Implement Document Similarity Engine

[gregv]

Title: Create Core Document Similarity Detection System
Priority: High
Type: Feature Implementation
Estimated Time: 3-4 days

Description

Implement the core document similarity engine using sentence transformers and FAISS for efficient similarity search.

Tasks

• Set up Python backend environment with FastAPI
• Implement document vectorization:

  class DocumentVectorizer:
      def __init__(self):
          self.model = SentenceTransformer('all-MiniLM-L6-v2')
          self.file_processors = {
              'pdf': PDFProcessor(),
              'ppt': PPTProcessor(),
              'jpg': ImageProcessor(),
          }

• Create FAISS index implementation
• Build document clustering system
• Implement similarity threshold configuration
• Add background processing for large documents
• Create API endpoints for similarity search
• Implement caching system for vectors

Acceptance Criteria

• ✅ Can process PDF, PPT, and JPG files
• ✅ Similarity search completes in <2 seconds for 1000 documents
• ✅ API endpoints properly documented with OpenAPI
• ✅ Proper error handling for corrupt/invalid files
• ✅ Caching system reduces repeated processing
• ✅ Unit tests achieve 80%+ coverage