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fix(api/core/model_runtime/model_providers/baichuan,localai): Parse ToolPromptMessage. #4943 (#5138)

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Co-authored-by: -LAN- <laipz8200@outlook.com>
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yanghx 2024-06-13 05:08:30 +00:00 committed by GitHub
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commit adc948e87c
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2 changed files with 88 additions and 54 deletions
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46
api/core/model_runtime/model_providers/baichuan/llm/llm.py
View File

@ -7,6 +7,7 @@ from core.model_runtime.entities.message_entities import (
PromptMessage,
PromptMessageTool,
SystemPromptMessage,
ToolPromptMessage,
UserPromptMessage,
)
from core.model_runtime.errors.invoke import (
@ -32,20 +33,21 @@ from core.model_runtime.model_providers.baichuan.llm.baichuan_turbo_errors impor
class BaichuanLarguageModel(LargeLanguageModel):
def _invoke(self, model: str, credentials: dict,
prompt_messages: list[PromptMessage], model_parameters: dict,
tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
def _invoke(self, model: str, credentials: dict,
prompt_messages: list[PromptMessage], model_parameters: dict,
tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
stream: bool = True, user: str | None = None) \
-> LLMResult | Generator:
return self._generate(model=model, credentials=credentials, prompt_messages=prompt_messages,
model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
tools: list[PromptMessageTool] | None = None) -> int:
return self._num_tokens_from_messages(prompt_messages)
def _num_tokens_from_messages(self, messages: list[PromptMessage],) -> int:
def _num_tokens_from_messages(self, messages: list[PromptMessage], ) -> int:
"""Calculate num tokens for baichuan model"""
def tokens(text: str):
return BaichuanTokenizer._get_num_tokens(text)
@ -85,9 +87,20 @@ class BaichuanLarguageModel(LargeLanguageModel):
elif isinstance(message, SystemPromptMessage):
message = cast(SystemPromptMessage, message)
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, ToolPromptMessage):
# copy from core/model_runtime/model_providers/anthropic/llm/llm.py
message = cast(ToolPromptMessage, message)
message_dict = {
"role": "user",
"content": [{
"type": "tool_result",
"tool_use_id": message.tool_call_id,
"content": message.content
}]
}
else:
raise ValueError(f"Unknown message type {type(message)}")
return message_dict
def validate_credentials(self, model: str, credentials: dict) -> None:
@ -106,13 +119,13 @@ class BaichuanLarguageModel(LargeLanguageModel):
except Exception as e:
raise CredentialsValidateFailedError(f"Invalid API key: {e}")
def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
model_parameters: dict, tools: list[PromptMessageTool] | None = None,
stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
model_parameters: dict, tools: list[PromptMessageTool] | None = None,
stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-> LLMResult | Generator:
if tools is not None and len(tools) > 0:
raise InvokeBadRequestError("Baichuan model doesn't support tools")
instance = BaichuanModel(
api_key=credentials['api_key'],
secret_key=credentials.get('secret_key', '')
@ -129,11 +142,12 @@ class BaichuanLarguageModel(LargeLanguageModel):
]
# invoke model
response = instance.generate(model=model, stream=stream, messages=messages, parameters=model_parameters, timeout=60)
response = instance.generate(model=model, stream=stream, messages=messages, parameters=model_parameters,
timeout=60)
if stream:
return self._handle_chat_generate_stream_response(model, prompt_messages, credentials, response)
return self._handle_chat_generate_response(model, prompt_messages, credentials, response)
def _handle_chat_generate_response(self, model: str,
@ -141,7 +155,9 @@ class BaichuanLarguageModel(LargeLanguageModel):
credentials: dict,
response: BaichuanMessage) -> LLMResult:
# convert baichuan message to llm result
usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=response.usage['prompt_tokens'], completion_tokens=response.usage['completion_tokens'])
usage = self._calc_response_usage(model=model, credentials=credentials,
prompt_tokens=response.usage['prompt_tokens'],
completion_tokens=response.usage['completion_tokens'])
return LLMResult(
model=model,
prompt_messages=prompt_messages,
@ -158,7 +174,9 @@ class BaichuanLarguageModel(LargeLanguageModel):
response: Generator[BaichuanMessage, None, None]) -> Generator:
for message in response:
if message.usage:
usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=message.usage['prompt_tokens'], completion_tokens=message.usage['completion_tokens'])
usage = self._calc_response_usage(model=model, credentials=credentials,
prompt_tokens=message.usage['prompt_tokens'],
completion_tokens=message.usage['completion_tokens'])
yield LLMResultChunk(
model=model,
prompt_messages=prompt_messages,

96
api/core/model_runtime/model_providers/localai/llm/llm.py
View File

@ -27,6 +27,7 @@ from core.model_runtime.entities.message_entities import (
PromptMessage,
PromptMessageTool,
SystemPromptMessage,
ToolPromptMessage,
UserPromptMessage,
)
from core.model_runtime.entities.model_entities import (
@ -51,13 +52,13 @@ from core.model_runtime.utils import helper
class LocalAILanguageModel(LargeLanguageModel):
def _invoke(self, model: str, credentials: dict,
prompt_messages: list[PromptMessage], model_parameters: dict,
tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
def _invoke(self, model: str, credentials: dict,
prompt_messages: list[PromptMessage], model_parameters: dict,
tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
stream: bool = True, user: str | None = None) \
-> LLMResult | Generator:
return self._generate(model=model, credentials=credentials, prompt_messages=prompt_messages,
model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
tools: list[PromptMessageTool] | None = None) -> int:
@ -67,8 +68,9 @@ class LocalAILanguageModel(LargeLanguageModel):
def _num_tokens_from_messages(self, messages: list[PromptMessage], tools: list[PromptMessageTool]) -> int:
"""
Calculate num tokens for baichuan model
LocalAI does not supports
LocalAI does not supports
"""
def tokens(text: str):
"""
We cloud not determine which tokenizer to use, cause the model is customized.
@ -124,7 +126,7 @@ class LocalAILanguageModel(LargeLanguageModel):
num_tokens += self._num_tokens_for_tools(tools)
return num_tokens
def _num_tokens_for_tools(self, tools: list[PromptMessageTool]) -> int:
"""
Calculate num tokens for tool calling
@ -133,6 +135,7 @@ class LocalAILanguageModel(LargeLanguageModel):
:param tools: tools for tool calling
:return: number of tokens
"""
def tokens(text: str):
return self._get_num_tokens_by_gpt2(text)
@ -193,7 +196,7 @@ class LocalAILanguageModel(LargeLanguageModel):
completion_model = LLMMode.COMPLETION.value
else:
raise ValueError(f"Unknown completion type {credentials['completion_type']}")
rules = [
ParameterRule(
name='temperature',
@ -227,7 +230,7 @@ class LocalAILanguageModel(LargeLanguageModel):
)
]
model_properties = {
model_properties = {
ModelPropertyKey.MODE: completion_model,
} if completion_model else {}
@ -246,11 +249,11 @@ class LocalAILanguageModel(LargeLanguageModel):
return entity
def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
model_parameters: dict, tools: list[PromptMessageTool] | None = None,
stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
model_parameters: dict, tools: list[PromptMessageTool] | None = None,
stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-> LLMResult | Generator:
kwargs = self._to_client_kwargs(credentials)
# init model client
client = OpenAI(**kwargs)
@ -271,7 +274,7 @@ class LocalAILanguageModel(LargeLanguageModel):
extra_model_kwargs['functions'] = [
helper.dump_model(tool) for tool in tools
]
if completion_type == 'chat_completion':
result = client.chat.completions.create(
messages=[self._convert_prompt_message_to_dict(m) for m in prompt_messages],
@ -294,24 +297,24 @@ class LocalAILanguageModel(LargeLanguageModel):
if stream:
if completion_type == 'completion':
return self._handle_completion_generate_stream_response(
model=model, credentials=credentials, response=result, tools=tools,
model=model, credentials=credentials, response=result, tools=tools,
prompt_messages=prompt_messages
)
return self._handle_chat_generate_stream_response(
model=model, credentials=credentials, response=result, tools=tools,
model=model, credentials=credentials, response=result, tools=tools,
prompt_messages=prompt_messages
)
if completion_type == 'completion':
return self._handle_completion_generate_response(
model=model, credentials=credentials, response=result,
model=model, credentials=credentials, response=result,
prompt_messages=prompt_messages
)
return self._handle_chat_generate_response(
model=model, credentials=credentials, response=result, tools=tools,
model=model, credentials=credentials, response=result, tools=tools,
prompt_messages=prompt_messages
)
def _to_client_kwargs(self, credentials: dict) -> dict:
"""
Convert invoke kwargs to client kwargs
@ -321,7 +324,7 @@ class LocalAILanguageModel(LargeLanguageModel):
"""
if not credentials['server_url'].endswith('/'):
credentials['server_url'] += '/'
client_kwargs = {
"timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
"api_key": "1",
@ -351,9 +354,20 @@ class LocalAILanguageModel(LargeLanguageModel):
elif isinstance(message, SystemPromptMessage):
message = cast(SystemPromptMessage, message)
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, ToolPromptMessage):
# copy from core/model_runtime/model_providers/anthropic/llm/llm.py
message = cast(ToolPromptMessage, message)
message_dict = {
"role": "user",
"content": [{
"type": "tool_result",
"tool_use_id": message.tool_call_id,
"content": message.content
}]
}
else:
raise ValueError(f"Unknown message type {type(message)}")
return message_dict
def _convert_prompt_message_to_completion_prompts(self, messages: list[PromptMessage]) -> str:
@ -373,14 +387,14 @@ class LocalAILanguageModel(LargeLanguageModel):
prompts += f'{message.content}\n'
else:
raise ValueError(f"Unknown message type {type(message)}")
return prompts
def _handle_completion_generate_response(self, model: str,
prompt_messages: list[PromptMessage],
credentials: dict,
response: Completion,
) -> LLMResult:
prompt_messages: list[PromptMessage],
credentials: dict,
response: Completion,
) -> LLMResult:
"""
Handle llm chat response
@ -393,7 +407,7 @@ class LocalAILanguageModel(LargeLanguageModel):
"""
if len(response.choices) == 0:
raise InvokeServerUnavailableError("Empty response")
assistant_message = response.choices[0].text
# transform assistant message to prompt message
@ -407,7 +421,8 @@ class LocalAILanguageModel(LargeLanguageModel):
)
completion_tokens = self._num_tokens_from_messages(messages=[assistant_prompt_message], tools=[])
usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens)
response = LLMResult(
model=model,
@ -436,7 +451,7 @@ class LocalAILanguageModel(LargeLanguageModel):
"""
if len(response.choices) == 0:
raise InvokeServerUnavailableError("Empty response")
assistant_message = response.choices[0].message
# convert function call to tool call
@ -452,7 +467,8 @@ class LocalAILanguageModel(LargeLanguageModel):
prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
completion_tokens = self._num_tokens_from_messages(messages=[assistant_prompt_message], tools=tools)
usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens)
response = LLMResult(
model=model,
@ -465,10 +481,10 @@ class LocalAILanguageModel(LargeLanguageModel):
return response
def _handle_completion_generate_stream_response(self, model: str,
prompt_messages: list[PromptMessage],
credentials: dict,
response: Stream[Completion],
tools: list[PromptMessageTool]) -> Generator:
prompt_messages: list[PromptMessage],
credentials: dict,
response: Stream[Completion],
tools: list[PromptMessageTool]) -> Generator:
full_response = ''
for chunk in response:
@ -496,9 +512,9 @@ class LocalAILanguageModel(LargeLanguageModel):
completion_tokens = self._num_tokens_from_messages(messages=[temp_assistant_prompt_message], tools=[])
usage = self._calc_response_usage(model=model, credentials=credentials,
usage = self._calc_response_usage(model=model, credentials=credentials,
prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
yield LLMResultChunk(
model=model,
prompt_messages=prompt_messages,
@ -538,7 +554,7 @@ class LocalAILanguageModel(LargeLanguageModel):
if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ''):
continue
# check if there is a tool call in the response
function_calls = None
if delta.delta.function_call:
@ -562,9 +578,9 @@ class LocalAILanguageModel(LargeLanguageModel):
prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
completion_tokens = self._num_tokens_from_messages(messages=[temp_assistant_prompt_message], tools=[])
usage = self._calc_response_usage(model=model, credentials=credentials,
usage = self._calc_response_usage(model=model, credentials=credentials,
prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
yield LLMResultChunk(
model=model,
prompt_messages=prompt_messages,
@ -613,7 +629,7 @@ class LocalAILanguageModel(LargeLanguageModel):
)
tool_calls.append(tool_call)
return tool_calls
return tool_calls
@property
def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: