H2O.ai · Schema
Extractor
CompanyArtificial IntelligenceMachine LearningMLOpsGenerative AILLMRAGData ScienceModel DeploymentAI AgentsEnterprise AI
Properties
| Name | Type | Description |
|---|---|---|
| name | string | Human-readable name |
| description | string | What this extractor does |
| llm | string | (Optional) Identifier or version of the language model the extractor uses |
| schema | string | (Optional) JSONSchema (or other spec) that the extractor outputs |
| prompt | string | (Optional) Prompt text for the extractor |
| pre_prompt_summary | string | (Optional) Pre-prompt summary text for the extractor |
| keep_intermediate_results | boolean | (Optional) Flag indicating whether to keep intermediate results during extraction |
| system_prompt | string | (Optional) System prompt text for the extractor |
| max_num_chunks | integer | (Optional) Maximum number of chunks to process |
| vision | string | (Optional) Vision mode setting |
| vision_llm | string | (Optional) Vision LLM model identifier |
| image_batch_image_prompt | string | (Optional) Image batch processing prompt |
| image_batch_final_prompt | string | (Optional) Final prompt for image batch processing |
| guardrails_settings | object | |
| id | string | Unique identifier of the extractor |
| is_public | boolean | Flag indicating if the extractor is public |
| created_at | string | When the extractor definition was created |
JSON Schema
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "https://raw.githubusercontent.com/api-evangelist/h2o-ai/main/json-schema/h2o-ai-extractor-schema.json",
"title": "Extractor",
"x-generated": "2026-10-03",
"x-method": "derived",
"x-generator": "derive-json-schema.py",
"x-source": "openapi/h2o-ai-extractors-api-openapi.yml#/components/schemas/Extractor",
"type": "object",
"required": [
"name",
"description",
"id",
"created_at",
"is_public"
],
"properties": {
"name": {
"type": "string",
"description": "Human-readable name"
},
"description": {
"type": "string",
"description": "What this extractor does"
},
"llm": {
"type": "string",
"description": "(Optional) Identifier or version of the language model the extractor uses"
},
"schema": {
"type": "string",
"description": "(Optional) JSONSchema (or other spec) that the extractor outputs"
},
"prompt": {
"type": "string",
"description": "(Optional) Prompt text for the extractor"
},
"pre_prompt_summary": {
"type": "string",
"description": "(Optional) Pre-prompt summary text for the extractor"
},
"keep_intermediate_results": {
"type": "boolean",
"description": "(Optional) Flag indicating whether to keep intermediate results during extraction"
},
"system_prompt": {
"type": "string",
"description": "(Optional) System prompt text for the extractor"
},
"max_num_chunks": {
"type": "integer",
"description": "(Optional) Maximum number of chunks to process"
},
"vision": {
"type": "string",
"description": "(Optional) Vision mode setting"
},
"vision_llm": {
"type": "string",
"description": "(Optional) Vision LLM model identifier"
},
"image_batch_image_prompt": {
"type": "string",
"description": "(Optional) Image batch processing prompt"
},
"image_batch_final_prompt": {
"type": "string",
"description": "(Optional) Final prompt for image batch processing"
},
"guardrails_settings": {
"$ref": "#/$defs/GuardrailsSettings"
},
"id": {
"type": "string",
"description": "Unique identifier of the extractor"
},
"is_public": {
"type": "boolean",
"description": "Flag indicating if the extractor is public"
},
"created_at": {
"type": "string",
"format": "date-time",
"description": "When the extractor definition was created"
}
},
"$defs": {
"GuardrailsSettings": {
"type": "object",
"properties": {
"exception_message": {
"description": "A message that will be returned in case some guardrails settings are violated.",
"type": "string"
},
"show_violated_responses": {
"description": "Admin-only toggle to view the exact content of violated responses. When enabled, admins can see what triggered a guardrail violation. This is disabled by default for privacy and security. Only administrators can enable this setting and view violated content.",
"type": "boolean",
"default": false
},
"guardrails_on_transcriptions": {
"description": "When enabled, audio and video transcriptions are checked for content safety violations using the guardrails LLM, in addition to the existing PII detection.",
"type": "boolean",
"default": false
},
"vision_guardrails_enabled": {
"description": "When enabled, images and video frames are checked for content safety violations using a vision-capable guardrails LLM during document ingestion.",
"type": "boolean",
"default": false
},
"guardrails_vision_llm": {
"description": "Vision-capable LLM for image and video content safety checks. Defaults to auto which selects the best available vision model.",
"type": "string",
"default": "auto"
},
"column_redaction_config": {
"type": "array",
"description": "List of [column_pattern, redaction_label], both strings.",
"items": {
"type": "array",
"minItems": 2,
"maxItems": 2,
"items": {
"type": "string"
}
}
},
"disallowed_regex_patterns": {
"description": "A list of regular expressions that match custom PII.",
"type": "array",
"items": {
"type": "string"
}
},
"presidio_labels_to_flag": {
"description": "A list of entities to be flagged as PII by the built-in Presidio model.",
"type": "array",
"items": {
"type": "string"
}
},
"pii_labels_to_flag": {
"description": "A list of entities to be flagged as PII by the built-in PII model.",
"type": "array",
"items": {
"type": "string"
}
},
"pii_detection_parse_action": {
"description": "What to do when PII is detected during parsing of documents. The 'redact' option will replace disallowed content in the ingested documents with redaction bars.",
"type": "string",
"enum": [
"allow",
"redact",
"fail"
]
},
"pii_detection_llm_input_action": {
"description": "What to do when PII is detected in the input to the LLM (document content and user prompts). The 'redact' option will replace disallowed content with placeholders.",
"type": "string",
"enum": [
"allow",
"redact",
"fail"
]
},
"pii_detection_llm_output_action": {
"description": "What to do when PII is detected in the output of the LLM. The 'redact' option will replace disallowed content with placeholders.",
"type": "string",
"enum": [
"allow",
"redact",
"fail"
]
},
"prompt_guard_labels_to_flag": {
"description": "A list of entities to be flagged as safety violations in user prompts by the built-in prompt guard model.",
"type": "array",
"items": {
"type": "string"
}
},
"guardrails_labels_to_flag": {
"description": "A list of entities to be flagged as safety violations in user prompts. Must be a subset of guardrails_entities, if provided.",
"type": "array",
"items": {
"type": "string"
}
},
"guardrails_llm": {
"description": "LLM to use for Guardrails and PII detection",
"type": "string"
},
"guardrails_safe_category": {
"description": "Name of the safe category for guardrails. Must be a key in guardrails_entities, if provided. Otherwise uses system defaults.",
"type": "string"
},
"guardrails_entities": {
"description": "Dictionary of entities and their descriptions for the guardrails model to classify. The first entry is the \"safe\" class, the rest are \"unsafe\" classes.",
"type": "object",
"additionalProperties": {
"type": "string"
}
},
"column_redaction_custom_entities_to_flag": {
"description": "Deprecated - Use custom_pii_entities_to_flag. A list of entities to redact in tabular data files. Must be a subset of column_redaction_custom_entities, if provided.",
"type": "array",
"items": {
"type": "string"
}
},
"column_redaction_custom_entities": {
"description": "(Optional) Deprecated - Use custom_pii_entities. A dictionary of entities and a short description for the LLM to check for and redact columns containing PII in tabular data files.",
"type": "object",
"additionalProperties": {
"type": "string"
}
},
"custom_pii_entities_to_flag": {
"description": "A list of entities to redact in tabular data files. Must be a subset of custom_pii_entities, if provided.",
"type": "array",
"items": {
"type": "string"
}
},
"custom_pii_entities": {
"description": "(Optional) A dictionary of entities and a short description for the LLM to check for and redact columns containing PII in tabular data files.",
"type": "object",
"additionalProperties": {
"type": "string"
}
}
}
}
}
}
Work with this as data
Every JSON Schema here is available over the APIs.io API and to AI agents over MCP.
MCP server
One button, every client — Claude, Cursor, VS Code and the rest.
https://apis.io/mcp
Tools for schemas
4 MCP tools reach this
find_json_schemasBrowse and filter every JSON Schema in the catalog.apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.resolveTurn a domain, URL or GitHub org into the provider it belongs to.find_cohortsEvery scored population of providers in the catalog.
Call it yourself
curl for this page
This JSON Schema
curl "https://apis.io/api/v1/json-schemas/h2o-ai-extractor"
All schemas
curl "https://apis.io/api/v1/json-schemas?limit=25"
Discovery needs no key. Ratings and market analysis are Pro.
Get an API key
Free tier, no form to fill in. Signing in shares your email address with us — we store it to create your key and to recognise you if you sign in with another provider. See our Privacy Policy and Terms.
A second provider on the same verified email joins the account you already have.