Extract Details from Receipts via Telegram with Tesseract and Llama — Flujo de trabajo n8n

Media complejidad Disparador13 nodos🏷️ Invoice Processing👁 1,165 vistaspor Khairul Muhtadin

Descripción general

Tesseract - Money Mate Workflow Description

Disclaimer: This template requires the n8n-nodes-tesseractjs community node, which is only available on self-hosted n8n instances. You’ll need a self-hosted n8n setup to use this workflow.

Who is this for? This workflow is designed for individuals, freelancers, or small business owners who want an easy way to track expenses using Telegram. It’s ideal for anyone looking to digitize receipts—whether from photos or text messages—using free tools, withou

Nodos utilizados

HTTP RequestTelegramCodeBasic LLM ChainStructured Output ParserOpenRouter Chat Model

Vista previa del flujo de trabajo

📲 Telegram Trigger
Receives user messages (text or photo) from Telegram to
🔍 Check for Image
Determines whether the input is an image or text to rou
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📬 Send Expense Summary
Sends a summary of the recognized receipt or input to t
📝 Extract Text Input
Handles plain text inputs when no image is provided by
📊 Parse Receipt Data
Converts AI output into structured JSON with store, tra
📦 Get Telegram File
Fetches the file ID of a receipt image uploaded via Tel
⬇️ Download Image
Downloads the receipt photo and prepares it for text ex
🖼️ Text Extractor
Extract text from given image
🧾 Format Message
Creates a clear summary of expenses with store, date, i
🧠 AI Categorizer
Uses AI to classify input into categories like income o
❗ Send Error Message
Sends a warning to the user if the input is invalid or
🚫 Check Invalid Input
Detects and flags incorrect or zero-value entries befor
🤖 OpenRouter AI Model
Configures the language model to extract structured inf
modelparser
Format Summary Message
Get Telegram File
Download Image
T
Telegram Trigger
AI Categorizer
C
Check Invalid Input
E
Extract Text Input
C
Check for Image
Receipt Parser
AI Analyzer
Send Error Message
Send Expense Summary
E
Extract Value From Image
13 nodes13 edges

Cómo funciona

  1. 1

    Disparador

    El flujo de trabajo comienza con un disparador disparador.

  2. 2

    Procesamiento

    Los datos fluyen a través de 13 nodos, connecting chainllm, code, httprequest.

  3. 3

    Salida

    El flujo de trabajo completa su automatización y entrega el resultado al destino configurado.

Detalles de nodos (13)

HT

HTTP Request

httpRequest

#1
TE

Telegram

telegram

#2
CO

Code

code

#3
BA

Basic LLM Chain

n8n-nodes-langchain.chainLlm

#4
ST

Structured Output Parser

n8n-nodes-langchain.outputParserStructured

#5
OP

OpenRouter Chat Model

n8n-nodes-langchain.lmChatOpenRouter

#6

Cómo importar este flujo de trabajo

  1. 1Haz clic en el botón Descargar JSON a la derecha para guardar el archivo del flujo de trabajo.
  2. 2Abre tu instancia de n8n. Ve a Flujos de trabajo → Nuevo → Importar desde archivo.
  3. 3Selecciona el archivo extract-details-from-receipts-via-telegram-with-tesseract-and-llama descargado y haz clic en Importar.
  4. 4Configura las credenciales para cada nodo de servicio (claves API, OAuth, etc.).
  5. 5Haz clic en Probar flujo de trabajo para verificar que todo funcione, luego actívalo.

O pega directamente en n8n → Importar desde JSON:

{ "name": " Extract Details from Receipts via Telegram with Tesseract and Llama", "nodes": [...], ...}

Integraciones

chainllmcodehttprequestiflmchatopenroutern8n-nodes-tesseractjs.tesseractnodeoutputparserstructuredsettelegramtelegramtrigger

Obtener este flujo de trabajo

Descarga e importa con un solo clic

Descargar JSONVer en n8n.io
Nodos13
Complejidadmedium
Disparadortrigger
Vistas1,165

Creado por

Khairul Muhtadin

Khairul Muhtadin

@khmuhtadin

Etiquetas

chainllmcodehttprequestiflmchatopenroutern8n-nodes-tesseractjs.tesseractnodeoutputparserstructuredsettelegramtelegramtrigger

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