# Agentic Workflow for ViewMo Platform ## Introduction **Agentic Workflow** is a research-focused project designed to enhance the **ViewMo** platform, a comprehensive research tool that empowers users to interact with their documents in innovative ways. The project introduces intelligent agents to simplify complex concepts, generate high-quality assessments, and provide actionable insights for users. Additionally, it offers a **CLI tool** and **Python SDK** for developers to integrate these features into their own products seamlessly. ![viewmo.png](viewmo.png) --- ## Key Features 1. **Document Interaction**: - Users can upload documents (PDFs, Docs) and ask questions based on the content. - The agent simplifies explanations to make complex concepts easier to understand.

2. **Graphical Notation for Complex Concepts**: - Users can highlight sections of the document they find difficult to understand. - Based on the selected difficulty level (Easy, Medium, Hard), the system generates graphical notations to explain the concepts.

3. **Knowledge Evaluation**: - The system generates high-standard multiple-choice questions (MCQs) based on the uploaded documents. - Users can select the number of questions, and the agent will handle the rest. - Tracks the time taken to answer questions and provides insights into areas where the user needs improvement (future implementation).

4. **Developer-Friendly CLI Tool**: - Provides the **Intellibot** Python package, available on [PyPI](https://pypi.org/project/intellibot/). - Developers can customize agentic workflows using JSON schemas. - Embed chatbots into their products by training them with documents uploaded to the ViewMo platform. - Advanced security features like **service keys** ensure secure integration.

5. **Secure and Scalable Infrastructure**: - Each user has a secure, private **vectorDB** and **cloud bucket** for their documents. - Uses **JWT tokens** and **service accounts** to secure the platform from unauthorized access. --- ## Technologies Used - **Frameworks**: - CrewAI - Langchain - FastAPI (for API development) - **Database**: - GCP-based PostgreSQL - **Vector Database**: - FAISS - **Cloud Platform**: - Google Cloud Platform (GCP) - **Security**: - JWT Tokens - Service Accounts - **Package Management**: - PyPI (for Intellibot Python package) --- ## How It Works 1. **User Interaction**: - Users log in to the **ViewMo** platform and upload documents. - They can ask questions, highlight difficult sections, or request MCQ generation.

2. **Agentic Workflow**: - Agents process the documents, simplify explanations, generate graphical notations, and create MCQs. - Insights are provided based on user interactions and performance.

3. **Developer Integration**: - Developers use the **Intellibot** Python package to integrate agentic workflows into their products. - JSON schemas define custom workflows, and service accounts manage permissions. --- ## Setup Instructions ### For Users: 1. Log in to the **ViewMo** platform. 2. Upload your documents (PDFs, Docs). 3. Use the features like question answering, graphical notation, and MCQ generation. ### For Developers: 1. Install the **Intellibot** package: ```bash pip install intellibot ``` 2. Use the provided Python SDK to integrate agentic workflows into your product. 3. Upload documents and define workflows using JSON schemas. 4. Secure your integration using service keys. --- ## Usage ### For Users: - Upload documents and interact with them using the platform's features. - Evaluate your knowledge using the MCQ generator and track your progress. ### For Developers: - Use the **Intellibot** package to embed chatbots and agentic workflows into your applications. - Train chatbots by uploading documents to the ViewMo platform. - Manage permissions and security using service accounts. --- ## API and SDK Links - **Agents API**: [GitHub Repository](https://github.com/PinsaraPerera/intellihack_research_agent.git) - **Intellibot PyPI**: [PyPI Page](https://pypi.org/project/intellibot/) --- ## Contributors - [Pawan Perera](https://github.com/PinsaraPerera) - Email: [1pawanpinsara@gmail.com](mailto:1pawanpinsara@gmail.com) - LinkedIn: [Pawan Perera](https://www.linkedin.com/in/pawan-perera) - Website: [pawanperera.com](https://www.pawanperera.com) --- ## License This project is licensed under the **MIT License**. See the [LICENSE](https://github.com/PinsaraPerera/intellihack_research_agent/blob/main/LICENSE) file for details. --- ## Future Scope - Implement insights for MCQ performance to identify areas where users need improvement. - Expand the graphical notation system to support more document formats. - Add support for additional third-party integrations. --- ## Contact Information For questions, collaborations, or feedback, feel free to reach out: - Email: [1pawanpinsara@gmail.com](mailto:1pawanpinsara@gmail.com) - GitHub: [PinsaraPerera](https://github.com/PinsaraPerera) - LinkedIn: [Pawan Perera](https://www.linkedin.com/in/pawan-perera) - Website: [pawanperera.com](https://www.pawanperera.com)