From 18661f2c54ae64066ce47e88651fa62d256a4493 Mon Sep 17 00:00:00 2001 From: Prashant1git Date: Tue, 8 Apr 2025 16:46:58 +0530 Subject: [PATCH] Create application_Prashant Nayak_Dashbot.md Dashbot project application submission. I have researched and understood the project requirements well then only i am submitting this file. --- .../application_Prashant Nayak_Dashbot.md | 155 ++++++++++++++++++ 1 file changed, 155 insertions(+) create mode 100644 doc/proposals/2025/gsoc/application_Prashant Nayak_Dashbot.md diff --git a/doc/proposals/2025/gsoc/application_Prashant Nayak_Dashbot.md b/doc/proposals/2025/gsoc/application_Prashant Nayak_Dashbot.md new file mode 100644 index 00000000..93d528b8 --- /dev/null +++ b/doc/proposals/2025/gsoc/application_Prashant Nayak_Dashbot.md @@ -0,0 +1,155 @@ +# GSoC Proposal: Dashbot For APIDash + ## About +- FULL NAME:PRASHANT NAYAK + - EMAIL : hydraprashant8@gmail.com +- PHONE:+917394060751 +- Discord Handle : prashant_1n_80322 +- Github Profile : https://github.com/Prashant1git +- Time zone : Asia /Jhansi ( IST) +- Resume Link : + https://drive.google.com/file/d/1Dt08bxtUQdnUL9UxTiOSt74zE1iwaAxg/view?usp=drivesdk + ## University Info + - University Name: Bundelkhad university + -Program: information and technology + -Year: 2nd Year (2025 Batch) + -Expected Graduation Date: 2027 + -Motivation & Past Experience +## 1. FOSS Contributions + I haven't contributed to FOSS projects yet, but I recently downloaded the APIDash codebase to + mylocal machine and started exploring it to understand its structure and functionality. +## 2. Proud Achievement + One of myproudest achievements was winning a university-level hackathon where I built a fully + functional Ai supported mobile application within just 24 hours. The event challenged + participants to solve real-world problems under intense time pressure, and I took it as an + opportunity to push my limits. Using Python, Flutter and Dart ,I developed a complete app—from + UI design to backend integration—that impressed the judges with its functionality, performance, +GSoCProposal: DashbotForAPIDash + and user experience. This experience not only boosted my confidence as a developer but also + reinforced my ability to work efficiently under pressure, think creatively, and deliver high-quality + results within tight deadlines. +## 3. Challenges that Motivate Me + Challenges that push me to step out of my comfort zone and learn something new are what + truly motivate me. Whether it's solving a complex coding bug, building a feature I've never tried + before, or working under tight deadlines—I see these situations as opportunities to grow. I enjoy + the process of breaking down problems, finding solutions, and seeing the impact of my work. + The feeling of overcoming a tough challenge and turning it into a success keeps me driven and + passionate about what I do as a developer. +## 4. GSoC Commitment + I will be working part-time on GSoC, as I am a 2nd-year student and need to balance my studies + alongside the project. +## 5. Syncing with Mentors + Yes, I am open to regular sync-ups with project mentors to ensure steady progress. +## 6. Interest in APIDash + APIDash stands out because of its lightweight, Flutter-based architecture, making it a + highly efficient alternative to tools like Postman. I am particularly excited about the + potential of expanding its modular design, enhancing API discovery, and integrating AI +based automation for better API management. +## 7. Project Improvements + While APIDash provides a great developer experience, some areas for improvement include: + Improving UI responsiveness on lower-end devices. + Expanding API import/export options for better interoperability. + Enhancing API security validation and error handling mechanisms. +##GSoCProposal: DashbotForAPIDash + Project Proposal Information + Proposal Title: Dashbot for APIDash +## Conceptual ; + DashBot is an intelligent assistant built for API Dash that helps developers save time and boost + productivity by handling common API tasks through natural language. From explaining + responses and debugging errors to generating documentation, tests, visualizations, and + frontend integration code (like React or Flutter), DashBot is designed to be both powerful and + flexible. It features a modular architecture and includes benchmarking tools to help users + choose the best-performing LLM backend for their needs. The project brings together AI, Python, + Dart, and Flutter to create a seamless, developer-friendly experience +# Weekly Timeline ; + + + +### 4.Weekly Timeline + + +| Week | Goals/Activities | Deliverables | +|------|-------------------------------------------------------------|-----------------------------------------------------------------| +| 1 | Planning & Setup.| Set up project structure & repo integration Create evaluation metrics for LLM benchmarking. | +| 2 | Natural Language Input Parsing. | Design prompt engineering systemCreate evaluation dataset for parsing accuracy. | +| 3 |Explain & Identify Discrepancie. | Implement module to explain API responses Add detection for discrepancies between response & expected schemaBenchmark explanation accuracy across LLMs. | +| 4 | Debug Based on Status/Error. |Build module to debug based on response codes & messages Integrate context (headers, payload, previous requests)Evaluate LLMs on debugging accuracy with predefined errorsGSoCProposal: DashbotForAPIDash. | +| 5 | Generate API Documentation. | Design prompt & output template for docs Support OpenAPI + natural descriptions Compare LLMoutput with real-world. | +| 6 | Generate Tests from API . | Build functionality to create tests (e.g., unit/integration tests)Target frameworks: Postman, pytest, etc.Validate test coverage & LLM consistency. | +| 7 |Visualizations of API Responses . | Implement module to convert JSON to charts (Bar, Line, Pie)Use plotting libraries like Plotly, Chart.jsd options for user customization. | +| 8 |Generate Frontend Integration Code . | Generate API integration snippets (React, Flutter Include authentication, headers, error handlingEvaluate code quality and syntactic correctness. | +| 9 |Modular Agent & Plugin System. |Design modular architecture for DashBot (plug-and-play Each module works independently with shared context/state Add agent loop with memory/context switchingLLMEvaluation FrameworkBuild evaluation UI/CLI to compare model outputs . | +| 10 |LLMEvaluation Framework. | Build evaluation UI/CLI to compare model outputs Define metrics: accuracy, coherence, latency, token usageDocument how to test with different backends. | +| 11 | Testing & Documentation. | Unit + integration testing across modules Create usage guide for developers Document each module’s LLM prompt structure. | +| 12 |Polish, Deploy & Community Feedback . | Polish UI/UX Create demo videos & example use cases Gather feedback from community & iterate TechnicalFlowchart. | +; + + +#FlowChart + + - USERINTERFACE + Flutter (Desktop)| +### +- + NATURALLANGUAGEINPUT│ + (Flutter ➜ Python API) + + + + COREPYTHONBACKEND(AGENTSYSTEM) │ +-Intent Parser (LangChain/OpenAI) │ + -Context Manager & Memory + -TaskRouter (Decides module) + + + + + -EXPLAIN -DEBUGMODULE -DOCGEN + │RESPONSE -Status/Error -OpenAPI/NL + + + - + TESTGEN PLOTS/VIS FRONTEND + (pytest) (Plotly) SNIPPETS + + + - LLMINTEGRATIONLAYER + - + -OpenAI / Claude / Local (via LangChain) + -Prompts + Output Parsers + Benchmarks + + -EVALUATIONFRAMEWORK + - Accuracy, Speed, Cost + - Compare LLMBackends + - CLI/GUI Benchmarks + + + + + -TESTING LAYER + + - Unit + Integration Tests │ + - Test LLMdeterminism + - CI/CD Integration + + +## Key Technologies by Layer + Frontend (Flutter/Dart): UI to input natural language and display results. + Backend (Python): Handles AI agent logic, modular routing, and LLM + interaction. + Agent System: Parses intent, routes to correct module (e.g., debugging, doc + gen). + LLMs: Handles understanding, generation, explanation tasks. + Evaluation Framework: Benchmarks LLM outputs across different + providers. +GSoCProposal: DashbotForAPIDash + Testing: Ensures correctness of outputs, stability of agents/modules. +## Conclusion + DashBot makes API Dash smarter by using AI to help developers with tasks like + debugging, writing docs, and generating code using natural language. It's built with + Python and Flutter, and designed to be flexible and easy to improve. It also helps + compare different AI models, making it a helpful open-source tool for developers. + Thisprojectaligns + wellwithmyskillsandinterests,andIa + meagertocontributetothe + APIDashecosyste + mthroughthisproject