Welcome to the CodeXpert, an advanced, state-of-the-art framework designed to analyze, explain, and optimize Python codebases. This repository leverages CodeLlama, LangChain, and FAISS to deliver a seamless, interactive experience for code comprehension and improvement.

The Code Analysis Pipeline provides an automated solution for:

• Code Understanding: Analyze Python code for functionality and structure.
• Knowledge Extraction: Generate clear and actionable insights using LLMs.
• Code Optimization: Suggest performance improvements and best practices.
• Technical Education: Simplify complex code concepts for learners and professionals.

1. Document Loading & Splitting:
   • Recursively scans the specified directory for Python files.
   • Splits large files into manageable chunks for efficient processing.
2. Semantic Embedding Generation:
   • Extracts embeddings using a HuggingFace embedding model.
3. Vector Store Creation:
   • Builds a FAISS vector store for semantic search and retrieval.
4. Question Answering (QA):
   • Processes user queries through a QA Chain with a retriever.
5. Code Analysis & Explanation:
   • Analyzes results using CodeLlama and simplifies explanations with templates.
6. Improvement Suggestions:
   • Leverages LLMs to suggest actionable optimizations.

• 📂 Recursive Document Loading: Processes entire directories with customizable file extensions.
• ✂️ Text Splitting: Splits large files into smaller chunks for precise embeddings.
• 🧠 Advanced Embedding Models: Uses HuggingFace's embeddings for high-quality vector representations.
• 🔍 Efficient Retrieval: Semantic search powered by FAISS.
• 🦙 LLM-Powered Analysis: Code analysis and explanations via CodeLlama.
• 📈 Optimization Suggestions: Provides practical tips for code improvements.
• 🔗 Seamless Integration: Designed to integrate with other AI tools and pipelines.

git clone https://github.com/MohammedNasserAhmed/CodeXpert.git
cd code-analysis-pipeline

Install required libraries with:

pip install -r requirements.txt

Create a .env file or export these variables directly:

MODEL=<YOUR_LLAMA_MODEL_VERSION>
HUGGINGFACEHUB_API_TOKEN=<Your_HuggingFace_Token>
REPO_ID=<Your_HuggingFace_Repo_ID>
CODEBASE_DIR=<Path_to_Your_Codebase>
EMBEDDING_MODEL=<HuggingFace_Embedding_Model>

python app.py

Provide a query like:

How to replace FAISS with CHORMA .

+--------------------+       +--------------------+       +----------------------+
| Document Loader    |-----> | Text Splitter      |-----> | Embedding Generator  |
+--------------------+       +--------------------+       +----------------------+
                                                         |
                                                         v
                                    +----------------------------------+
                                    | FAISS Vector Store               |
                                    +----------------------------------+
                                                         |
                                                         v
                                    +----------------------------------+
                                    | Retrieval-Based QA Chain         |
                                    +----------------------------------+
                                                         |
                                                         v
                            +--------------------------------------------+
                            | CodeLlama Agent for Analysis & Explanations |
                            +--------------------------------------------+
                                                         |
                                                         v
                                   +----------------------------------+
                                   | Suggestions for Code Improvement |
                                   +----------------------------------+

CodeXpert/
│
├── codexpert/
│   ├── components/
│   │   ├── load_document.py           # Handles >> document loading from the codebase
│   │   ├── split_text.py              # Splits >> documents into manageable chunks
│   │   ├── get_embeddings.py          # Generates >> embeddings using HuggingFace models
│   │   ├── codellama_agent.py         # Code analysis agent >> powered by Llama models
│   │   ├── vector_store.py            # Manages >> FAISS vector store initialization
│   │   ├── llm_agent.py               # Handles LLM setup and question-answering
│   │
│   ├── config/
│   │   ├── constants.py               # Contains >> configurations like API tokens and file paths
│
├── tests/                             # Contains >> unit tests for all components
│   ├── test_load_document.py          # Tests >> for the document loader
│   ├── test_split_text.py             # Tests >> for the text splitter
│   ├── test_get_embeddings.py         # Tests >> for the embedding generator
│   ├── test_codellama_agent.py        # Tests >> for the CodeLlama agent
│   ├── test_vector_store.py           # Tests >> for the FAISS vector store
│   └── test_llm_agent.py              # Tests >> for the LLM setup and QA chain
│
├── .gitignore                         # Specifies >> files and folders to ignore in version control
├── requirements.txt                   # Dependencies >> required for the project
├── README.md                          # Project documentation (you are here!)

To verify the functionality of the components, use pytest:

Run all tests:

pytest CodeXpert/tests/

Run tests with detailed output:

pytest -v

Run tests for a specific component:

pytest CodeXpert/tests/test_<component_name>.py

Generate a coverage report (requires pytest-cov):

pip install pytest-cov
pytest --cov=CodeXpert/codechat

• Developers: Enhance understanding of complex codebases.
• Educators: Provide clear code explanations for learners.
• Researchers: Analyze algorithmic code for optimization.
• Organizations: Maintain clean, optimized, and well-documented repositories.

• File Types: Ensure the target codebase contains supported extensions (e.g., .py).
• Environment Setup: Use a virtual environment to isolate dependencies.
• Model Performance: Adjust embedding and LLM parameters for optimal results.

We welcome contributions! If you'd like to improve the pipeline, please:

1. Fork this repository.
2. Create a new branch for your feature or fix.
3. Submit a pull request with a detailed description.

• M. N. Gaber

This project is licensed under the Apache License. See the LICENSE file for details.

Feel free to reach out for questions or feedback:

• 📧 Email: abunasserip@gmail.com
• 🐦 LinkedIn: @M.N.Gaber

Special thanks to:

• HuggingFace for hosting world-class AI models.
• LangChain for simplifying LLM workflows.
• FAISS for fast and efficient retrieval.

🚀 Ready to revolutionize code analysis? Dive in today and supercharge your development process! 🦾