Telegram RAG PDF
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Process PDF documents sent via Telegram using Retrieval Augmented Generation (RAG) with OpenAI embeddings and Pinecone vector store.
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About This Workflow
Overview
This workflow enables users to send PDF documents via Telegram. The workflow then processes these documents by generating embeddings using OpenAI and storing them in a Pinecone vector store. Subsequent messages can be processed to retrieve information from the stored documents and generate answers using a language model.
Key Features
- Receive PDF documents via Telegram.
- Convert document format for processing.
- Generate embeddings using OpenAI.
- Store embeddings in Pinecone.
- Process text messages to query stored documents.
- Generate answers using a language model (Groq).
- Respond to users via Telegram.
How To Use
- Configure Credentials: Set up your OpenAI API, Pinecone API, and Telegram Bot tokens.
- Telegram Trigger: Ensure your Telegram bot is running and accessible.
- Document Ingestion: Send a PDF file to your Telegram bot.
- Chat Interaction: Send text messages to the bot to ask questions about the uploaded PDF.
- Observe Responses: The bot will provide answers based on the PDF content.
Apps Used
Workflow JSON
{
"id": "4756ffed-b7a9-42d0-9c3b-3a6b5729f03a",
"name": "Telegram RAG PDF",
"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.
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ID: 4756ffed-b7a9...
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