Added web rag search documentation

[sauravpanda]

Improve Documentation Search Experience

• Purpose:
  Implement a fully client-side Retrieval Augmented Generation (RAG) system for the AkiraDocs documentation platform.
• Key Changes:
  • Offline indexing of documentation using a GTE-small model during build time.
  • Browser-based components including a SQLite database, local embedding model, and local LLaMA model for query processing and response generation.
  • No API costs or rate limits, as all processing happens directly in the user's browser.
• Impact:
  Provides a privacy-first, low-latency documentation search experience with unlimited chat interactions, without any external API dependencies.

✨ Generated with love by Kaizen ❤️

Original Description

## 🔍 Description

Type

• 🐛 Bug Fix
• ✨ Feature
• 📚 Documentation
• 🔧 Other: _____

Checklist

• Tested locally
• Updated docs (if needed)
• Added/updated tests (if needed)

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Name	Status	Preview	Comments	Updated (UTC)
akira-docs	🔄 Building (Inspect)	Visit Preview	💬 Add feedback	Dec 26, 2024 6:38pm

[kaizen-bot]

🔍 Code Review Summary

❗ Attention Required: This push has potential issues. 🚨

Overview

• Total Feedbacks: 1 (Critical: 1, Refinements: 0)
• Files Affected: 1
• Code Quality: [█████████████████░░░] 85% (Good)

🚨 Critical Issues

security (1 issues)

1. Potential data privacy concerns with local processing.

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📁 File: README.md
🔍 Reasoning:
While the documentation states that no data leaves the user's device, it is essential to clarify how data is handled locally to ensure user trust and compliance with privacy regulations.

💡 Solution:
Include a section on data handling practices and user privacy measures to enhance transparency.

Current Code:

 - 🔒 **Privacy-First**: No data leaves the user's device

Suggested Code:

 - 🔒 **Privacy-First**: No data leaves the user's device. All data is processed locally and is not stored or transmitted externally.

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Test Cases

2 file need updates to their tests. Run !unittest to generate create and update tests.

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