Telegram RAG PDF Workflow
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Automate RAG (Retrieval Augmented Generation) for PDF documents received via Telegram.
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About This Workflow
Overview
This n8n workflow enables you to build a Retrieval Augmented Generation (RAG) system that processes PDF documents sent through Telegram. When a PDF is received, the workflow extracts, chunks, embeds, and stores the document in a Pinecone vector database. It can then answer questions based on the stored PDF content.
Key Features
- Receives PDF files via Telegram.
- Extracts and processes PDF content.
- Chunks text for efficient retrieval.
- Generates embeddings using OpenAI.
- Stores document embeddings in Pinecone.
- Supports question answering based on stored documents.
How To Use
- Import this workflow into your n8n instance.
- Configure the
Telegram Triggernode with your Telegram bot token. - Configure the
Embeddings OpenAInode with your OpenAI API key. - Configure the
Pinecone Vector Store1node with your Pinecone API key and environment. - Set the
pineconeIndexparameter in thePinecone Vector Store1node to your desired index name (e.g., 'telegram'). - Send a PDF file to your Telegram bot to initiate the process.
- To query, send a text message to your Telegram bot.
Apps Used
Workflow JSON
{
"id": "aa89c9f6-cf57-417c-a193-9c7e67a96a77",
"name": "Telegram RAG PDF Workflow",
"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: aa89c9f6-cf57...
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