Build a Telegram RAG PDF Chatbot with OpenAI and Pinecone

Name: Build a Telegram RAG PDF Chatbot with OpenAI and Pinecone
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Author: Free N8N

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AI & Machine LearningAIChatbotLLMOpenAIPDFPineconeRAGTelegram

This workflow enables users to chat with PDF documents via Telegram. It uses OpenAI for embeddings and question answering, and Pinecone for vector storage, creating a powerful Retrieval-Augmented Generation (RAG) system.

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About This Workflow

Overview

This n8n workflow automates the process of creating a searchable knowledge base from PDF documents, accessible through a Telegram bot. When a user uploads a PDF, the workflow extracts the text, splits it into manageable chunks, generates embeddings using OpenAI, and stores them in a Pinecone vector database. Subsequently, when a user sends a text message, the workflow retrieves relevant information from Pinecone based on the query and uses OpenAI to formulate a precise answer, all delivered via Telegram.

Key Features

Upload PDFs via Telegram to build a knowledge base.
Utilize OpenAI for text embedding and question answering.
Store and retrieve document chunks using Pinecone vector database.
Respond to user queries with AI-generated answers based on uploaded documents.
Streamlined RAG implementation within n8n.

How To Use

Set up Credentials: Configure your OpenAI and Pinecone API credentials in n8n.
Configure Telegram Bot: Set up your Telegram bot token in the Telegram Trigger and Telegram Response nodes.
Initialize Pinecone Index: Ensure your Pinecone index named 'telegram' is created and ready to receive data.
Upload a PDF: Send a PDF file to your Telegram bot.
Chat with the PDF: Once the upload is processed, send text messages to your bot to ask questions about the PDF content.

Apps Used

Chatbot

LLM

OpenAI

PDF

Pinecone

RAG

Workflow JSON

{
  "id": "deffbfa2-3781-42ad-a138-8a4cdae44a42",
  "name": "Build a Telegram RAG PDF Chatbot with OpenAI and Pinecone",
  "nodes": 0,
  "category": "AI & Machine Learning",
  "status": "active",
  "version": "1.0.0"
}

Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.