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POST /feedback

The /feedback endpoint assigns feedback to a particular inference or episode. Each feedback is associated with a metric that is defined in the configuration file.

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Request

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dryrun

  • Type: boolean
  • Required: no
If true, the feedback request will be executed but won’t be stored to the database (i.e. no-op). This field is primarily for debugging and testing, and you should ignore it in production.

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episode_id

  • Type: UUID
  • Required: when the metric level is episode
The episode ID to provide feedback for. You should use this field when the metric level is episode. Only use episode IDs that were returned by the TensorZero gateway.

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inference_id

  • Type: UUID
  • Required: when the metric level is inference
The inference ID to provide feedback for. You should use this field when the metric level is inference. Only use inference IDs that were returned by the TensorZero gateway.

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metric_name

  • Type: string
  • Required: yes
The name of the metric to provide feedback. For example, if your metric is defined as [metrics.draft_accepted] in your configuration file, then you would set metric_name: "draft_accepted". The metric names comment and demonstration are reserved for special types of feedback. A comment is free-form text (string) that can be assigned to either an inference or an episode. The demonstration metric accepts values that would be a valid output. See Metrics & Feedback for more details.

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tags

  • Type: flat JSON object with string keys and values
  • Required: no
User-provided tags to associate with the feedback. For example, {"user_id": "123"} or {"author": "Alice"}.

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value

  • Type: varies
  • Required: yes
The value of the feedback. The type of the value depends on the metric type (e.g. boolean for a metric with type = "boolean").

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Response

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feedback_id

  • Type: UUID
The ID assigned to the feedback.

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Examples

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Inference-Level Boolean Metric
Configuration
// tensorzero.toml
# ...
[metrics.draft_accepted]
type = "boolean"
level = "inference"
# ...
Request
  • Python
  • HTTP
POST /feedback
from tensorzero import AsyncTensorZeroGateway

async with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    result = await client.feedback(
        inference_id="00000000-0000-0000-0000-000000000000",
        metric_name="draft_accepted",
        value=True,
    )
Response
POST /feedback
{ "feedback_id": "11111111-1111-1111-1111-111111111111" }

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Episode-Level Float Metric
Configuration
// tensorzero.toml
# ...
[metrics.user_rating]
type = "float"
level = "episode"
# ...
Request
  • Python
  • HTTP
POST /feedback
from tensorzero import AsyncTensorZeroGateway

async with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    result = await client.feedback(
        episode_id="00000000-0000-0000-0000-000000000000",
        metric_name="user_rating",
        value=10,
    )
Response
POST /feedback
{ "feedback_id": "11111111-1111-1111-1111-111111111111" }