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222 lines
10 KiB
Markdown
222 lines
10 KiB
Markdown
# GSoC Proposal
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## DashBot - AI-Powered API Assistant
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## About
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1. **Full Name**: Loghadhith J R
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2. **Email**: logha6824@gmail.com
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3. **Phone-no**: +91-6380572051
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4. **Discord Handle**: Loghadhith
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5. **Home Page**: nil
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6. **Blog**: nil
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7. **GitHub Profile Link**: [github.com/Loghadhith](https://github.com/Loghadhith/)
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8. **Twitter**: nil
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9. **LinkedIn**: nil
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10. **Time Zone**: Indian Standard Time (IST, UTC+5:30)
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11. **Link to a Resume**: [Resume](https://drive.google.com/file/d/19UzV2vpNRMRkDMYV2NbjoB06mdw_GJgR/view?usp=sharing)
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---
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## University Info
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- **University Name**: Chennai Insititute of Technology
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- **Program**: B.E in Computer Science and Engineering (Artificial Intelligence and Machine Learning)
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- **Year**: 3rd Year (Started in 2022)
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- **Expected Graduation Date**: May 2026
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---
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## Motivation & Past Experience
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1. **Have you worked on or contributed to a FOSS project before? Can you attach repo links or relevant PRs?**
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No
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2. **What is your one project/achievement that you are most proud of? Why?**
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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.
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3. **What kind of problems or challenges motivate you the most to solve them?**
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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.
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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?**
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Yes, I’ll be working full-time on GSoC.
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5. **Do you mind regularly syncing up with the project mentors?**
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Not at all, I’m happy to sync up regularly.
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6. **What interests you the most about API Dash?**
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Its lightweight, developer-focused design and potential for smart automation.
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7. **Can you mention some areas where the project can be improved?**
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Add LLM-powered plugins for automated insights, testing, and integration.
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---
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## Project Proposal Information
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### 1. Proposal Title
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**DashBot - AI-Powered API Assistant for API Dash**
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### 2. Abstract
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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.
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### 3. Detailed Description
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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:
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- **Response Explanation:** Converts raw API responses into easy-to-understand English.
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- **Debugging Assistant:** Diagnoses failed API calls using status codes and error messages.
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- **Documentation Generator:** Produces markdown/OpenAPI-style docs automatically.
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- **Test Generator:** Creates integration/unit test code based on API behavior.
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- **Visualizer:** Generates plots from JSON responses.
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- **Frontend Integration Code Generator:** Outputs working code snippets for frameworks like React, Vue, Flutter, etc.
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DashBot will include:
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- An **NLP + prompt generation layer** to parse natural language.
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- A **modular routing system** to direct tasks to appropriate LLM-backed modules.
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- Integration hooks to read request/response data from API Dash’s store.
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- UI components to embed DashBot seamlessly inside Apidash’s existing interface.
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The entire system will be modular and extensible, ensuring future contributions and new features can plug into the core easily.
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---
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### 4. Architecture Diagram
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---
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### 5. Weekly Timeline (12 Weeks)
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| Week | Activities |
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|------|------------|
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| 1 | Finalize architecture, scope, and module breakdown |
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| 2 | Build DashBot UI inside Apidash; develop NLP intent router |
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| 3–4 | Implement Orchestration Layer + LLM Gateway |
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| 4–5 | Develop core modules: Explainer, Debugger, Test Generator |
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| 6–7| Build Visualizer & Frontend Code Generator modules |
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| 8–9| Documentation generator + context-aware prompt enhancements |
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| 10-11 | Logging, feedback collection, performance improvements |
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| 11 | End-to-end testing with real APIs; edge case handling |
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| 12 | Final polish, documentation, and MVP release 🎉 |
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---
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#### **Week 1 – Planning & Architecture**
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- Community bonding period
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- Finalize overall scope based on GSoC proposal.
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- Define system architecture: DashBot core, AI modules, orchestration layer, LLM integration, UI interface.
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- Plan modular structure for each feature (e.g., explainer, visualizer).
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- Choose tech stack, finalize third-party API providers (OpenAI, Ollama, etc.).
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- Setup project skeleton: repository structure, linting, CI, and code quality tools.
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---
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#### **Week 2 – UI & Intent Recognition**
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- Design and integrate DashBot’s user interface within Apidash:
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- Natural language input box
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- Output/response pane
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- Quick-action suggestions
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- Implement an **Intent Router**:
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- Parse natural language to route user queries (e.g., "Explain this response", "Generate test")
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- Map intents to module calls using regex or lightweight NLP libraries.
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---
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#### **Weeks 3–4 – Orchestration Layer & LLM Gateway**
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- Develop the **Orchestration Layer** to:
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- Receive user input
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- Call appropriate AI module
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- Return result to frontend
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- Build a **pluggable LLM gateway** to support multiple model providers (OpenAI, Claude, Gemini, Ollama).
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- Add retry logic, error handling, and token usage monitoring.
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---
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#### **Weeks 4–5 – Core Modules: Explainer, Debugger, Test Generator**
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- **Explainer**: Parses API response and uses LLM to output plain-English explanations.
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- **Debugger**: Analyzes failed requests based on status code, headers, and error messages. Suggests causes and fixes.
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- **Test Generator**: Produces unit/integration test cases using popular frameworks (e.g., Jest, Postman, or Python’s `unittest`).
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---
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#### **Weeks 6–7 – Visualizer & Frontend Code Generator**
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- **Visualizer**:
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- Extracts structured data from JSON
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- Generates basic plots (line, bar, pie) using lightweight charting libs
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- Allows user customization (fields, chart type)
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- **Frontend Code Generator**:
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- Converts API call structure into ready-to-use code snippets
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- Supports React (fetch/axios), Flutter (Dio/http), Vue, etc.
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- Customizes auth headers, parameters, and error handling
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---
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#### **Weeks 8–9 – Documentation & Context Enhancements**
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- **Documentation Generator**:
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- Automatically converts API calls and responses into structured Markdown or OpenAPI-style documentation.
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- Supports headers, body, query params, and sample responses.
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- Enhance prompts and modules using **context-aware input**:
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- Include request metadata (method, headers, params) in prompt
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- Maintain short-term context history (last few interactions)
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---
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#### **Weeks 10–11 – Logging, Feedback & Edge Cases**
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- Implement:
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- Logging system to track DashBot usage and performance
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- Anonymous feedback system for collecting suggestions/ratings
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- Improve:
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- Prompt quality (edge case prompts)
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- Error-handling for null/undefined/malformed responses
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- Optimize:
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- Caching repeated LLM outputs for performance
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- Limit token usage for cost efficiency
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---
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#### **Week 12 – Final Polish & MVP Release**
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- Finalize UX for all modules.
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- Conduct full test suite (unit, integration, manual flows).
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- Clean up codebase, remove dev tools, finalize docs.
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- Release DashBot MVP with full documentation and usage instructions.
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- Prepare final demo and handoff plan (if needed).
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---
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## 6.Techstack
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#### 1. **NLP Module (Intent + Entity Extraction)**
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- **SpaCy**: For tokenization and entity recognition.
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- **Hugging Face Transformers**: For intent detection with pre-trained models (e.g., BERT, GPT).
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- **Rasa/Dialogflow**: For conversational AI (optional).
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#### 2. **AI Feature Modules (Response Explainer, Debugging, etc.)**
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- **OpenAI API**: For text generation (GPT-3/4, Codex for code).
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- **LangChain**: For complex chaining and interactions of LLMs.
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- **GPT-J**: Open-source alternative for local processing.
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#### 3. **Orchestration Layer**
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- **Celery**: For managing asynchronous tasks.
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- **Redux** (Flutter equivalent: **Riverpod** or **Provider**): For state management and conversation context.
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- **BullMQ**: For background task management (Node.js backend, if any).
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#### 4. **Knowledge Base / Contextual Data Store**
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- **Elasticsearch**: For indexing and searching API specs, logs.
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- **MongoDB**: Flexible NoSQL database for storing user interactions and configurations.
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- **Redis**: For quick access to session and context data.
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- **SQLite**: Lightweight local database for storing interaction history.
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#### 5. **Logging & Feedback**
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- **Sentry**: For error monitoring and real-time feedback.
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- **LogRocket**: For frontend user session logging (Flutter can integrate via native bridge).
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- **Google Analytics / Mixpanel**: For usage analytics and tracking.
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#### 6. **UI & Frontend (Flutter)**
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- **Flutter**: Cross-platform UI framework for DashBot integration.
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- **Riverpod** or **Provider**: For state management in Flutter.
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- **Tailwind CSS**: If required in Flutter web apps (via Flutter Web integration).
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- **Chart.js / Plotly**: For generating visualizations.
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#### 7. **Code Integration & Test Generation**
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- **Swagger/OpenAPI**: For API documentation generation.
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- **Jest/Mocha**: For generating and running unit tests (via Node.js backend, if any).
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- **Yeoman**: For scaffolding code templates (if necessary).
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#### 8. **Other**
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- **Redis**: For quick caching and session data.
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- **PostgreSQL**: If relational data is needed.
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