🔬 Analyze legal contract risk with Google Gemini hybrid RAG and Supabase

⚡ 210 views · 🔬 Document Extraction & Analysis

Description

🚀 What This Workflow Does

This workflow transforms any PDF legal contract into a detailed AI-powered risk report — in under 5 minutes. Upload a contract, and the system automatically splits it into clauses, analyses each one using Hybrid RAG (semantic + keyword search), scores risk as HIGH / MEDIUM / LOW, and delivers plain-English explanations with safer alternative wording.

🔥 Why Hybrid RAG?

Most dangerous clauses don’t use obvious legal keywords. “The Client accepts full responsibility for all third-party claims” is an indemnification clause — but keyword search misses it. Hybrid RAG combines:

• Vector Search (pgvector) — finds semantically similar risky patterns
• BM25 Keyword Search — catches explicit legal red flags
• RRF Reranking — merges both results with clause-type boosting

🔍 What It Does

• Accepts a PDF contract via webhook (with async job_id tracking)
• Splits contract into individual numbered clauses
• Classifies each clause type using Google Gemini (indemnification, IP, termination, etc.)
• Generates vector embeddings and searches a Supabase knowledge base
• Scores each clause HIGH / MEDIUM / LOW using regex + AI
• AI Agent (Gemini Flash) explains risk in plain language + suggests safer wording
• Aggregates all results into a single JSON report
• Saves report to Supabase (frontend polls for result asynchronously)

⚙️ Architecture (Two Pipelines)

Pipeline 1 — Ingestion: Builds the knowledge base of risky clause patterns in Supabase Pipeline 2 — Query: Analyses new contracts against the knowledge base

Both pipelines run in the same workflow — the branch splits at Extract Embedding.

🧠 Key Technical Decisions

• Async architecture — Frontend fires request + polls Supabase. No timeout issues.
• job_id tracking — Preserved across all nodes via …$json spread
• RRF Reranking — Combines vector + BM25 scores with type-based boost multipliers
• Regex Risk Scorer — First-pass risk classification before expensive LLM call
• Gemini Flash — Fast, cost-efficient LLM for per-clause annotation

📦 Requirements

• Google Gemini API key — for clause classification + embeddings + AI Agent
• Supabase project — with pgvector extension enabled
• Supabase tables: legal_clauses (knowledge base) + reports (results)
• Supabase functions: match_clauses() + keyword_search_clauses()
• Frontend (optional): HTML/CSS/JS web app hosted on Netlify

💡 Example Use Cases

• Freelancers reviewing client contracts before signing
• Startups evaluating vendor or investor agreements
• Legal ops teams standardising contract review at scale
• Business owners catching risky clauses without legal fees

🎯 Output

• Per-clause: risk_level, plain-English explanation, risk_reason, safer_alternative, key_obligations, legal_area
• Summary: overall_risk_score, risk_distribution, legal_areas map, high_risk_clauses list
• Stored as JSON in Supabase reports table, keyed by job_id

🔗 Nodes Used

HTTP Request, Webhook, Supabase, AI Agent, Extract from File, Google Gemini Chat Model

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup