Minimum Viable Product (MVP)

The MVP is a conversational AI chat interface where users ask questions about the Vedas and receive contextual, referenced answers powered by the Vedic knowledge base, web search, and Sarvam AI.

Explore: veda-guru.com — landing page with all entry points

Scope

Area Included in MVP Future
Chat Text-based Q&A with AI Voice, guided tutorials
Vedic KB DharmicData (Rig/Yajur/Atharva) + HF IndianVedasOriginal (Sama, Krishna Yajur) — BM25 search Upanishads, Brahmanas
Search Vedic corpus (tried first) → Tavily → Wikipedia → DuckDuckGo → Google Fine-tuned Vedic model
LLM Sarvam AI (sarvam-105b, 128K context) Fine-tuned Vedic model
TTS Sarvam Bulbul v3 with browser fallback Multiple voices, languages
Languages English 10+ Indian languages
References Source labels from Vedic corpus (text citations) + web URLs Clickable verse references
Auth Email/password register + login SSO, OAuth providers
History Auto-saved conversations per user Search, export, share
Voice Maharshi page with mic + TTS (Beta) Full voice conversation
Security Rate limiting, CORS lock, JWT auth Per-session tokens, 2FA
UI/UX Landing page, auto-resize textarea, smart scroll, stop button, follow-up suggestions Full design system
Learning Gurukula learning page (/learn) with 3 tiers, 36 adhyayas, daily reading, pronunciation guides, auto-generated quizzes, progress tracking (localStorage + server sync) Advanced analytics, peer discussions

How it works

  1. User lands on the dharmic-themed landing page (/) and clicks Enter Chat
  2. Chat page (/chat) loads, connects to the backend
  3. User asks a question (e.g. “What does the Rig Veda say about creation?”)
  4. Backend searches the Vedic corpus first (BM25 over DharmicData + HuggingFace Vedas)
  5. If corpus results found → used as context; if not → falls back to web search
  6. Context + question is fed to Sarvam AI (sarvam-105b)
  7. Sarvam AI synthesizes an answer with source citations
  8. Response is rendered with markdown formatting in the chat UI
  9. If logged in, the exchange is auto-saved to conversation history
  10. Follow-up suggestions are fetched in the background and shown as clickable chips

User flow

Landing page (/)
      │
      ▼  Click "Enter Chat"
Chat interface (/chat)
      │
      ▼  Ask a question
Vedic corpus search (BM25 — tried first)
      │
      ├── Found? → Use as context
      │
      └── Not found? → Web search (Tavily → Wikipedia → DuckDuckGo)
                           │
                           ▼
Context + question sent to Sarvam AI
      │
      ▼
AI synthesizes answer with source citations
      │
      ▼
Response rendered with markdown in chat UI
      │
      ├── Auto-saved to history (if logged in)
      │
      └── Follow-up suggestions fetched and displayed

Tech stack (MVP)

Component Choice
LLM Sarvam AI (sarvam-105b, 128K context)
TTS Sarvam AI bulbul:v3, shubh voice
Vedic KB BM25 in-memory (DharmicData JSON + HuggingFace IndianVedasOriginal)
Backend Python FastAPI (GCP Cloud Run)
Frontend Vanilla HTML/CSS/JS (Cloudflare Pages) — 4 pages: landing, chat, learning, maharshi
Database Neon Postgres (persistent) or SQLite fallback
Auth HMAC-signed JWT tokens, SHA-256 password hashing
Web search Tavily (primary) + Wikipedia API + DuckDuckGo + Google
Rate limiting slowapi (30 req/min per IP)
Markdown render marked.js with Vedic-themed code block styles
Speech-to-text Browser Web Speech API (free, no API calls)
Corpus download GitHub API (version-tracked) + HuggingFace direct download

Success metrics