README.md

metadata

title: JanMat
emoji: 🏛️
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false

JanMat

A chatbot that reads Indian legislative bills — their PRS Legislative Research PDF briefs in full, the bill pages themselves, related articles, and public opinion — and answers questions grounded in that material, with sources cited. Supports both typed and spoken questions (voice via Sarvam AI), and speaks answers back.

This is a single Gradio Space: the chat UI, the retrieval engine, and the ingestion pipeline all run here — no separate frontend deployment needed.

Required secrets (Space Settings → Variables and secrets)

Add each of these as a Secret:

Secret name	Where to get it
SUPABASE_URL	Supabase project → Settings → API → Project URL
SUPABASE_SERVICE_KEY	Supabase project → Settings → API → service_role key
GROQ_API_KEY	console.groq.com → API Keys
SARVAM_API_KEY	sarvam.ai → dashboard → API keys
CRON_SECRET	any random string you make up yourself

First-time setup

1. Create a Supabase project, open its SQL Editor, and run supabase/schema.sql from this repo once. This creates the tables and the vector search function.
2. Add the five secrets above in this Space's settings.
3. Once the Space is running, open the "Admin: refresh the knowledge base" section at the bottom of the app, enter your CRON_SECRET, and click "Run ingestion now". This scrapes PRS bills + their PDFs and fills the knowledge base — it takes a few minutes.
4. Once ingestion finishes, ask a question in the chat box above.

How it works

• Reading: pdf_ingest.py downloads and reads every page of every linked PDF (and every paragraph of every HTML page) in full — nothing is summarized at this stage.
• Storage: embeddings.py + db.py chunk and embed that text (locally, free, no extra API key) and store it in Supabase with pgvector.
• Answering: rag.py finds the most relevant chunks for a question and asks Groq to answer using only those chunks, citing which document each part came from.
• Voice: voice.py calls Sarvam AI for speech-to-text (your question) and text-to-speech (the spoken answer).