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Text Classifier and Information Extractor integration

Save yourself the work of writing custom integrations for Text Classifier and Information Extractor and use n8n instead. Build adaptable and scalable AI, and Langchain workflows that work with your technology stack. All within a building experience you will love.

How to connect Text Classifier and Information Extractor

  • Step 1: Create a new workflow
  • Step 2: Add and configure nodes
  • Step 3: Connect
  • Step 4: Customize and extend your integration
  • Step 5: Test and activate your workflow

Step 1: Create a new workflow and add the first step

In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.

Text Classifier and Information Extractor integration: Create a new workflow and add the first step

Step 2: Add and configure Text Classifier and Information Extractor nodes

You can find Text Classifier and Information Extractor in the nodes panel. Drag them onto your workflow canvas, selecting their actions. Click each node, choose a credential, and authenticate to grant n8n access. Configure Text Classifier and Information Extractor nodes one by one: input data on the left, parameters in the middle, and output data on the right.

Text Classifier and Information Extractor integration: Add and configure Text Classifier and Information Extractor nodes

Step 3: Connect Text Classifier and Information Extractor

A connection establishes a link between Text Classifier and Information Extractor (or vice versa) to route data through the workflow. Data flows from the output of one node to the input of another. You can have single or multiple connections for each node.

Text Classifier and Information Extractor integration: Connect Text Classifier and Information Extractor

Step 4: Customize and extend your Text Classifier and Information Extractor integration

Use n8n's core nodes such as If, Split Out, Merge, and others to transform and manipulate data. Write custom JavaScript or Python in the Code node and run it as a step in your workflow. Connect Text Classifier and Information Extractor with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

Text Classifier and Information Extractor integration: Customize and extend your Text Classifier and Information Extractor integration

Step 5: Test and activate your Text Classifier and Information Extractor workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Text Classifier to Information Extractor or vice versa. Easily debug your workflow: you can check past executions to isolate and fix the mistake. Once you've tested everything, make sure to save your workflow and activate it.

Text Classifier and Information Extractor integration: Test and activate your Text Classifier and Information Extractor workflow

API Schema Extractor

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet.

🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies.

How it works
Stage 1 - Research:
Fetches pending services from a Google Sheet
Uses Google search to find API documentation
Employs Apify for web scraping to filter relevant pages
Stores webpage contents and metadata in Qdrant (vector database)
Updates progress status in Google Sheet (pending, ok, or error)

Stage 2 - Extraction:
Processes services that completed research successfully
Queries vector store to identify products and offerings
Further queries for relevant API documentation
Uses Gemini (LLM) to extract API operations
Records extracted operations in Google Sheet
Updates progress status (pending, ok, or error)

Stage 3 - Generation:
Takes services with successful extraction
Retrieves all API operations from the database
Combines and groups operations into a custom schema
Uploads final schema to Google Drive
Updates final status in sheet with file location

Ideal for:
Development teams needing to catalog multiple APIs
API documentation initiatives
Creating standardized API schema collections
Automating API discovery and documentation

Accounts required:
Google account (for Sheets and Drive access)
Apify account (for web scraping)
Qdrant database
Gemini API access

Set up instructions:
Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID.
Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini.
Ensure proper permissions for Google Drive access.

Nodes used in this workflow

Popular Text Classifier and Information Extractor workflows

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BambooHR AI-Powered Company Policies and Benefits Chatbot

How it works This workflow enables companies to provide instant HR support by automating responses to employee queries about policies and benefits: Retrieves company policies, benefits, and HR documents from BambooHR. Uses AI to analyze and answer employee questions based on company records. Identifies the most relevant contact person for escalations. Seamlessly integrates with company systems to provide real-time HR assistance. Set up steps: Estimated time: ~20 minutes Connect your BambooHR account to allow policy retrieval. Configure AI parameters and access control settings. (Optional) Set up the employee lookup tool for personalized responses. Test the chatbot to ensure accurate responses and seamless integration. Benefits This workflow is perfect for HR teams looking to enhance employee support while reducing manual inquiries. Outperform BambooHR's "Ask BambooHR" Chatbot #1. Superior specificity of replies to general inquiries #2. More appropriate escalations when responding to sensitive employee concerns
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API Schema Extractor

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet. 🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies. How it works Stage 1 - Research: Fetches pending services from a Google Sheet Uses Google search to find API documentation Employs Apify for web scraping to filter relevant pages Stores webpage contents and metadata in Qdrant (vector database) Updates progress status in Google Sheet (pending, ok, or error) Stage 2 - Extraction: Processes services that completed research successfully Queries vector store to identify products and offerings Further queries for relevant API documentation Uses Gemini (LLM) to extract API operations Records extracted operations in Google Sheet Updates progress status (pending, ok, or error) Stage 3 - Generation: Takes services with successful extraction Retrieves all API operations from the database Combines and groups operations into a custom schema Uploads final schema to Google Drive Updates final status in sheet with file location Ideal for: Development teams needing to catalog multiple APIs API documentation initiatives Creating standardized API schema collections Automating API discovery and documentation Accounts required: Google account (for Sheets and Drive access) Apify account (for web scraping) Qdrant database Gemini API access Set up instructions: Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID. Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini. Ensure proper permissions for Google Drive access.

Visualize your SQL Agent queries with OpenAI and Quickchart.io

Overview This workflow aims to provide data visualization capabilities to a native SQL Agent. Together, they can help foster data analysis and data visualization within a team. It uses the native SQL Agent that works well and adds visualization capabilities thanks to OpenAI’s Structured Output and Quickchart.io. How it works Information Extraction: The Information Extractor identifies and extracts the user's question. If the question includes a visualization aspect, the SQL Agent alone may not respond accurately. SQL Querying: It leverages a regular SQL Agent: it connects to a database, queries it, and translates the response into a human-readable format. Chart Decision: The Text Classifier determines whether the user would benefit from a chart to support the SQL Agent's response. Chart Generation: If a chart is needed, the sub-workflow dynamically generates a chart and appends it to the SQL Agent’s response. If not, the SQL Agent’s response is output as is. Calling OpenAI for Chart Definition: The sub-workflow calls OpenAI via the HTTP Request node to retrieve a chart definition. Building and Returning the Chart: In the "Set Response" node, the chart definition is appended to a Quickchart.io URL, generating the final chart image. The AI Agent returns the response along with the chart. How to use it Use an existing database or create a new one. For example, I've used this Kaggle dataset and uploaded it to a Supabase DB. Add the PostgreSQL or MySQL credentials. Alternatively, you can use SQLite binary files (check this template). Activate the workflow. Start chatting with the AI SQL Agent. If the Text Classifier determines a chart would be useful, it will generate one in addition to the SQL Agent's response. Notes The full Quickchart.io specifications have not been fully integrated, so there may be some glitches (e.g., radar graphs may not display properly due to size limitations).

Build your own Text Classifier and Information Extractor integration

Create custom Text Classifier and Information Extractor workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.

Text Classifier and Information Extractor integration details

FAQs

  • Can Text Classifier connect with Information Extractor?

  • Can I use Text Classifier’s API with n8n?

  • Can I use Information Extractor’s API with n8n?

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  • How to get started with Text Classifier and Information Extractor integration in n8n.io?

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