CrossPlatform-TechSupport-Chatbot

A multi-platform chatbot that provides intelligent customer and tech support using OpenAI, Dialogflow, and a Retrieval-Augmented Generation (RAG) system.

Table of Contents

• Features
• Demo
• How It Works
• Installation
• Tech Stack
• License
• Contact

Features

• Multi-platform Support: Messenger, Telegram, LINE, Instagram, or custom web page.
• Retrieval-Augmented Generation (RAG): Intelligent, document-based responses using semantic search.
• Dynamic Intent Handling: Dialogflow for intent matching and tagging documents for improved efficiency.
• Context-aware Responses: Combines RAG and OpenAI models for enhanced conversational AI.
• Document Processing: Upload, chunk, and store documents with embeddings for semantic search.

Demo

• Presentation slides: Support chatbot with RAG
• Live Demo: Chatbot demo with custom frontend

How It Works

1. Intent Handling:
   • Dialogflow matches user inputs with known intents and tags uploaded documents.
   • Only documents matching specific tags are searched to improve efficiency.
2. RAG Process:
   • Uploaded documents are chunked with overlapping sections.
   • Embeddings are generated and stored for semantic search.
   • Relevant chunks are retrieved using a weighted combination of cosine similarity and fuzzy matching scores.
   • Retrieved context is added to prompts for response generation using GPT models.
3. Cross-platform Integration:
   • APIs for Messenger, LINE, Telegram, and Instagram.
   • Custom web frontend built with React.

Tech Stack

• Frontend: React
• Backend: Go (Gin framework)
• Database: PostgreSQL
• APIs: OpenAI, Dialogflow, Telegram, LINE, META
• Cloud Deployment: Heroku (backend), GitHub Pages (frontend)
• Tools: PDF processing libraries for text extraction

Installation

• Clone the Repository:

  git clone https://github.com/petersun1937/cross-platform-chatbot-rag.git
  cd cross-platform-chatbot-rag

• Backend Setup:

1. Prerequisites:

   • Install Go (v1.16 or higher) and Python (v3.6 or higher).
   • Verify installation:

     go version
     python --version

2. Install Python Packages:

   • The backend requires several Python packages for processing PDFs. To install them, run:

   pip install pdfplumber pytesseract pdf2image PyPDF2

   • These packages handle PDF text extraction and Optical Character Recognition (OCR) for images within PDFs.

3. Set Up Environment Variables:

   • Create a .env file in the configs/ directory to store variables such as API keys and database configurations (refer to sample.env).

4. Run the Backend Server:

   • Navigate to the project directory and start the server:

     go run main.go

Frontend Setup (Optional)

• Refer to my custom frontend repo

Potential Use Cases

• Streamlining FAQs and troubleshooting for customer support.
• Automating knowledge retrieval for internal teams.
• Enhancing collaborative workflows with intelligent document processing.
• Acting as a meeting assistant for document organization and context provision.
• Extending to incorporate custom-trained language models.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

If you have any questions or suggestions, feel free to reach out to cxs1937@psu.edu.