🎯 Challenges We Ran Into

🤖 AI Content Verification System

• Challenge: Needed to verify legal document authenticity and cross-reference evidence
• Problem: AI would sometimes lose context between different pieces of evidence
• Solution:

  # Implemented a content verification system using file-based context
  os.makedirs('content-verification', exist_ok=True)
  file_path = os.path.join('content-verification', 'case.txt')
  with open(file_path, 'w', encoding='utf-8') as f:
      f.write(case_data.description)
  # For each piece of evidence
  for file in evidence_data.evidences:
      reference_file_path = os.path.join('content-verification/references', 
          f"{file.original_name.split('.')[0]}.txt")
      with open(reference_file_path, 'w', encoding='utf-8') as f:
          f.write(file.description)
  

📝 Real-time PDF Generation

• Challenge: Needed to generate court documents in real-time as conversations progressed
• Problem: PDF merging would sometimes corrupt files during concurrent updates
• Solution:

  def append_to_case_pdf(case_id: str, conversation: LawyerContext):
      try:
          # Create temporary PDF for new content
          temp_pdf = f'case_reports/temp_{case_id}.pdf'
          doc = SimpleDocTemplate(temp_pdf, pagesize=letter)
          
          # Merge with existing PDF using PyPDF2
          merger = PdfMerger()
          if os.path.exists(pdf_filename):
              merger.append(PdfReader(open(pdf_filename, 'rb')))
          merger.append(PdfReader(open(temp_pdf, 'rb')))
  

🔄 WebSocket Implementation Hurdles

• Initial Challenge: Implementing real-time bidirectional communication between human lawyers and AI agents proved complex
• Problem: WebSocket connections would drop during long AI processing times
• Outcome: Stable connections maintained even during extended AI processing