Skip to content

FIXES_SUMMARY.md

Latest commit

 

History

History
190 lines (150 loc) · 5.15 KB

FIXES_SUMMARY.md

File metadata and controls

190 lines (150 loc) · 5.15 KB

Fixes Summary - All Errors Resolved! ✅

Issues Fixed

1. ✅ Import Path Errors

Problem: ModuleNotFoundError: No module named 'src.document_processor'

Solution: Updated all example files to use new project structure:

  • src.document_processor → src.processing.document_processor
  • src.enhanced_rag → src.retrieval.enhanced_rag
  • src.document_search → src.retrieval.base_rag
  • src.database_manager → src.storage.database_manager

Files Fixed:

  • ✅ examples/process_documents.py
  • ✅ examples/query_enhanced_rag.py
  • ✅ examples/use_local_qdrant.py
  • ✅ tests/test_document_search.py
  • ✅ src/__init__.py

2. ✅ EnhancedDocumentRAG Parameter Error

Problem: TypeError: DocumentSearchRAG.__init__() got an unexpected keyword argument 'collection_name'

Root Cause: EnhancedDocumentRAG was trying to pass individual parameters to DocumentSearchRAG, but the base class only accepts config_path.

Solution: Changed EnhancedDocumentRAG.__init__() to match base class signature:

Before:

def __init__(
    self,
    collection_name: str = "enhanced_documents",
    embedding_model: str = "...",
    qdrant_url: str = "localhost",
    qdrant_port: int = 6333,
    openai_model: str = "gpt-4",
    ...
):
    super().__init__(
        collection_name=collection_name,  # ❌ Base doesn't accept this
        embedding_model=embedding_model,
        ...
    )

After:

def __init__(
    self,
    config_path: str = "config.yaml",  # ✅ Base accepts this
    chunk_size: int = 512,
    use_semantic_chunking: bool = True,
    embedding_model: str = "..."
):
    super().__init__(config_path=config_path)  # ✅ Works!

3. ✅ Pytest Coverage Error

Problem: pytest: error: unrecognized arguments: --cov=src

Solution: Installed pytest-cov package

Result:

  • ✅ All 13 tests passing
  • ✅ 82% coverage on base_rag.py
  • ✅ HTML coverage report generated

Current Status

✅ All Working:

  • Import paths fixed across all files
  • Enhanced RAG initialization fixed
  • Tests running successfully (13/13 passing)
  • Coverage reporting working
  • Example scripts ready to run

📝 To Use the System:

Option 1: Base RAG (Simple)

from src.retrieval.base_rag import DocumentSearchRAG, Document

rag = DocumentSearchRAG()
doc = Document(id="1", title="Test", content="Hello world")
rag.index_documents([doc])
results = rag.search("hello")

Option 2: Enhanced RAG (Advanced)

from src.retrieval.enhanced_rag import EnhancedDocumentRAG

# Initialize (requires OPENAI_API_KEY in .env)
rag = EnhancedDocumentRAG()

# Process real documents (PDF, DOCX, etc.)
stats = rag.process_and_index_documents("test_documents/")

# Search with filters
results = rag.enhanced_search(
    "machine learning",
    category_filter="AI/ML",
    include_tables=True
)

Run Examples:

# Activate environment
source .venv/bin/activate

# Process documents
python examples/process_documents.py test_documents/

# Query system
python examples/query_enhanced_rag.py

# Test local Qdrant
python examples/use_local_qdrant.py

Run Tests:

pytest                    # Run all tests
pytest -v                 # Verbose output
pytest --no-cov          # Skip coverage (faster)
open htmlcov/index.html  # View coverage report

Project Structure (Updated)

document_search/
├── src/
│   ├── __init__.py                    ✅ Fixed imports
│   ├── config/
│   │   └── settings.py
│   ├── core/
│   │   ├── pipeline.py
│   │   └── rag_system.py
│   ├── processing/
│   │   └── document_processor.py     ← Docling + Chonkie
│   ├── retrieval/
│   │   ├── base_rag.py               ← DocumentSearchRAG
│   │   └── enhanced_rag.py           ← EnhancedDocumentRAG ✅ Fixed
│   └── storage/
│       ├── database_manager.py
│       └── vector_store.py
├── examples/
│   ├── process_documents.py          ✅ Fixed imports
│   ├── query_enhanced_rag.py         ✅ Fixed imports
│   └── use_local_qdrant.py           ✅ Fixed imports
├── tests/
│   └── test_document_search.py       ✅ Fixed imports
├── test_documents/                    ← Sample documents
├── qdrant_db/                         ← Local vector storage
├── config.yaml                        ← Configuration
├── .env                               ← API keys (create from sample.env)
└── pyproject.toml

Important Notes

  1. API Key Required: Both RAG systems need OPENAI_API_KEY in .env file
  2. Config File: Settings are in config.yaml (collection name, model, etc.)
  3. Local Qdrant: Vector database stored in ./qdrant_db/ (no server needed)
  4. Test Documents: Use files in test_documents/ for testing

Next Steps

  1. ✅ All errors fixed - system is ready to use!
  2. Add your OpenAI API key to .env file
  3. Run example scripts to see the system in action
  4. Process your own documents
  5. Build your RAG application!

🎉 Everything is working!