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.
3-Tier
RAG Architecture
5
RBAC Roles
3072
Embedding Dims
5-Turn
Memory Buffer
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.