feat: support NOT_GIVEN type

ai21/clients/bedrock/resources/bedrock_completion.py

@@ -1,45 +1,45 @@
-from typing import Optional, List
+from __future__ import annotations
+
+from typing import List
 
 from ai21.clients.bedrock.resources.bedrock_resource import BedrockResource
 from ai21.models import Penalty, CompletionsResponse
+from ai21.types import NotGiven, NOT_GIVEN
+from ai21.utils.typing import remove_not_given
 
 
 class BedrockCompletion(BedrockResource):
     def create(
         self,
         prompt: str,
         *,
-        max_tokens: Optional[int] = None,
-        num_results: Optional[int] = 1,
-        min_tokens: Optional[int] = 0,
-        temperature: Optional[float] = 0.7,
-        top_p: Optional[int] = 1,
-        top_k_return: Optional[int] = 0,
-        stop_sequences: Optional[List[str]] = None,
-        frequency_penalty: Optional[Penalty] = None,
-        presence_penalty: Optional[Penalty] = None,
-        count_penalty: Optional[Penalty] = None,
+        max_tokens: int | NotGiven = NOT_GIVEN,
+        num_results: int | NotGiven = NOT_GIVEN,
+        min_tokens: int | NotGiven = NOT_GIVEN,
+        temperature: float | NotGiven = NOT_GIVEN,
+        top_p: float | NotGiven = NOT_GIVEN,
+        top_k_return: int | NotGiven = NOT_GIVEN,
+        stop_sequences: List[str] | NotGiven = NOT_GIVEN,
+        frequency_penalty: Penalty | NotGiven = NOT_GIVEN,
+        presence_penalty: Penalty | NotGiven = NOT_GIVEN,
+        count_penalty: Penalty | NotGiven = NOT_GIVEN,
         **kwargs,
     ) -> CompletionsResponse:
-        body = {
-            "prompt": prompt,
-            "maxTokens": max_tokens,
-            "numResults": num_results,
-            "minTokens": min_tokens,
-            "temperature": temperature,
-            "topP": top_p,
-            "topKReturn": top_k_return,
-            "stopSequences": stop_sequences or [],
-        }
-
-        if frequency_penalty is not None:
-            body["frequencyPenalty"] = frequency_penalty.to_dict()
-
-        if presence_penalty is not None:
-            body["presencePenalty"] = presence_penalty.to_dict()
-
-        if count_penalty is not None:
-            body["countPenalty"] = count_penalty.to_dict()
+        body = remove_not_given(
+            {
+                "prompt": prompt,
+                "maxTokens": max_tokens,
+                "numResults": num_results,
+                "minTokens": min_tokens,
+                "temperature": temperature,
+                "topP": top_p,
+                "topKReturn": top_k_return,
+                "stopSequences": stop_sequences or [],
+                "frequencyPenalty": frequency_penalty.to_dict() if frequency_penalty else frequency_penalty,
+                "presencePenalty": presence_penalty.to_dict() if presence_penalty else presence_penalty,
+                "countPenalty": count_penalty.to_dict() if count_penalty else count_penalty,
+            }
+        )
 
         model_id = kwargs.get("model_id", self._model_id)
 

ai21/clients/common/completion_base.py

@@ -1,7 +1,11 @@
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
-from typing import Optional, List, Dict, Any
+from typing import List, Dict, Any
 
 from ai21.models import Penalty, CompletionsResponse
+from ai21.types import NOT_GIVEN, NotGiven
+from ai21.utils.typing import remove_not_given
 
 
 class Completion(ABC):
@@ -13,18 +17,18 @@ def create(
         model: str,
         prompt: str,
         *,
-        max_tokens: int = 64,
-        num_results: int = 1,
-        min_tokens=0,
-        temperature=0.7,
-        top_p=1,
-        top_k_return=0,
-        custom_model: Optional[str] = None,
-        stop_sequences: Optional[List[str]] = (),
-        frequency_penalty: Optional[Penalty] = None,
-        presence_penalty: Optional[Penalty] = None,
-        count_penalty: Optional[Penalty] = None,
-        epoch: Optional[int] = None,
+        max_tokens: int | NotGiven = NOT_GIVEN,
+        num_results: int | NotGiven = NOT_GIVEN,
+        min_tokens: int | NotGiven = NOT_GIVEN,
+        temperature: float | NOT_GIVEN = NOT_GIVEN,
+        top_p: float | NotGiven = NOT_GIVEN,
+        top_k_return: int | NotGiven = NOT_GIVEN,
+        custom_model: str | NotGiven = NOT_GIVEN,
+        stop_sequences: List[str] | NotGiven = NOT_GIVEN,
+        frequency_penalty: Penalty | NotGiven = NOT_GIVEN,
+        presence_penalty: Penalty | NotGiven = NOT_GIVEN,
+        count_penalty: Penalty | NotGiven = NOT_GIVEN,
+        epoch: int | NotGiven = NOT_GIVEN,
         **kwargs,
     ) -> CompletionsResponse:
         """
@@ -54,32 +58,34 @@ def _create_body(
         self,
         model: str,
         prompt: str,
-        max_tokens: Optional[int],
-        num_results: Optional[int],
-        min_tokens: Optional[int],
-        temperature: Optional[float],
-        top_p: Optional[int],
-        top_k_return: Optional[int],
-        custom_model: Optional[str],
-        stop_sequences: Optional[List[str]],
-        frequency_penalty: Optional[Penalty],
-        presence_penalty: Optional[Penalty],
-        count_penalty: Optional[Penalty],
-        epoch: Optional[int],
+        max_tokens: int | NotGiven,
+        num_results: int | NotGiven,
+        min_tokens: int | NotGiven,
+        temperature: float | NotGiven,
+        top_p: float | NotGiven,
+        top_k_return: int | NotGiven,
+        custom_model: str | NotGiven,
+        stop_sequences: List[str] | NotGiven,
+        frequency_penalty: Penalty | NotGiven,
+        presence_penalty: Penalty | NotGiven,
+        count_penalty: Penalty | NotGiven,
+        epoch: int | NotGiven,
     ):
-        return {
-            "model": model,
-            "customModel": custom_model,
-            "prompt": prompt,
-            "maxTokens": max_tokens,
-            "numResults": num_results,
-            "minTokens": min_tokens,
-            "temperature": temperature,
-            "topP": top_p,
-            "topKReturn": top_k_return,
-            "stopSequences": stop_sequences or [],
-            "frequencyPenalty": None if frequency_penalty is None else frequency_penalty.to_dict(),
-            "presencePenalty": None if presence_penalty is None else presence_penalty.to_dict(),
-            "countPenalty": None if count_penalty is None else count_penalty.to_dict(),
-            "epoch": epoch,
-        }
+        return remove_not_given(
+            {
+                "model": model,
+                "customModel": custom_model,
+                "prompt": prompt,
+                "maxTokens": max_tokens,
+                "numResults": num_results,
+                "minTokens": min_tokens,
+                "temperature": temperature,
+                "topP": top_p,
+                "topKReturn": top_k_return,
+                "stopSequences": stop_sequences,
+                "frequencyPenalty": NOT_GIVEN if frequency_penalty is NOT_GIVEN else frequency_penalty.to_dict(),
+                "presencePenalty": NOT_GIVEN if presence_penalty is NOT_GIVEN else presence_penalty.to_dict(),
+                "countPenalty": NOT_GIVEN if count_penalty is NOT_GIVEN else count_penalty.to_dict(),
+                "epoch": epoch,
+            }
+        )

ai21/clients/sagemaker/resources/sagemaker_completion.py

@@ -1,45 +1,43 @@
-from typing import Optional, List
+from typing import List
 
 from ai21.clients.sagemaker.resources.sagemaker_resource import SageMakerResource
 from ai21.models import Penalty, CompletionsResponse
+from ai21.types import NotGiven, NOT_GIVEN
+from ai21.utils.typing import remove_not_given
 
 
 class SageMakerCompletion(SageMakerResource):
     def create(
         self,
         prompt: str,
         *,
-        max_tokens: Optional[int] = None,
-        num_results: Optional[int] = 1,
-        min_tokens: Optional[int] = 0,
-        temperature: Optional[float] = 0.7,
-        top_p: Optional[int] = 1,
-        top_k_return: Optional[int] = 0,
-        stop_sequences: Optional[List[str]] = None,
-        frequency_penalty: Optional[Penalty] = None,
-        presence_penalty: Optional[Penalty] = None,
-        count_penalty: Optional[Penalty] = None,
+        max_tokens: int | NotGiven = NOT_GIVEN,
+        num_results: int | NotGiven = NOT_GIVEN,
+        min_tokens: int | NotGiven = NOT_GIVEN,
+        temperature: float | NotGiven = NOT_GIVEN,
+        top_p: float | NotGiven = NOT_GIVEN,
+        top_k_return: int | NotGiven = NOT_GIVEN,
+        stop_sequences: List[str] | NotGiven = NOT_GIVEN,
+        frequency_penalty: Penalty | NotGiven = NOT_GIVEN,
+        presence_penalty: Penalty | NotGiven = NOT_GIVEN,
+        count_penalty: Penalty | NotGiven = NOT_GIVEN,
         **kwargs,
     ) -> CompletionsResponse:
-        body = {
-            "prompt": prompt,
-            "maxTokens": max_tokens,
-            "numResults": num_results,
-            "minTokens": min_tokens,
-            "temperature": temperature,
-            "topP": top_p,
-            "topKReturn": top_k_return,
-            "stopSequences": stop_sequences or [],
-        }
-
-        if frequency_penalty is not None:
-            body["frequencyPenalty"] = frequency_penalty.to_dict()
-
-        if presence_penalty is not None:
-            body["presencePenalty"] = presence_penalty.to_dict()
-
-        if count_penalty is not None:
-            body["countPenalty"] = count_penalty.to_dict()
+        body = remove_not_given(
+            {
+                "prompt": prompt,
+                "maxTokens": max_tokens,
+                "numResults": num_results,
+                "minTokens": min_tokens,
+                "temperature": temperature,
+                "topP": top_p,
+                "topKReturn": top_k_return,
+                "stopSequences": stop_sequences or [],
+                "frequencyPenalty": frequency_penalty.to_dict() if frequency_penalty else frequency_penalty,
+                "presencePenalty": presence_penalty.to_dict() if presence_penalty else presence_penalty,
+                "countPenalty": count_penalty.to_dict() if count_penalty else count_penalty,
+            }
+        )
 
         raw_response = self._invoke(body)
 

ai21/clients/studio/resources/studio_completion.py

@@ -1,8 +1,11 @@
-from typing import Optional, List
+from __future__ import annotations
+
+from typing import List
 
 from ai21.clients.common.completion_base import Completion
 from ai21.clients.studio.resources.studio_resource import StudioResource
 from ai21.models import Penalty, CompletionsResponse
+from ai21.types import NOT_GIVEN, NotGiven
 
 
 class StudioCompletion(StudioResource, Completion):
@@ -11,23 +14,23 @@ def create(
         model: str,
         prompt: str,
         *,
-        max_tokens: Optional[int] = None,
-        num_results: Optional[int] = 1,
-        min_tokens: Optional[int] = 0,
-        temperature: Optional[float] = 0.7,
-        top_p: Optional[float] = 1,
-        top_k_return: Optional[int] = 0,
-        custom_model: Optional[str] = None,
-        stop_sequences: Optional[List[str]] = None,
-        frequency_penalty: Optional[Penalty] = None,
-        presence_penalty: Optional[Penalty] = None,
-        count_penalty: Optional[Penalty] = None,
-        epoch: Optional[int] = None,
+        max_tokens: int | NotGiven = NOT_GIVEN,
+        num_results: int | NotGiven = NOT_GIVEN,
+        min_tokens: int | NotGiven = NOT_GIVEN,
+        temperature: float | NotGiven = NOT_GIVEN,
+        top_p: float | NotGiven = NOT_GIVEN,
+        top_k_return: int | NotGiven = NOT_GIVEN,
+        custom_model: str | NotGiven = NOT_GIVEN,
+        stop_sequences: List[str] | NotGiven = NOT_GIVEN,
+        frequency_penalty: Penalty | NotGiven = NOT_GIVEN,
+        presence_penalty: Penalty | NotGiven = NOT_GIVEN,
+        count_penalty: Penalty | NotGiven = NOT_GIVEN,
+        epoch: int | NotGiven = NOT_GIVEN,
         **kwargs,
     ) -> CompletionsResponse:
         url = f"{self._client.get_base_url()}/{model}"
 
-        if custom_model is not None:
+        if custom_model:
             url = f"{url}/{custom_model}"
 
         url = f"{url}/{self._module_name}"

ai21/models/ai21_base_model_mixin.py

@@ -1,7 +1,10 @@
 from dataclasses_json import LetterCase, DataClassJsonMixin
 
+from ai21.utils.typing import is_not_given
+
 
 class AI21BaseModelMixin(DataClassJsonMixin):
     dataclass_json_config = {
         "letter_case": LetterCase.CAMEL,
+        "exclude": is_not_given,
     }

ai21/models/penalty.py

@@ -1,14 +1,16 @@
+from __future__ import annotations
+
 from dataclasses import dataclass
-from typing import Optional
 
+from ai21.types import NOT_GIVEN, NotGiven
 from ai21.models.ai21_base_model_mixin import AI21BaseModelMixin
 
 
 @dataclass
 class Penalty(AI21BaseModelMixin):
     scale: float
-    apply_to_whitespaces: Optional[bool] = None
-    apply_to_punctuation: Optional[bool] = None
-    apply_to_numbers: Optional[bool] = None
-    apply_to_stopwords: Optional[bool] = None
-    apply_to_emojis: Optional[bool] = None
+    apply_to_whitespaces: bool | NotGiven = NOT_GIVEN
+    apply_to_punctuation: bool | NotGiven = NOT_GIVEN
+    apply_to_numbers: bool | NotGiven = NOT_GIVEN
+    apply_to_stopwords: bool | NotGiven = NOT_GIVEN
+    apply_to_emojis: bool | NotGiven = NOT_GIVEN

ai21/types.py

@@ -0,0 +1,30 @@
+from typing_extensions import Literal
+
+
+# Sentinel class used until PEP 0661 is accepted
+class NotGiven:
+    """
+    A sentinel singleton class used to distinguish omitted keyword arguments
+    from those passed in with the value None (which may have different behavior).
+
+    For example:
+
+    ```py
+    def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
+        ...
+
+
+    get(timeout=1)  # 1s timeout
+    get(timeout=None)  # No timeout
+    get()  # Default timeout behavior, which may not be statically known at the method definition.
+    ```
+    """
+
+    def __bool__(self) -> Literal[False]:
+        return False
+
+    def __repr__(self) -> str:
+        return "NOT_GIVEN"
+
+
+NOT_GIVEN = NotGiven()

ai21/utils/typing.py

@@ -0,0 +1,22 @@
+from typing import Any, Dict
+
+from ai21.types import NotGiven
+
+
+def is_not_given(value: Any) -> bool:
+    return isinstance(value, NotGiven)
+
+
+def remove_not_given(body: Dict[str, Any]) -> Dict[str, Any]:
+    return {k: v for k, v in body.items() if not is_not_given(v)}
+
+
+def to_camel_case(snake_str: str) -> str:
+    return "".join(x.capitalize() for x in snake_str.lower().split("_"))
+
+
+def to_lower_camel_case(snake_str: str) -> str:
+    # We capitalize the first letter of each component except the first one
+    # with the 'capitalize' method and join them together.
+    camel_string = to_camel_case(snake_str)
+    return snake_str[0].lower() + camel_string[1:]