GoGreen DOC-AI
Legal Document Intelligence with 3-Tier RAG
Upload legal documents and get AI-powered summaries, Q&A with citations, and contract risk scoring. Built on a 3-tier RAG architecture — Basic RAG, LangChain RAG (ChatAnthropic + QdrantVectorStore + 5-turn memory), and Graph RAG (entity extraction + BM25 + RRF) — for unmatched accuracy and context.
3-Tier RAG
Basic, LangChain, and Graph RAG for maximum retrieval accuracy
LangChain Powered
ChatAnthropic + QdrantVectorStore + 5-turn conversational memory
5-Role RBAC
Admin, Manager, Lawyer, Paralegal, and Viewer access control
Powered by LangChain
LangChain Integration
- ChatAnthropic for Claude-powered generation
- QdrantVectorStore for vector retrieval
- ConversationBufferWindowMemory (5 turns)
- RetrievalQAChain with source documents
- Streaming responses via LangChain callbacks
- Custom document loaders and splitters
Why LangChain?
- Modular chain composition for complex workflows
- Built-in memory management across sessions
- Seamless Anthropic Claude integration
- Production-ready vector store connectors
- Extensible retrieval strategies
- Active open-source ecosystem
Platform Features
Document Upload & Processing
Upload legal documents in any format. AI extracts text, metadata, and structure for intelligent analysis.
- PDF, DOCX, and image upload support
- OCR for scanned documents
- Automatic metadata extraction
- Document versioning and history
- MinIO object storage backend
- Batch upload and processing
AI Summarization & Q&A
Get instant summaries and ask natural language questions about your documents with cited answers.
- One-click document summarization
- Natural language Q&A with citations
- Page-level source references
- Multi-document cross-referencing
- Powered by Claude via LangChain ChatAnthropic
- 5-turn conversational memory
Contract Analysis & Risk Scoring
AI-powered contract review that identifies risks, obligations, and key clauses with confidence scoring.
- Automated risk scoring (Low/Medium/High/Critical)
- Obligation and deadline extraction
- Key clause identification
- Non-standard term detection
- Liability and indemnification analysis
- Renewal and termination tracking
LangChain RAG Pipeline
Advanced retrieval-augmented generation using LangChain with ChatAnthropic, QdrantVectorStore, and 5-turn memory.
- LangChain ChatAnthropic integration
- QdrantVectorStore for vector search
- OpenAI Embeddings (3072-dimensional)
- 5-turn conversational memory buffer
- Contextual retrieval with re-ranking
- Streaming response generation
3-Tier RAG Architecture
Three levels of retrieval — Basic RAG, LangChain RAG, and Graph RAG — for maximum accuracy and context.
- Tier 1: Basic RAG (cosine similarity search)
- Tier 2: LangChain RAG (ChatAnthropic + QdrantVectorStore)
- Tier 3: Graph RAG (entity extraction + relationships)
- BM25 sparse retrieval for keyword matching
- Reciprocal Rank Fusion (RRF) scoring
- Hybrid dense + sparse retrieval
5-Role RBAC & Multi-Tenancy
Enterprise role-based access control with Admin, Manager, Lawyer, Paralegal, and Viewer roles.
- 5 roles: Admin, Manager, Lawyer, Paralegal, Viewer
- Multi-tenant workspace isolation
- Document-level permission controls
- Audit trail for all actions
- Team collaboration features
- SSO and OAuth integration
3-Tier RAG Architecture
Tier 1: Basic RAG
Direct cosine similarity search against Qdrant vector store with OpenAI 3072-dimensional embeddings.
Tier 2: LangChain RAG
ChatAnthropic + QdrantVectorStore + 5-turn conversational memory buffer for contextual multi-turn Q&A.
Tier 3: Graph RAG
Entity extraction, relationship mapping, BM25 sparse retrieval, and Reciprocal Rank Fusion (RRF) for maximum accuracy.
Tech Stack
AI Models
Transform Your Legal Document Workflow
Upload, analyze, and understand legal documents in seconds with 3-tier RAG intelligence powered by LangChain and Claude.