# RAG Capstone Project - Code Review Report

**Date:** January 1, 2026  
**Project:** RAG Capstone Project  
**Reviewer:** Code Analysis System

---

## Executive Summary

✅ **Code Organization Improved**: Moved 7 unused/utility scripts to `archived_scripts/` folder  
✅ **Core System Architecture**: Well-structured with clear separation of concerns  
⚠️ **Minor Improvements Recommended**: Code quality is good; some refactoring opportunities exist

---

## 1. FILES MOVED TO ARCHIVED_SCRIPTS

The following files have been moved to the `archived_scripts/` directory as they are not actively used by the main application:

### 1.1 Utility/Diagnostic Scripts
- **`audit_collection_names.py`** - Direct SQLite query script for debugging collection metadata
- **`cleanup_chroma.py`** - Cleanup utility for ChromaDB and cache
- **`create_architecture_diagram.py`** - Standalone diagram generation script
- **`create_ppt_presentation.py`** - Standalone PowerPoint presentation generator
- **`create_trace_flow_diagrams.py`** - Standalone flow diagram creation script

### 1.2 Example/Alternative Implementation
- **`example.py`** - Example usage script (not part of production pipeline)
- **`api.py`** - FastAPI backend (appears to be alternative/incomplete implementation)

**Rationale**: These files are not imported by the main application (`run.py` or `streamlit_app.py`). They serve as:
- Development/debugging utilities
- Documentation examples
- Alternative API implementations
- Presentation materials

---

## 2. ACTIVE PRODUCTION FILES

### 2.1 Core Entry Points
| File | Purpose | Status |
|------|---------|--------|
| `streamlit_app.py` | Main web interface | ✅ Active |
| `run.py` | Quick start launcher | ✅ Active |
| `streamlit_app.py` | Interactive chat UI | ✅ Active |

### 2.2 Core Modules (Actively Used)
| File | Purpose | Dependencies | Status |
|------|---------|--------------|--------|
| `config.py` | Configuration management | Pydantic Settings | ✅ Good |
| `vector_store.py` | ChromaDB integration | ChromaDB, embedding_models, chunking_strategies | ✅ Well-structured |
| `llm_client.py` | Groq LLM integration | Groq API, rate limiting logic | ✅ Good |
| `embedding_models.py` | Multi-model embedding factory | Sentence Transformers, PyTorch | ✅ Well-designed |
| `chunking_strategies.py` | Document chunking factory | - | ✅ Good |
| `dataset_loader.py` | Dataset loading from RAGBench | HuggingFace Datasets | ✅ Good |
| `trace_evaluator.py` | TRACE metric calculation | NumPy | ✅ Core evaluation |
| `evaluation_pipeline.py` | Evaluation orchestration | advanced_rag_evaluator, trace_evaluator | ✅ Good |
| `advanced_rag_evaluator.py` | Advanced metrics (RMSE, AUC-ROC) | NumPy, scikit-learn | ✅ Advanced |

### 2.3 Utility/Recovery Scripts (Maintenance)
| File | Purpose | Status |
|------|---------|--------|
| `rebuild_chroma_index.py` | Rebuild corrupted ChromaDB | ✅ Recovery tool |
| `rebuild_sqlite_direct.py` | Direct SQLite rebuild | ✅ Recovery tool |
| `recover_chroma_advanced.py` | Advanced recovery | ✅ Recovery tool |
| `recover_collections.py` | Collection recovery | ✅ Recovery tool |
| `rename_collections.py` | Collection renaming utility | ✅ Utility |
| `reset_sqlite_index.py` | Reset SQLite index | ✅ Utility |
| `test_llm_audit_trail.py` | Audit trail testing | ✅ Test script |
| `test_rmse_aggregation.py` | RMSE testing | ✅ Test script |

---

## 3. CODE QUALITY ASSESSMENT

### 3.1 Strengths

#### ✅ Architecture & Design
- **Factory Pattern**: Well-implemented in `EmbeddingFactory` and `ChunkingFactory`
- **Separation of Concerns**: Clear module boundaries between data, embedding, LLM, evaluation
- **Modular Design**: Easy to swap components (chunking strategies, embedding models, LLM)

#### ✅ Configuration Management
```python
# config.py uses Pydantic for type-safe settings
class Settings(BaseSettings):
    groq_api_key: str = ""
    chroma_persist_directory: str = "./chroma_db"
    embedding_models: list = [...]
    # Good: Supports .env file, environment variables
```

#### ✅ Rate Limiting
```python
# llm_client.py includes intelligent rate limiting
class RateLimiter:
    - Tracks requests within sliding 1-minute window
    - Provides both sync and async acquire methods
    - Configurable RPM limits (default: 30)
```

#### ✅ Vector Storage
```python
# vector_store.py handles ChromaDB with metadata
- Persistent storage with metadata tracking
- Automatic collection cleanup and recreation
- Reconnection handling for fault tolerance
```

### 3.2 Areas for Improvement

#### ⚠️ Error Handling
**Current Issue**: Some try-except blocks are too broad
```python
# vector_store.py line ~75
try:
    self.client.delete_collection(collection_name)
except:  # ← Too broad, silently ignores all errors
    pass
```

**Recommendation**:
```python
try:
    self.client.delete_collection(collection_name)
except chromadb.errors.InvalidCollectionError:
    pass  # Collection doesn't exist, which is fine
except Exception as e:
    logger.warning(f"Unexpected error deleting collection: {e}")
```

#### ⚠️ Logging
**Current Issue**: Mix of print() statements instead of proper logging
```python
print(f"Loaded {len(dataset)} samples")  # ← Should use logger
print("=" * 50)  # ← Should use logger.info()
```

**Recommendation**: Add logging configuration
```python
import logging

logger = logging.getLogger(__name__)

# In config.py:
logging_level: str = "INFO"
logging_format: str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
```

#### ⚠️ Type Hints
**Current Status**: Partially implemented  
**Good**: `llm_client.py`, `vector_store.py`, `trace_evaluator.py`  
**Needs Work**: Some functions lack return type hints

**Example to improve**:
```python
# Current (missing return type)
def create_collection(self, collection_name: str, embedding_model_name: str):
    ...

# Improved
def create_collection(
    self,
    collection_name: str,
    embedding_model_name: str,
    metadata: Optional[Dict] = None
) -> chromadb.Collection:
    ...
```

#### ⚠️ Constants and Magic Numbers
**Found in**: Multiple files  
**Example**:
```python
# config.py line ~16
rate_limit_delay: float = 2.5  # Magic number without explanation
groq_rpm_limit: int = 30

# Better would be:
class RateLimits:
    GROQ_RPM = 30
    RATE_LIMIT_SAFETY_MARGIN = 2.5
    MIN_REQUESTS_PER_MINUTE = 24  # Conservative estimate
```

---

## 4. DEPENDENCY ANALYSIS

### 4.1 External Dependencies (from requirements.txt)
✅ **Production Dependencies**:
- `streamlit` - Web UI framework
- `chromadb` - Vector database
- `sentence-transformers` - Embedding models
- `groq` - LLM API client
- `fastapi` - REST API framework
- `pandas` - Data processing
- `numpy` - Numerical computing
- `scikit-learn` - ML metrics (RMSE, AUC-ROC)
- `datasets` - HuggingFace datasets
- `torch` - PyTorch for embeddings
- `transformers` - HuggingFace transformers

### 4.2 Dependency Relationships
```
streamlit_app.py
├── config.py
├── dataset_loader.py (datasets, pandas)
├── vector_store.py
│   ├── embedding_models.py (torch, sentence-transformers)
│   └── chunking_strategies.py
├── llm_client.py (groq)
├── trace_evaluator.py (numpy)
└── evaluation_pipeline.py
    ├── trace_evaluator.py
    └── advanced_rag_evaluator.py (numpy, sklearn)
```

---

## 5. RECOMMENDED IMPROVEMENTS

### Priority 1: High Impact (Do First)

#### 1.1 Add Structured Logging
```python
# Create logging.py
import logging
import logging.config

LOGGING_CONFIG = {
    'version': 1,
    'disable_existing_loggers': False,
    'formatters': {
        'default': {
            'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        },
        'detailed': {
            'format': '%(asctime)s - %(name)s - %(levelname)s - %(funcName)s:%(lineno)d - %(message)s'
        },
    },
    'handlers': {
        'console': {
            'class': 'logging.StreamHandler',
            'formatter': 'default',
        },
        'file': {
            'class': 'logging.FileHandler',
            'filename': 'app.log',
            'formatter': 'detailed',
        },
    },
    'loggers': {
        '': {  # Root logger
            'handlers': ['console', 'file'],
            'level': 'INFO',
        },
    },
}

logging.config.dictConfig(LOGGING_CONFIG)
```

#### 1.2 Improve Error Handling
Replace broad `except:` with specific exceptions:
```python
# Before
try:
    self.client.delete_collection(collection_name)
except:
    pass

# After
try:
    self.client.delete_collection(collection_name)
except Exception as e:
    logger.debug(f"Collection {collection_name} not found (expected): {e}")
```

### Priority 2: Medium Impact (Nice to Have)

#### 2.1 Add Input Validation
```python
# In vector_store.py
def load_dataset_into_collection(
    self,
    collection_name: str,
    embedding_model_name: str,
    dataset_data: List[Dict],
    **kwargs
) -> None:
    """Load dataset into collection with validation."""
    # Validate inputs
    if not collection_name or not isinstance(collection_name, str):
        raise ValueError("collection_name must be a non-empty string")
    if not dataset_data or not isinstance(dataset_data, list):
        raise ValueError("dataset_data must be a non-empty list")
    
    # Proceed with loading
    ...
```

#### 2.2 Add Performance Monitoring
```python
# Create metrics.py
import time
from contextlib import contextmanager
from typing import Optional

@contextmanager
def timer(operation_name: str) -> None:
    """Context manager to measure operation duration."""
    start = time.time()
    try:
        yield
    finally:
        duration = time.time() - start
        logger.info(f"{operation_name} took {duration:.2f}s")

# Usage
with timer("Vector search"):
    results = collection.query(query_embeddings, n_results=5)
```

### Priority 3: Low Impact (Polish)

#### 3.1 Add Constants File
```python
# constants.py
class Config:
    # Rate limiting
    GROQ_RPM_LIMIT = 30
    RATE_LIMIT_SAFETY_MARGIN = 2.5
    
    # Vector search
    DEFAULT_TOP_K = 5
    MIN_SIMILARITY_SCORE = 0.3
    
    # Chunking
    DEFAULT_CHUNK_SIZE = 512
    DEFAULT_CHUNK_OVERLAP = 50

class ErrorMessages:
    INVALID_COLLECTION = "Collection '{name}' not found"
    API_KEY_MISSING = "API key not configured in environment"
    INVALID_EMBEDDING_MODEL = "Embedding model '{model}' not supported"
```

#### 3.2 Add Unit Tests
```python
# tests/test_config.py
import pytest
from config import settings

def test_settings_loads_from_env():
    """Test that settings load from environment variables."""
    assert settings.groq_api_key  # Should be set in .env

def test_embedding_models_available():
    """Test that embedding models list is not empty."""
    assert len(settings.embedding_models) > 0

# tests/test_vector_store.py
def test_create_collection():
    """Test collection creation."""
    vector_store = ChromaDBManager()
    collection = vector_store.create_collection(
        "test_collection",
        "sentence-transformers/all-MiniLM-L6-v2"
    )
    assert collection is not None
    assert collection.name == "test_collection"
```

---

## 6. FOLDER STRUCTURE AFTER CLEANUP

```
RAG Capstone Project/
├── archived_scripts/              # ← NEWLY CREATED - Unused scripts
│   ├── api.py                      # Alternative FastAPI implementation
│   ├── audit_collection_names.py   # SQLite debugging script
│   ├── cleanup_chroma.py           # Cleanup utility
│   ├── create_architecture_diagram.py
│   ├── create_ppt_presentation.py
│   ├── create_trace_flow_diagrams.py
│   └── example.py                  # Example usage script
│
├── CORE APPLICATION FILES
├── run.py                          # Entry point (launcher)
├── streamlit_app.py                # Main web interface
├── config.py                       # Settings management
├── vector_store.py                 # ChromaDB integration
├── llm_client.py                   # Groq LLM client
├── embedding_models.py             # Embedding factory
├── chunking_strategies.py          # Chunking factory
├── dataset_loader.py               # Dataset loading
├── trace_evaluator.py              # TRACE metrics
├── evaluation_pipeline.py          # Evaluation orchestration
├── advanced_rag_evaluator.py       # Advanced metrics
│
├── RECOVERY/UTILITY SCRIPTS
├── rebuild_chroma_index.py
├── rebuild_sqlite_direct.py
├── recover_chroma_advanced.py
├── recover_collections.py
├── rename_collections.py
├── reset_sqlite_index.py
│
├── TEST SCRIPTS
├── test_llm_audit_trail.py
├── test_rmse_aggregation.py
│
├── CONFIGURATION & DATA
├── .env                            # Environment variables
├── .env.example                    # Example environment
├── config.py                       # Settings
├── requirements.txt                # Python dependencies
├── docker-compose.yml              # Docker setup
├── Dockerfile                      # Container definition
├── Procfile                        # Deployment manifest
│
├── DATA & PERSISTENCE
├── chroma_db/                      # Vector database
├── data_cache/                     # Cached datasets
│
├── DOCUMENTATION
├── docs/                           # Documentation files
├── README.md                       # Main readme
├── CODE_REVIEW_REPORT.md          # ← THIS FILE
│
└── BUILD ARTIFACTS
    ├── RAG_Architecture_Diagram.png
    ├── RAG_Data_Flow_Diagram.png
    └── RAG_Capstone_Project_Presentation.pptx
```

---

## 7. SUMMARY OF CHANGES

### Actions Completed ✅

1. **Created `archived_scripts/` directory** for unused files
2. **Moved 7 unused files** to archive:
   - `api.py` (alternative FastAPI implementation)
   - `audit_collection_names.py` (debugging utility)
   - `cleanup_chroma.py` (maintenance utility)
   - `create_architecture_diagram.py` (documentation)
   - `create_ppt_presentation.py` (documentation)
   - `create_trace_flow_diagrams.py` (documentation)
   - `example.py` (example usage)

3. **Created this Code Review Report** with:
   - File classification and rationale
   - Code quality assessment
   - Improvement recommendations
   - Priority-based action items

### Benefits

- **🗂️ Better Organization**: Unused code separated from production code
- **📦 Cleaner Main Directory**: Main folder now focuses on active, production code
- **📚 Better Navigation**: Easier to identify which files are critical
- **🔍 Clearer Architecture**: Core modules are clearly distinguishable from utilities
- **📋 Documented Decisions**: This report explains why files were moved

### Next Steps

**Recommended follow-up actions**:
1. ✅ Review archived files periodically (delete if no longer needed)
2. ⚠️ Implement structured logging (Priority 1)
3. ⚠️ Improve error handling (Priority 1)
4. 💡 Add input validation (Priority 2)
5. 📊 Add performance monitoring (Priority 2)

---

## 8. NOTES FOR TEAM

### For Developers
- The `archived_scripts/` folder contains historically useful but currently unused code
- Feel free to reference these scripts for implementation ideas
- If functionality is needed, migrate code from archive to main modules

### For Maintenance
- **Recovery Scripts** (rebuild_*.py, recover_*.py) should stay in main directory
- These are critical for database maintenance and troubleshooting
- Document any new utility scripts with clear purpose

### For Documentation
- The archived scripts contain good examples of system capabilities
- Consider extracting useful patterns into reusable utilities
- Keep the presentation/diagram generation for future updates

---

**End of Code Review Report**

*Generated on: January 1, 2026*  
*Review Scope: File organization and code quality assessment*