🧠 CODE-ASSISTANT: RAG-Based Python Code Assistant

A Retrieval-Augmented Generation (RAG) powered assistant built to understand and answer coding questions using real codebases. It integrates LangChain, FAISS, and Transformers to deliver context-aware and accurate Python code support — like your AI pair programmer.

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🚀 Features

• 🔍 Retrieval-Augmented Generation (RAG) for accurate code responses
• 🤖 Uses FAISS for fast vector-based similarity search
• 🔗 Built with LangChain for prompt orchestration and memory
• 🔤 Embeddings powered by Transformers
• 📂 Supports multiple libraries: langchain, transformers, sklearn, etc.

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🛠 Tech Stack

Tool/Library	Role
LangChain	Prompt management & chaining
FAISS	Embedding indexing & search
Hugging Face	Model and file hosting
Transformers	Embedding generation
Python	Core logic and backend

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📁 Project Structure

PROJECT/ ├── data/ │ ├── merged_faiss_index/ │ │ ├── index.faiss │ │ └── index.pkl │ └── transformers_embeddings.pkl # (optional, >100MB) ├── main.py ├── utils.py ├── run.py ├── .env # Not pushed to repo ├── requirements.txt └── README.md

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⚙️ How It Works

1. Code chunks are extracted and embedded using Transformer models.
2. These embeddings are indexed using FAISS.
3. At runtime, LangChain uses a retriever to find the most relevant chunks.
4. The relevant chunks are passed into an LLM to generate code-based answers.

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📦 Setup Instructions

# Create and activate your virtual environment
python -m venv venv
source venv/bin/activate  # or .\venv\Scripts\activate on Windows

# Install dependencies
pip install -r requirements.txt
⚠️ You'll need to set environment variables in a .env file (not included). Example:

env
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Edit
OPENAI_API_KEY=your_key_here

## SAMPLE OUTPUT
 You: write detailed code of linear regression

 ANSWER: ```python
# Import the necessary libraries
from sklearn.linear_model import LinearRegression  # Import the LinearRegression class
from sklearn.datasets import load_boston  # Import the load_boston function to load the Boston housing dataset

# Load the Boston housing dataset
boston = load_boston()

# Extract the features (X) and the target (y) from the dataset
X = boston.data
y = boston.target

# Create a linear regression model
model = LinearRegression()

# Train the model
model.fit(X, y)  # Fit the model to the data

# Make predictions
predictions = model.predict(X)  # Use the predict method to make predictions

# Print the coefficients
print("Coefficients:", model.coef_)  # Print the coefficients of the linear model

# Print the intercept
print("Intercept:", model.intercept_)  # Print the intercept of the linear model

# Print the mean squared error
print("Mean squared error:", model.score(X, y))  # Print the mean squared error of the model

This code first imports the necessary libraries and loads the Boston housing dataset. It then creates a LinearRegression model and trains it on the dataset using the fit method. The predict method is then used to make predictions, and the coefficients, intercept, and mean squared error are printed.