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Code Issues Packages Projects Releases Wiki Activity

Feat/blocking function call (#2247)

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This commit is contained in:
Yeuoly
2024-01-30 15:25:37 +08:00
committed by GitHub
parent 1ea18a2922
commit 6d5b386394
33 changed files with 429 additions and 94 deletions
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5
api/core/model_runtime/model_providers/azure_openai/_constant.py
View File

@@ -36,6 +36,7 @@ LLM_BASE_MODELS = [
features=[
ModelFeature.AGENT_THOUGHT,
ModelFeature.MULTI_TOOL_CALL,
ModelFeature.STREAM_TOOL_CALL,
],
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
@@ -80,6 +81,7 @@ LLM_BASE_MODELS = [
features=[
ModelFeature.AGENT_THOUGHT,
ModelFeature.MULTI_TOOL_CALL,
ModelFeature.STREAM_TOOL_CALL,
],
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
@@ -124,6 +126,7 @@ LLM_BASE_MODELS = [
features=[
ModelFeature.AGENT_THOUGHT,
ModelFeature.MULTI_TOOL_CALL,
ModelFeature.STREAM_TOOL_CALL,
],
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
@@ -198,6 +201,7 @@ LLM_BASE_MODELS = [
features=[
ModelFeature.AGENT_THOUGHT,
ModelFeature.MULTI_TOOL_CALL,
ModelFeature.STREAM_TOOL_CALL,
],
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
@@ -272,6 +276,7 @@ LLM_BASE_MODELS = [
features=[
ModelFeature.AGENT_THOUGHT,
ModelFeature.MULTI_TOOL_CALL,
ModelFeature.STREAM_TOOL_CALL,
],
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={

28
api/core/model_runtime/model_providers/azure_openai/llm/llm.py
View File

@@ -324,6 +324,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
tools: Optional[list[PromptMessageTool]] = None) -> Generator:
index = 0
full_assistant_content = ''
delta_assistant_message_function_call_storage: ChoiceDeltaFunctionCall = None
real_model = model
system_fingerprint = None
completion = ''
@@ -333,12 +334,32 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
delta = chunk.choices[0]
if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ''):
if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == '') and \
delta.delta.function_call is None:
continue
# assistant_message_tool_calls = delta.delta.tool_calls
assistant_message_function_call = delta.delta.function_call
# extract tool calls from response
if delta_assistant_message_function_call_storage is not None:
# handle process of stream function call
if assistant_message_function_call:
# message has not ended ever
delta_assistant_message_function_call_storage.arguments += assistant_message_function_call.arguments
continue
else:
# message has ended
assistant_message_function_call = delta_assistant_message_function_call_storage
delta_assistant_message_function_call_storage = None
else:
if assistant_message_function_call:
# start of stream function call
delta_assistant_message_function_call_storage = assistant_message_function_call
if delta_assistant_message_function_call_storage.arguments is None:
delta_assistant_message_function_call_storage.arguments = ''
continue
# extract tool calls from response
# tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
function_call = self._extract_response_function_call(assistant_message_function_call)
@@ -489,7 +510,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
else:
raise ValueError(f"Got unknown type {message}")
if message.name is not None:
if message.name:
message_dict["name"] = message.name
return message_dict
@@ -586,7 +607,6 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
num_tokens = 0
for tool in tools:
num_tokens += len(encoding.encode('type'))
num_tokens += len(encoding.encode(tool.get("type")))
num_tokens += len(encoding.encode('function'))
# calculate num tokens for function object

6
api/core/model_runtime/model_providers/chatglm/llm/llm.py
View File

@@ -5,7 +5,7 @@ from typing import Generator, List, Optional, cast
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageFunction,
PromptMessageTool, SystemPromptMessage, UserPromptMessage)
PromptMessageTool, SystemPromptMessage, UserPromptMessage, ToolPromptMessage)
from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
from core.model_runtime.errors.validate import CredentialsValidateFailedError
@@ -194,6 +194,10 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
elif isinstance(message, SystemPromptMessage):
message = cast(SystemPromptMessage, message)
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, ToolPromptMessage):
# check if last message is user message
message = cast(ToolPromptMessage, message)
message_dict = {"role": "function", "content": message.content}
else:
raise ValueError(f"Unknown message type {type(message)}")

2
api/core/model_runtime/model_providers/minimax/llm/abab5.5-chat.yaml
View File

@@ -4,6 +4,8 @@ label:
model_type: llm
features:
- agent-thought
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 16384

2
api/core/model_runtime/model_providers/minimax/llm/abab6-chat.yaml
View File

@@ -4,6 +4,8 @@ label:
model_type: llm
features:
- agent-thought
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 32768

3
api/core/model_runtime/model_providers/minimax/llm/chat_completion.py
View File

@@ -16,7 +16,7 @@ class MinimaxChatCompletion(object):
"""
def generate(self, model: str, api_key: str, group_id: str,
prompt_messages: List[MinimaxMessage], model_parameters: dict,
tools: Dict[str, Any], stop: List[str] | None, stream: bool, user: str) \
tools: List[Dict[str, Any]], stop: List[str] | None, stream: bool, user: str) \
-> Union[MinimaxMessage, Generator[MinimaxMessage, None, None]]:
"""
generate chat completion
@@ -162,7 +162,6 @@ class MinimaxChatCompletion(object):
continue
for choice in choices:
print(choice)
message = choice['delta']
yield MinimaxMessage(
content=message,

46
api/core/model_runtime/model_providers/minimax/llm/chat_completion_pro.py
View File

@@ -17,7 +17,7 @@ class MinimaxChatCompletionPro(object):
"""
def generate(self, model: str, api_key: str, group_id: str,
prompt_messages: List[MinimaxMessage], model_parameters: dict,
tools: Dict[str, Any], stop: List[str] | None, stream: bool, user: str) \
tools: List[Dict[str, Any]], stop: List[str] | None, stream: bool, user: str) \
-> Union[MinimaxMessage, Generator[MinimaxMessage, None, None]]:
"""
generate chat completion
@@ -82,6 +82,10 @@ class MinimaxChatCompletionPro(object):
**extra_kwargs
}
if tools:
body['functions'] = tools
body['function_call'] = { 'type': 'auto' }
try:
response = post(
url=url, data=dumps(body), headers=headers, stream=stream, timeout=(10, 300))
@@ -135,6 +139,7 @@ class MinimaxChatCompletionPro(object):
"""
handle stream chat generate response
"""
function_call_storage = None
for line in response.iter_lines():
if not line:
continue
@@ -148,7 +153,7 @@ class MinimaxChatCompletionPro(object):
msg = data['base_resp']['status_msg']
self._handle_error(code, msg)
if data['reply']:
if data['reply'] or 'usage' in data and data['usage']:
total_tokens = data['usage']['total_tokens']
message = MinimaxMessage(
role=MinimaxMessage.Role.ASSISTANT.value,
@@ -160,6 +165,12 @@ class MinimaxChatCompletionPro(object):
'total_tokens': total_tokens
}
message.stop_reason = data['choices'][0]['finish_reason']
if function_call_storage:
function_call_message = MinimaxMessage(content='', role=MinimaxMessage.Role.ASSISTANT.value)
function_call_message.function_call = function_call_storage
yield function_call_message
yield message
return
@@ -168,11 +179,28 @@ class MinimaxChatCompletionPro(object):
continue
for choice in choices:
message = choice['messages'][0]['text']
if not message:
continue
message = choice['messages'][0]
if 'function_call' in message:
if not function_call_storage:
function_call_storage = message['function_call']
if 'arguments' not in function_call_storage or not function_call_storage['arguments']:
function_call_storage['arguments'] = ''
continue
else:
function_call_storage['arguments'] += message['function_call']['arguments']
continue
else:
if function_call_storage:
message['function_call'] = function_call_storage
function_call_storage = None
yield MinimaxMessage(
content=message,
role=MinimaxMessage.Role.ASSISTANT.value
)
minimax_message = MinimaxMessage(content='', role=MinimaxMessage.Role.ASSISTANT.value)
if 'function_call' in message:
minimax_message.function_call = message['function_call']
if 'text' in message:
minimax_message.content = message['text']
yield minimax_message

43
api/core/model_runtime/model_providers/minimax/llm/llm.py
View File

@@ -2,7 +2,7 @@ from typing import Generator, List
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageTool,
SystemPromptMessage, UserPromptMessage)
SystemPromptMessage, UserPromptMessage, ToolPromptMessage)
from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
from core.model_runtime.errors.validate import CredentialsValidateFailedError
@@ -84,6 +84,13 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
"""
client: MinimaxChatCompletionPro = self.model_apis[model]()
if tools:
tools = [{
"name": tool.name,
"description": tool.description,
"parameters": tool.parameters
} for tool in tools]
response = client.generate(
model=model,
api_key=credentials['minimax_api_key'],
@@ -109,7 +116,19 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
elif isinstance(prompt_message, UserPromptMessage):
return MinimaxMessage(role=MinimaxMessage.Role.USER.value, content=prompt_message.content)
elif isinstance(prompt_message, AssistantPromptMessage):
if prompt_message.tool_calls:
message = MinimaxMessage(
role=MinimaxMessage.Role.ASSISTANT.value,
content=''
)
message.function_call={
'name': prompt_message.tool_calls[0].function.name,
'arguments': prompt_message.tool_calls[0].function.arguments
}
return message
return MinimaxMessage(role=MinimaxMessage.Role.ASSISTANT.value, content=prompt_message.content)
elif isinstance(prompt_message, ToolPromptMessage):
return MinimaxMessage(role=MinimaxMessage.Role.FUNCTION.value, content=prompt_message.content)
else:
raise NotImplementedError(f'Prompt message type {type(prompt_message)} is not supported')
@@ -151,6 +170,28 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
finish_reason=message.stop_reason if message.stop_reason else None,
),
)
elif message.function_call:
if 'name' not in message.function_call or 'arguments' not in message.function_call:
continue
yield LLMResultChunk(
model=model,
prompt_messages=prompt_messages,
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(
content='',
tool_calls=[AssistantPromptMessage.ToolCall(
id='',
type='function',
function=AssistantPromptMessage.ToolCall.ToolCallFunction(
name=message.function_call['name'],
arguments=message.function_call['arguments']
)
)]
),
),
)
else:
yield LLMResultChunk(
model=model,

10
api/core/model_runtime/model_providers/minimax/llm/types.py
View File

@@ -7,13 +7,23 @@ class MinimaxMessage:
USER = 'USER'
ASSISTANT = 'BOT'
SYSTEM = 'SYSTEM'
FUNCTION = 'FUNCTION'
role: str = Role.USER.value
content: str
usage: Dict[str, int] = None
stop_reason: str = ''
function_call: Dict[str, Any] = None
def to_dict(self) -> Dict[str, Any]:
if self.function_call and self.role == MinimaxMessage.Role.ASSISTANT.value:
return {
'sender_type': 'BOT',
'sender_name': '专家',
'text': '',
'function_call': self.function_call
}
return {
'sender_type': self.role,
'sender_name': '我' if self.role == 'USER' else '专家',

1
api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-0613.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 4096

1
api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-1106.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 16385

1
api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-16k-0613.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 16385

1
api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-16k.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 16385

1
api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 4096

1
api/core/model_runtime/model_providers/openai/llm/gpt-4-0125-preview.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 128000

1
api/core/model_runtime/model_providers/openai/llm/gpt-4-1106-preview.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 128000

1
api/core/model_runtime/model_providers/openai/llm/gpt-4-32k.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 32768

1
api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-preview.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 128000

1
api/core/model_runtime/model_providers/openai/llm/gpt-4.yaml
View File

@@ -6,6 +6,7 @@ model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
context_size: 8192

2
api/core/model_runtime/model_providers/openai/llm/llm.py
View File

@@ -671,7 +671,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
else:
raise ValueError(f"Got unknown type {message}")
if message.name is not None:
if message.name:
message_dict["name"] = message.name
return message_dict

31
api/core/model_runtime/model_providers/xinference/llm/llm.py
View File

@@ -3,14 +3,14 @@ from typing import Generator, Iterator, List, Optional, Union, cast
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageTool,
SystemPromptMessage, UserPromptMessage)
SystemPromptMessage, UserPromptMessage, ToolPromptMessage)
from core.model_runtime.entities.model_entities import (AIModelEntity, FetchFrom, ModelPropertyKey, ModelType,
ParameterRule, ParameterType)
ParameterRule, ParameterType, ModelFeature)
from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.model_runtime.model_providers.xinference.llm.xinference_helper import (XinferenceHelper,
from core.model_runtime.model_providers.xinference.xinference_helper import (XinferenceHelper,
XinferenceModelExtraParameter)
from core.model_runtime.utils import helper
from openai import (APIConnectionError, APITimeoutError, AuthenticationError, ConflictError, InternalServerError,
@@ -33,6 +33,12 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
see `core.model_runtime.model_providers.__base.large_language_model.LargeLanguageModel._invoke`
"""
if 'temperature' in model_parameters:
if model_parameters['temperature'] < 0.01:
model_parameters['temperature'] = 0.01
elif model_parameters['temperature'] > 1.0:
model_parameters['temperature'] = 0.99
return self._generate(
model=model, credentials=credentials, prompt_messages=prompt_messages, model_parameters=model_parameters,
tools=tools, stop=stop, stream=stream, user=user,
@@ -65,6 +71,9 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
credentials['completion_type'] = 'completion'
else:
raise ValueError(f'xinference model ability {extra_param.model_ability} is not supported')
if extra_param.support_function_call:
credentials['support_function_call'] = True
except RuntimeError as e:
raise CredentialsValidateFailedError(f'Xinference credentials validate failed: {e}')
@@ -220,6 +229,9 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
elif isinstance(message, SystemPromptMessage):
message = cast(SystemPromptMessage, message)
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, ToolPromptMessage):
message = cast(ToolPromptMessage, message)
message_dict = {"tool_call_id": message.tool_call_id, "role": "tool", "content": message.content}
else:
raise ValueError(f"Unknown message type {type(message)}")
@@ -237,7 +249,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
label=I18nObject(
zh_Hans='温度',
en_US='Temperature'
)
),
),
ParameterRule(
name='top_p',
@@ -282,6 +294,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
completion_type = LLMMode.COMPLETION.value
else:
raise ValueError(f'xinference model ability {extra_args.model_ability} is not supported')
support_function_call = credentials.get('support_function_call', False)
entity = AIModelEntity(
model=model,
@@ -290,6 +304,9 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
),
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_type=ModelType.LLM,
features=[
ModelFeature.TOOL_CALL
] if support_function_call else [],
model_properties={
ModelPropertyKey.MODE: completion_type,
},
@@ -310,6 +327,12 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
extra_model_kwargs can be got by `XinferenceHelper.get_xinference_extra_parameter`
"""
if 'server_url' not in credentials:
raise CredentialsValidateFailedError('server_url is required in credentials')
if credentials['server_url'].endswith('/'):
credentials['server_url'] = credentials['server_url'][:-1]
client = OpenAI(
base_url=f'{credentials["server_url"]}/v1',
api_key='abc',

23
api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py
View File

@@ -2,7 +2,7 @@ import time
from typing import Optional
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType, PriceType
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType, PriceType, ModelPropertyKey
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
@@ -10,6 +10,7 @@ from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
from xinference_client.client.restful.restful_client import Client, RESTfulEmbeddingModelHandle, RESTfulModelHandle
from core.model_runtime.model_providers.xinference.xinference_helper import XinferenceHelper
class XinferenceTextEmbeddingModel(TextEmbeddingModel):
"""
@@ -35,7 +36,10 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
"""
server_url = credentials['server_url']
model_uid = credentials['model_uid']
if server_url.endswith('/'):
server_url = server_url[:-1]
client = Client(base_url=server_url)
try:
@@ -102,8 +106,15 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
:return:
"""
try:
server_url = credentials['server_url']
model_uid = credentials['model_uid']
extra_args = XinferenceHelper.get_xinference_extra_parameter(server_url=server_url, model_uid=model_uid)
if extra_args.max_tokens:
credentials['max_tokens'] = extra_args.max_tokens
self._invoke(model=model, credentials=credentials, texts=['ping'])
except InvokeAuthorizationError:
except (InvokeAuthorizationError, RuntimeError):
raise CredentialsValidateFailedError('Invalid api key')
@property
@@ -160,6 +171,7 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
"""
used to define customizable model schema
"""
entity = AIModelEntity(
model=model,
label=I18nObject(
@@ -167,7 +179,10 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
),
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_type=ModelType.TEXT_EMBEDDING,
model_properties={},
model_properties={
ModelPropertyKey.MAX_CHUNKS: 1,
ModelPropertyKey.CONTEXT_SIZE: 'max_tokens' in credentials and credentials['max_tokens'] or 512,
},
parameter_rules=[]
)

25
api/core/model_runtime/model_providers/xinference/llm/xinference_helper.py → api/core/model_runtime/model_providers/xinference/xinference_helper.py
View File

@@ -1,6 +1,7 @@
from threading import Lock
from time import time
from typing import List
from os import path
from requests import get
from requests.adapters import HTTPAdapter
@@ -12,11 +13,16 @@ class XinferenceModelExtraParameter(object):
model_format: str
model_handle_type: str
model_ability: List[str]
max_tokens: int = 512
support_function_call: bool = False
def __init__(self, model_format: str, model_handle_type: str, model_ability: List[str]) -> None:
def __init__(self, model_format: str, model_handle_type: str, model_ability: List[str],
support_function_call: bool, max_tokens: int) -> None:
self.model_format = model_format
self.model_handle_type = model_handle_type
self.model_ability = model_ability
self.support_function_call = support_function_call
self.max_tokens = max_tokens
cache = {}
cache_lock = Lock()
@@ -49,7 +55,7 @@ class XinferenceHelper:
get xinference model extra parameter like model_format and model_handle_type
"""
url = f'{server_url}/v1/models/{model_uid}'
url = path.join(server_url, 'v1/models', model_uid)
# this methid is surrounded by a lock, and default requests may hang forever, so we just set a Adapter with max_retries=3
session = Session()
@@ -66,10 +72,12 @@ class XinferenceHelper:
response_json = response.json()
model_format = response_json['model_format']
model_ability = response_json['model_ability']
model_format = response_json.get('model_format', 'ggmlv3')
model_ability = response_json.get('model_ability', [])
if model_format == 'ggmlv3' and 'chatglm' in response_json['model_name']:
if response_json.get('model_type') == 'embedding':
model_handle_type = 'embedding'
elif model_format == 'ggmlv3' and 'chatglm' in response_json['model_name']:
model_handle_type = 'chatglm'
elif 'generate' in model_ability:
model_handle_type = 'generate'
@@ -78,8 +86,13 @@ class XinferenceHelper:
else:
raise NotImplementedError(f'xinference model handle type {model_handle_type} is not supported')
support_function_call = 'tools' in model_ability
max_tokens = response_json.get('max_tokens', 512)
return XinferenceModelExtraParameter(
model_format=model_format,
model_handle_type=model_handle_type,
model_ability=model_ability
model_ability=model_ability,
support_function_call=support_function_call,
max_tokens=max_tokens
)

4
api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml
View File

@@ -2,6 +2,10 @@ model: glm-3-turbo
label:
en_US: glm-3-turbo
model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
parameter_rules:

4
api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml
View File

@@ -2,6 +2,10 @@ model: glm-4
label:
en_US: glm-4
model_type: llm
features:
- multi-tool-call
- agent-thought
- stream-tool-call
model_properties:
mode: chat
parameter_rules:

21
api/core/model_runtime/model_providers/zhipuai/llm/llm.py
View File

@@ -194,6 +194,27 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
'content': prompt_message.content,
'tool_call_id': prompt_message.tool_call_id
})
elif isinstance(prompt_message, AssistantPromptMessage):
if prompt_message.tool_calls:
params['messages'].append({
'role': 'assistant',
'content': prompt_message.content,
'tool_calls': [
{
'id': tool_call.id,
'type': tool_call.type,
'function': {
'name': tool_call.function.name,
'arguments': tool_call.function.arguments
}
} for tool_call in prompt_message.tool_calls
]
})
else:
params['messages'].append({
'role': 'assistant',
'content': prompt_message.content
})
else:
params['messages'].append({
'role': prompt_message.role.value,