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June — Review Table

AI-powered document review with verifiable citations. Upload PDFs, define extraction columns, and let AI populate every cell with answers, reasoning, and exact source references — all auto-saved, all verifiable, all agent-ready.

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Tech Stack

Layer Technology Why
API Server Python / FastAPI Best CRUD framework, Pydantic validation, AI ecosystem
Extraction Worker Python OpenAI/Anthropic SDKs, RAG pipeline, IO-bound
Document Worker Python + LiteParse Local PDF parsing with OCR + bounding boxes for citations
SSE Service Go Goroutines, 2KB/connection, purpose-built for long-lived connections
Frontend React / TypeScript / shadcn Type safety, component library, Zustand state
Database PostgreSQL + pgvector Relational data + vector similarity search in one
Queue / Events Redis Task queues + pub/sub for real-time events
Storage S3 / MinIO PDF storage, presigned URL uploads
Monorepo Taskfile Language-agnostic task runner, no framework lock-in

Docs

Document Description
Product What we're building, for whom, scope, user journey
Architecture System design, data flows, decisions, scale estimates
API Reference Every endpoint — request, response, status codes
Database Schema Tables, columns, types, indexes, relationships
API Server FastAPI internals, route organization, config
Document Worker LiteParse pipeline, chunking, embeddings
Extraction Worker RAG pipeline, LLM prompts, cell extraction
SSE Service Go service, connection management, event routing
Frontend Components, state, layout system, design system
Infrastructure Docker, Redis queues, S3, event catalog
Local Setup Clone to running in 5 minutes

Quick Start

git clone https://github.com/june-legal/review-table.git
cd review-table
cp .env.example .env
task dev
# Open http://localhost:3000

About

open source ai workspace for legal teams - document review, table extraction, and more. self-hostable alternative to harvey and legora.

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