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azure add o1-mini、o1-preview models (#9088)

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Co-authored-by: crazywoola <100913391+crazywoola@users.noreply.github.com>
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Charlie.Wei 2024-10-09 16:15:03 +08:00 committed by GitHub
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commit 55679b4389
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3 changed files with 196 additions and 13 deletions
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75
api/core/model_runtime/model_providers/azure_openai/_constant.py
View File

@ -1081,8 +1081,81 @@ LLM_BASE_MODELS = [
),
),
),
AzureBaseModel(
base_model_name="o1-preview",
entity=AIModelEntity(
model="fake-deployment-name",
label=I18nObject(
en_US="fake-deployment-name-label",
),
model_type=ModelType.LLM,
features=[
ModelFeature.AGENT_THOUGHT,
],
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
ModelPropertyKey.MODE: LLMMode.CHAT.value,
ModelPropertyKey.CONTEXT_SIZE: 128000,
},
parameter_rules=[
ParameterRule(
name="response_format",
label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
type="string",
help=I18nObject(
zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
),
required=False,
options=["text", "json_object"],
),
_get_max_tokens(default=512, min_val=1, max_val=32768),
],
pricing=PriceConfig(
input=15.00,
output=60.00,
unit=0.000001,
currency="USD",
),
),
),
AzureBaseModel(
base_model_name="o1-mini",
entity=AIModelEntity(
model="fake-deployment-name",
label=I18nObject(
en_US="fake-deployment-name-label",
),
model_type=ModelType.LLM,
features=[
ModelFeature.AGENT_THOUGHT,
],
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
ModelPropertyKey.MODE: LLMMode.CHAT.value,
ModelPropertyKey.CONTEXT_SIZE: 128000,
},
parameter_rules=[
ParameterRule(
name="response_format",
label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
type="string",
help=I18nObject(
zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
),
required=False,
options=["text", "json_object"],
),
_get_max_tokens(default=512, min_val=1, max_val=65536),
],
pricing=PriceConfig(
input=3.00,
output=12.00,
unit=0.000001,
currency="USD",
),
),
),
]
EMBEDDING_BASE_MODELS = [
AzureBaseModel(
base_model_name="text-embedding-ada-002",

12
api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml
View File

@ -120,6 +120,18 @@ model_credential_schema:
show_on:
- variable: __model_type
value: llm
- label:
en_US: o1-mini
value: o1-mini
show_on:
- variable: __model_type
value: llm
- label:
en_US: o1-preview
value: o1-preview
show_on:
- variable: __model_type
value: llm
- label:
en_US: gpt-4o-mini
value: gpt-4o-mini

122
api/core/model_runtime/model_providers/azure_openai/llm/llm.py
View File

@ -312,20 +312,118 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
if user:
extra_model_kwargs["user"] = user
# chat model
messages = [self._convert_prompt_message_to_dict(m) for m in prompt_messages]
response = client.chat.completions.create(
messages=messages,
model=model,
stream=stream,
**model_parameters,
**extra_model_kwargs,
# clear illegal prompt messages
prompt_messages = self._clear_illegal_prompt_messages(model, prompt_messages)
block_as_stream = False
if model.startswith("o1"):
if stream:
block_as_stream = True
stream = False
if "stream_options" in extra_model_kwargs:
del extra_model_kwargs["stream_options"]
if "stop" in extra_model_kwargs:
del extra_model_kwargs["stop"]
# chat model
response = client.chat.completions.create(
messages=[self._convert_prompt_message_to_dict(m) for m in prompt_messages],
model=model,
stream=stream,
**model_parameters,
**extra_model_kwargs,
)
if stream:
return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)
block_result = self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
if block_as_stream:
return self._handle_chat_block_as_stream_response(block_result, prompt_messages, stop)
return block_result
def _handle_chat_block_as_stream_response(
self,
block_result: LLMResult,
prompt_messages: list[PromptMessage],
stop: Optional[list[str]] = None,
) -> Generator[LLMResultChunk, None, None]:
"""
Handle llm chat response
:param model: model name
:param credentials: credentials
:param response: response
:param prompt_messages: prompt messages
:param tools: tools for tool calling
:param stop: stop words
:return: llm response chunk generator
"""
text = block_result.message.content
text = cast(str, text)
if stop:
text = self.enforce_stop_tokens(text, stop)
yield LLMResultChunk(
model=block_result.model,
prompt_messages=prompt_messages,
system_fingerprint=block_result.system_fingerprint,
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(content=text),
finish_reason="stop",
usage=block_result.usage,
),
)
if stream:
return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)
def _clear_illegal_prompt_messages(self, model: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
"""
Clear illegal prompt messages for OpenAI API
return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
:param model: model name
:param prompt_messages: prompt messages
:return: cleaned prompt messages
"""
checklist = ["gpt-4-turbo", "gpt-4-turbo-2024-04-09"]
if model in checklist:
# count how many user messages are there
user_message_count = len([m for m in prompt_messages if isinstance(m, UserPromptMessage)])
if user_message_count > 1:
for prompt_message in prompt_messages:
if isinstance(prompt_message, UserPromptMessage):
if isinstance(prompt_message.content, list):
prompt_message.content = "\n".join(
[
item.data
if item.type == PromptMessageContentType.TEXT
else "[IMAGE]"
if item.type == PromptMessageContentType.IMAGE
else ""
for item in prompt_message.content
]
)
if model.startswith("o1"):
system_message_count = len([m for m in prompt_messages if isinstance(m, SystemPromptMessage)])
if system_message_count > 0:
new_prompt_messages = []
for prompt_message in prompt_messages:
if isinstance(prompt_message, SystemPromptMessage):
prompt_message = UserPromptMessage(
content=prompt_message.content,
name=prompt_message.name,
)
new_prompt_messages.append(prompt_message)
prompt_messages = new_prompt_messages
return prompt_messages
def _handle_chat_generate_response(
self,
@ -560,7 +658,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
tokens_per_message = 4
# if there's a name, the role is omitted
tokens_per_name = -1
elif model.startswith("gpt-35-turbo") or model.startswith("gpt-4"):
elif model.startswith("gpt-35-turbo") or model.startswith("gpt-4") or model.startswith("o1"):
tokens_per_message = 3
tokens_per_name = 1
else: