"""Schemas for the LangSmith API."""

from __future__ import annotations

from collections.abc import Iterator
from datetime import datetime, timedelta, timezone
from decimal import Decimal
from enum import Enum
from pathlib import Path
from typing import (
    Annotated,
    Any,
    NamedTuple,
    Optional,
    Protocol,
    Union,
    runtime_checkable,
)
from uuid import UUID

from pydantic import (
    BaseModel,
    ConfigDict,
    Field,
    PrivateAttr,
    StrictBool,
    StrictFloat,
    StrictInt,
)
from typing_extensions import Literal, NotRequired, TypedDict

SCORE_TYPE = Union[StrictBool, StrictInt, StrictFloat, None]
VALUE_TYPE = Union[dict, str, StrictBool, StrictInt, StrictFloat, None]


class Attachment(NamedTuple):
    """Annotated type that will be stored as an attachment if used.

    Examples:
        ```python
        from langsmith import traceable
        from langsmith.schemas import Attachment


        @traceable
        def my_function(bar: int, my_val: Attachment):
            # my_val will be stored as an attachment
            # bar will be stored as inputs
            return bar
        ```
    """

    mime_type: str
    data: Union[bytes, Path]


Attachments = dict[str, Union[tuple[str, bytes], Attachment, tuple[str, Path]]]
"""Attachments associated with the run.

Each entry is a tuple of `(mime_type, bytes)`, or `(mime_type, file_path)`
"""


@runtime_checkable
class BinaryIOLike(Protocol):
    """Protocol for binary IO-like objects."""

    def read(self, size: int = -1) -> bytes:
        """Read function."""
        ...

    def seek(self, offset: int, whence: int = 0) -> int:
        """Seek function."""
        ...

    def getvalue(self) -> bytes:
        """Get value function."""
        ...


class ExampleBase(BaseModel):
    """Example base model."""

    dataset_id: UUID
    inputs: Optional[dict[str, Any]] = Field(default=None)
    outputs: Optional[dict[str, Any]] = Field(default=None)
    metadata: Optional[dict[str, Any]] = Field(default=None)

    model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True)


class _AttachmentDict(TypedDict):
    mime_type: str
    data: Union[bytes, Path]


_AttachmentLike = Union[
    Attachment, _AttachmentDict, tuple[str, bytes], tuple[str, Path]
]


class ExampleCreate(BaseModel):
    """Example upload with attachments."""

    id: Optional[UUID] = None
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    inputs: Optional[dict[str, Any]] = Field(default=None)
    outputs: Optional[dict[str, Any]] = Field(default=None)
    metadata: Optional[dict[str, Any]] = Field(default=None)
    split: Optional[Union[str, list[str]]] = None
    attachments: Optional[dict[str, _AttachmentLike]] = None
    use_source_run_io: bool = False
    use_source_run_attachments: Optional[list[str]] = None
    source_run_id: Optional[UUID] = None

    def __init__(self, **data):
        """Initialize from dict."""
        super().__init__(**data)


ExampleUploadWithAttachments = ExampleCreate


class ExampleUpsertWithAttachments(ExampleCreate):
    """Example create with attachments."""

    dataset_id: UUID


class AttachmentInfo(TypedDict):
    """Info for an attachment."""

    presigned_url: str
    reader: BinaryIOLike
    mime_type: Optional[str]


class Example(ExampleBase):
    """Example model."""

    id: UUID
    created_at: datetime = Field(
        default_factory=lambda: datetime.fromtimestamp(0, tz=timezone.utc)
    )
    dataset_id: UUID = Field(default=UUID("00000000-0000-0000-0000-000000000000"))
    modified_at: Optional[datetime] = Field(default=None)
    source_run_id: Optional[UUID] = None
    attachments: Optional[dict[str, AttachmentInfo]] = Field(default=None)
    """Dictionary with attachment names as keys and a tuple of the S3 url
    and a reader of the data for the file."""
    _host_url: Optional[str] = PrivateAttr(default=None)
    _tenant_id: Optional[UUID] = PrivateAttr(default=None)

    def __init__(
        self,
        _host_url: Optional[str] = None,
        _tenant_id: Optional[UUID] = None,
        **kwargs: Any,
    ) -> None:
        """Initialize a Dataset object."""
        super().__init__(**kwargs)
        self._host_url = _host_url
        self._tenant_id = _tenant_id

    @property
    def url(self) -> Optional[str]:
        """URL of this run within the app."""
        if self._host_url:
            path = f"/datasets/{self.dataset_id}/e/{self.id}"
            if self._tenant_id:
                return f"{self._host_url}/o/{str(self._tenant_id)}{path}"
            return f"{self._host_url}{path}"
        return None

    def __repr__(self):
        """Return a string representation of the RunBase object."""
        return f"{self.__class__}(id={self.id}, dataset_id={self.dataset_id}, link='{self.url}')"


class AttachmentsOperations(BaseModel):
    """Operations to perform on attachments."""

    rename: dict[str, str] = Field(
        default_factory=dict, description="Mapping of old attachment names to new names"
    )
    retain: list[str] = Field(
        default_factory=list, description="List of attachment names to keep"
    )


class ExampleUpdate(BaseModel):
    """Example update with attachments."""

    id: UUID
    dataset_id: Optional[UUID] = None
    inputs: Optional[dict[str, Any]] = Field(default=None)
    outputs: Optional[dict[str, Any]] = Field(default=None)
    metadata: Optional[dict[str, Any]] = Field(default=None)
    split: Optional[Union[str, list[str]]] = None
    attachments: Optional[Attachments] = None
    attachments_operations: Optional[AttachmentsOperations] = None

    model_config = ConfigDict(frozen=True)

    def __init__(self, **data):
        """Initialize from dict."""
        super().__init__(**data)


ExampleUpdateWithAttachments = ExampleUpdate


class DataType(str, Enum):
    """Enum for dataset data types."""

    kv = "kv"
    llm = "llm"
    chat = "chat"


class DatasetBase(BaseModel):
    """Dataset base model."""

    name: str
    description: Optional[str] = None
    data_type: Optional[DataType] = None

    model_config = ConfigDict(frozen=True)


DatasetTransformationType = Literal[
    "remove_system_messages",
    "convert_to_openai_message",
    "convert_to_openai_tool",
    "remove_extra_fields",
    "extract_tools_from_run",
]


class DatasetTransformation(TypedDict, total=False):
    """Schema for dataset transformations."""

    path: list[str]
    transformation_type: Union[DatasetTransformationType, str]


class Dataset(DatasetBase):
    """Dataset ORM model."""

    id: UUID
    created_at: datetime
    modified_at: Optional[datetime] = Field(default=None)
    example_count: Optional[int] = None
    session_count: Optional[int] = None
    last_session_start_time: Optional[datetime] = None
    inputs_schema: Optional[dict[str, Any]] = None
    outputs_schema: Optional[dict[str, Any]] = None
    transformations: Optional[list[DatasetTransformation]] = None
    metadata: Optional[dict[str, Any]] = None
    _host_url: Optional[str] = PrivateAttr(default=None)
    _tenant_id: Optional[UUID] = PrivateAttr(default=None)
    _public_path: Optional[str] = PrivateAttr(default=None)

    def __init__(
        self,
        _host_url: Optional[str] = None,
        _tenant_id: Optional[UUID] = None,
        _public_path: Optional[str] = None,
        **kwargs: Any,
    ) -> None:
        """Initialize a Dataset object."""
        if "inputs_schema_definition" in kwargs:
            kwargs["inputs_schema"] = kwargs.pop("inputs_schema_definition")

        if "outputs_schema_definition" in kwargs:
            kwargs["outputs_schema"] = kwargs.pop("outputs_schema_definition")

        super().__init__(**kwargs)
        self._host_url = _host_url
        self._tenant_id = _tenant_id
        self._public_path = _public_path

    @property
    def url(self) -> Optional[str]:
        """URL of this run within the app."""
        if self._host_url:
            if self._public_path:
                return f"{self._host_url}{self._public_path}"
            if self._tenant_id:
                return f"{self._host_url}/o/{str(self._tenant_id)}/datasets/{self.id}"
            return f"{self._host_url}/datasets/{self.id}"
        return None


class DatasetVersion(BaseModel):
    """Class representing a dataset version."""

    tags: Optional[list[str]] = None
    as_of: datetime


def _default_extra():
    return {"metadata": {}}


class RunBase(BaseModel):
    """Base Run schema.

    A Run is a span representing a single unit of work or operation within your LLM app.
    This could be a single call to an LLM or chain, to a prompt formatting call,
    to a runnable lambda invocation. If you are familiar with OpenTelemetry,
    you can think of a run as a span.
    """

    id: UUID
    """Unique identifier for the run."""

    name: str
    """Human-readable name for the run."""

    start_time: datetime
    """Start time of the run."""

    run_type: str
    """The type of run, such as tool, chain, llm, retriever,
    embedding, prompt, parser."""

    end_time: Optional[datetime] = None
    """End time of the run, if applicable."""

    extra: Optional[dict] = Field(default_factory=_default_extra)
    """Additional metadata or settings related to the run."""

    error: Optional[str] = None
    """Error message, if the run encountered any issues."""

    serialized: Optional[dict] = None
    """Serialized object that executed the run for potential reuse."""

    events: Optional[list[dict]] = None
    """List of events associated with the run, like
    start and end events."""

    inputs: dict = Field(default_factory=dict)
    """Inputs used for the run."""

    outputs: Optional[dict] = None
    """Outputs generated by the run, if any."""

    reference_example_id: Optional[UUID] = None
    """Reference to an example that this run may be based on."""

    parent_run_id: Optional[UUID] = None
    """Identifier for a parent run, if this run is a sub-run."""

    tags: Optional[list[str]] = None
    """Tags for categorizing or annotating the run."""

    attachments: Union[Attachments, dict[str, AttachmentInfo]] = Field(
        default_factory=dict
    )
    """Attachments associated with the run.

    Each entry is a tuple of `(mime_type, bytes)`.
    """

    @property
    def metadata(self) -> dict[str, Any]:
        """Retrieve the metadata (if any)."""
        if self.extra is None:
            self.extra = {}
        return self.extra.setdefault("metadata", {})

    @property
    def revision_id(self) -> Optional[UUID]:
        """Retrieve the revision ID (if any)."""
        return self.metadata.get("revision_id")

    @property
    def latency(self) -> Optional[float]:
        """Latency in seconds."""
        if self.end_time is None:
            return None
        return (self.end_time - self.start_time).total_seconds()

    def __repr__(self):
        """Return a string representation of the RunBase object."""
        return f"{self.__class__}(id={self.id}, name='{self.name}', run_type='{self.run_type}')"

    model_config = ConfigDict(arbitrary_types_allowed=True)


class Run(RunBase):
    """Run schema when loading from the DB."""

    session_id: Optional[UUID] = None
    """The project ID this run belongs to."""
    child_run_ids: Optional[list[UUID]] = None
    """Deprecated: The child run IDs of this run."""
    child_runs: Optional[list[Run]] = None
    """The child runs of this run, if instructed to load using the client
    These are not populated by default, as it is a heavier query to make."""
    feedback_stats: Optional[dict[str, Any]] = None
    """Feedback stats for this run."""
    app_path: Optional[str] = None
    """Relative URL path of this run within the app."""
    manifest_id: Optional[UUID] = None
    """Unique ID of the serialized object for this run."""
    status: Optional[str] = None
    """Status of the run (e.g., 'success')."""
    prompt_tokens: Optional[int] = None
    """Number of tokens used for the prompt."""
    completion_tokens: Optional[int] = None
    """Number of tokens generated as output."""
    total_tokens: Optional[int] = None
    """Total tokens for prompt and completion."""
    prompt_token_details: Optional[dict[str, int]] = None
    """Breakdown of prompt (input) token counts.

    Does *not* need to sum to full prompt token count.
    """
    completion_token_details: Optional[dict[str, int]] = None
    """Breakdown of completion (output) token counts.

    Does *not* need to sum to full completion token count.
    """
    first_token_time: Optional[datetime] = None
    """Time the first token was processed."""
    total_cost: Optional[Decimal] = None
    """The total estimated LLM cost associated with the completion tokens."""
    prompt_cost: Optional[Decimal] = None
    """The estimated cost associated with the prompt (input) tokens."""
    completion_cost: Optional[Decimal] = None
    """The estimated cost associated with the completion tokens."""
    prompt_cost_details: Optional[dict[str, Decimal]] = None
    """Breakdown of prompt (input) token costs.

    Does *not* need to sum to full prompt token cost.
    """
    completion_cost_details: Optional[dict[str, Decimal]] = None
    """Breakdown of completion (output) token costs.

    Does *not* need to sum to full completion token cost.
    """
    parent_run_ids: Optional[list[UUID]] = None
    """List of parent run IDs."""
    trace_id: UUID
    """Unique ID assigned to every run within this nested trace."""
    dotted_order: str = Field(default="")
    """Dotted order for the run.

    This is a string composed of {time}{run-uuid}.* so that a trace can be
    sorted in the order it was executed.

    Example:
        - Parent: 20230914T223155647Z1b64098b-4ab7-43f6-afee-992304f198d8
        - Children:
        - 20230914T223155647Z1b64098b-4ab7-43f6-afee-992304f198d8.20230914T223155649Z809ed3a2-0172-4f4d-8a02-a64e9b7a0f8a
        - 20230915T223155647Z1b64098b-4ab7-43f6-afee-992304f198d8.20230914T223155650Zc8d9f4c5-6c5a-4b2d-9b1c-3d9d7a7c5c7c
    """  # noqa: E501
    in_dataset: Optional[bool] = None
    """Whether this run is in a dataset."""
    _host_url: Optional[str] = PrivateAttr(default=None)

    def __init__(self, _host_url: Optional[str] = None, **kwargs: Any) -> None:
        """Initialize a Run object."""
        if not kwargs.get("trace_id"):
            kwargs = {"trace_id": kwargs.get("id"), **kwargs}
        inputs = kwargs.pop("inputs", None) or {}
        super().__init__(**kwargs, inputs=inputs)
        self._host_url = _host_url
        if self.start_time.tzinfo is None:
            self.start_time = self.start_time.replace(tzinfo=timezone.utc)
        if self.end_time is not None and self.end_time.tzinfo is None:
            self.end_time = self.end_time.replace(tzinfo=timezone.utc)
        if not self.dotted_order.strip() and not self.parent_run_id:
            self.dotted_order = f"{self.start_time.isoformat()}{self.id}"

    @property
    def url(self) -> Optional[str]:
        """URL of this run within the app."""
        if self._host_url and self.app_path:
            return f"{self._host_url}{self.app_path}"
        return None

    @property
    def input_tokens(self) -> int | None:
        """Alias for prompt_tokens."""
        return self.prompt_tokens

    @property
    def output_tokens(self) -> int | None:
        """Alias for completion_tokens."""
        return self.completion_tokens

    @property
    def input_cost(self) -> Decimal | None:
        """Alias for prompt_cost."""
        return self.prompt_cost

    @property
    def output_cost(self) -> Decimal | None:
        """Alias for completion_cost."""
        return self.completion_cost

    @property
    def input_token_details(self) -> dict[str, int] | None:
        """Alias for prompt_token_details."""
        return self.prompt_token_details

    @property
    def output_token_details(self) -> dict[str, int] | None:
        """Alias for output_token_details."""
        return self.completion_token_details

    @property
    def input_cost_details(self) -> dict[str, Decimal] | None:
        """Alias for prompt_cost_details."""
        return self.prompt_cost_details

    @property
    def output_cost_details(self) -> dict[str, Decimal] | None:
        """Alias for completion_cost_details."""
        return self.completion_cost_details


class RunTypeEnum(str, Enum):
    """(Deprecated) Enum for run types. Use string directly."""

    tool = "tool"
    chain = "chain"
    llm = "llm"
    retriever = "retriever"
    embedding = "embedding"
    prompt = "prompt"
    parser = "parser"


class RunLikeDict(TypedDict, total=False):
    """Run-like dictionary, for type-hinting."""

    name: str
    run_type: RunTypeEnum
    start_time: datetime
    inputs: Optional[dict]
    outputs: Optional[dict]
    end_time: Optional[datetime]
    extra: Optional[dict]
    error: Optional[str]
    serialized: Optional[dict]
    parent_run_id: Optional[UUID]
    manifest_id: Optional[UUID]
    events: Optional[list[dict]]
    tags: Optional[list[str]]
    inputs_s3_urls: Optional[dict]
    outputs_s3_urls: Optional[dict]
    id: Optional[UUID]
    session_id: Optional[UUID]
    session_name: Optional[str]
    reference_example_id: Optional[UUID]
    input_attachments: Optional[dict]
    output_attachments: Optional[dict]
    trace_id: UUID
    dotted_order: str
    attachments: Attachments


class RunWithAnnotationQueueInfo(RunBase):
    """Run schema with annotation queue info."""

    last_reviewed_time: Optional[datetime] = None
    """The last time this run was reviewed."""
    added_at: Optional[datetime] = None
    """The time this run was added to the queue."""


class FeedbackSourceBase(BaseModel):
    """Base class for feedback sources.

    This represents whether feedback is submitted from the API, model, human labeler,
        etc.
    """

    type: str
    """The type of the feedback source."""
    metadata: Optional[dict[str, Any]] = Field(default_factory=dict)
    """Additional metadata for the feedback source."""
    user_id: Optional[Union[UUID, str]] = None
    """The user ID associated with the feedback source."""
    user_name: Optional[str] = None
    """The user name associated with the feedback source."""


class APIFeedbackSource(FeedbackSourceBase):
    """API feedback source."""

    type: Literal["api"] = "api"


class ModelFeedbackSource(FeedbackSourceBase):
    """Model feedback source."""

    type: Literal["model"] = "model"


class FeedbackSourceType(Enum):
    """Feedback source type."""

    API = "api"
    """General feedback submitted from the API."""
    MODEL = "model"
    """Model-assisted feedback."""


class FeedbackBase(BaseModel):
    """Feedback schema."""

    id: UUID
    """The unique ID of the feedback."""
    created_at: Optional[datetime] = None
    """The time the feedback was created."""
    modified_at: Optional[datetime] = None
    """The time the feedback was last modified."""
    run_id: Optional[UUID]
    """The associated run ID this feedback is logged for."""
    trace_id: Optional[UUID]
    """The associated trace ID this feedback is logged for."""
    key: str
    """The metric name, tag, or aspect to provide feedback on."""
    score: SCORE_TYPE = None
    """Value or score to assign the run."""
    value: VALUE_TYPE = None
    """The display value, tag or other value for the feedback if not a metric."""
    comment: Optional[str] = None
    """Comment or explanation for the feedback."""
    correction: Union[str, dict, None] = None
    """Correction for the run."""
    feedback_source: Optional[FeedbackSourceBase] = None
    """The source of the feedback."""
    session_id: Optional[UUID] = None
    """The associated project ID (Session = Project) this feedback is logged for."""
    start_time: Optional[datetime] = None
    """The start time of the run this feedback is associated with."""
    comparative_experiment_id: Optional[UUID] = None
    """If logged within a 'comparative experiment', this is the ID of the experiment."""
    feedback_group_id: Optional[UUID] = None
    """For preference scoring, this group ID is shared across feedbacks for each

    run in the group that was being compared."""
    extra: Optional[dict] = None
    """The metadata of the feedback."""

    model_config = ConfigDict(frozen=True)


class FeedbackCategory(TypedDict, total=False):
    """Specific value and label pair for feedback."""

    value: float
    """The numeric value associated with this feedback category."""
    label: Optional[str]
    """An optional label to interpret the value for this feedback category."""


class FeedbackConfig(TypedDict, total=False):
    """Represents _how_ a feedback value ought to be interpreted."""

    type: Literal["continuous", "categorical", "freeform"]
    """The type of feedback."""
    min: Optional[float]
    """The minimum value for continuous feedback."""
    max: Optional[float]
    """The maximum value for continuous feedback."""
    categories: Optional[list[FeedbackCategory]]
    """If feedback is categorical, this defines the valid categories the server will accept.
    Not applicable to continuous or freeform feedback types."""  # noqa


class FeedbackCreate(FeedbackBase):
    """Schema used for creating feedback."""

    feedback_source: FeedbackSourceBase
    """The source of the feedback."""
    feedback_config: Optional[FeedbackConfig] = None
    """The config for the feedback"""
    error: Optional[bool] = None


class Feedback(FeedbackBase):
    """Schema for getting feedback."""

    id: UUID
    created_at: datetime
    """The time the feedback was created."""
    modified_at: datetime
    """The time the feedback was last modified."""
    feedback_source: Optional[FeedbackSourceBase] = None
    """The source of the feedback. In this case"""


class TracerSession(BaseModel):
    """TracerSession schema for the API.

    Sessions are also referred to as "Projects" in the UI.
    """

    id: UUID
    """The ID of the project."""
    start_time: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    """The time the project was created."""
    end_time: Optional[datetime] = None
    """The time the project was ended."""
    description: Optional[str] = None
    """The description of the project."""
    name: Optional[str] = None
    """The name of the session."""
    extra: Optional[dict[str, Any]] = None
    """Extra metadata for the project."""
    tenant_id: UUID
    """The tenant ID this project belongs to."""
    reference_dataset_id: Optional[UUID]
    """The reference dataset IDs this project's runs were generated on."""

    _host_url: Optional[str] = PrivateAttr(default=None)

    def __init__(self, _host_url: Optional[str] = None, **kwargs: Any) -> None:
        """Initialize a Run object."""
        super().__init__(**kwargs)
        self._host_url = _host_url
        if self.start_time.tzinfo is None:
            self.start_time = self.start_time.replace(tzinfo=timezone.utc)

    @property
    def url(self) -> Optional[str]:
        """URL of this run within the app."""
        if self._host_url:
            return f"{self._host_url}/o/{self.tenant_id}/projects/p/{self.id}"
        return None

    @property
    def metadata(self) -> dict[str, Any]:
        """Retrieve the metadata (if any)."""
        if self.extra is None or "metadata" not in self.extra:
            return {}
        return self.extra["metadata"]

    @property
    def tags(self) -> list[str]:
        """Retrieve the tags (if any)."""
        if self.extra is None or "tags" not in self.extra:
            return []
        return self.extra["tags"]


class TracerSessionResult(TracerSession):
    """A project, hydrated with additional information.

    Sessions are also referred to as "Projects" in the UI.
    """

    run_count: Optional[int] = None
    """The number of runs in the project."""
    latency_p50: Optional[timedelta] = None
    """The median (50th percentile) latency for the project."""
    latency_p99: Optional[timedelta] = None
    """The 99th percentile latency for the project."""
    total_tokens: Optional[int] = None
    """The total number of tokens consumed in the project."""
    prompt_tokens: Optional[int] = None
    """The total number of prompt tokens consumed in the project."""
    completion_tokens: Optional[int] = None
    """The total number of completion tokens consumed in the project."""
    last_run_start_time: Optional[datetime] = None
    """The start time of the last run in the project."""
    feedback_stats: Optional[dict[str, Any]] = None
    """Feedback stats for the project."""
    session_feedback_stats: Optional[dict[str, Any]] = None
    """Summary feedback stats for the project."""
    run_facets: Optional[list[dict[str, Any]]] = None
    """Facets for the runs in the project."""
    total_cost: Optional[Decimal] = None
    """The total estimated LLM cost associated with the completion tokens."""
    prompt_cost: Optional[Decimal] = None
    """The estimated cost associated with the prompt (input) tokens."""
    completion_cost: Optional[Decimal] = None
    """The estimated cost associated with the completion tokens."""
    first_token_p50: Optional[timedelta] = None
    """The median (50th percentile) time to process the first token."""
    first_token_p99: Optional[timedelta] = None
    """The 99th percentile time to process the first token."""
    error_rate: Optional[float] = None
    """The error rate for the project."""


@runtime_checkable
class BaseMessageLike(Protocol):
    """A protocol representing objects similar to BaseMessage."""

    content: str
    """The content of the message."""
    additional_kwargs: dict[Any, Any]
    """Additional keyword arguments associated with the message."""

    @property
    def type(self) -> str:
        """Type of the Message, used for serialization."""


class DatasetShareSchema(TypedDict, total=False):
    """Represents the schema for a dataset share."""

    dataset_id: UUID
    """The ID of the dataset."""
    share_token: UUID
    """The token for sharing the dataset."""
    url: str
    """The URL of the shared dataset."""


class AnnotationQueueRubricItem(TypedDict, total=False):
    """Represents a rubric item assigned to an annotation queue.

    Links a feedback config to a queue with optional per-queue customization.
    """

    feedback_key: str
    """The feedback key to include in this queue's rubric."""
    description: Optional[str]
    """Instructions for annotators on how to evaluate this item."""
    value_descriptions: Optional[dict[str, str]]
    """Display text for categorical feedback values."""
    score_descriptions: Optional[dict[str, str]]
    """Display text for score ranges."""
    is_required: Optional[bool]
    """Whether feedback for this rubric item is required before submission."""


class AnnotationQueue(BaseModel):
    """Represents an annotation queue."""

    id: UUID
    """The unique identifier of the annotation queue."""
    name: str
    """The name of the annotation queue."""
    description: Optional[str] = None
    """An optional description of the annotation queue."""
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    """The timestamp when the annotation queue was created."""
    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    """The timestamp when the annotation queue was last updated."""
    tenant_id: UUID
    """The ID of the tenant associated with the annotation queue."""


class AnnotationQueueWithDetails(AnnotationQueue):
    """Represents an annotation queue with details."""

    rubric_instructions: Optional[str] = None
    """The rubric instructions for the annotation queue."""


class BatchIngestConfig(TypedDict, total=False):
    """Configuration for batch ingestion."""

    use_multipart_endpoint: bool
    """Whether to use the multipart endpoint for batch ingestion."""
    scale_up_qsize_trigger: int
    """The queue size threshold that triggers scaling up."""
    scale_up_nthreads_limit: int
    """The maximum number of threads to scale up to."""
    scale_down_nempty_trigger: int
    """The number of empty threads that triggers scaling down."""
    size_limit: int
    """The maximum size limit for the batch."""
    size_limit_bytes: Optional[int]
    """The maximum size limit in bytes for the batch."""


class LangSmithInfo(BaseModel):
    """Information about the LangSmith server."""

    version: str = ""
    """The version of the LangSmith server."""
    license_expiration_time: Optional[datetime] = None
    """The time the license will expire."""
    batch_ingest_config: Optional[BatchIngestConfig] = None
    """The instance flags."""
    instance_flags: Optional[dict[str, Any]] = None


Example.model_rebuild()


class LangSmithSettings(BaseModel):
    """Settings for the LangSmith tenant."""

    id: str
    """The ID of the tenant."""
    display_name: str
    """The display name of the tenant."""
    created_at: datetime
    """The creation time of the tenant."""
    tenant_handle: Optional[str] = None


class FeedbackIngestToken(BaseModel):
    """Represents the schema for a feedback ingest token."""

    id: UUID
    """The ID of the feedback ingest token."""
    url: str
    """The URL to GET when logging the feedback."""
    expires_at: datetime
    """The expiration time of the token."""


class RunEvent(TypedDict, total=False):
    """Run event schema."""

    name: str
    """Type of event."""
    time: Union[datetime, str]
    """Time of the event."""
    kwargs: Optional[dict[str, Any]]
    """Additional metadata for the event."""


class TimeDeltaInput(TypedDict, total=False):
    """Timedelta input schema."""

    days: int
    """Number of days."""
    hours: int
    """Number of hours."""
    minutes: int
    """Number of minutes."""


class DatasetDiffInfo(BaseModel):
    """Represents the difference information between two datasets."""

    examples_modified: list[UUID]
    """A list of UUIDs representing the modified examples."""
    examples_added: list[UUID]
    """A list of UUIDs representing the added examples."""
    examples_removed: list[UUID]
    """A list of UUIDs representing the removed examples."""


class ComparativeExperiment(BaseModel):
    """Represents a comparative experiment.

    This information summarizes evaluation results comparing
    two or more models on a given dataset.
    """

    id: UUID
    """The unique identifier for the comparative experiment."""
    name: Optional[str] = None
    """The optional name of the comparative experiment."""
    description: Optional[str] = None
    """An optional description of the comparative experiment."""
    tenant_id: UUID
    """The identifier of the tenant associated with this experiment."""
    created_at: datetime
    """The timestamp when the comparative experiment was created."""
    modified_at: datetime
    """The timestamp when the comparative experiment was last modified."""
    reference_dataset_id: UUID
    """The identifier of the reference dataset used in this experiment."""
    extra: Optional[dict[str, Any]] = None
    """Optional additional information about the experiment."""
    experiments_info: Optional[list[dict]] = None
    """Optional list of dictionaries containing information about individual experiments."""
    feedback_stats: Optional[dict[str, Any]] = None
    """Optional dictionary containing feedback statistics for the experiment."""

    @property
    def metadata(self) -> dict[str, Any]:
        """Retrieve the metadata (if any)."""
        if self.extra is None or "metadata" not in self.extra:
            return {}
        return self.extra["metadata"]


class PromptCommit(BaseModel):
    """Represents a Prompt with a manifest."""

    model_config = ConfigDict(populate_by_name=True)

    owner: str
    """The handle of the owner of the prompt."""
    repo: str
    """The name of the prompt."""
    commit_hash: str
    """The commit hash of the prompt."""
    manifest: dict[str, Any]
    """The manifest of the prompt."""
    examples: list[dict]
    """The list of examples."""
    description: Optional[str] = None
    """Optional human-readable description for the commit."""
    hub_model_config: Optional[dict] = Field(default=None, alias="model_config")
    """The model configuration for the prompt."""
    hub_model_provider: Optional[str] = Field(default=None, alias="model_provider")
    """The model provider (e.g. ChatOpenAI)."""


class ListedPromptCommit(BaseModel):
    """Represents a listed prompt commit with associated metadata."""

    id: UUID
    """The unique identifier for the prompt commit."""

    owner: str
    """The owner of the prompt commit."""

    repo: str
    """The repository name of the prompt commit."""

    manifest_id: Optional[UUID] = None
    """The optional identifier for the manifest associated with this commit."""

    repo_id: Optional[UUID] = None
    """The optional identifier for the repository."""

    parent_id: Optional[UUID] = None
    """The optional identifier for the parent commit."""

    commit_hash: Optional[str] = None
    """The optional hash of the commit."""

    created_at: Optional[datetime] = None
    """The optional timestamp when the commit was created."""

    updated_at: Optional[datetime] = None
    """The optional timestamp when the commit was last updated."""

    example_run_ids: Optional[list[UUID]] = Field(default_factory=list)
    """A list of example run identifiers associated with this commit."""

    num_downloads: Optional[int] = 0
    """The number of times this commit has been downloaded."""

    num_views: Optional[int] = 0
    """The number of times this commit has been viewed."""

    parent_commit_hash: Optional[str] = None
    """The optional hash of the parent commit."""

    description: Optional[str] = None
    """Optional human-readable description for the commit."""


class Prompt(BaseModel):
    """Represents a Prompt with metadata."""

    repo_handle: str
    """The name of the prompt."""
    description: Optional[str] = None
    """The description of the prompt."""
    readme: Optional[str] = None
    """The README of the prompt."""
    id: str
    """The ID of the prompt."""
    tenant_id: str
    """The tenant ID of the prompt owner."""
    created_at: datetime
    """The creation time of the prompt."""
    updated_at: datetime
    """The last update time of the prompt."""
    is_public: bool
    """Whether the prompt is public."""
    is_archived: bool
    """Whether the prompt is archived."""
    tags: list[str]
    """The tags associated with the prompt."""
    original_repo_id: Optional[str] = None
    """The ID of the original prompt, if forked."""
    upstream_repo_id: Optional[str] = None
    """The ID of the upstream prompt, if forked."""
    owner: Optional[str]
    """The handle of the owner of the prompt."""
    full_name: str
    """The full name of the prompt. (owner + repo_handle)"""
    num_likes: int
    """The number of likes."""
    num_downloads: int
    """The number of downloads."""
    num_views: int
    """The number of views."""
    liked_by_auth_user: Optional[bool] = None
    """Whether the prompt is liked by the authenticated user."""
    last_commit_hash: Optional[str] = None
    """The hash of the last commit."""
    num_commits: int
    """The number of commits."""
    original_repo_full_name: Optional[str] = None
    """The full name of the original prompt, if forked."""
    upstream_repo_full_name: Optional[str] = None
    """The full name of the upstream prompt, if forked."""


class ListPromptsResponse(BaseModel):
    """A list of prompts with metadata."""

    repos: list[Prompt]
    """The list of prompts."""
    total: int
    """The total number of prompts."""


class PromptSortField(str, Enum):
    """Enum for sorting fields for prompts."""

    num_downloads = "num_downloads"
    """Number of downloads."""
    num_views = "num_views"
    """Number of views."""
    updated_at = "updated_at"
    """Last updated time."""
    num_likes = "num_likes"
    """Number of likes."""


class FileEntry(BaseModel):
    """A file with inline content."""

    type: Literal["file"] = "file"
    """The entry type."""
    content: str
    """The file content."""


class AgentEntry(BaseModel):
    """A link to another agent repo."""

    type: Literal["agent"] = "agent"
    """The entry type."""
    repo_handle: str
    """The handle of the linked repo."""
    commit_id: Optional[UUID] = None
    """The commit ID of the linked repo, if pinned."""
    owner: Optional[str] = None
    """The owner of the linked repo."""
    commit_hash: Optional[str] = None
    """The commit hash of the linked repo."""


class SkillEntry(BaseModel):
    """A link to a skill repo."""

    type: Literal["skill"] = "skill"
    """The entry type."""
    repo_handle: str
    """The handle of the linked repo."""
    commit_id: Optional[UUID] = None
    """The commit ID of the linked repo, if pinned."""
    owner: Optional[str] = None
    """The owner of the linked repo."""
    commit_hash: Optional[str] = None
    """The commit hash of the linked repo."""


Entry = Annotated[Union[FileEntry, AgentEntry, SkillEntry], Field(discriminator="type")]
"""A hub directory entry, discriminated by `type`."""


class AgentContext(BaseModel):
    """An agent pulled from hub."""

    commit_id: UUID
    """The commit ID."""
    commit_hash: str
    """The commit hash."""
    files: dict[str, Entry]
    """The files in the agent."""


class SkillContext(BaseModel):
    """A skill pulled from hub."""

    commit_id: UUID
    """The commit ID."""
    commit_hash: str
    """The commit hash."""
    files: dict[str, Entry]
    """The files in the skill."""


class DirectoryCommitInfo(BaseModel):
    """Commit details returned from a directory commit."""

    id: UUID
    """The commit ID."""
    commit_hash: str
    """The commit hash."""
    created_at: datetime
    """When the commit was created."""


class DirectoryCommitResponse(BaseModel):
    """Response body for ``POST /directories/commits``."""

    commit: DirectoryCommitInfo
    """The created commit."""


class InputTokenDetails(TypedDict, total=False):
    """Breakdown of input token counts.

    Does *not* need to sum to full input token count. Does *not* need to have all keys.
    """

    audio: int
    """Audio input tokens."""
    cache_creation: int
    """Input tokens that were cached and there was a cache miss.

    Since there was a cache miss, the cache was created from these tokens.
    """
    cache_read: int
    """Input tokens that were cached and there was a cache hit.
    Since there was a cache hit, the tokens were read from the cache. More precisely,
    the model state given these tokens was read from the cache.
    """
    cache_read_over_200k: int
    """Input tokens that were cached and there was a cache hit over 200k. Part of Gemini pricing.
    """
    ephemeral_5m_input_tokens: int
    """Input tokens used to create a 5-minute ephemeral cache entry (Anthropic)."""
    ephemeral_1h_input_tokens: int
    """Input tokens used to create a 1-hour ephemeral cache entry (Anthropic)."""


class OutputTokenDetails(TypedDict, total=False):
    """Breakdown of output token counts.

    Does *not* need to sum to full output token count. Does *not* need to have all keys.
    """

    audio: int
    """Audio output tokens."""
    reasoning: int
    """Reasoning output tokens.

    Tokens generated by the model in a chain of thought process (i.e. by OpenAI's o1
    models) that are not returned as part of model output.
    """


class InputCostDetails(TypedDict, total=False):
    """Breakdown of input token costs.

    Does *not* need to sum to full input cost. Does *not* need to have all keys.
    """

    audio: float
    """Cost of audio input tokens."""
    cache_creation: float
    """Cost of input tokens that were cached and there was a cache miss.

    Since there was a cache miss, the cache was created from these tokens.
    """
    cache_read: float
    """Cost of input tokens that were cached and there was a cache hit.

    Since there was a cache hit, the tokens were read from the cache. More precisely,
    the model state given these tokens was read from the cache.
    """


class OutputCostDetails(TypedDict, total=False):
    """Breakdown of output token costs.

    Does *not* need to sum to full output cost. Does *not* need to have all keys.
    """

    audio: float
    """Cost of audio output tokens."""
    reasoning: float
    """Cost of reasoning output tokens.

    Tokens generated by the model in a chain of thought process (i.e. by OpenAI's o1
    models) that are not returned as part of model output.
    """


class UsageMetadata(TypedDict):
    """Usage metadata for a message, such as token counts.

    This is a standard representation of token usage that is consistent across models.
    """

    input_tokens: int
    """Count of input (or prompt) tokens. Sum of all input token types."""
    output_tokens: int
    """Count of output (or completion) tokens. Sum of all output token types."""
    total_tokens: int
    """Total token count. Sum of input_tokens + output_tokens."""
    input_token_details: NotRequired[InputTokenDetails]
    """Breakdown of input token counts.

    Does *not* need to sum to full input token count. Does *not* need to have all keys.
    """
    output_token_details: NotRequired[OutputTokenDetails]
    """Breakdown of output token counts.

    Does *not* need to sum to full output token count. Does *not* need to have all keys.
    """
    input_cost: NotRequired[float]
    """The cost of the input tokens."""
    output_cost: NotRequired[float]
    """The cost of the output tokens."""
    total_cost: NotRequired[float]
    """The total cost of the tokens."""
    input_cost_details: NotRequired[InputCostDetails]
    """The cost details of the input tokens."""
    output_cost_details: NotRequired[OutputCostDetails]
    """The cost details of the output tokens."""


class ExtractedUsageMetadata(TypedDict, total=False):
    """Usage metadata dictionary extracted from a run.

    Should be the same as UsageMetadata, but does not require all
    keys to be present.
    """

    input_tokens: int
    """The number of tokens used for the prompt."""
    output_tokens: int
    """The number of tokens generated as output."""
    total_tokens: int
    """The total number of tokens used."""
    input_token_details: InputTokenDetails
    """The details of the input tokens."""
    output_token_details: OutputTokenDetails
    """The details of the output tokens."""
    input_cost: float
    """The cost of the input tokens."""
    output_cost: float
    """The cost of the output tokens."""
    total_cost: float
    """The total cost of the tokens."""
    input_cost_details: InputCostDetails
    """The cost details of the input tokens."""
    output_cost_details: OutputCostDetails
    """The cost details of the output tokens."""


class UpsertExamplesResponse(TypedDict):
    """Response object returned from the upsert_examples_multipart method."""

    count: int
    """The number of examples that were upserted."""
    example_ids: list[str]
    """The ids of the examples that were upserted."""
    as_of: NotRequired[str | None]
    """The timestamp when the examples were created/updated. None if backend doesn't support it."""


class ExampleWithRuns(Example):
    """Example with runs."""

    runs: list[Run] = Field(default_factory=list)

    """The runs of the example."""


class ExperimentRunStats(TypedDict):
    """Run statistics for an experiment."""

    run_count: Optional[int]
    """The number of runs in the project."""
    latency_p50: Optional[timedelta]
    """The median (50th percentile) latency for the project."""
    latency_p99: Optional[timedelta]
    """The 99th percentile latency for the project."""
    total_tokens: Optional[int]
    """The total number of tokens consumed in the project."""
    prompt_tokens: Optional[int]
    """The total number of prompt tokens consumed in the project."""
    completion_tokens: Optional[int]
    """The total number of completion tokens consumed in the project."""
    last_run_start_time: Optional[datetime]
    """The start time of the last run in the project."""
    run_facets: Optional[list[dict[str, Any]]]
    """Facets for the runs in the project."""
    total_cost: Optional[Decimal]
    """The total estimated LLM cost associated with the completion tokens."""
    prompt_cost: Optional[Decimal]
    """The estimated cost associated with the prompt (input) tokens."""
    completion_cost: Optional[Decimal]
    """The estimated cost associated with the completion tokens."""
    first_token_p50: Optional[timedelta]
    """The median (50th percentile) time to process the first token."""
    first_token_p99: Optional[timedelta]
    """The 99th percentile time to process the first token."""
    error_rate: Optional[float]
    """The error rate for the project."""


class ExperimentResults(TypedDict):
    """Results container for experiment data with stats and examples.

    Breaking change in v0.4.32:
        The 'stats' field has been split into 'feedback_stats' and 'run_stats'.
    """

    feedback_stats: dict
    """Feedback statistics for the experiment."""
    run_stats: ExperimentRunStats
    """Run statistics (latency, token count, etc.)."""
    examples_with_runs: Iterator[ExampleWithRuns]


class InsightsReport(BaseModel):
    """An Insights Report created by the Insights Agent over a tracing project."""

    id: UUID | str
    name: str
    status: str
    error: str | None = None
    project_id: UUID | str
    host_url: str
    tenant_id: UUID | str

    @property
    def link(self) -> str:
        """URL to view this Insights Report in LangSmith UI."""
        return f"{self.host_url}/o/{str(self.tenant_id)}/projects/p/{str(self.project_id)}?tab=4&clusterJobId={str(self.id)}"

    def _repr_html_(self) -> str:
        return f'<a href="{self.link}", target="_blank" rel="noopener">InsightsReport(\'{self.name}\')</a>'


class InsightsHighlightedTrace(BaseModel):
    """A trace highlighted in an insights report summary."""

    run_id: UUID | str
    cluster_id: UUID | str | None = None
    cluster_name: str | None = None
    rank: int
    highlight_reason: str
    summary: str | None = None


class InsightsSummaryReport(BaseModel):
    """High-level summary of an insights job: key points and highlighted traces."""

    key_points: list[str] = Field(default_factory=list)
    title: str | None = None
    highlighted_traces: list[InsightsHighlightedTrace] = Field(default_factory=list)
    created_at: datetime | None = None


class InsightsCluster(BaseModel):
    """A single cluster of runs in an insights report."""

    id: UUID | str
    parent_id: UUID | str | None = None
    level: int
    name: str
    description: str
    parent_name: str | None = None
    num_runs: int
    stats: dict[str, Any] | None = None


def _fetch_insights_runs(
    client: Any,
    session_id: str | UUID,
    job_id: str | UUID,
    *,
    cluster_id: str | UUID | None = None,
) -> list[dict[str, Any]]:
    """Fetch runs for an Insights job, optionally filtered to a single cluster."""
    from langsmith import utils as ls_utils  # local import to avoid circular deps

    all_runs: list[dict[str, Any]] = []
    limit = 100
    next_offset: int | None = 0
    while next_offset is not None:
        params: dict[str, Any] = {"limit": limit, "offset": next_offset}
        if cluster_id is not None:
            params["cluster_id"] = str(cluster_id)

        resp = client.request_with_retries(
            "GET",
            f"/sessions/{session_id}/insights/{job_id}/runs",
            params=params,
        )
        ls_utils.raise_for_status_with_text(resp)
        body = resp.json()
        batch = body.get("runs", []) or []
        all_runs.extend(batch)
        returned_offset = body.get("offset")
        if returned_offset is None:
            next_offset = None
        else:
            next_offset = int(returned_offset)
            if not batch:
                next_offset = None
    return all_runs


class _ClusterWithTraces:
    """Cluster wrapper that can load its traces from the API. Returned by report.clusters[name]."""

    __slots__ = ("_cluster", "_report")

    def __init__(self, cluster: InsightsCluster, report: InsightsReportResult) -> None:
        self._cluster = cluster
        self._report = report

    def __getattr__(self, name: str) -> Any:
        return getattr(self._cluster, name)

    def __dir__(self) -> list[str]:
        return list(set(dir(self.__class__)) | set(dir(self._cluster)))

    def __repr__(self) -> str:
        return f"_ClusterWithTraces(name={self._cluster.name!r})"

    def load_traces(self) -> list[dict[str, Any]]:
        """Fetch run dicts for this cluster from the API."""
        client = getattr(self._report, "_client", None)
        if client is None:
            raise ValueError(
                "Report not attached to a client; get reports via Client.get_insights_report()"
            )
        session_id = getattr(self._report, "_session_id", None)
        job_id = getattr(self._report, "_job_id", None)
        if session_id is None or job_id is None:
            raise ValueError("Report missing session/job ids")

        return _fetch_insights_runs(
            client=client,
            session_id=session_id,
            job_id=job_id,
            cluster_id=self._cluster.id,
        )


class _ClustersMap:
    """List-like map of clusters by name. Supports report.clusters['name'] and .load_traces()."""

    __slots__ = ("_clusters", "_report")

    def __init__(
        self,
        clusters: list[InsightsCluster],
        report: InsightsReportResult,
    ) -> None:
        self._clusters = list(clusters)
        self._report = report

    def __getitem__(self, key: str | int) -> _ClusterWithTraces:
        if isinstance(key, int):
            cluster = self._clusters[key]
        else:
            by_name = {c.name: c for c in self._clusters}
            if key not in by_name:
                raise KeyError(
                    f"Cluster {key!r} not found; available: {list(by_name.keys())}"
                )
            cluster = by_name[key]
        return _ClusterWithTraces(cluster, self._report)

    def __iter__(self):
        return (_ClusterWithTraces(c, self._report) for c in self._clusters)

    def __len__(self) -> int:
        return len(self._clusters)

    def __repr__(self) -> str:
        return f"_ClustersMap(n_clusters={len(self._clusters)})"


class InsightsReportResult(BaseModel):
    """Full result of fetching an Insights report (job + clusters + summary + optional runs)."""

    id: UUID | str
    name: str
    status: str
    start_time: datetime | str | None = None
    end_time: datetime | str | None = None
    created_at: datetime | str | None = None
    metadata: dict[str, Any] | None = None
    shape: dict[str, int] | None = None
    error: str | None = None
    config_id: UUID | str | None = None
    clusters: list[InsightsCluster] = Field(default_factory=list)
    report: InsightsSummaryReport | None = None
    runs: list[dict[str, Any]] = Field(default_factory=list)
    """Run dicts when fetched with include_runs=True."""

    _client: Any = PrivateAttr(default=None)
    _session_id: Any = PrivateAttr(default=None)
    _job_id: Any = PrivateAttr(default=None)

    model_config = ConfigDict(extra="allow")

    def _attach_client(
        self,
        client: Any,
        session_id: str | UUID,
        job_id: str | UUID,
    ) -> None:
        """Attach client and ids so clusters can load traces. Called by Client.get_insights_report."""
        object.__setattr__(self, "_client", client)
        object.__setattr__(self, "_session_id", str(session_id))
        object.__setattr__(self, "_job_id", str(job_id))
        object.__setattr__(
            self,
            "clusters",
            _ClustersMap(self.clusters, self),
        )

    def to_json(self) -> dict[str, Any]:
        """Return the report as a JSON-serializable dict (raw JSON shape)."""
        clusters_val = self.clusters
        if hasattr(clusters_val, "_clusters"):
            object.__setattr__(self, "clusters", clusters_val._clusters)
        try:
            return self.model_dump(mode="json")
        finally:
            if hasattr(clusters_val, "_clusters"):
                object.__setattr__(self, "clusters", clusters_val)

    def model_dump_json_dict(self) -> dict[str, Any]:
        """Return the report as a JSON-serializable dict (e.g. for saving to file)."""
        return self.model_dump(mode="json")


class FeedbackFormulaWeightedVariable(BaseModel):
    """A feedback key and weight used when calculating feedback formulas."""

    part_type: Literal["weighted_key"]
    weight: float
    key: Annotated[str, Field(min_length=1)]


class FeedbackFormulaCreate(BaseModel):
    """Schema used for creating a feedback formula."""

    dataset_id: Optional[UUID] = None
    session_id: Optional[UUID] = None
    feedback_key: str
    aggregation_type: Literal["sum", "avg"]
    formula_parts: list[FeedbackFormulaWeightedVariable] = Field(
        ..., min_length=1, max_length=50
    )


class FeedbackFormulaUpdate(BaseModel):
    """Schema used for updating a feedback formula."""

    feedback_key: str
    aggregation_type: Literal["sum", "avg"]
    formula_parts: list[FeedbackFormulaWeightedVariable] = Field(
        ..., min_length=1, max_length=50
    )


class FeedbackFormula(FeedbackFormulaCreate):
    """Schema for getting feedback formulas."""

    id: UUID
    created_at: datetime
    modified_at: datetime


class FeedbackConfigSchema(BaseModel):
    """Represents a feedback configuration for a tenant's feedback key.

    Feedback configurations define how feedback with a given key should be
    interpreted, including its type (continuous, categorical, or freeform),
    scoring bounds, and valid categories.
    """

    feedback_key: str
    """The unique key identifying this feedback configuration."""
    feedback_config: FeedbackConfig
    """The configuration specifying the type, bounds, and categories."""
    tenant_id: UUID
    """The ID of the tenant that owns this feedback configuration."""
    modified_at: datetime
    """When this feedback configuration was last modified."""
    is_lower_score_better: Optional[bool] = None
    """Whether a lower score is considered better for this feedback key."""
