clm-system/PLANNING/design.md

CLM System Architecture Design

Design Patterns

1. Monolithic Architecture

Single FastAPI application with modular components:

• Document Ingestion Module: Handles multiple file formats (PDF, DOCX, TXT)
• RAG Module: Manages vector embeddings and retrieval
• AI Agent Module: Daily contract monitoring and reporting
• Chatbot Module: User interface for contract queries

2. Direct File Operations

• Simple utility functions for file I/O
• Direct file system operations for document storage
• No abstraction layer needed for this scope

3. Direct File Processing

• Simple file type detection and processing functions
• Direct embedding generation using selected model

4. Strategy Pattern

• ChunkingStrategy: Basic fixed-size chunking
• EmbeddingModel: Single model (OpenAI or local)

5. Chain of Responsibility

• Document processing pipeline:
  1. FileValidator → 2. OCRProcessor → 3. TextExtractor → 4. Chunker → 5. Embedder → 6. VectorStore

6. Singleton Pattern

• ConfigurationManager: Global config access
• VectorDatabaseConnection: Single connection
• Logger: Basic error logging

Data Flow

1. Document Ingestion: File → Validation → Processing → Storage
2. Query Processing: User Query → RAG Pipeline → Context Retrieval → Response Generation
3. Daily Monitoring: Scheduled Trigger → Contract Scan → Conflict Detection → Report Generation

Technology Stack

• Framework: FastAPI (async support, automatic docs)
• Vector DB: ChromaDB (lightweight, easy setup)
• LLM Framework: LangChain
• Container: Docker + Docker Compose

Implementation Priority

1. Document ingestion and indexing
2. Basic RAG pipeline
3. AI agent for daily reports
4. Simple chatbot interface
5. Document similarity function