INTEGRATION_SUMMARY.md

LlamaParse Integration Summary

Changes Made

1. core/data_loaders.py - Complete Replacement

Status: ✅ Complete

Changes:

• ❌ Removed: PyMuPDF4LLMLoader and TesseractBlobParser
• ✅ Added: LlamaParse and SimpleDirectoryReader from llama-index
• ✅ Added: os module for environment variable handling

New Functions:

1. load_pdf_documents(pdf_path, api_key=None) - Basic LlamaParse loader
2. load_pdf_documents_advanced(pdf_path, api_key=None, premium_mode=False) - Advanced loader with premium features
3. load_multiple_pdfs(pdf_directory, api_key=None, file_pattern="*.pdf") - Batch processing

Key Features:

• Medical document optimized parsing instructions
• Accurate page numbering with split_by_page=True
• Preserves borderless tables and complex layouts
• Enhanced metadata tracking
• Premium mode option for GPT-4o parsing

---

2. core/config.py - Configuration Updates

Status: ✅ Complete

Changes:

# Added to Settings class
LLAMA_CLOUD_API_KEY: str | None = None
LLAMA_PREMIUM_MODE: bool = False

Purpose:

• Store LlamaParse API key from environment variables
• Control premium/basic parsing mode
• Centralized configuration management

---

3. core/utils.py - Pipeline Integration

Status: ✅ Complete

Changes:

1. Import Update (Line 12):

   from .config import get_embedding_model, VECTOR_STORE_DIR, CHUNKS_PATH, NEW_DATA, PROCESSED_DATA, settings
   

2. Function Update _load_documents_for_file() (Lines 118-141):

   def _load_documents_for_file(file_path: Path) -> List[Document]:
       try:
           if file_path.suffix.lower() == '.pdf':
               # Use advanced LlamaParse loader with settings from config
               api_key = settings.LLAMA_CLOUD_API_KEY
               premium_mode = settings.LLAMA_PREMIUM_MODE
               
               return data_loaders.load_pdf_documents_advanced(
                   file_path,
                   api_key=api_key,
                   premium_mode=premium_mode
               )
           return data_loaders.load_markdown_documents(file_path)
       except Exception as e:
           logger.error(f"Failed to load {file_path}: {e}")
           return []
   

Impact:

• All PDF processing now uses LlamaParse automatically
• Reads configuration from environment variables
• Maintains backward compatibility with markdown files

---

New Files Created

1. LLAMAPARSE_INTEGRATION.md

Complete documentation including:

• Setup instructions
• Configuration guide
• Usage examples
• Cost considerations
• Troubleshooting
• Migration guide

2. test_llamaparse.py

Test suite with:

• Configuration checker
• Single PDF test
• Batch processing test
• Full pipeline test

3. INTEGRATION_SUMMARY.md (this file)

Quick reference for all changes

---

Environment Variables Required

Add to your .env file:

# Required for LlamaParse
LLAMA_CLOUD_API_KEY=llx-your-api-key-here

# Optional: Enable premium mode (default: False)
LLAMA_PREMIUM_MODE=False

# Existing (still required)
OPENAI_API_KEY=your-openai-key

---

Installation Requirements

pip install llama-parse llama-index-core

---

How to Use

Automatic Processing (Recommended)

1. Set LLAMA_CLOUD_API_KEY in .env
2. Place PDFs in data/new_data/PROVIDER/
3. Run your application - documents are processed automatically on startup

Manual Processing

from core.utils import process_new_data_and_update_vector_store

# Process all new documents
vector_store = process_new_data_and_update_vector_store()

Direct PDF Loading

from pathlib import Path
from core.data_loaders import load_pdf_documents_advanced

pdf_path = Path("data/new_data/SASLT/guideline.pdf")
documents = load_pdf_documents_advanced(pdf_path)

---

Testing

Run the test suite:

python test_llamaparse.py

This will:

1. ✅ Check configuration
2. ✅ Test single PDF loading
3. ✅ (Optional) Test batch processing
4. ✅ (Optional) Test full pipeline

---

Backward Compatibility

✅ Fully backward compatible:

• Existing processed documents remain valid
• Vector store continues to work
• Markdown processing unchanged
• No breaking changes to API

---

Benefits

Aspect	Before (PyMuPDF4LLMLoader)	After (LlamaParse)
Borderless Tables	❌ Poor	✅ Excellent
Complex Layouts	⚠️ Moderate	✅ Excellent
Medical Terminology	⚠️ Moderate	✅ Excellent
Page Numbering	✅ Good	✅ Excellent
Processing Speed	✅ Fast (local)	⚠️ Slower (cloud)
Cost	✅ Free	⚠️ ~$0.003-0.01/page
Accuracy	⚠️ Moderate	✅ High

---

Cost Estimation

Basic Mode (~$0.003/page)

• 50-page guideline: ~$0.15
• 100-page guideline: ~$0.30

Premium Mode (~$0.01/page)

• 50-page guideline: ~$0.50
• 100-page guideline: ~$1.00

Note: LlamaParse caches results, so re-processing is free.

---

Workflow Example

1. User places PDF in data/new_data/SASLT/
   └── new_guideline.pdf

2. Application startup triggers processing
   ├── Detects new PDF
   ├── Calls load_pdf_documents_advanced()
   ├── LlamaParse processes with medical optimizations
   ├── Extracts 50 pages with accurate metadata
   └── Returns Document objects

3. Pipeline continues
   ├── Splits into 245 chunks
   ├── Updates vector store
   └── Moves to data/processed_data/SASLT/new_guideline_20251111_143022.pdf

4. Ready for RAG queries
   └── Vector store contains new guideline content

---

Next Steps

1. ✅ Set LLAMA_CLOUD_API_KEY in .env
2. ✅ Install dependencies: pip install llama-parse llama-index-core
3. ✅ Test with: python test_llamaparse.py
4. ✅ Place PDFs in data/new_data/PROVIDER/
5. ✅ Run application and verify processing

---

Support & Troubleshooting

Common Issues

1. API Key Not Found

ValueError: LlamaCloud API key not found

→ Set LLAMA_CLOUD_API_KEY in .env

2. Import Errors

ModuleNotFoundError: No module named 'llama_parse'

→ Run: pip install llama-parse llama-index-core

3. Slow Processing → Normal for cloud processing (30-60s per document) → Subsequent runs use cache (much faster)

Logs

Check logs/app.log for detailed processing information

---

Integration Date: November 11, 2025
Status: ✅ Production Ready
Version: 1.0