Project Motivation

This project I'm aiming as a Prerequisites for analysis of my application(proposal) to work on the project - 2 in GSOC for https://github.com/keploy/keploy keploy

Features

• Real-time Codebase Indexing: Automatically indexes code files upon changes, with real-time updates.
• Vector Database Search: Utilizes FAISS or a similar vector database for fast, efficient code search using embeddings.
• Conversational Coding Assistance: Integrates OpenAI's GPT models to provide contextual code suggestions, improvements, and fixes.
• Configurable Settings: Environment-specific settings are managed using a .env file for API keys, model selection, and directories.

Tech Stack

• OpenAI API: Leverages GPT-4o (or any other OpenAI model) for conversational and coding improvements.
• Python: Core functionality and API interactions.
• FAISS (Facebook AI Similarity Search): For vector-based searching.
• python-dotenv: For managing environment variables.
• Retrieval-Augmented Generation (RAG): Combines search and generative models.

Setup Instructions

Prerequisites

• Python 3.8+
• OpenAI API Key (You can get one here)
• FAISS

Step 1: Clone the Repository

Step 2: Install Dependencies

Create a virtual environment (recommended):

python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install required packages:

pip install -r requirements.txt

Step 3: Configure Environment Variables

Create a .env file in the root of the project and add the following variables:

OPENAI_API_KEY=your_openai_api_key
OPENAI_EMBEDDING_MODEL=text-embedding-ada-002
OPENAI_CHAT_MODEL=gpt-4o
WATCHED_DIR=path_to_your_code_directory
FAISS_INDEX_FILE=path_to_faiss_index
EMBEDDING_DIM=1536  # Modify if you're using a different embedding model

Step 4: Run the Application

1. Start the Backend:

   To start the backend (indexing, embeddings, and monitoring):

   python main.py

2. Start the Frontend:

   To launch the Streamlit UI:

   streamlit run app.py

Usage

1. Ask a Question: Type your question or code request into the interface. The model will search the indexed codebase and provide suggestions or improvements.
2. Review Suggestions: You'll receive a merged or fixed version of the code based on the model's analysis.
3. Conversational History: The system keeps track of your queries and the AI responses for better context in future interactions.

Project Structure

• main.py: The main script to run the application.
• prompt_flow.py: Handles querying OpenAI's API and manages the search and conversational history.
• keployrag/config.py: Stores configuration and environment variables.
• keployrag/search.py: Manages vector database (FAISS) searches for relevant code snippets.
• .env: Holds environment-specific settings (OpenAI API keys, model configuration, etc.).
• requirements.txt: Lists the Python dependencies needed to run the project.