Move files

great_ai/views/__init__.py

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+from .api_metadata import ApiMetadata
+from .cache_statistics import CacheStatistics
+from .evaluation_feedback_request import EvaluationFeedbackRequest
+from .filter import Filter
+from .function_metadata import FunctionMetadata
+from .health_check_response import HealthCheckResponse
+from .model import Model
+from .operators import operators
+from .query import Query
+from .sort_by import SortBy
+from .trace import Trace

great_ai/views/api_metadata.py

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+from typing import Any
+
+from pydantic import BaseModel
+
+
+class ApiMetadata(BaseModel):
+    name: str
+    version: str
+    documentation: str
+    configuration: Any

great_ai/views/cache_statistics.py

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+from pydantic import BaseModel
+
+
+class CacheStatistics(BaseModel):
+    hits: int
+    misses: int
+    size: int
+    max_size: int

great_ai/views/evaluation_feedback_request.py

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+from typing import Any
+
+from pydantic import BaseModel
+
+
+class EvaluationFeedbackRequest(BaseModel):
+    feedback: Any

great_ai/views/filter.py

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+from typing import Union
+
+from pydantic import BaseModel
+
+from .operators import Operator
+
+
+class Filter(BaseModel):
+    property: str
+    operator: Operator
+    value: Union[float, str]

great_ai/views/function_metadata.py

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+from typing import List
+
+from pydantic import BaseModel
+
+
+class FunctionMetadata(BaseModel):
+    input_parameter_names: List[str] = []
+    model_parameter_names: List[str] = []
+    is_finalised: bool = False

great_ai/views/health_check_response.py

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+from pydantic import BaseModel
+
+from .cache_statistics import CacheStatistics
+
+
+class HealthCheckResponse(BaseModel):
+    is_healthy: bool
+    cache_statistics: CacheStatistics

great_ai/views/model.py

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+from pydantic import BaseModel
+
+
+class Model(BaseModel):
+    key: str
+    version: int

great_ai/views/operators.py

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+from typing import List, Literal
+
+Operator = Literal[">=", "<=", "<", ">", "!=", "=", "contains"]
+
+operators: List[Operator] = [">=", "<=", "<", ">", "!=", "=", "contains"]

great_ai/views/query.py

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+from datetime import datetime
+from typing import List, Optional, Sequence
+
+from pydantic import BaseModel
+
+from .filter import Filter
+from .sort_by import SortBy
+
+
+class Query(BaseModel):
+    filter: List[Filter] = []
+    sort: List[SortBy] = []
+    conjunctive_tags: Sequence[str] = []
+    since: Optional[datetime] = None
+    until: Optional[datetime] = None
+    has_feedback: Optional[bool] = None
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "filter": [
+                    {
+                        "property": "original_execution_time_ms",
+                        "operator": ">",
+                        "value": 100,
+                    }
+                ],
+                "sort": [
+                    {"column_id": "original_execution_time_ms", "direction": "asc"},
+                    {"column_id": "id", "direction": "desc"},
+                ],
+                "conjunctive_tags": ["online"],
+                "has_feedback": False,
+            }
+        }

great_ai/views/sort_by.py

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+from typing import Literal
+
+from pydantic import BaseModel
+
+
+class SortBy(BaseModel):
+    column_id: str
+    direction: Literal["asc", "desc"]

great_ai/views/trace.py

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+from pprint import pformat
+from typing import Any, Dict, Generic, List, Optional, TypeVar
+from uuid import uuid4
+
+from pydantic import BaseModel, Extra, validator
+
+from ..helper import HashableBaseModel
+from .model import Model
+
+T = TypeVar("T")
+
+
+class Trace(Generic[T], HashableBaseModel):
+    trace_id: Optional[str]
+    created: str
+    original_execution_time_ms: float
+    logged_values: Dict[str, Any]
+    models: List[Model]
+    exception: Optional[str]
+    output: T
+    feedback: Any = None
+    tags: List[str]
+
+    class Config:
+        extra = Extra.ignore
+
+    @validator("trace_id", always=True)
+    def generate_id(cls, v: Optional[str], values: Dict[str, Any]) -> Optional[str]:
+        if not v:
+            return str(uuid4())
+        return v
+
+    @property
+    def input(self) -> Any:
+        return (
+            self.logged_values["input"]
+            if list(self.logged_values.keys()) == ["input"]
+            else self.logged_values
+        )
+
+    @property
+    def models_flat(self) -> str:
+        return ", ".join(f"{m.key}:{m.version}" for m in self.models)
+
+    @property
+    def output_flat(self) -> str:
+        return pformat(self.output, indent=2, compact=True)
+
+    @property
+    def exception_flat(self) -> str:
+        return (
+            "null"
+            if self.exception is None
+            else pformat(self.exception, indent=2, compact=True)
+        )
+
+    @property
+    def feedback_flat(self) -> str:
+        return (
+            "null"
+            if self.feedback is None
+            else pformat(self.feedback, indent=2, compact=True)
+        )
+
+    @property
+    def tags_flat(self) -> str:
+        return ",\n".join(self.tags)
+
+    def to_flat_dict(self, include_original: bool = True) -> Dict[str, Any]:
+        return {
+            **(
+                self.dict()
+                if include_original
+                else {
+                    "trace_id": self.trace_id,
+                    "created": self.created,
+                    "original_execution_time_ms": self.original_execution_time_ms,
+                }
+            ),
+            **{
+                k: v.dict() if isinstance(v, BaseModel) else v
+                for k, v in self.logged_values.items()
+            },
+            "models_flat": self.models_flat,
+            "exception_flat": self.exception_flat,
+            "output_flat": self.output_flat,
+            "feedback_flat": self.feedback_flat,
+            "tags_flat": self.tags_flat,
+        }