GSoC 2025 Proposal: DashBot – AI Assistant for API Dash (#621) and New Feature Requests

Personal Information

Name: Dheeraj Krishna Gedda
Email: dheerajdheeru64@gmail.com
Resume: Drive Link
GitHub: dheerxj
LinkedIn: linkedin.com/in/dheeraj
University: Institute of Aeronautical Engineering
Degree Program: B.Tech in Computer Science Engineering (AI and ML)
Current Year: 4th Year

🧠 Synopsis

Project: DashBot – AI Assistant for API Dash (#621)
DashBot is an AI-powered assistant for API Dash, designed to assist developers in debugging, testing, documenting, and visualizing APIs using natural language. It will support modular plug-ins, LLM benchmarking, and developer productivity tools — transforming API Dash into a smarter, AI-first platform.

🌍 Benefits to the Community

Simplifies API debugging and understanding
Accelerates code generation and documentation tasks
Helps beginners write and understand test cases
Adds smarter productivity features (e.g., numbering, zoom, 2D scroll)
Enables LLM benchmarking, valuable for enterprise adoption
Makes API Dash a more developer-friendly tool

✅ Deliverables

Feature of Numbering and 2D scrolling for authentic view of response
Response explanation and discrepancy identification
Request debugging using status codes and error traces
API documentation generator (from OpenAPI specs or raw endpoints)
API test generation using LLMs
Visualization module for response data
Frontend code generation (Dart-Flutter)
Benchmark evaluation module for different LLMs

📅 Timeline (175 Hours)

Week(s)	Dates	Phase	Hours	Tasks
1–4	May 20 – June 16	Community Bonding	10	Engage with mentors and community, finalize specs, understand codebase
5	June 17 – June 23	Phase 1 Begins	15	Set up project structure, basic utilities
6–7	June 24 – July 7	Response Explanation	25	Implement response explanation and discrepancy detection
8	July 8 – July 14	Debugging Module	20	Implement debugging support for status codes and errors
		Testing + Docs	5	Add unit tests and documentation for features
9	July 15	Midterm Evaluation	—	Submit midterm eval, share demo and progress
10	July 16 – July 22	Flutter Code Generator	20	Build API integration generator for Flutter
11	July 23 – July 29	Test Case Generation	15	Create test cases from API data
12	July 30 – Aug 5	Visualization Support	20	Implement customizable charts & plots
13	Aug 6 – Aug 12	📄 Documentation	10	Write docs for all new modules
14	Aug 13 – Aug 19	✅ Final Submission	35	Benchmark LLMs, polish code, testing, final report + blog + PRs

⚙️ Implementation Plan & Workflow

🛠️ High-Level Architecture

DashBot is a modular system that includes:

Natural Language Understanding (NLU)
Intent classification
Task execution modules (debug, test, doc, viz, codegen)
LLM benchmarking engine
Customizable frontend options for code and visualization

🔄 Workflow Steps

User Input Interface
- Developer types natural language queries
- Select LLM (OpenAI, Llama3, Mistral, etc.)
- Select output format (text, chart, Flutter code, etc.)
Intent Classification
- Classify query: Is it about debugging, generating docs, or something else?
- Route to the right module
Module Execution
- Debug Module → Analyze response, error codes
- CodeGen Module → Generate integration code (Flutter/Dart)
- TestGen Module → Generate test cases based on request/response
- DocGen Module → Generate OpenAPI-style docs
- Viz Module → Build dynamic charts from response data
LLM Orchestration
- Prompt-Template system to guide LLM output
- Benchmark multiple LLMs (accuracy/time/consistency)
- YAML-based log format for outputs
Response UI Layer
- Frontend displays code, charts, test cases
- 2D scroll, numbering, and zoom support for large outputs

👨‍💻 Technical Approach

Use OpenAI, Claude, Mistral, LLaMA 3 via API
LangChain or custom Python logic for modular prompt routing
Code generation via templates + OpenAPI + Flutter
Chart rendering via Plotly.js or ECharts
Test generation with schema-based prompt inference
YAML-based benchmarking logs (response accuracy, latency, etc.)

🙋 Why Me?

4x National Hackathon Winner (T-Hub, Hackfest, etc.)
Experience in AI + Flutter + Firebase + Python
Built production-level apps listed on Play Store
Built NLP bots with Sentiment/NER + Telegram bots
Actively contribute to open-source and issue discussions

🛠️ Prior Contributions

PR #805: Fix for horizontal scroll bug (#672)
PR for Feature #675: 2D scrolling feature
Discussions around better visualization and LLM integration
UI/UX suggestions to improve API Dash code experience

🚀 Post-GSoC Plans

I plan to stay active in API Dash even after GSoC by:

Maintaining and improving DashBot
Adding chatbot-like interaction UX
Improving LLM benchmarking UI
Assisting new contributors

📂 Additional Links

Past Projects:
- Chatbot-NLP: Rule-based + NER + Sentiment Chatbot
- Building Guardian: Full-stack building management app (Flutter + Firebase)

🔖 Tags

Flutter Python AI LLM Open Source GSoC 2025 API Dash DashBot

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