🧠 Python Documentation RAG Chatbot

An intelligent, context-aware chatbot that helps users improve their coding skills through interactive conversations. Powered by Retrieval-Augmented Generation (RAG), the system pulls information from official Python documentation to provide accurate and relevant responses.

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📌 About the Project

Our project is an intelligent chatbot designed to help users improve their Python coding skills through conversation-based learning. The chatbot leverages Retrieval-Augmented Generative (RAG) techniques to fetch information from official Python documentation and provide accurate, grounded, and context-aware answers.

The chatbot is trained on Python 3.13 documentation and enables:

• Conversational search
• Programming guidance
• Real-time assistance using language models and document retrieval

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🎯 Goal

Our goal is to fulfill the following core functionalities that assist users in everyday learning:

• Interactive Learning
  Engage users in real-world conversations that enhance learning.

• Library Assistance
  Suggest Python libraries based on user needs and queries.

• Programming Correction
  Help users debug, understand, or improve their code using relevant documentation.

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🔍 Scope

• 🖥️ More Prettier UI
  Build a clean, modern UI using Next.js for a better user experience.

• 📚 One-Stop Shop for Python Library
  The bot will support multiple Python libraries and fetch accurate documentation in response to user questions.

• ⚙️ Check Program Efficiency

  • Retrieve and rank the top 3 most relevant documents from the vector database.
  • Provide suggestions with context and explanations.

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🏗️ Tech Stack

Component	Tech
Frontend (Planned)	Next.js
Backend	LangChain, FastAPI
LLM	OpenAI GPT-3.5 / GPT-4
Embeddings	OpenAI Embeddings
Vector Store	FAISS (local) or Pinecone/Chroma (cloud)
Data Source	Python 3.13 Documentation (Plain Text)
Tools	Jupyter, Git, Databricks (for data prep)

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👨‍👩‍👧‍👦 Team Members

• We will collaborate to implement the RAG pipeline and improve answer accuracy.
• We will use vector databases to support semantic document search.
• We will spread out data collection, aggregation, and cleaning.
• We will use Databricks for large-scale processing and feature exploration.
• We will iterate to handle real-world issues like ambiguity, noise, or conflicting documentation.

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🧪 What the Code Does / Steps to Run

🔄 Pipeline Breakdown

1. Unzip Python 3.13 documentation
   The plain-text .zip file is extracted into a local folder using Python's zipfile.

2. Load all .txt files
   Reads every file in the extracted folder into memory using LangChain's Document class.

3. Split documents into chunks
   Uses RecursiveCharacterTextSplitter to break content into small, overlapping pieces for better semantic matching.

4. Generate embeddings
   OpenAI Embeddings are created for each chunk and stored in a FAISS vector store for fast retrieval.

5. Run Retrieval-Augmented Generation
   A LangChain RetrievalQA or ConversationalRetrievalChain is used to answer user questions based on retrieved content.

6. Interactive chat
   A loop runs in the notebook where the user can ask questions about Python, and the bot responds using official docs.

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🔐 How to Get OpenAI API Key & Run the Bot

To use OpenAI’s GPT models for answering questions, you'll need to generate an API key from your OpenAI account.

🧾 Step-by-Step Instructions

1. Create/Open an OpenAI account
   Go to: https://platform.openai.com/signup
   (Sign up or log in with your existing credentials)

2. Generate your API key

   • Go to: https://platform.openai.com/account/api-keys
   • Click “Create new secret key”
   • Copy and save the key somewhere safe (you won’t be able to see it again!)

3. Set the API key in your code
   You can pass it securely using Python:

   import os
   from getpass import getpass
   
   os.environ["OPENAI_API_KEY"] = getpass("🔐 Enter your OpenAI API key: ")

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🚀 Setup Instructions

1. Install Jupyter Notebook if not done: https://jupyter.org/

2. Go to openai and get api key: https://platform.openai.com/settings/organization/api-keys

3. Download the python documentation: https://docs.python.org/3/download.html

4. Clone the repository:

   git clone https://github.com/yourusername/cs6320_project.git
   cd python-rag-chatbot