Merge pull request #778 from Loghadhith/gsoc-loghadhith

GSOC: Dashbot

Author: animator

doc/proposals/2025/gsoc/application_Loghadhith_Dashbot.md

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+# GSoC Proposal
+## DashBot - AI-Powered API Assistant
+
+
+## About
+
+1. **Full Name**: Loghadhith J R
+2. **Email**: [email protected]  
+3. **Phone-no**: +91-6380572051
+4. **Discord Handle**: Loghadhith
+5. **Home Page**: nil
+6. **Blog**: nil
+7. **GitHub Profile Link**: [github.com/Loghadhith](https://github.com/Loghadhith/)  
+8. **Twitter**: nil
+9. **LinkedIn**: nil
+10. **Time Zone**: Indian Standard Time (IST, UTC+5:30)  
+11. **Link to a Resume**: [Resume](https://drive.google.com/file/d/19UzV2vpNRMRkDMYV2NbjoB06mdw_GJgR/view?usp=sharing)  
+---
+## University Info
+- **University Name**: Chennai Insititute of Technology
+- **Program**: B.E in Computer Science and Engineering (Artificial Intelligence and Machine Learning) 
+- **Year**: 3rd Year (Started in 2022)
+- **Expected Graduation Date**: May 2026  
+---
+## Motivation & Past Experience
+
+1. **Have you worked on or contributed to a FOSS project before? Can you attach repo links or relevant PRs?**  
+   No
+
+2. **What is your one project/achievement that you are most proud of? Why?**  
+One project I’m most proud of is THREADS, an open-source e-commerce assistant that uses similarity search and voice assistance to guide non-tech savvy users. It taught me how to design complex architecture with LLMs, and I'm proud of how it combines technology with real-world user experience to make online shopping more accessible.
+   
+
+3. **What kind of problems or challenges motivate you the most to solve them?**
+I’m motivated by challenges that simplify complex systems and improve user experience, especially for non-tech savvy individuals. Solving problems with new technologies, like LLMs, excites me because it allows me to create impactful, accessible solutions.
+   
+
+4. **Will you be working on GSoC full-time? In case not, what will you be studying or working on while working on the project?**  
+   Yes, I’ll be working full-time on GSoC.
+
+5. **Do you mind regularly syncing up with the project mentors?**  
+   Not at all, I’m happy to sync up regularly.
+
+6. **What interests you the most about API Dash?**  
+   Its lightweight, developer-focused design and potential for smart automation.
+
+7. **Can you mention some areas where the project can be improved?**  
+   Add LLM-powered plugins for automated insights, testing, and integration.
+---
+## Project Proposal Information
+
+### 1. Proposal Title  
+**DashBot - AI-Powered API Assistant for API Dash**
+
+### 2. Abstract  
+API Dash simplifies API interaction, but repetitive tasks like debugging, documentation, and testing still demand developer effort. DashBot introduces a natural-language-based AI assistant inside API Dash that helps explain, debug, document, test, and visualize APIs. By leveraging LLMs and context-aware routing, DashBot will supercharge developer productivity and reduce cognitive load.
+
+### 3. Detailed Description
+DashBot will be a modular AI assistant built into API Dash to handle natural-language queries and automate critical but repetitive developer tasks. It will support the following core features:
+- **Response Explanation:** Converts raw API responses into easy-to-understand English.
+- **Debugging Assistant:** Diagnoses failed API calls using status codes and error messages.
+- **Documentation Generator:** Produces markdown/OpenAPI-style docs automatically.
+- **Test Generator:** Creates integration/unit test code based on API behavior.
+- **Visualizer:** Generates plots from JSON responses.
+- **Frontend Integration Code Generator:** Outputs working code snippets for frameworks like React, Vue, Flutter, etc.
+
+DashBot will include:
+- An **NLP + prompt generation layer** to parse natural language.
+- A **modular routing system** to direct tasks to appropriate LLM-backed modules.
+- Integration hooks to read request/response data from API Dash’s store.
+- UI components to embed DashBot seamlessly inside Apidash’s existing interface.
+
+The entire system will be modular and extensible, ensuring future contributions and new features can plug into the core easily.
+
+---
+### 4. Architecture Diagram
+![1743958514](https://github.com/user-attachments/assets/ef3d1d7c-5c58-4183-864a-8711bcd1f62e)
+---
+
+### 5. Weekly Timeline (12 Weeks)
+
+| Week | Activities |
+|------|------------|
+|  1  | Finalize architecture, scope, and module breakdown |
+|  2  | Build DashBot UI inside Apidash; develop NLP intent router |
+| 3–4  | Implement Orchestration Layer + LLM Gateway |
+| 4–5  | Develop core modules: Explainer, Debugger, Test Generator |
+| 6–7| Build Visualizer & Frontend Code Generator modules |
+| 8–9| Documentation generator + context-aware prompt enhancements |
+| 10-11   | Logging, feedback collection, performance improvements |
+| 11   | End-to-end testing with real APIs; edge case handling |
+| 12   | Final polish, documentation, and MVP release 🎉 |
+
+---
+
+#### **Week 1 – Planning & Architecture**
+- Community bonding period
+- Finalize overall scope based on GSoC proposal.
+- Define system architecture: DashBot core, AI modules, orchestration layer, LLM integration, UI interface.
+- Plan modular structure for each feature (e.g., explainer, visualizer).
+- Choose tech stack, finalize third-party API providers (OpenAI, Ollama, etc.).
+- Setup project skeleton: repository structure, linting, CI, and code quality tools.
+
+---
+
+#### **Week 2 – UI & Intent Recognition**
+- Design and integrate DashBot’s user interface within Apidash:
+  - Natural language input box
+  - Output/response pane
+  - Quick-action suggestions
+- Implement an **Intent Router**:
+  - Parse natural language to route user queries (e.g., "Explain this response", "Generate test")
+  - Map intents to module calls using regex or lightweight NLP libraries.
+
+---
+
+#### **Weeks 3–4 – Orchestration Layer & LLM Gateway**
+- Develop the **Orchestration Layer** to:
+  - Receive user input
+  - Call appropriate AI module
+  - Return result to frontend
+- Build a **pluggable LLM gateway** to support multiple model providers (OpenAI, Claude, Gemini, Ollama).
+- Add retry logic, error handling, and token usage monitoring.
+
+---
+
+#### **Weeks 4–5 – Core Modules: Explainer, Debugger, Test Generator**
+- **Explainer**: Parses API response and uses LLM to output plain-English explanations.
+- **Debugger**: Analyzes failed requests based on status code, headers, and error messages. Suggests causes and fixes.
+- **Test Generator**: Produces unit/integration test cases using popular frameworks (e.g., Jest, Postman, or Python’s `unittest`).
+
+---
+
+#### **Weeks 6–7 – Visualizer & Frontend Code Generator**
+- **Visualizer**:
+  - Extracts structured data from JSON
+  - Generates basic plots (line, bar, pie) using lightweight charting libs
+  - Allows user customization (fields, chart type)
+- **Frontend Code Generator**:
+  - Converts API call structure into ready-to-use code snippets
+  - Supports React (fetch/axios), Flutter (Dio/http), Vue, etc.
+  - Customizes auth headers, parameters, and error handling
+
+---
+
+#### **Weeks 8–9 – Documentation & Context Enhancements**
+- **Documentation Generator**:
+  - Automatically converts API calls and responses into structured Markdown or OpenAPI-style documentation.
+  - Supports headers, body, query params, and sample responses.
+- Enhance prompts and modules using **context-aware input**:
+  - Include request metadata (method, headers, params) in prompt
+  - Maintain short-term context history (last few interactions)
+
+---
+
+#### **Weeks 10–11 – Logging, Feedback & Edge Cases**
+- Implement:
+  - Logging system to track DashBot usage and performance
+  - Anonymous feedback system for collecting suggestions/ratings
+- Improve:
+  - Prompt quality (edge case prompts)
+  - Error-handling for null/undefined/malformed responses
+- Optimize:
+  - Caching repeated LLM outputs for performance
+  - Limit token usage for cost efficiency
+
+---
+
+#### **Week 12 – Final Polish & MVP Release**
+- Finalize UX for all modules.
+- Conduct full test suite (unit, integration, manual flows).
+- Clean up codebase, remove dev tools, finalize docs.
+- Release DashBot MVP with full documentation and usage instructions.
+- Prepare final demo and handoff plan (if needed).
+
+---
+
+## 6.Techstack
+
+
+#### 1. **NLP Module (Intent + Entity Extraction)**
+- **SpaCy**: For tokenization and entity recognition.
+- **Hugging Face Transformers**: For intent detection with pre-trained models (e.g., BERT, GPT).
+- **Rasa/Dialogflow**: For conversational AI (optional).
+
+#### 2. **AI Feature Modules (Response Explainer, Debugging, etc.)**
+- **OpenAI API**: For text generation (GPT-3/4, Codex for code).
+- **LangChain**: For complex chaining and interactions of LLMs.
+- **GPT-J**: Open-source alternative for local processing.
+
+#### 3. **Orchestration Layer**
+- **Celery**: For managing asynchronous tasks.
+- **Redux** (Flutter equivalent: **Riverpod** or **Provider**): For state management and conversation context.
+- **BullMQ**: For background task management (Node.js backend, if any).
+  
+#### 4. **Knowledge Base / Contextual Data Store**
+- **Elasticsearch**: For indexing and searching API specs, logs.
+- **MongoDB**: Flexible NoSQL database for storing user interactions and configurations.
+- **Redis**: For quick access to session and context data.
+- **SQLite**: Lightweight local database for storing interaction history.
+
+#### 5. **Logging & Feedback**
+- **Sentry**: For error monitoring and real-time feedback.
+- **LogRocket**: For frontend user session logging (Flutter can integrate via native bridge).
+- **Google Analytics / Mixpanel**: For usage analytics and tracking.
+
+#### 6. **UI & Frontend (Flutter)**
+- **Flutter**: Cross-platform UI framework for DashBot integration.
+- **Riverpod** or **Provider**: For state management in Flutter.
+- **Tailwind CSS**: If required in Flutter web apps (via Flutter Web integration).
+- **Chart.js / Plotly**: For generating visualizations.
+
+#### 7. **Code Integration & Test Generation**
+- **Swagger/OpenAPI**: For API documentation generation.
+- **Jest/Mocha**: For generating and running unit tests (via Node.js backend, if any).
+- **Yeoman**: For scaffolding code templates (if necessary).
+
+#### 8. **Other**
+- **Redis**: For quick caching and session data.
+- **PostgreSQL**: If relational data is needed.
+