Telegram AI Chatbot with Document-Based Answers Using OpenAI and pgvector RAG

Overview

This workflow automates a Telegram chatbot that provides intelligent, document-based answers by leveraging OpenAI's language models and pgvector-powered Retrieval-Augmented Generation (RAG). It integrates Google Drive for document management and uses advanced AI to deliver context-aware responses.

Key Features

• AI-Powered Chatbot: Utilizes OpenAI's chat model for natural language understanding and response generation.
• Document Retrieval: Loads and processes documents from Google Drive, extracting relevant information for user queries.
• Embeddings & RAG: Employs OpenAI embeddings and pgvector to match user questions with document content, enhancing answer accuracy.
• Automated Triggers: Monitors Google Drive for new or updated files, ensuring the knowledge base is always current.

Benefits

• Enhanced Customer Support: Delivers instant, accurate answers based on up-to-date documents.
• Time Savings: Automates manual response and document lookup tasks, freeing up staff resources.
• Scalable Knowledge Management: Easily adapts to growing document repositories and user queries.

Use Cases

• Customer support chatbots for product documentation.
• Internal helpdesks for HR or IT knowledge bases.
• Automated FAQ bots for websites or social channels.

Integrations & Automation

Key integrations include Google Drive for document storage, OpenAI for AI-powered responses, and Telegram for user interaction. The workflow orchestrates file monitoring, text extraction, embedding generation, and AI-driven chat seamlessly.