Extract & Categorize Receipt Data with Google OCR, OpenRouter AI & Telegram — n8n ワークフロー

複雑度 トリガー13個のノード🏷️ Invoice Processing👁 1,536回閲覧作成者:Khairul Muhtadin

概要

Effortlessly track your expenses with MoneyMate, an n8n workflow that transforms receipts into organized financial insights.

Upload a photo or text via Telegram, and let MoneyMate extract key details—store info, transaction dates, items, and totals—using Google Vision OCR and AI-powered parsing via OpenRouter.

It categorizes expenses (e.g., Food & Beverages, Transport, Household) and delivers a clean, emoji-rich summary back to your Telegram chat. Handles zero-total errors with a friendly nudg

使用ノード

HTTP RequestTelegramCodeBasic LLM ChainStructured Output ParserOpenRouter Chat Model

ワークフロープレビュー

📲 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
MoneyMate is free to use!
Got feedback, ideas, or just wanna chat? Find me at khm
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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

仕組み

  1. 1

    トリガー

    このワークフローは トリガー トリガーで開始します。

  2. 2

    処理

    データは 13 個のノードを流れます, connecting chainllm, code, httprequest。

  3. 3

    出力

    ワークフローは自動化を完了し、設定された宛先に結果を配信します。

ノード詳細 (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

このワークフローのインポート方法

  1. 1右側の JSONをダウンロード ボタンをクリックしてワークフローファイルを保存します。
  2. 2n8nインスタンスを開き、ワークフロー → 新規 → ファイルからインポート に進みます。
  3. 3ダウンロードした extract-categorize-receipt-data-with-google-ocr-openrouter-ai-telegram ファイルを選択し、インポートをクリックします。
  4. 4各サービスノードの 認証情報(APIキー、OAuthなど)を設定します。
  5. 5ワークフローをテスト をクリックして動作確認し、有効化します。

またはn8nの JSONからインポート に直接貼り付け:

{ "name": "Extract & Categorize Receipt Data with Google OCR, OpenRouter AI & Telegram", "nodes": [...], ...}

インテグレーション

chainllmcodehttprequestiflmchatopenroutern8n-nodes-tesseractjs.tesseractnodeoutputparserstructuredsettelegramtelegramtrigger

このワークフローを取得

ワンクリックでダウンロード&インポート

JSONをダウンロードn8n.ioで見る
ノード13
複雑度medium
トリガーtrigger
閲覧数1,536
カテゴリInvoice Processing

作成者

Khairul Muhtadin

Khairul Muhtadin

@khmuhtadin

タグ

chainllmcodehttprequestiflmchatopenroutern8n-nodes-tesseractjs.tesseractnodeoutputparserstructuredsettelegramtelegramtrigger

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