docs/CODE_REVIEW_REPORT.md

RAG Capstone Project - Code Review Report

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

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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

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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

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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

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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

# 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

# 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

# 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

# vector_store.py line ~75
try:
    self.client.delete_collection(collection_name)
except:  # ← Too broad, silently ignores all errors
    pass

Recommendation:

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

print(f"Loaded {len(dataset)} samples")  # ← Should use logger
print("=" * 50)  # ← Should use logger.info()

Recommendation: Add logging configuration

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:

# 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:

# 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

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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)

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5. RECOMMENDED IMPROVEMENTS

Priority 1: High Impact (Do First)

1.1 Add Structured Logging

# 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:

# 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

# 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

# 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

# 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

# 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"

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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

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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)

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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

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End of Code Review Report

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