Actions132
- AI Actions
- Extract Text From Document
- Check Content Policy
- Detect Adult Content
- Detect Brand
- Detect Color
- Detect Email Type
- Detect Faces
- Extract Entities
- Extract Contact Information
- Extract Customizable Information From Text
- Fuzzy Text Matcher
- Generate Image
- Generate JavaScript Code
- Generate Python Code
- Detect Language
- Detect Mood
- Extract Text From PDF
- Recognize Objects in Image
- Extract Text From Image
- Redact Sensitive Information From PDF
- Remove Background Image
- Summarize Text
- Transcribe
- Translate Text
- Image Actions
- Business Actions
- Convert Actions
- Generate Actions
- Crypto Actions
- Operator Actions
- Storage Actions
- Scheduler Actions
- Text Actions
- Chart Actions
- Code Actions
- Calculate Actions
- Date and Time Actions
- PDF Actions
- Compress PDF File
- Count PDF Pages
- Create Empty PDF
- Decrypt PDF
- Convert DOCX to PDF
- Draw Image on PDF
- Draw Text on PDF
- Encrypt PDF
- Convert HTML to PDF
- Convert Markdown to PDF
- Manage PDF Metadata
- Merge PDF Files
- Add PDF Pages
- Remove PDF Pages
- Resize PDF Pages
- Rotate PDF Pages
- Convert PDF to Base64
- Convert PDF to Image
- Split PDF File
- Add Image Watermark to PDF
- Add Text Watermark to PDF
- Shortened URL Actions
AI → Extract Entities
AI-generatedOverview
The node provides an entity extraction feature under the AI resource. It analyzes a given text input to identify and extract named entities such as people, locations, organizations, dates, and other relevant information. This is useful in scenarios like processing customer feedback, extracting key information from documents, or automating data entry by recognizing structured data within unstructured text.
For example, given the input text "John Smith from New York works at OpenAI," the node will detect entities like the person name "John Smith," the location "New York," and the organization "OpenAI."
Properties
| Name | Meaning |
|---|---|
| Text Content | The text content to analyze for entity extraction. Example: "John Smith from New York works at OpenAI" |
Output
The output contains a JSON object with the extracted entities from the input text. Each entity typically includes details such as the entity type (e.g., person, location, organization), the exact text matched, and possibly additional metadata depending on the underlying service's response.
If the node supports binary data output, it would be related to any attachments or files processed, but for this operation, the focus is purely on JSON textual data representing recognized entities.
Dependencies
- Requires an API key credential for authentication to the external AI service endpoint.
- The node sends a POST request to the
/ai/entitydetectionendpoint of the configured base URL (https://prod.0codekit.com). - Proper network access and valid credentials are necessary for successful execution.
Troubleshooting
Common issues:
- Missing or invalid API key credential can cause authentication failures.
- Empty or improperly formatted text input may result in no entities detected or errors.
- Network connectivity problems can prevent reaching the external API endpoint.
Error messages:
- Authentication errors indicate issues with the provided API key; verify and update credentials.
- Validation errors suggest required properties like "Text Content" are missing or empty; ensure input is correctly set.
- Timeout or connection errors imply network issues; check internet connectivity and firewall settings.
Links and References
- Named Entity Recognition (NER) Overview
- 0CodeKit API Documentation (if publicly available) (hypothetical link based on base URL)