This Adaptive Code Assistant is designed to facilitate querying within large codebases through a natural language processing system. It leverages a combination of machine learning models and indexing technologies to parse, index, and retrieve code snippets efficiently, responding to user queries through a conversational interface.

1. File Reading and Indexing: The system scans specified directories for source code files (specifically .cpp,.c,.h,.hpp,.pdf,.md files (supports other file formats)), reading and indexing them to create a searchable dataset.

2. Embedding Generation: Utilizing the SentenceTransformer model, the script generates embeddings for each line of code. These embeddings capture the semantic meaning of the code, allowing for efficient similarity-based retrieval.

3. FAISS Index Creation: The embeddings are then used to create a FAISS index, which facilitates fast and efficient similarity searches within the dataset.

4. Conversational Interface: The application uses Streamlit to create a chat-style interface, where users can input their queries and receive responses. The responses are generated by a language model, which uses the indexed data to provide relevant and context-aware answers.

5. Language Model Integration: At the core of the response generation is a language model from the CTransformers class, configured to handle conversational queries about the code. This model is integrated into a LangChain workflow that combines retrieval and generative capabilities.

Access all necessary files for the ACA project via the following Google Drive link:
ACA Project Files

Modify the target_path in the file /AdaptiveCodeAssistant/src/ui/adaptive_code_assistant.py at line 128. Change it from:

target_path = os.path.join(root_directory, 'dataset')

to:

target_path = "C:/x/x"

To activate the virtual environment, either double-click or run the following PowerShell script from the command line:

C:/XX/XX/AdaptiveCodeAssistant/Scripts/Activate.ps1

Start the project by running the following command:

streamlit run C:/XX/XX/AdaptiveCodeAssistant/src/ui/adaptive_code_assistant.py

Note: If you download the project from the provided Drive link, no additional steps are necessary.

• Python 3.8+
• Libraries: streamlit, transformers, faiss, numpy, torch, sentence-transformers, langchain
• Adequate storage for embeddings and index files, depending on the size of the codebase.

Install the required Python packages using pip:

pip install streamlit transformers faiss-cpu numpy torch sentence-transformers langchain

To run the application, execute the following command from the terminal:

streamlit run adaptive_code_assistant.py