A beautiful, intelligent AI chatbot with Retrieval-Augmented Generation (RAG) capabilities. Upload your documents (PDFs, HTML, code files) and chat with an AI that understands your content, or use it as a general-purpose chatbot without any documents.

• Dual Mode Operation

  • 🟢 RAG Mode: Upload documents and get AI responses based on your content
  • 🔵 General Mode: Chat with AI without any documents loaded

• Multiple File Format Support

  • 📄 PDF Documents
  • 🌐 HTML Files (.html, .htm)
  • 💻 Code Files (.py, .java, .js, .cpp, .c, .h, .cs, .rb, .go, .rs, .php, .swift, .kt, .ts)
  • 📝 Text & Markdown (.txt, .md)

• Beautiful Web Interface

  • Modern, responsive design
  • File upload with drag-and-drop support
  • Real-time chat with typing indicators
  • Source citations for RAG responses

• Powerful Technology Stack

  • 🤖 Ollama (SmolLM2) for AI responses
  • 🔍 ChromaDB for vector storage
  • 📊 Sentence Transformers for embeddings
  • 🌐 Flask for web interface

1. Python 3.8+ installed on your system
2. Ollama installed and running (Download Ollama)
3. Pull the SmolLM2 model:

   ollama pull smollm2:135m # or for better quality (CLI only): ollama pull smollm2:1.7b

1. Clone or download this repository

2. Install dependencies:

   pip install -r requirements.txt

3. Start the application:

   python app.py

4. Open your browser: Navigate to http://localhost:5000

1. Upload Documents (Optional)

   • Click the "Upload Files" button
   • Select one or more files (PDF, HTML, code files, etc.)
   • Wait for processing (creates vector embeddings)
   • RAG mode is now active! 🟢

2. Chat with the AI

   • Type your question in the input box
   • Press Enter to send
   • Get responses based on your documents (RAG mode) or general AI knowledge

3. Manage Documents

   • Clear Docs: Remove all uploaded documents and return to general mode
   • Clear Chat: Clear the conversation history

Run the chatbot in terminal mode:

python chatbot.py

Commands:

• Type your questions naturally
• sources - View sources used in the last RAG response
• quit or exit - Exit the chatbot

To manually process documents from the data/ folder:

1. Place your files in the data/ directory
2. Run:

   python ingest_documents.py

168_rag_chatbot/ ├── app.py # Flask web application ├── chatbot.py # CLI chatbot interface ├── ingest_documents.py # Document processing script ├── requirements.txt # Python dependencies ├── data/ # Upload folder for documents ├── chroma_db/ # Vector database storage ├── templates/ │ ├── index.html # Main chat interface │ └── error.html # Error page └── static/ ├── style.css # Styling └── script.js # Frontend logic 

Edit the configuration variables in app.py, chatbot.py, or ingest_documents.py:

# Model Configuration OLLAMA_MODEL = "smollm2:135m" # Ollama model to use EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2" # Embeddings model TOP_K_RESULTS = 4 # Number of context chunks to retrieve # Paths DATA_PATH = "data/" # Document upload folder CHROMA_PATH = "chroma_db" # Vector database location

• Learning Assistant: Upload course materials and get answers based on your lessons
• Code Documentation: Upload code files and ask questions about implementation
• Research Helper: Process research papers and get insights
• Knowledge Base: Create a searchable knowledge base from your documents
• General Chat: Use without documents for general AI assistance

• All processing happens locally on your machine
• Documents are stored locally in the data/ folder
• No data is sent to external services (except Ollama API calls)

• Make sure Ollama is running: ollama serve
• Verify the model is downloaded: ollama list

• Install dependencies: pip install -r requirements.txt

• Check that files are in supported formats
• Ensure files are not corrupted
• Check console output for specific errors

• Clear and recreate: Click "Clear Docs" in the web interface
• Or manually delete the chroma_db folder

To modify or extend the chatbot:

1. Change AI Model: Edit OLLAMA_MODEL in the configuration
2. Add File Types: Extend the loaders in ingest_documents.py
3. Customize UI: Modify templates/index.html and static/style.css
4. Adjust RAG Behavior: Change chunk size, overlap, or retrieval count

Contributions are welcome! Feel free to:

• Report bugs
• Suggest new features
• Submit pull requests

This project is open source and available under the MIT License.

• Ollama - Local AI model hosting
• LangChain - Document processing and RAG framework
• ChromaDB - Vector database
• Sentence Transformers - Text embeddings

Made with ❤️ using Python, Flask, and AI for Coding Questions

A Retrieval-Augmented Generation (RAG) chatbot that answers coding questions based on your PDF learning materials using the SmolLM2:1.7b model from Ollama.

• 📚 PDF Processing: Automatically processes PDF documents from the data/ folder
• 🔍 Semantic Search: Uses vector embeddings to find relevant content
• 🤖 Local LLM: Powered by SmolLM2:1.7b via Ollama (runs locally)
• 💬 Interactive Chat: Simple command-line interface for asking questions
• 🎯 Context-Aware: Answers based on your specific learning materials

1. Python 3.8+ installed
2. Ollama installed with SmolLM2:1.7b model
   • Install Ollama from: https://ollama.ai
   • Pull the model: ollama pull smollm2:1.7b

# Activate virtual environment .\master\Scripts\activate # Install required packages pip install -r requirements.txt

Place your PDF files in the data/ folder. Currently includes:

• learning_java.pdf
• Learning_Python.pdf

Process the PDFs and create the vector database:

python ingest_documents.py

This will:

• Load all PDF files from the data/ folder
• Split them into manageable chunks
• Create embeddings using sentence-transformers
• Store them in a ChromaDB vector database

python chatbot.py

• "How do I create a list in Python?"
• "What is inheritance in Java?"
• "Explain Python decorators"
• "How do I handle exceptions in Java?"
• "What are Python list comprehensions?"
• "Explain Java interfaces"

• Type your question and press Enter
• Type sources to see the source documents for the last answer
• Type quit or exit to end the conversation

1. Document Ingestion (ingest_documents.py):

   • Loads PDF files
   • Splits into chunks (1000 chars with 200 overlap)
   • Creates embeddings using all-MiniLM-L6-v2
   • Stores in ChromaDB

2. RAG Pipeline (chatbot.py):

   • Takes user question
   • Finds relevant chunks using semantic search
   • Creates context from top 4 results
   • Sends context + question to SmolLM2:1.7b
   • Returns contextual answer

rag_chatbot/ ├── data/ # PDF documents │ ├── learning_java.pdf │ └── Learning_Python.pdf ├── chroma_db/ # Vector database (created after ingestion) ├── master/ # Virtual environment ├── ingest_documents.py # Document processing script ├── chatbot.py # Main chatbot application ├── requirements.txt # Python dependencies └── README.md # This file 

Run python ingest_documents.py first to create the vector database.

• Make sure Ollama is running
• Verify the model is installed: ollama list
• Pull the model if needed: ollama pull smollm2:1.7b

• Reduce TOP_K_RESULTS in chatbot.py (default: 4)
• Reduce chunk_size in ingest_documents.py (default: 1000)

Edit OLLAMA_MODEL in chatbot.py:

OLLAMA_MODEL = "your-model-name"

Edit chunk_size in ingest_documents.py:

chunk_size=1000, # Increase or decrease chunk_overlap=200 # Adjust overlap

Edit TOP_K_RESULTS in chatbot.py:

TOP_K_RESULTS = 4 # Increase for more context

1. Add PDF, text, or code files to the data/ folder
2. Re-run the ingestion: python ingest_documents.py
3. The chatbot will now include the new materials

MIT License - Feel free to use and modify as needed!