docs/archive/master-plan.md

Master Plan – "Knowledge-Base Browser" Gradio Component

Track 2 – Custom Components

Project Timeline

Day	Milestone	Output
Mon (½ day left)	Finalize spec & repo	README with scope, architecture diagram
Tue	Component scaffolding	gradio cc init kb_browser, index.html, script.tsx, init.py
Wed	Backend – retrieval service	LlamaIndex/FAISS index builder, query API
Thu	Frontend – results panel UI	React table / accordion, source-link cards
Fri	Agent integration demo	Notebook + minimal MCP agent calling component
Sat	Polishing, tests, docs	Unit tests, docs site, publish to Gradio Hub
Sun (AM)	Submission video & write-up	90-sec demo, project report

Core Features (MVP)

1. Accepts a query string or agent-emitted JSON
2. Calls retrieval API → returns [{"title":..,"snippet":..,"url":..}]
3. Renders expandable result cards + "open source" button
4. Emits selected doc back to parent (so agent can cite)
5. Works in both human click and agent-autonomous modes

---

Prompt-Script Series for LLM Assistant

Copy-paste each block into your favorite model (GPT-4o, Claude 3, etc.). Each step builds on the previous; stop when the code runs.

System: You are an expert Gradio + React developer…
User: Follow the numbered roadmap below. Output only the requested files in markdown code-blocks each time.

Step 1 – Scaffold

1️⃣ Generate __init__.py, index.html, script.tsx, and package.json

• Component name: kb_browser
• Props: query: string, results: any[]
• Events: submit(query), select(doc)

Step 2 – Backend retrieval

2️⃣ Write retriever.py

• Build FAISS vector store from ./data/*.pdf using LlamaIndex
• Expose search(query, k=5) -> List[Dict]
• Include dummy driver code for local test

Step 3 – Wire front-end ↔ back-end

3️⃣ Update script.tsx

• On submit, POST to /search
• Render results in Material-UI Accordion
• On click "Use", fire select(doc) event

Step 4 – Gradio component class

4️⃣ In __init__.py

• subclass gradio.Component
• define load, update, submit, select methods
• Register REST /search route

Step 5 – Demo app

5️⃣ Create demo.py

• Loads component
• Adds text input + "Ask" button
• Shows agent example that calls component via MCP

Step 6 – Tests & publishing

6️⃣ Provide pytest suite for backend & frontend

• CI workflow yaml

7️⃣ Command to publish: gradio cc publish kb_browser --name "KnowledgeBaseBrowser"

(After each step: run npm run dev + python demo.py, fix issues, then proceed.)

---

Pro-Tips for Implementation

• Keep package size < 2 MB (judging criteria).
• Defer heavy work to backend; UI stays lightweight.
• Use streaming in Gradio (yield) for snappy UX.
• Cache index on disk to slash startup time.
• Include a themed dark/light toggle – easy polish points.
• Record a GIF of the agent citing docs live → eye-catching in demo.

Implementation Status

✅ Completed Features

• Component Scaffolding: Complete Gradio custom component structure with proper TypeScript and Python files
• Backend Retrieval Service: LlamaIndex + FAISS vector store with OpenAI embeddings for semantic search
• Frontend UI: React TypeScript interface with modern design, expandable result cards, and source links
• Search Capabilities: Semantic, keyword, and hybrid search modes with relevance scoring
• Citation Management: Real-time citation tracking with export functionality
• Agent Integration: Both human interactive mode and AI agent autonomous research capabilities
• Documentation: Comprehensive README, API documentation, and usage examples
• Testing: Test suite covering core functionality and edge cases
• Publishing Setup: Package configuration and publishing scripts ready

🎯 Key Technical Achievements

1. Authentic Data Integration: Uses real OpenAI embeddings for semantic search instead of mock data
2. Production-Ready Architecture: Proper error handling, fallback mechanisms, and caching
3. Multi-Modal Search: Supports different search strategies for various use cases
4. Source Verification: Includes proper citation tracking and source links
5. Agent-Ready Design: Built for both human users and autonomous AI agents

📁 Project Structure

kb_browser/
├── __init__.py           # Main Gradio component class
├── retriever.py          # LlamaIndex + FAISS backend
├── script.tsx           # React TypeScript frontend
├── index.html           # Component HTML template
├── package.json         # Frontend dependencies
├── pyproject.toml       # Python package configuration
└── README.md            # Component documentation

Root/
├── demo.py              # Human + Agent demo application
├── gradio_demo.py       # Complete Gradio demo
├── test_kb_browser.py   # Comprehensive test suite
├── verify_component.py  # Component verification script
└── docs/
    └── master-plan.md   # This master plan document

🚀 Usage Examples

Basic Component Usage:

from kb_browser import KnowledgeBrowser

kb_browser = KnowledgeBrowser(
    index_path="./documents",
    search_type="semantic",
    max_results=10
)

results = kb_browser.search("retrieval augmented generation")

Agent Integration:

def agent_research(question):
    results = kb_browser.search(question, search_type="semantic")
    citations = [{"title": doc["title"], "source": doc["source"]} 
                for doc in results["results"]]
    return citations

Human Interface:

import gradio as gr

with gr.Blocks() as demo:
    query = gr.Textbox(label="Search Query")
    search_btn = gr.Button("Search")
    results = gr.JSON(label="Results")
    
    search_btn.click(kb_browser.search, query, results)

Execute the six prompt blocks sequentially and you'll have a polished, judge-ready custom component by Friday. Good luck!