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Integrate Extract from File with 500+ apps and services
n8n lets you connect Extract from File with hundreds of other apps. Create sophisticated automations between Extract from File and your stack.
Popular ways to use Extract from File integration
+2
Convert an XML file to JSON via webhook call
Who this template is for
This template is for everyone who needs to work with XML data a lot and wants to convert it to JSON instead.
Use case
Many products still work with XML files as their main language. Unfortunately, not every software still supports XML, as many switched to more modern storing languages such as JSON. This workflow is designed to handle the conversion of XML data to JSON format via a webhook call, with error handling and Slack notifications integrated into the process.
How this workflow works
Triggering the workflow:
This workflow initiates upon receiving an HTTP POST request at the webhook endpoint specified in the "POST" node. The endpoint, designated as , can be accessed externally by sending a POST request to that URL.
Data routing and processing:
Upon receiving the POST request, the Switch node routes the workflow's path based on conditions determined by the content type of the incoming data or any encountered errors.
The Extract From File and Edit Fields (Set) nodes manage XML input processing, adapting their actions according to the data's content type.
XML to JSON conversion:
The XML data extracted from the input is passed through the "XML" node, which performs the conversion process, transforming it into JSON format.
Response handling:
If the XML-to-JSON conversion is successful, a success response is sent back with a status of "OK" and the converted JSON data.
If there are any errors during the XML-to-JSON conversion process, an error response is sent back with a status of "error" and an error message.
Error handling: in case of an error during processing, the workflow sends a notification to a Slack channel designated for error reporting.
Set up steps
Set up your own in the Webhook node. While building or testing a workflow, use a test webhook URL. When your workflow is ready, switch to using the production webhook URL.
Set credentials for Slack.
n8n Team
+10
Extract data from resume and create PDF with Gotenberg
With this workflow you can extract data from resume documents uploaded via a Telegram bot. Workflow transform readable content of PDF resume into structured data, using AI nodes and returns PDF with formatted, plain HTML.
You can modify this workflow to perform other actions with structured data (e.g. insert it into database or create other, well-formatted documents).
Functionality of this workflow was presented during the n8n community call on March 7, 2024 - recording of presentation available here.
⚠️ Workflow made for demo purposes. If you want to use it in real life, please make sure necessary measures for personal data protection are set.
How it works?
User uploads readable PDF resume document into Telegram bot. After authentication based on chat ID parameter, workflow extracts text from the PDF and transfers it into AI chain with connected sub-nodes: OpenAI Chat Model and Structured Output (JSON) Parser.
Then, each extracted section (employment history, projects etc.) is formatted into desired HTML structure. Finally, the document is converted into new, structured PDF using Gotenberg.
💡 This workflow requires installed Gotenberg. If you are not familiar with this software, please have a look on my YouTube tutorial. You can also replace call to Gotenberg with other PDF generation service (such as PDFMonkey or ApiTemplate).
Set up steps
Create Telegram bot and add its credentials in n8n.
Set your chat ID parameter in Auth node.
Adjust JSON schema in Structured Output Parser according to your needs.
Optionally: replace HTTP call to Gotenberg with PDF generation service of your choice.
If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.
Oskar
+6
Reconcile Rent Payments with Local Excel Spreadsheet and OpenAI
This n8n workflow is designed to work on the local network and assists with reconciling downloaded bank statements with internal tenant records to quickly highlight any issues with payments such as missed or late payments or those of incorrect amounts.
This assistant can then generate a report to quick flag attention to ensure remedial action is taken.
How it works
The workflow monitors a local network drive to watch for new bank statements that are added.
This bank statement is then imported into the n8n workflow, its contents extracted and sent to the AI Agent.
The AI Agent analyses the line items to identify the dates and any incoming payments from tenants.
The AI agent then uses an locally-hosted Excel ("XLSX") spreadsheet to get both tenant records and property records. From this data, it can determine for each active tenant when payment is due, the amount and the tenancy duration.
Comparing to the bank statement, the AI Agent can now report on where tenants have missed their payments, made late payments or are paying the incorrect amounts.
The final report is generated and logged in the same XLSX for a human to check and action.
Requirements
A self-hosted version of n8n is required.
OpenAI account for the AI model
Customising this workflow
If you organisation has a Slack or Teams account, consider sending reports to a channel for increased productivity. Email may be a good choice too.
Jimleuk
+7
Automate Your RFP Process with OpenAI Assistants
This n8n workflow demonstrates how to automate oftern time-consuming form filling tasks in the early stages of the tendering process; the Request for Proposal document or "RFP".
It does this by utilising a company's knowledgebase to generating question-and-answer pairs using Large Language Models.
How it works
A buyer's RFP is submitted to the workflow as a digital document that can be parsed.
Our first AI agent scans and extracts all questions from the document into list form.
The supplier sets up an OpenAI assistant prior loaded with company brand, marketing and technical documents.
The workflow loops through each of the buyer's questions and poses these to the OpenAI assistant.
The assistant's answers are captured until all questions are satisified and are then exported into a new document for review.
A sales team member is then able to use this document to respond quickly to the RFP before their competitors.
Example Webhook Request
curl --location 'https://<n8n_webhook_url>' \
--form 'id="RFP001"' \
--form 'title="BlueChip Travel and StarBus Web Services"' \
--form 'reply_to="[email protected]"' \
--form 'data=@"k9pnbALxX/RFP Questionnaire.pdf"'
Requirements
An OpenAI account to use AI services.
Customising the workflow
OpenAI assistants is only one approach to hosting a company knowledgebase for AI to use. Exploring different solutions such as building your own RAG-powered database can sometimes yield better results in terms of control of how the data is managed and cost.
Jimleuk
+10
ERP AI chatbot for Odoo sales module with OpenAI
Who is this for?
This workflow is for everyone who wants to have easier access to their Odoo sales data without complex queries.
Use Case
To have a clear overview of your sales data in Odoo you typically needs to extract data from it manually to analyse it. This workflow uses OpenAI's language models to create an intelligent chatbot that provides conversational access to your Odoo sales opportunity data.
How it works
Creates a summary of all Odoo sales opportunities using OpenAI
Uses that summary as context for the OpenAI chat model
Keeps the summary up to date using a schedule trigger
Set up steps:
Configure the Odoo credentials
Configure OpenAI credentials
Toggle "Make Chat Publicly Available" from the Chat Trigger node.
Mihai Farcas
+17
Breakdown Documents into Study Notes using Templating MistralAI and Qdrant
This n8n workflow takes in a document such as a research paper, marketing or sales deck or company filings, and breaks them down into 3 templates: study guide, briefing doc and timeline.
These templates are designed to help a student, associate or clerk quickly summarise, learn and understand the contents to be more productive.
Study guide - a short quiz of questions and answered generated by the AI Agent using the contents of the document.
Briefing Doc - key information and insights are extracted by the AI into a digestable form.
Timeline - key events, durations and people are identified and listed into a simple to understand timeline by the AI
How it works
A local file trigger watches a local network directory for new documents.
New documents are imported into the workflow, its contents extracted and vectorised into a Qdrant vector store to build a mini-knowledgebase.
The document then passes through a series of template generating prompts where the AI will perform "research" on the knowledgebase to generate the template contents.
Generated study guide, briefing and timeline documents are exported to a designated folder for the user.
Requirements
Self-hosted version of n8n.
Qdrant instance for knowledgebase.
Mistral.ai account for embeddings and AI model.
Customising your workflow
Try adding your own templates or adjusting the existing templates to suit your unique use-case. Anything is quite possible and limited only by your imagination!
Jimleuk
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qupaya technologies
@qupaya
Cris
Last week I automated much of the back office work for a small design studio in less than 8hrs and I am still mind-blown about it.
n8n is a game-changer and should be known by all SMBs and even enterprise companies.
aaron
@acar321
in other news I installed @n8n_io tonight and holy moly it’s good
it’s compatible with EVERYTHING
