📄 PDF/JSON documents ↓ 2️⃣ Process 📝 Chunk, tag metadata, summarize (Vertex AI), embed (HuggingFace BGE) ↓ 3️⃣ Index 🗂️ Store embeddings + metadata in MongoDB Atlas (vector index) ↓ 4️⃣ Retrieve 🔍 Semantic search + user selects reference documents ↓ 5️⃣ Interact 🤖 Ask questions & get cited answers via Vertex AI on Streamlit UI ``` ### 1. Data Preparation KnowBot’s dataset combines publications from **Ready Tensor** with a diverse range of theses, academic papers, and conference proceedings sourced globally. The content spans a broad spectrum—from large language models to psychotherapy and counseling. #### 🧠 TL;DR – Dataset Summary ##### 🔢 Most Frequent Tags - `Machine Learning`: 20 - `Artificial Intelligence`: 8 - `Prostate Cancer`: 6 - `Psychometrics`: 5 - Others: Vary from 2–5 times ##### 🗂️ Common Themes - **AI & Software**: - Machine Learning, Artificial Intelligence, Deep Learning, NLP, Python Programming - **Medical & Psychology**: - Prostate Cancer, Anxiety Assessment, Medical Research ![tag_chart.png](tag_chart.png) > [!Note] Consider normalizing tag casing and merging semantically similar tags to improve data quality. To prepare the data for retrieval-augmented tasks, each document undergoes: - **Chunking**: Split into coherent semantic units for manageable processing. - **Summarization**: Condensed by LLMs to support fast, human-readable previews. - **Tagging**: Enriched with metadata and topical tags to aid classification and filtering. - **Embedding & Indexing**: Transformed into vector representations via HuggingFace models and stored in **MongoDB Atlas** for efficient semantic search. ### 2. Model Integration KnowBot integrates several key AI components: - **Semantic Search**: Powered by HuggingFace embedding models optimized for academic language. - **Summarization & QA**: Uses Vertex AI large language models to generate both document summaries and context-grounded answers. ```python SUMMARY_PROMPT = """ You are a helpful assistant. Your task is to summarize a single document. Instructions: - If the document is short, provide a concise summary in one sentence. - If the document covers multiple key ideas, present them as a bulleted list. - Do **not** begin with generic phrases like "This document is about...". - Focus solely on the core content, highlighting the most important insights. """ ``` - **Follow-up Generation**: Optionally suggests related or deeper questions based on user queries and document context. ```python REFERENCE_PROMPT = """You are a helpful assistant. Your task is to answer the user's question using only the information provided in the reference documents below. Instructions: - Use **only** the provided references. Do not use external knowledge or assumptions. - Always begin your answer with a brief **summary** (1-2 sentences). - Follow the summary with a detailed explanation, **formatted in Markdown**, with clear paragraph breaks. - At the end of **each paragraph**, add a **footnote reference** in Markdown style linking to the supporting document(s), e.g., `[^1]`. - If the answer is not found in the references, explicitly state that. - Finish with 2–3 thoughtful, open-ended follow-up questions formatted as a Markdown blockquote. Example format: ### Summary > Brief summary here. ### Detailed Answer First paragraph of detailed explanation.[^2] Second paragraph continuing the explanation.[^3] Third paragraph adding further details.[^2][^4] [^1]: [Document Title 2](#doc1) [^2]: [Document Title 3](#doc2) [^3]: [Document Title 4](#doc3) ### 💡 Follow-up Questions > #### Question 1? > #### Question 2? > #### Question 3? Question: {{user_question}} References: {{references}} """ ``` - **Reference Attribution**: All model outputs are accompanied by citations linking back to source documents. ### 3. User Interface The interface is specifically designed to support detailed academic exploration and research workflows, providing the following features: - **Search & Filter**: Enables users to perform semantic searches across the document corpus and apply filters based on document relevance, publication date, or other metadata to quickly narrow down results. - **Reference Selection**: Allows users to manually select, add, or remove specific documents as contextual references, giving full control over which sources inform the answer generation. - **Preview & Summarize**: Displays inline previews of documents alongside concise, LLM-generated summaries formatted in markdown, helping users assess document relevance without needing to open full files. - **Contextual Follow-up Q&A**: Supports iterative questioning where users can ask targeted follow-up questions based on previously selected documents and answers. Each response is explicitly tied to clearly cited source documents, with answer length constrained for clarity and ease of reading. ### 4. Deployment & CI/CD - **Deployment**: Configurable for local, containerized, or cloud-based environments. - **Monitoring**: Basic logging and telemetry are implemented to track system performance.

--DIVIDER--# Results 💡 **Summary:** KnowBot delivers accurate academic retrieval and transparent responses. Semantic search and chunk-level summarization enable users to quickly find and assess relevant content. Feedback highlights clear citation, easy document preview, and enhanced control over reference selection—streamlining academic workflows. ## 🚀 Getting Started 1. Clone the repository: ```bash git clone git@gitlab.com:sc310542-group/KnowBot.git cd KnowBot ``` 2. Install dependencies: ```bash bash script/build-docker-image.sh ``` 3. Set up your `.env` file: > 📌 **Note:** You can choose one method to start model inference, just leave the other `""`, and the app will automatically choose the one available - Copy `.env.example` to `.env` - Fill in the required keys: - `MONGODB_URI` - `MONGODB_NAME` - `COLLECTION` - `INDEX_NAME` - `OPENAI_API_KEY` - `VERTEX_PROJECT_ID` - `VERTEX_LOCATION` - `VERTEX_MODEL_NAME` - `GOOGLE_APPLICATION_CREDENTIALS` (credential filepath) 4. Launch the app: ```bash cd deploy docker-compose up -d # for development bash script/run-dev-mode.sh streamlit run app.py --server.port=7860 ``` ## 🛠️ How to Use 1. **Search for academic content** Enter your question in the search panel. The system performs semantic search over ingested JSON/PDF documents. 2. **Explore and select references** Browse the retrieved documents and their AI-generated summaries. Choose relevant ones by clicking **"Add to Ref"**. 3. **Ask your question** With references selected, ask a question. The system uses only those documents to generate accurate, grounded answers. 4. **View source and follow-ups** Check the sources of the answer, and explore suggested follow-up questions to continue your research. ## Limitations & Future Work | Current Limitations | Planned Improvements | | ------------------------------------- | ----------------------------------- | | Fixed-size chunking may break context | Dynamic chunking algorithms | | Basic metadata & tagging only | Smart tagging & classification | | Limited file types supported | Expand to DOCX, HTML, etc. | | No quantitative eval yet | Benchmarking with academic datasets | --- # Licensing and Maintenance and Support - This repository follows semantic versioning, with updates planned regularly to improve features and fix bugs. - Issues and feature requests can be reported via the GitLab issue tracker: [GitHub Issues](https://github.com/stephanie0324/KnowBot/issues). - Contributions are welcome under the same **MIT License**.