Classify
🤖 AI Drafted (Not reviewed)
Categorize document type
|
|
| Tool id |
classify |
| Category |
vision |
| Uses a language model |
yes |
| Needs a generative model |
no |
| Runs over many items |
yes |
| Item handling |
elementwise |
| Structured output |
yes |
| Human-verified |
not yet |
What it reads
| Port |
Type |
Required |
What it is |
Files (files) |
files |
yes |
Image files |
Context (context) |
any |
no |
Previous text/transcription |
Metadata (metadata) |
json |
no |
Existing metadata |
Documents (documents) |
json |
no |
Document metadata |
What it emits
| Port |
Type |
Required |
What it is |
Text (text) |
text |
— |
Raw text response |
Value (value) |
any |
— |
Parsed value |
Texts (texts) |
array |
— |
Per-item texts |
Values (values) |
array |
— |
Per-item values |
Results (results) |
json |
— |
Full results |
Records (records) |
array |
— |
Per-document text records [{doc_id, text}, …]. |
Artifacts (artifacts) |
json |
— |
Artifact IDs |
Options
| Option |
Type |
Default |
What it does |
categories |
array |
[“photograph”, “letter”, “receipt”, “invoice”, “diagram”, “map”, “handwriting”, “screenshot”, “artwork”, “form”, “certificate”, “newspaper”, “book_page”, “presentation”, “other”] |
Document categories. |
choices |
array |
— |
Valid choices. (Not shown in the editor.) |
chunk_size_chars |
integer |
0 |
Chunk large input text above this character budget (0=auto). |
force_ocr |
boolean |
no |
Force image processing instead of existing text. |
match_mode |
string |
prefer |
Match mode. One of: prefer, strict, inform. |
max_image_dimension |
integer |
8192 |
Max image size. |
max_items |
integer |
10 |
List max items. |
max_tokens |
integer |
8192 |
Max response. |
max_words |
integer |
50 |
Word limit. |
metadata_field |
string |
— |
Save to field. |
model_name |
string |
— |
Model name. |
multi_label |
boolean |
no |
Allow multiple categories. |
output_format |
string |
text |
Response format. One of: text, boolean, choice, number, words, list, json. |
prompt |
string |
— |
Custom prompt. |
provider_name |
string |
— |
LLM provider. One of: openai, anthropic, google, ollama, lmstudio, groq, together, deepseek, mistral, openrouter, dashscope, xai, perplexity, fireworks, deepl. |
quality_gate |
boolean |
yes |
Stop the run if output is unreadable. |
reference_values |
object |
— |
Known values to match. (Not shown in the editor.) |
save_to_db |
boolean |
yes |
Save to library. |
save_to_file |
boolean |
no |
Export to file. |
temperature |
number |
0.7 |
Creativity. |
thinking_mode |
string |
off |
Chain-of-thought reasoning depth. One of: off, short, medium, long. |
vision_mode |
string |
auto |
Vision engine. ‘auto’ picks based on the resolved provider: apple → Apple Vision OCR; anything else → LLM vision path. ‘kraken’ → on-device neural line segmentation (baselines/polygons, no text). One of: auto, apple, llm, kraken. (Not shown in the editor.) |
The prompt it sends
This is what the tool asks a model, with every option left at its default. Changing the options above changes this text.
Classify this image/document into exactly ONE category.
Categories: "photograph", "letter", "receipt", "invoice", "diagram", "map", "handwriting", "screenshot", "artwork", "form", "certificate", "newspaper", "book_page", "presentation", "other"
Respond with exactly one category name from the list above.