LLM

Bring model access and AI services alongside your data with llm.* operations.

Overview

Intelligence connects models to the data and documents your agents and apps already use. You don't need provider keys or prompt plumbing: send content and instructions, and get results back.

Models: Choose a configured catalog model with model, or omit it to use DEFAULT_MODEL for generation. Available models depend on the deployed catalog; an unknown generation model is rejected. Embeddings use their own model default.

Key features:

Generation - Write, translate, summarize, categorize, extract, and analyze sentiment
Structured output - JSON responses shaped by a JSON Schema
Embeddings - Vectors for one or many documents

Use cases: Content tools, classification pipelines, data extraction, semantic search preparation

Access: Generation and embeddings require a signed-in user (web key plus bearer token), or a server or permitted agent key without a bearer token. chat.ask remains public with a valid project key, subject to retrieval and attachment permissions.

For persisted chats with history and Chat with Documents, see Conversation.

Operations

Operation	Description	Required payload
`llm.generate`	Generate content with a named built-in template	`content` or `input`
`llm.write`	Write content	`content` or `input`
`llm.translate`	Translate content	`content` or `input`
`llm.summarize`	Summarize content	`content` or `input`
`llm.categorize`	Classify content	`content` or `input`
`llm.extract`	Extract entities or data	`content` or `input`
`llm.analyze`	Analyze sentiment	`content` or `input`
`llm.embed`	Create embeddings	`documents`

For chats, see Conversation.

Generation

The seven generation operations share one payload. Each selects a built-in prompt for its task.

Generation parameters:

Field	Description
`content`	String, object, or list of strings/objects; up to 200,000 converted characters
`input`	Request, up to 4,000 characters; alias `user_input`
`output`	`text` (default), `markdown`, or `json`; alias `format`
`json_schema`	JSON structure; requires `output: "json"`
`model`	Catalog model key; default `DEFAULT_MODEL`
`prompt_template`	Built-in template name on `llm.generate` only; default `generic`
`system`	Own instruction on `llm.generate` only; at most 8,000 characters

content is what the model reads, input is what you ask it to do with it. At least one is required and nonblank. Objects become structured Markdown; list items are converted without a Data Records title and joined with a blank line. Object items in multi-item lists have ## Item N headings numbered by position; strings and single objects have none. Fixed operations ignore prompt_template and system. An explicitly sent prompt_template wins over system, including generic; otherwise nonblank system wins, falling back to generic. system alone cannot replace the required content/input. Named templates must exist in the deployed prompt catalog. Unknown names return PROMPT_NOT_FOUND.

Breaking change: custom_instructions, variables, prompt_name, prompt_md, params, and options are silently ignored in generation. Use input for instructions. output is now an answer shape, not a dot path; output: null is invalid. Embedding and chat keep their own payloads.

Response:

json

{"data": {"result": "SignalPeak\nUptimeLens\nPingWise"}, "meta": {"request_id": "req_123", "exec_time": 0.84}, "exec_time": 0.87}

JSON output returns parsed JSON in data.result. Each successful generation or embedding call records the existing LLM_CALL usage event. See Templates for template syntax.

llm.generate

json

{
  "operation": "llm.generate",
  "payload": {
    "input": "Suggest three names for an API monitoring tool, one per line.",
    "prompt_template": "generic"
  }
}

llm.write

json

{
  "operation": "llm.write",
  "payload": {
    "content": {"product": "Wireless headphones", "battery_hours": 30},
    "input": "Write a friendly product description, under 60 words."
  }
}

llm.translate

json

{
  "operation": "llm.translate",
  "payload": {
    "content": "Welcome to our developer platform.",
    "input": "Translate into French."
  }
}

llm.summarize

json

{
  "operation": "llm.summarize",
  "payload": {
    "content": "The release adds direct file uploads, faster queries, and rotating refresh tokens. Existing clients must adopt the new request envelope.",
    "input": "Two bullets.",
    "output": "markdown"
  }
}

llm.categorize

json

{
  "operation": "llm.categorize",
  "payload": {
    "content": "I was charged twice for my subscription.",
    "input": "Choose one: billing, technical, general."
  }
}

llm.extract

json

{
  "operation": "llm.extract",
  "payload": {
    "content": "Jane Doe ordered two notebooks on October 3, 2026.",
    "input": "Extract customer, product, quantity, and order date.",
    "output": "json",
    "json_schema": {
      "type": "object",
      "properties": {
        "customer": { "type": "string" },
        "product": { "type": "string" },
        "quantity": { "type": "integer" },
        "order_date": { "type": "string" }
      }
    }
  }
}

llm.analyze

json

{
  "operation": "llm.analyze",
  "payload": {
    "content": "Setup was quick and the docs were easy to follow.",
    "input": "Return the sentiment and a one-sentence reason."
  }
}

Embeddings

llm.embed

Example - One string:

json

{
  "operation": "llm.embed",
  "payload": {
    "documents": "A short cancellation policy."
  }
}

Example - Several documents:

json

{
  "operation": "llm.embed",
  "payload": {
    "documents": [
      { "id": "policy-1", "content": "Cancel at any time." },
      { "id": "policy-2", "content": "Refunds take five days." }
    ],
    "embedding_content_path": "content"
  }
}

Parameters:

Field	Description
`documents`	A string, an object, or a non-empty array of strings and objects (required)
`embedding_content_path`	Field to embed in each object. Default `"content"`. Supports dot paths
`model`	Embedding model
`dimensions`	Positive vector size, when supported by the selected embedding model
`params`	Extra provider options

Results keep the input order. Embedding does not store anything; save the vectors yourself if you need them.

Common Errors

Message	Meaning
`AUTHENTICATION_REQUIRED`, `ACCESS_DENIED`	Missing user authentication or insufficient access for generation/embeddings
`PROMPT_NOT_FOUND`	The generation template does not exist
`CONTENT_TOO_LARGE`	Converted generation content exceeds 200,000 characters
`INVALID_LLM_EMBED_PAYLOAD`	Embedding inputs or options are invalid
`INVALID_PAYLOAD`	The payload does not match the operation
`KB_SOURCE_REQUIRED`	`kb` mode has no retrieval source or attachment
`RETRIEVAL_SOURCE_FORBIDDEN`	A web-key caller used a server-only source
`RETRIEVAL_SOURCE_MISMATCH`	`source` and `type` disagree
`DOCSET_NOT_FOUND`	A docset is missing, disabled, or not available to the caller
`INVALID_RETRIEVAL`	Invalid docset IDs, or the client sent docset definitions
`INVALID_QUERY_FOR_SOURCE`	The retrieval query does not fit its source
`ATTACHMENT_LIMIT`	More than 5 attachments
`ATTACHMENT_TOO_LARGE`	Inline or extracted attachment content is too large
`FILE_FORBIDDEN`	The user does not own the attached file
`LLM_REQUEST_FAILED`	The chat handler failed (HTTP 502)

Model validation failures return HTTP 422. Other generation or embedding failures return HTTP 502 with the underlying error message; do not assume one fixed message for every model failure. See Responses for the error envelope.

Next Steps

Conversation - Persisted chats and Chat with Documents
Data - Store the content your docsets search
Agents - Give AI agents scoped LLM permissions

Use Chat for one-off answers and persisted conversations, and JavaScript SDK for sbc.llm calls.

LLM ​

Overview ​

Operations ​

Generation ​

llm.generate ​

llm.write ​

llm.translate ​

llm.summarize ​

llm.categorize ​

llm.extract ​

llm.analyze ​

Embeddings ​

llm.embed ​

Common Errors ​

Next Steps ​

LLM

Overview

Operations

Generation

llm.generate

llm.write

llm.translate

llm.summarize

llm.categorize

llm.extract

llm.analyze

Embeddings

llm.embed

Common Errors

Next Steps