GLiNER extraction, classification, and relations

POST https://api.fastino.ai/v1/chat/completions

This endpoint uses an OpenAI-compatible envelope: an inference-capable model, a messages list, and a top-level schema describing the GLiNER task. See the Inference API overview for authentication and cold-start/retry behavior shared across Fastino’s inference interfaces.

POST /inference has been removed. Do not send its legacy fields such as model_id, text, task, format_results, or is_warmup.

Request

string

required

An inference-capable base-model ID or the UUID of a completed, deployable Fastino training job.

object[]

required

A non-empty list of messages. For GLiNER inference, put the text to analyze in a user message’s content.

object

Defines custom encoder tasks. Supply a dictionary containing one or more of entities, classifications, structures, or relations. Omit it only when the selected model has a configured default task.A flat array of entity labels is deprecated. Always use the dictionary shape.

number

default:"0.5"

Confidence threshold from 0 to 1. Lower values favor recall; higher values favor precision.

boolean

default:"true"

Include confidence values in extracted results.

boolean

default:"true"

Include half-open character offsets (start, end) in entity results.

boolean

default:"true"

Persist the inference. Set to false to opt out.

Entity schema

Use descriptive entity definitions when possible:

{
  "entities": [
    {
      "name": "organization",
      "description": "business or institution name"
    },
    {
      "name": "product",
      "description": "named commercial product"
    }
  ]
}

Classification schema

{
  "classifications": [
    {
      "task": "sentiment",
      "labels": ["positive", "negative", "neutral"],
      "multi_label": false,
      "top_k": 1
    }
  ]
}

Structured extraction schema

Structure fields use field::type::description specifications:

{
  "structures": {
    "product": [
      "name::str::product name",
      "price::str::listed price",
      "features::list::named features"
    ]
  }
}

Relation schema

The simplest relation schema is a flat list of relation names:

{
  "relations": ["works_for", "lives_in"]
}

You can also use a dictionary to add descriptions or per-relation configuration:

{
  "relations": {
    "works_for": {
      "description": "employment relationship",
      "threshold": 0.6
    }
  }
}

Do not send relation objects containing head and tail definitions.

Combined schema

Run multiple tasks over the same text by combining keys:

{
  "entities": [
    {
      "name": "organization",
      "description": "business or institution name"
    },
    {
      "name": "product",
      "description": "named commercial product"
    }
  ],
  "classifications": [
    {
      "task": "sentiment",
      "labels": ["positive", "negative", "neutral"],
      "multi_label": false,
      "top_k": 1
    }
  ]
}

The schema identifies the operation automatically. Do not send task or task_type.

Example

curl -X POST "https://api.fastino.ai/v1/chat/completions" \
  -H "Authorization: Bearer $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "fastino/gliner2.5-multi-v1",
    "messages": [
      {
        "role": "user",
        "content": "Apple announced the MacBook Pro at WWDC in Cupertino."
      }
    ],
    "schema": {
      "entities": [
        {
          "name": "organization",
          "description": "business or institution name"
        },
        {
          "name": "product",
          "description": "named commercial product"
        },
        {
          "name": "event",
          "description": "named conference or event"
        },
        {
          "name": "location",
          "description": "city, region, or place"
        }
      ]
    },
    "threshold": 0.5
  }'

The model value must currently support hosted inference. Use the live model catalog rather than assuming every Hugging Face checkpoint is available:

curl "https://api.fastino.ai/v1/base-models?supports_inference=true&task_type=encoder"

Response

The endpoint returns a chat-completion envelope. The GLiNER result is serialized as a JSON string in choices[0].message.content:

{
  "id": "chatcmpl_...",
  "object": "chat.completion",
  "created": 1790123456,
  "model": "fastino/gliner2.5-multi-v1",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "{\"entities\":[{\"organization\":[{\"text\":\"Apple\",\"confidence\":0.99,\"start\":0,\"end\":5}],\"product\":[{\"text\":\"MacBook Pro\",\"confidence\":0.98,\"start\":20,\"end\":31}],\"event\":[{\"text\":\"WWDC\",\"confidence\":0.97,\"start\":35,\"end\":39}],\"location\":[{\"text\":\"Cupertino\",\"confidence\":0.99,\"start\":43,\"end\":52}]}],\"classifications\":{},\"structures\":{},\"relations\":{}}"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 0,
    "completion_tokens": 0,
    "total_tokens": 0
  },
  "x_pioneer": {
    "inference_id": "..."
  }
}

Parse choices[0].message.content as JSON before reading task results. When store=true, x_pioneer.inference_id identifies the persisted inference.

Repeated inputs

The endpoint accepts one conversation per request. It does not support the removed native endpoint’s text: string[] batch shape. Send separate requests concurrently when processing multiple inputs.

Errors

Status	Likely cause	Action
`400`	Invalid request or schema	Correct the request fields or schema
`401`	Missing, malformed, or invalid API key	Export a valid `fast_sk_...` key
`402`	Insufficient credits, funding required, or an owner-set daily/monthly spend limit	Add credits or adjust the spend limit
`403`	Payment method, card verification, credit ceiling, or account policy denial	Resolve the billing or account requirement
`404`	Unknown or unsupported model	Use an inference-capable catalog ID or deployable job UUID
`409`	Fine-tuned model exists but is not deployed	Wait for or repair the model deployment
`422`	Request validation failed	Read and correct the field-level validation details
`425`	Deployment is warming	Retry after the indicated delay
`429`	Rate or capacity limit	Respect `Retry-After` and retry with backoff
`503`	Cold start or temporary provider capacity issue	Retry before treating it as terminal

See Cold starts and retries for the shared retry pattern.

Legacy request migration

Removed `/inference` field	Current field
`model_id`	`model`
`text`	`messages: [{"role": "user", "content": "..."}]`
Flat `schema` entity list	`schema` dictionary
`task` or `task_type`	Remove; the schema identifies the operation
`text: string[]`	Send separate requests
`format_results` or `is_warmup`	Remove
Native result body	Parse `choices[0].message.content`

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Models

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GLiNER extraction, classification, and relations

Request

Entity schema

Classification schema

Structured extraction schema

Relation schema

Combined schema

Example

Response

Repeated inputs

Errors

Legacy request migration

​Request

​Entity schema

​Classification schema

​Structured extraction schema

​Relation schema

​Combined schema

​Example

​Response

​Repeated inputs

​Errors

​Legacy request migration

Request

Entity schema

Classification schema

Structured extraction schema

Relation schema

Combined schema

Example

Response

Repeated inputs

Errors

Legacy request migration