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fix: update decorators for openai extension #280

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May 9, 2025
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fix: update decorators for openai extension #280

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fa8caa3
update decorators for openai extension
manvkaur May 2, 2025
603d3a7
add commas
manvkaur May 5, 2025
0794adb
update openai file
manvkaur May 5, 2025
8a715e6
update casing error
manvkaur May 5, 2025
f848161
qa validations
manvkaur May 5, 2025
c33bd76
fix flake8 validations
manvkaur May 6, 2025
158a42f
use union for acceptable type
manvkaur May 6, 2025
859fe88
address review comments
manvkaur May 6, 2025
397505e
add deprecation information
manvkaur May 9, 2025
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134 changes: 103 additions & 31 deletions azure/functions/decorators/function_app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1694,7 +1694,6 @@ def assistant_skill_trigger(self,
function_description: str,
function_name: Optional[str] = None,
parameter_description_json: Optional[str] = None, # NoQA
model: Optional[OpenAIModels] = OpenAIModels.DefaultChatModel, # NoQA
data_type: Optional[
Union[DataType, str]] = None,
**kwargs: Any) -> Callable[..., Any]:
Expand Down Expand Up @@ -1723,7 +1722,6 @@ def assistant_skill_trigger(self,
:param parameter_description_json: A JSON description of the function
parameter, which is provided to the LLM.
If no description is provided, the description will be autogenerated.
:param model: The OpenAI chat model to use.
:param data_type: Defines how Functions runtime should treat the
parameter value.
:param kwargs: Keyword arguments for specifying additional binding
Expand All @@ -1741,7 +1739,6 @@ def decorator():
function_description=function_description,
function_name=function_name,
parameter_description_json=parameter_description_json,
model=model,
data_type=parse_singular_param_to_enum(data_type,
DataType),
**kwargs))
Expand Down Expand Up @@ -3220,10 +3217,13 @@ def decorator():
def text_completion_input(self,
arg_name: str,
prompt: str,
model: Optional[OpenAIModels] = OpenAIModels.DefaultChatModel, # NoQA
chat_model: Optional
[Union[str, OpenAIModels]]
= OpenAIModels.DefaultChatModel,
temperature: Optional[str] = "0.5",
top_p: Optional[str] = None,
max_tokens: Optional[str] = "100",
is_reasoning_model: Optional[bool] = False,
data_type: Optional[Union[DataType, str]] = None,
**kwargs) \
-> Callable[..., Any]:
Expand All @@ -3243,7 +3243,10 @@ def text_completion_input(self,
:param arg_name: The name of binding parameter in the function code.
:param prompt: The prompt to generate completions for, encoded as a
string.
:param model: the ID of the model to use.
:param model: @deprecated. Use chat_model instead. The model parameter
is unused and will be removed in future versions.
:param chat_model: The deployment name or model name of OpenAI Chat
Completion API. The default value is "gpt-3.5-turbo".
:param temperature: The sampling temperature to use, between 0 and 2.
Higher values like 0.8 will make the output more random, while lower
values like 0.2 will make it more focused and deterministic.
Expand All @@ -3255,7 +3258,10 @@ def text_completion_input(self,
:param max_tokens: The maximum number of tokens to generate in the
completion. The token count of your prompt plus max_tokens cannot
exceed the model's context length. Most models have a context length of
2048 tokens (except for the newest models, which support 4096).
2048 tokens (except for the newest models, which support 4096)
:param is_reasoning_model: Whether the configured chat completion model
is a reasoning model or not. Properties max_tokens and temperature are not
supported for reasoning models.
:param data_type: Defines how Functions runtime should treat the
parameter value
:param kwargs: Keyword arguments for specifying additional binding
Expand All @@ -3271,10 +3277,11 @@ def decorator():
binding=TextCompletionInput(
name=arg_name,
prompt=prompt,
model=model,
chat_model=chat_model,
temperature=temperature,
top_p=top_p,
max_tokens=max_tokens,
is_reasoning_model=is_reasoning_model,
data_type=parse_singular_param_to_enum(data_type,
DataType),
**kwargs))
Expand Down Expand Up @@ -3371,9 +3378,15 @@ def decorator():
def assistant_post_input(self, arg_name: str,
id: str,
user_message: str,
model: Optional[str] = None,
chat_model: Optional
[Union[str, OpenAIModels]]
= OpenAIModels.DefaultChatModel,
chat_storage_connection_setting: Optional[str] = "AzureWebJobsStorage", # noqa: E501
collection_name: Optional[str] = "ChatState", # noqa: E501
collection_name: Optional[str] = "ChatState", # noqa: E501
temperature: Optional[str] = "0.5",
top_p: Optional[str] = None,
max_tokens: Optional[str] = "100",
is_reasoning_model: Optional[bool] = False,
data_type: Optional[
Union[DataType, str]] = None,
**kwargs) \
Expand All @@ -3386,12 +3399,30 @@ def assistant_post_input(self, arg_name: str,
:param id: The ID of the assistant to update.
:param user_message: The user message that user has entered for
assistant to respond to.
:param model: The OpenAI chat model to use.
:param model: @deprecated. Use chat_model instead. The model parameter
is unused and will be removed in future versions.
:param chat_model: The deployment name or model name of OpenAI Chat
Completion API. The default value is "gpt-3.5-turbo".
:param chat_storage_connection_setting: The configuration section name
for the table settings for assistant chat storage. The default value is
"AzureWebJobsStorage".
:param collection_name: The table collection name for assistant chat
storage. The default value is "ChatState".
:param temperature: The sampling temperature to use, between 0 and 2.
Higher values like 0.8 will make the output more random, while lower
values like 0.2 will make it more focused and deterministic.
:param top_p: An alternative to sampling with temperature, called
nucleus sampling, where the model considers the results of the tokens
with top_p probability mass. So 0.1 means only the tokens comprising
the top 10% probability mass are considered. It's generally recommend
to use this or temperature
:param max_tokens: The maximum number of tokens to generate in the
completion. The token count of your prompt plus max_tokens cannot
exceed the model's context length. Most models have a context length of
2048 tokens (except for the newest models, which support 4096)
:param is_reasoning_model: Whether the configured chat completion model
is a reasoning model or not. Properties max_tokens and temperature are
not supported for reasoning models.
:param data_type: Defines how Functions runtime should treat the
parameter value
:param kwargs: Keyword arguments for specifying additional binding
Expand All @@ -3408,9 +3439,13 @@ def decorator():
name=arg_name,
id=id,
user_message=user_message,
model=model,
chat_model=chat_model,
chat_storage_connection_setting=chat_storage_connection_setting, # noqa: E501
collection_name=collection_name,
temperature=temperature,
top_p=top_p,
max_tokens=max_tokens,
is_reasoning_model=is_reasoning_model,
data_type=parse_singular_param_to_enum(data_type,
DataType),
**kwargs))
Expand All @@ -3424,7 +3459,9 @@ def embeddings_input(self,
arg_name: str,
input: str,
input_type: InputType,
model: Optional[str] = None,
embeddings_model: Optional
[Union[str, OpenAIModels]]
= OpenAIModels.DefaultEmbeddingsModel,
max_chunk_length: Optional[int] = 8 * 1024,
max_overlap: Optional[int] = 128,
data_type: Optional[
Expand All @@ -3441,7 +3478,10 @@ def embeddings_input(self,
:param input: The input source containing the data to generate
embeddings for.
:param input_type: The type of the input.
:param model: The ID of the model to use.
:param model: @deprecated. Use embeddings_model instead. The model
parameter is unused and will be removed in future versions.
:param embeddings_model: The deployment name or model name for OpenAI
Embeddings. The default value is "text-embedding-ada-002".
:param max_chunk_length: The maximum number of characters to chunk the
input into. Default value: 8 * 1024
:param max_overlap: The maximum number of characters to overlap
Expand All @@ -3462,7 +3502,7 @@ def decorator():
name=arg_name,
input=input,
input_type=input_type,
model=model,
embeddings_model=embeddings_model,
max_chunk_length=max_chunk_length,
max_overlap=max_overlap,
data_type=parse_singular_param_to_enum(data_type,
Expand All @@ -3476,13 +3516,21 @@ def decorator():

def semantic_search_input(self,
arg_name: str,
connection_name: str,
search_connection_name: str,
collection: str,
query: Optional[str] = None,
embeddings_model: Optional[OpenAIModels] = OpenAIModels.DefaultEmbeddingsModel, # NoQA
chat_model: Optional[OpenAIModels] = OpenAIModels.DefaultChatModel, # NoQA
embeddings_model: Optional
[Union[str, OpenAIModels]]
= OpenAIModels.DefaultEmbeddingsModel,
chat_model: Optional
[Union[str, OpenAIModels]]
= OpenAIModels.DefaultChatModel,
system_prompt: Optional[str] = semantic_search_system_prompt, # NoQA
max_knowledge_count: Optional[int] = 1,
temperature: Optional[str] = "0.5",
top_p: Optional[str] = None,
max_tokens: Optional[str] = "100",
is_reasoning_model: Optional[bool] = False,
data_type: Optional[
Union[DataType, str]] = None,
**kwargs) \
Expand All @@ -3499,19 +3547,34 @@ def semantic_search_input(self,
Ref: https://platform.openai.com/docs/guides/embeddings

:param arg_name: The name of binding parameter in the function code.
:param connection_name: app setting or environment variable which
contains a connection string value.
:param search_connection_name: app setting or environment variable
which contains a vector search connection setting value.
:param collection: The name of the collection or table to search or
store.
:param query: The semantic query text to use for searching.
:param embeddings_model: The ID of the model to use for embeddings.
The default value is "text-embedding-ada-002".
:param chat_model: The name of the Large Language Model to invoke for
chat responses. The default value is "gpt-3.5-turbo".
:param embeddings_model: The deployment name or model name for OpenAI
Embeddings. The default value is "text-embedding-ada-002".
:param chat_model: The deployment name or model name of OpenAI Chat
Completion API. The default value is "gpt-3.5-turbo".
:param system_prompt: Optional. The system prompt to use for prompting
the large language model.
:param max_knowledge_count: Optional. The number of knowledge items to
inject into the SystemPrompt. Default value: 1
:param temperature: The sampling temperature to use, between 0 and 2.
Higher values like 0.8 will make the output more random, while lower
values like 0.2 will make it more focused and deterministic.
:param top_p: An alternative to sampling with temperature, called
nucleus sampling, where the model considers the results of the tokens
with top_p probability mass. So 0.1 means only the tokens comprising
the top 10% probability mass are considered. It's generally recommend
to use this or temperature
:param max_tokens: The maximum number of tokens to generate in the
completion. The token count of your prompt plus max_tokens cannot
exceed the model's context length. Most models have a context length of
2048 tokens (except for the newest models, which support 4096)
:param is_reasoning_model: Whether the configured chat completion model
is a reasoning model or not. Properties max_tokens and temperature are
not supported for reasoning models.
:param data_type: Optional. Defines how Functions runtime should treat
the parameter value. Default value: None
:param kwargs: Keyword arguments for specifying additional binding
Expand All @@ -3526,13 +3589,17 @@ def decorator():
fb.add_binding(
binding=SemanticSearchInput(
name=arg_name,
connection_name=connection_name,
search_connection_name=search_connection_name,
collection=collection,
query=query,
embeddings_model=embeddings_model,
chat_model=chat_model,
system_prompt=system_prompt,
max_knowledge_count=max_knowledge_count,
temperature=temperature,
top_p=top_p,
max_tokens=max_tokens,
is_reasoning_model=is_reasoning_model,
data_type=parse_singular_param_to_enum(data_type,
DataType),
**kwargs))
Expand All @@ -3546,9 +3613,11 @@ def embeddings_store_output(self,
arg_name: str,
input: str,
input_type: InputType,
connection_name: str,
store_connection_name: str,
collection: str,
model: Optional[OpenAIModels] = OpenAIModels.DefaultEmbeddingsModel, # NoQA
embeddings_model: Optional
[Union[str, OpenAIModels]]
= OpenAIModels.DefaultEmbeddingsModel,
max_chunk_length: Optional[int] = 8 * 1024,
max_overlap: Optional[int] = 128,
data_type: Optional[
Expand All @@ -3568,10 +3637,13 @@ def embeddings_store_output(self,
:param arg_name: The name of binding parameter in the function code.
:param input: The input to generate embeddings for.
:param input_type: The type of the input.
:param connection_name: The name of an app setting or environment
variable which contains a connection string value
:param store_connection_name: The name of an app setting or environment
variable which contains a vectore store connection setting value
:param collection: The collection or table to search.
:param model: The ID of the model to use.
:param model: @deprecated. Use embeddings_model instead. The model
parameter is unused and will be removed in future versions.
:param embeddings_model: The deployment name or model name for OpenAI
Embeddings. The default value is "text-embedding-ada-002".
:param max_chunk_length: The maximum number of characters to chunk the
input into.
:param max_overlap: The maximum number of characters to overlap between
Expand All @@ -3592,9 +3664,9 @@ def decorator():
name=arg_name,
input=input,
input_type=input_type,
connection_name=connection_name,
store_connection_name=store_connection_name,
collection=collection,
model=model,
embeddings_model=embeddings_model,
max_chunk_length=max_chunk_length,
max_overlap=max_overlap,
data_type=parse_singular_param_to_enum(data_type,
Expand Down
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