#Agora — RAG System for AI Governance Documents

Agora is a production-grade Retrieval-Augmented Generation system built for the ETO AGORA Corpus — a collection of AI governance documents, regulations, and laws from jurisdictions worldwide. It lets users ask natural-language questions about policy documents and get grounded, source-attributed answers instead of hallucinated summaries.

Agora answers questions over AI governance documentation with an emphasis on accuracy over speed. Every response is anchored in retrieved documents, source-attributed, and filtered through a governance-analyst persona that distinguishes between what documents prohibit, require, recommend, or permit.

Intelligent Query Processing

• Sub-query decomposition splits multi-part questions into 1–5 focused retrieval passes for comprehensive coverage
• Conversational intent detection short-circuits RAG for greetings and pleasantries
• Parallel embedding and retrieval across sub-queries

Context Management

• Multi-turn conversation memory via Upstash Redis (30-minute model context window, 7-day UI history)
• Automatic deduplication of repeated content
• Source citations with cosine similarity scores

Production Infrastructure

• Document ingestion pipeline supporting .txt and PDF formats
• Background task management for large uploads
• Namespace isolation for multiple document collections

The pipeline runs five sequential steps: sub-query decomposition classifies intent, parallel embedding and Pinecone retrieval fetch relevant chunks, context assembly deduplicates and ranks results, Gemini synthesizes a grounded answer, and conversation memory stores the exchange for future context injection.

A dedicated evaluation dashboard tracks retrieval and answer quality on the eval-dashboard branch, deployed live at evaluation-dashboard.streamlit.app. It gives a quantitative view of how well the system retrieves and grounds answers, rather than relying on qualitative spot-checks alone.

Try Agora live → · View the evaluation dashboard →