Extract Details from Receipts via Telegram with Tesseract and Llama — рабочий процесс n8n

Средняя сложность Триггер13 узлов🏷️ Invoice Processing👁 1,165 просмотровот Khairul Muhtadin

Обзор

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

Использованные узлы

HTTP RequestTelegramCodeBasic LLM ChainStructured Output ParserOpenRouter Chat Model

Предпросмотр рабочего процесса

📲 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

Как это работает

  1. 1

    Триггер

    Рабочий процесс запускается триггером триггер.

  2. 2

    Обработка

    Данные проходят через 13 узлов, connecting chainllm, code, httprequest.

  3. 3

    Вывод

    Рабочий процесс завершает автоматизацию и доставляет результат в настроенное место назначения.

Детали узлов (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

Как импортировать этот рабочий процесс

  1. 1Нажмите кнопку Скачать JSON справа, чтобы сохранить файл рабочего процесса.
  2. 2Откройте ваш экземпляр n8n. Перейдите в Рабочие процессы → Новый → Импорт из файла.
  3. 3Выберите скачанный файл extract-details-from-receipts-via-telegram-with-tesseract-and-llama и нажмите Импортировать.
  4. 4Настройте учётные данные для каждого узла сервиса (ключи API, OAuth и т.д.).
  5. 5Нажмите Протестировать рабочий процесс, чтобы убедиться в правильной работе, затем активируйте его.

Или вставьте напрямую в n8n → Импорт из JSON:

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

Интеграции

chainllmcodehttprequestiflmchatopenroutern8n-nodes-tesseractjs.tesseractnodeoutputparserstructuredsettelegramtelegramtrigger

Получить этот рабочий процесс

Скачайте и импортируйте одним кликом

Скачать JSONПросмотреть на n8n.io
Узлы13
Сложностьmedium
Триггерtrigger
Просмотры1,165
КатегорияInvoice Processing

Создан

Khairul Muhtadin

Khairul Muhtadin

@khmuhtadin

Теги

chainllmcodehttprequestiflmchatopenroutern8n-nodes-tesseractjs.tesseractnodeoutputparserstructuredsettelegramtelegramtrigger

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