Description
Overview
The extract text from PDF and image using Vertex AI into CSV automation workflow enables seamless data extraction from both PDFs and images stored in Google Drive. This no-code integration pipeline targets users who require structured transaction data extraction and categorization without manual data entry. The workflow is triggered by new file creation events in a specified Google Drive folder, leveraging a Google Drive Trigger to initiate processing.
Key Benefits
- Automates extraction of transaction data from PDFs and images without manual input.
- Utilizes AI-driven text recognition and natural language processing for accurate data parsing.
- Converts extracted text into structured CSV format with categorized transaction entries.
- Uploads output files back to Google Drive for centralized storage and access.
Product Overview
This automation workflow begins with a Google Drive Trigger node monitoring a designated folder for newly created PDF or image files. Upon detection, the workflow routes files based on MIME type, ensuring appropriate processing branches for PDFs or images. PDFs are downloaded and their raw text extracted using the Extract From File node. This text is then sent to an external AI service via HTTP request, instructing the model to parse bank statement transactions and export them as CSV including a categorized column. For images, the workflow downloads the file and sends it to Google Vertex AI (Gemini) through the LangChain integration for text extraction and transaction parsing. Both branches convert AI-generated text into CSV files before uploading them to a specified Google Drive folder. The workflow runs synchronously per file event with no explicit error handling configured beyond platform defaults. Authentication relies on Google Service Account credentials and HTTP header authorization for the external AI API.
Features and Outcomes
Core Automation
This extract text from PDF and image no-code integration receives new files as input, determines file type via MIME evaluation, and applies distinct extraction logic for PDFs and images. The branching logic is implemented using a Switch node, enabling single-pass evaluation for each file type.
- Deterministic routing based on MIME type ensures precise processing paths.
- Single-pass evaluation minimizes redundant processing steps.
- Integrated AI models handle both text and image data within one orchestration pipeline.
Integrations and Intake
The workflow integrates Google Drive for file intake and storage, Google Vertex AI for image text extraction, and an external AI API for PDF text parsing. Authentication uses Google Service Account credentials and HTTP Header Auth for API access. The intake expects files in PDF or image formats uploaded to a monitored Google Drive folder.
- Google Drive Trigger monitors file creation events in a specified folder.
- Google Vertex AI (Gemini) processes images for text extraction using AI-driven OCR.
- External AI API processes extracted PDF text to parse transactions via HTTP POST requests.
Outputs and Consumption
Extracted and parsed transaction data is output as CSV files, formatted and uploaded back to Google Drive. The workflow operates synchronously for each file event, delivering CSV files named by the current date. Output fields include transaction details and an AI-assigned category column.
- CSV format output for structured transaction data consumption.
- Uploads to a dedicated Google Drive folder for centralized access.
- Includes categorized transaction data as part of the CSV content.
Workflow — End-to-End Execution
Step 1: Trigger
The workflow initiates on a Google Drive Trigger node configured to poll every minute for newly created files within a specific folder named “Actual Budget.” It listens exclusively for file creation events, ensuring immediate response to new PDFs or images.
Step 2: Processing
After triggering, the workflow routes files based on MIME type using a Switch node. PDFs follow a branch where the file is downloaded and raw text extracted using the Extract From File node. Images are downloaded and sent to Google Vertex AI via LangChain for text extraction. Basic presence checks confirm file availability for downstream processing.
Step 3: Analysis
Extracted PDF text is sent to an external AI model (Meta LLaMA 3.1 instruct) over HTTP POST with a prompt to parse transactions and assign categories, returning only CSV data. For images, Google Vertex AI (Gemini) processes the binary to extract transaction data and categorize entries similarly. Both models operate deterministically based on provided prompts.
Step 4: Delivery
The workflow converts AI-generated text responses to CSV files using the Convert To File node, then uploads them to a designated Google Drive folder named “CSV Exports.” Each file is named with the current date, enabling chronological organization. Uploads use Google Service Account authentication.
Use Cases
Scenario 1
Financial teams manually extracting transaction data from PDFs face inefficiencies and risk of error. This workflow automates extraction and categorization of bank statement transactions from PDFs, returning structured CSV outputs automatically. Resulting data reduces manual entry and supports faster reconciliation processes.
Scenario 2
Organizations receiving scanned images of payment transactions require accurate data capture for accounting. This no-code integration pipeline uses Google Vertex AI to extract and categorize transactions from images, converting results into CSV format for accounting systems. It eliminates manual transcription and accelerates data availability.
Scenario 3
Companies managing mixed document formats in Google Drive need a unified extraction approach. This automation workflow detects PDFs and images in a single folder, processes each accordingly with AI models, and delivers consistent CSV outputs. It streamlines multi-format data ingestion with minimal configuration.
How to use
To deploy this extract text from PDF and image automation workflow within n8n, configure a Google Drive folder to receive PDFs and images. Set up Google Service Account credentials with appropriate Drive and Vertex AI permissions. Enable the Google Drive Trigger node to monitor the target folder. Configure HTTP Header Auth credentials for the external AI API. Activate the workflow to run live. Upon new file uploads, expect synchronized processing and CSV outputs uploaded back to Google Drive. Monitor workflow executions for errors via n8n’s interface.
Comparison — Manual Process vs. Automation Workflow
| Attribute | Manual/Alternative | This Workflow |
|---|---|---|
| Steps required | Multiple manual steps: download, read, transcribe, categorize, reformat | Automated single-pass evaluation with branching for file types |
| Consistency | Subject to human error and variability in transcription | Deterministic AI parsing ensures standardized CSV outputs |
| Scalability | Limited by manual throughput and labor availability | Scales with cloud APIs and event-driven processing |
| Maintenance | High ongoing effort to update scripts and manage errors | Low maintenance; relies on managed n8n nodes and cloud services |
Technical Specifications
| Environment | n8n workflow automation platform |
|---|---|
| Tools / APIs | Google Drive, Google Vertex AI (Gemini), External AI API (Meta LLaMA) |
| Execution Model | Event-driven, synchronous per file creation |
| Input Formats | PDF files, image files (MIME types application/pdf, image/*) |
| Output Formats | CSV files with transaction data and categories |
| Data Handling | Transient processing; no persistence beyond output upload |
| Known Constraints | Relies on availability of external AI API and Google Cloud services |
| Credentials | Google Service Account, HTTP Header Auth for AI API |
Implementation Requirements
- Google Drive folder configured for file upload and shared with n8n Google Service Account.
- Google Cloud project with Vertex AI enabled and appropriate permissions granted.
- API credentials for external AI service configured with HTTP Header Authentication.
Configuration & Validation
- Verify Google Drive Trigger node correctly detects new files in the target folder.
- Confirm Google Service Account has permissions for Drive file download and upload.
- Test AI API connectivity and authentication with sample PDF extracted text or image payloads.
Data Provenance
- Trigger: Google Drive Trigger monitoring specific folder for new files.
- Nodes: Switch node for MIME routing, Extract From File for PDFs, LangChain Vertex AI node for images.
- Credentials: Google Service Account for Drive access, HTTP Header Auth for external AI API.
FAQ
How is the extract text from PDF and image automation workflow triggered?
The workflow is triggered by a Google Drive Trigger node configured to poll every minute for new file creation events within a specific folder, initiating processing upon detecting PDFs or images.
Which tools or models does the orchestration pipeline use?
The pipeline uses Google Vertex AI (Gemini) for image text extraction and an external AI API running a Meta LLaMA instruct model for PDF transaction parsing, both integrated within the no-code automation workflow.
What does the response look like for client consumption?
Responses are formatted as CSV files containing parsed transaction data with an additional category column, uploaded to a designated Google Drive folder for client access.
Is any data persisted by the workflow?
Data is processed transiently within the workflow; only final CSV files are persisted by uploading back to Google Drive. No intermediate data storage occurs.
How are errors handled in this integration flow?
The workflow relies on platform default error handling; no explicit retry or backoff logic is configured within the JSON workflow.
Conclusion
This extract text from PDF and image automation workflow provides a reliable method for converting unstructured transaction data from PDFs and images into structured CSV outputs. It combines event-driven triggers, MIME-based routing, and AI-powered extraction models to streamline data processing with minimal manual effort. While it depends on external AI service availability and correct credential configuration, the workflow offers consistent, categorized transaction data outputs suitable for financial analysis and record keeping. Its design supports maintainability and scalability within n8n’s automation environment.
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