mirror of
https://github.com/langbot-app/LangBot.git
synced 2025-11-25 11:29:39 +08:00
fix:del some print ,and amend respback on stream judge ,and del in dingtalk this is_stream_output_supported() use
This commit is contained in:
Dong_master
@@ -39,11 +39,9 @@ class SendResponseBackStage(stage.PipelineStage):
|
||||
|
||||
quote_origin = query.pipeline_config['output']['misc']['quote-origin']
|
||||
|
||||
has_chunks = any(isinstance(msg, llm_entities.MessageChunk) for msg in query.resp_messages)
|
||||
print(has_chunks)
|
||||
if has_chunks and hasattr(query.adapter,'reply_message_chunk'):
|
||||
# has_chunks = any(isinstance(msg, llm_entities.MessageChunk) for msg in query.resp_messages)
|
||||
if await query.adapter.is_stream_output_supported():
|
||||
is_final = [msg.is_final for msg in query.resp_messages][0]
|
||||
print(is_final)
|
||||
await query.adapter.reply_message_chunk(
|
||||
message_source=query.message_event,
|
||||
message_id=query.resp_messages[-1].resp_message_id,
|
||||
@@ -58,10 +56,6 @@ class SendResponseBackStage(stage.PipelineStage):
|
||||
quote_origin=quote_origin,
|
||||
)
|
||||
|
||||
# await query.adapter.reply_message(
|
||||
# message_source=query.message_event,
|
||||
# message=query.resp_message_chain[-1],
|
||||
# quote_origin=quote_origin,
|
||||
# )
|
||||
|
||||
|
||||
return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
|
||||
|
||||
@@ -25,7 +25,6 @@ class MessagePlatformAdapter(metaclass=abc.ABCMeta):
|
||||
|
||||
logger: EventLogger
|
||||
|
||||
is_stream: bool
|
||||
|
||||
def __init__(self, config: dict, ap: app.Application, logger: EventLogger):
|
||||
"""初始化适配器
|
||||
@@ -62,26 +61,31 @@ class MessagePlatformAdapter(metaclass=abc.ABCMeta):
|
||||
quote_origin (bool, optional): 是否引用原消息. Defaults to False.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
async def reply_message_chunk(
|
||||
self,
|
||||
message_source: platform_events.MessageEvent,
|
||||
message_source: platform_events.MessageEvent,
|
||||
message_id: int,
|
||||
message: platform_message.MessageChain,
|
||||
quote_origin: bool = False,
|
||||
is_final: bool = False,
|
||||
):
|
||||
):
|
||||
"""回复消息（流式输出）
|
||||
Args:
|
||||
message_source (platform.types.MessageEvent): 消息源事件
|
||||
message_id (int): 消息ID
|
||||
message (platform.types.MessageChain): 消息链
|
||||
quote_origin (bool, optional): 是否引用原消息. Defaults to False.
|
||||
is_final (bool, optional): 流式是否结束. Defaults to False.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
async def create_message_card(self,message_id,event):
|
||||
'''创建卡片消息'''
|
||||
async def create_message_card(self, message_id:typing.Type[str,int], event:platform_events.MessageEvent) -> bool:
|
||||
"""创建卡片消息
|
||||
Args:
|
||||
message_id (str): 消息ID
|
||||
event (platform_events.MessageEvent): 消息源事件
|
||||
"""
|
||||
return False
|
||||
|
||||
async def is_muted(self, group_id: int) -> bool:
|
||||
@@ -117,11 +121,9 @@ class MessagePlatformAdapter(metaclass=abc.ABCMeta):
|
||||
async def run_async(self):
|
||||
"""异步运行"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
async def is_stream_output_supported(self) -> bool:
|
||||
"""是否支持流式输出"""
|
||||
self.is_stream = False
|
||||
return False
|
||||
|
||||
async def kill(self) -> bool:
|
||||
|
||||
@@ -148,7 +148,7 @@ class DingTalkAdapter(adapter.MessagePlatformAdapter):
|
||||
message: platform_message.MessageChain,
|
||||
quote_origin: bool = False,
|
||||
is_final: bool = False,
|
||||
):
|
||||
):
|
||||
event = await DingTalkEventConverter.yiri2target(
|
||||
message_source,
|
||||
)
|
||||
@@ -158,13 +158,12 @@ class DingTalkAdapter(adapter.MessagePlatformAdapter):
|
||||
|
||||
content, at = await DingTalkMessageConverter.yiri2target(message)
|
||||
|
||||
card_instance,card_instance_id = self.card_instance_id_dict[message_id]
|
||||
card_instance, card_instance_id = self.card_instance_id_dict[message_id]
|
||||
# print(card_instance_id)
|
||||
await self.bot.send_card_message(card_instance,card_instance_id,content,is_final)
|
||||
await self.bot.send_card_message(card_instance, card_instance_id, content, is_final)
|
||||
if is_final:
|
||||
self.card_instance_id_dict.pop(message_id)
|
||||
|
||||
|
||||
async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
|
||||
content = await DingTalkMessageConverter.yiri2target(message)
|
||||
if target_type == 'person':
|
||||
@@ -174,11 +173,11 @@ class DingTalkAdapter(adapter.MessagePlatformAdapter):
|
||||
|
||||
async def is_stream_output_supported(self) -> bool:
|
||||
is_stream = False
|
||||
if self.config.get("enable-stream-reply", None):
|
||||
if self.config.get('enable-stream-reply', None):
|
||||
is_stream = True
|
||||
return is_stream
|
||||
|
||||
async def create_message_card(self,message_id,event):
|
||||
async def create_message_card(self, message_id, event):
|
||||
card_template_id = self.config['card_template_id']
|
||||
incoming_message = event.source_platform_object.incoming_message
|
||||
# message_id = incoming_message.message_id
|
||||
@@ -186,7 +185,6 @@ class DingTalkAdapter(adapter.MessagePlatformAdapter):
|
||||
self.card_instance_id_dict[message_id] = (card_instance, card_instance_id)
|
||||
return True
|
||||
|
||||
|
||||
def register_listener(
|
||||
self,
|
||||
event_type: typing.Type[platform_events.Event],
|
||||
@@ -194,7 +192,6 @@ class DingTalkAdapter(adapter.MessagePlatformAdapter):
|
||||
):
|
||||
async def on_message(event: DingTalkEvent):
|
||||
try:
|
||||
await self.is_stream_output_supported()
|
||||
return await callback(
|
||||
await self.event_converter.target2yiri(event, self.config['robot_name']),
|
||||
self,
|
||||
|
||||
@@ -9,7 +9,6 @@ import re
|
||||
import base64
|
||||
import uuid
|
||||
import json
|
||||
import time
|
||||
import datetime
|
||||
import hashlib
|
||||
from Crypto.Cipher import AES
|
||||
@@ -344,12 +343,11 @@ class LarkAdapter(adapter.MessagePlatformAdapter):
|
||||
config: dict
|
||||
quart_app: quart.Quart
|
||||
ap: app.Application
|
||||
|
||||
message_id_to_card_id: typing.Dict[str, typing.Tuple[str, int]]
|
||||
|
||||
card_id_dict: dict[str, str]
|
||||
|
||||
seq: int
|
||||
card_id_dict: dict[str, str] # 消息id到卡片id的映射，便于创建卡片后的发送消息到指定卡片
|
||||
|
||||
seq: int # 用于在发送卡片消息中识别消息顺序，直接以seq作为标识
|
||||
|
||||
def __init__(self, config: dict, ap: app.Application, logger: EventLogger):
|
||||
self.config = config
|
||||
@@ -357,10 +355,9 @@ class LarkAdapter(adapter.MessagePlatformAdapter):
|
||||
self.logger = logger
|
||||
self.quart_app = quart.Quart(__name__)
|
||||
self.listeners = {}
|
||||
self.message_id_to_card_id = {}
|
||||
self.card_id_dict = {}
|
||||
self.seq = 1
|
||||
self.card_id_time = {}
|
||||
|
||||
|
||||
@self.quart_app.route('/lark/callback', methods=['POST'])
|
||||
async def lark_callback():
|
||||
@@ -405,15 +402,7 @@ class LarkAdapter(adapter.MessagePlatformAdapter):
|
||||
await self.logger.error(f'Error in lark callback: {traceback.format_exc()}')
|
||||
return {'code': 500, 'message': 'error'}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
async def on_message(event: lark_oapi.im.v1.P2ImMessageReceiveV1):
|
||||
|
||||
lb_event = await self.event_converter.target2yiri(event, self.api_client)
|
||||
|
||||
await self.listeners[type(lb_event)](lb_event, self)
|
||||
@@ -435,51 +424,49 @@ class LarkAdapter(adapter.MessagePlatformAdapter):
|
||||
|
||||
async def is_stream_output_supported(self) -> bool:
|
||||
is_stream = False
|
||||
if self.config.get("enable-stream-reply", None):
|
||||
if self.config.get('enable-stream-reply', None):
|
||||
is_stream = True
|
||||
return is_stream
|
||||
|
||||
async def create_card_id(self,message_id):
|
||||
async def create_card_id(self, message_id):
|
||||
try:
|
||||
is_stream = await self.is_stream_output_supported()
|
||||
if is_stream:
|
||||
self.ap.logger.debug('飞书支持stream输出,创建卡片......')
|
||||
self.ap.logger.debug('飞书支持stream输出,创建卡片......')
|
||||
|
||||
card_data = {"schema": "2.0", "header": {"title": {"content": "bot", "tag": "plain_text"}},
|
||||
"body": {"elements": [
|
||||
{"tag": "markdown", "content": "[思考中.....]", "element_id": "markdown_1"}]},
|
||||
"config": {"streaming_mode": True,
|
||||
"streaming_config": {"print_strategy": "delay"}}} # delay / fast
|
||||
card_data = {
|
||||
'schema': '2.0',
|
||||
'header': {'title': {'content': 'bot', 'tag': 'plain_text'}},
|
||||
'body': {'elements': [{'tag': 'markdown', 'content': '[思考中.....]', 'element_id': 'markdown_1'}]},
|
||||
'config': {'streaming_mode': True, 'streaming_config': {'print_strategy': 'delay'}},
|
||||
} # delay / fast 创建卡片模板，delay 延迟打印，fast 实时打印，可以自定义更好看的消息模板
|
||||
|
||||
request: CreateCardRequest = CreateCardRequest.builder() \
|
||||
.request_body(
|
||||
CreateCardRequestBody.builder()
|
||||
.type("card_json")
|
||||
.data(json.dumps(card_data)) \
|
||||
.build()
|
||||
).build()
|
||||
request: CreateCardRequest = (
|
||||
CreateCardRequest.builder()
|
||||
.request_body(CreateCardRequestBody.builder().type('card_json').data(json.dumps(card_data)).build())
|
||||
.build()
|
||||
)
|
||||
|
||||
# 发起请求
|
||||
response: CreateCardResponse = self.api_client.cardkit.v1.card.create(request)
|
||||
# 发起请求
|
||||
response: CreateCardResponse = self.api_client.cardkit.v1.card.create(request)
|
||||
|
||||
# 处理失败返回
|
||||
if not response.success():
|
||||
raise Exception(
|
||||
f"client.cardkit.v1.card.create failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}")
|
||||
# 处理失败返回
|
||||
if not response.success():
|
||||
raise Exception(
|
||||
f'client.cardkit.v1.card.create failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
|
||||
)
|
||||
|
||||
self.ap.logger.debug(f'飞书卡片创建成功,卡片ID: {response.data.card_id}')
|
||||
self.card_id_dict[message_id] = response.data.card_id
|
||||
self.ap.logger.debug(f'飞书卡片创建成功,卡片ID: {response.data.card_id}')
|
||||
self.card_id_dict[message_id] = response.data.card_id
|
||||
|
||||
card_id = response.data.card_id
|
||||
card_id = response.data.card_id
|
||||
return card_id
|
||||
|
||||
except Exception as e:
|
||||
self.ap.logger.error(f'飞书卡片创建失败,错误信息: {e}')
|
||||
|
||||
async def create_message_card(self,message_id,event) -> str:
|
||||
async def create_message_card(self, message_id, event) -> str:
|
||||
"""
|
||||
创建卡片消息。
|
||||
使用卡片消息是因为普通消息更新次数有限制，而大模型流式返回结果可能很多而超过限制，而飞书卡片没有这个限制
|
||||
使用卡片消息是因为普通消息更新次数有限制，而大模型流式返回结果可能很多而超过限制，而飞书卡片没有这个限制（api免费次数有限）
|
||||
"""
|
||||
# message_id = event.message_chain.message_id
|
||||
|
||||
@@ -487,7 +474,7 @@ class LarkAdapter(adapter.MessagePlatformAdapter):
|
||||
content = {
|
||||
'type': 'card',
|
||||
'data': {'card_id': card_id, 'template_variable': {'content': 'Thinking...'}},
|
||||
}
|
||||
} # 当收到消息时发送消息模板，可添加模板变量，详情查看飞书中接口文档
|
||||
request: ReplyMessageRequest = (
|
||||
ReplyMessageRequest.builder()
|
||||
.message_id(event.message_chain.message_id)
|
||||
@@ -545,7 +532,6 @@ class LarkAdapter(adapter.MessagePlatformAdapter):
|
||||
f'client.im.v1.message.reply failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
|
||||
)
|
||||
|
||||
|
||||
async def reply_message_chunk(
|
||||
self,
|
||||
message_source: platform_events.MessageEvent,
|
||||
@@ -557,56 +543,50 @@ class LarkAdapter(adapter.MessagePlatformAdapter):
|
||||
"""
|
||||
回复消息变成更新卡片消息
|
||||
"""
|
||||
lark_message = await self.message_converter.yiri2target(message, self.api_client)
|
||||
|
||||
|
||||
self.seq += 1
|
||||
|
||||
if (self.seq - 1) % 8 == 0 or is_final:
|
||||
lark_message = await self.message_converter.yiri2target(message, self.api_client)
|
||||
|
||||
|
||||
text_message = ''
|
||||
for ele in lark_message[0]:
|
||||
if ele['tag'] == 'text':
|
||||
text_message += ele['text']
|
||||
elif ele['tag'] == 'md':
|
||||
text_message += ele['text']
|
||||
print(text_message)
|
||||
text_message = ''
|
||||
for ele in lark_message[0]:
|
||||
if ele['tag'] == 'text':
|
||||
text_message += ele['text']
|
||||
elif ele['tag'] == 'md':
|
||||
text_message += ele['text']
|
||||
|
||||
content = {
|
||||
'type': 'card_json',
|
||||
'data': {'card_id': self.card_id_dict[message_id], 'elements': {'content': text_message}},
|
||||
}
|
||||
# content = {
|
||||
# 'type': 'card_json',
|
||||
# 'data': {'card_id': self.card_id_dict[message_id], 'elements': {'content': text_message}},
|
||||
# }
|
||||
|
||||
request: ContentCardElementRequest = ContentCardElementRequest.builder() \
|
||||
.card_id(self.card_id_dict[message_id]) \
|
||||
.element_id("markdown_1") \
|
||||
.request_body(ContentCardElementRequestBody.builder()
|
||||
# .uuid("a0d69e20-1dd1-458b-k525-dfeca4015204")
|
||||
.content(text_message)
|
||||
.sequence(self.seq)
|
||||
.build()) \
|
||||
.build()
|
||||
|
||||
if is_final:
|
||||
self.seq = 1
|
||||
self.card_id_dict.pop(message_id)
|
||||
# 发起请求
|
||||
response: ContentCardElementResponse = self.api_client.cardkit.v1.card_element.content(request)
|
||||
|
||||
|
||||
# 处理失败返回
|
||||
if not response.success():
|
||||
raise Exception(
|
||||
f'client.im.v1.message.patch failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
|
||||
request: ContentCardElementRequest = (
|
||||
ContentCardElementRequest.builder()
|
||||
.card_id(self.card_id_dict[message_id])
|
||||
.element_id('markdown_1')
|
||||
.request_body(
|
||||
ContentCardElementRequestBody.builder()
|
||||
# .uuid("a0d69e20-1dd1-458b-k525-dfeca4015204")
|
||||
.content(text_message)
|
||||
.sequence(self.seq)
|
||||
.build()
|
||||
)
|
||||
.build()
|
||||
)
|
||||
return
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
if is_final:
|
||||
self.seq = 1 # 消息回复结束之后重置seq
|
||||
self.card_id_dict.pop(message_id) # 清理已经使用过的卡片
|
||||
# 发起请求
|
||||
response: ContentCardElementResponse = self.api_client.cardkit.v1.card_element.content(request)
|
||||
|
||||
# 处理失败返回
|
||||
if not response.success():
|
||||
raise Exception(
|
||||
f'client.im.v1.message.patch failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
|
||||
)
|
||||
return
|
||||
|
||||
async def is_muted(self, group_id: int) -> bool:
|
||||
return False
|
||||
@@ -658,4 +638,4 @@ class LarkAdapter(adapter.MessagePlatformAdapter):
|
||||
# 所以要设置_auto_reconnect=False,让其不重连。
|
||||
self.bot._auto_reconnect = False
|
||||
await self.bot._disconnect()
|
||||
return False
|
||||
return False
|
||||
@@ -167,7 +167,7 @@ class TelegramAdapter(adapter.MessagePlatformAdapter):
|
||||
await self.listeners[type(lb_event)](lb_event, self)
|
||||
await self.is_stream_output_supported()
|
||||
except Exception:
|
||||
await self.logger.error(f"Error in telegram callback: {traceback.format_exc()}")
|
||||
await self.logger.error(f'Error in telegram callback: {traceback.format_exc()}')
|
||||
|
||||
self.application = ApplicationBuilder().token(self.config['token']).build()
|
||||
self.bot = self.application.bot
|
||||
@@ -206,7 +206,6 @@ class TelegramAdapter(adapter.MessagePlatformAdapter):
|
||||
|
||||
await self.bot.send_message(**args)
|
||||
|
||||
|
||||
async def reply_message_chunk(
|
||||
self,
|
||||
message_source: platform_events.MessageEvent,
|
||||
@@ -214,8 +213,7 @@ class TelegramAdapter(adapter.MessagePlatformAdapter):
|
||||
message: platform_message.MessageChain,
|
||||
quote_origin: bool = False,
|
||||
is_final: bool = False,
|
||||
):
|
||||
|
||||
):
|
||||
assert isinstance(message_source.source_platform_object, Update)
|
||||
components = await TelegramMessageConverter.yiri2target(message, self.bot)
|
||||
args = {}
|
||||
@@ -240,7 +238,6 @@ class TelegramAdapter(adapter.MessagePlatformAdapter):
|
||||
if self.config['markdown_card'] is True:
|
||||
args['parse_mode'] = 'MarkdownV2'
|
||||
|
||||
|
||||
send_msg = await self.bot.send_message(**args)
|
||||
send_msg_id = send_msg.message_id
|
||||
self.msg_stream_id[message_id] = send_msg_id
|
||||
@@ -264,16 +261,12 @@ class TelegramAdapter(adapter.MessagePlatformAdapter):
|
||||
if is_final:
|
||||
self.msg_stream_id.pop(message_id)
|
||||
|
||||
|
||||
async def is_stream_output_supported(self) -> bool:
|
||||
is_stream = False
|
||||
if self.config.get("enable-stream-reply", None):
|
||||
if self.config.get('enable-stream-reply', None):
|
||||
is_stream = True
|
||||
self.is_stream = is_stream
|
||||
|
||||
return is_stream
|
||||
|
||||
|
||||
async def is_muted(self, group_id: int) -> bool:
|
||||
return False
|
||||
|
||||
|
||||
@@ -17,14 +17,13 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
"""OpenAI ChatCompletion API 请求器"""
|
||||
|
||||
client: openai.AsyncClient
|
||||
is_content:bool
|
||||
is_content: bool
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
'base_url': 'https://api.openai.com/v1',
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
|
||||
async def initialize(self):
|
||||
self.client = openai.AsyncClient(
|
||||
api_key='',
|
||||
@@ -46,7 +45,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
args: dict,
|
||||
extra_body: dict = {},
|
||||
) -> chat_completion.ChatCompletion:
|
||||
|
||||
async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
|
||||
yield chunk
|
||||
|
||||
@@ -66,23 +64,23 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
|
||||
# deepseek的reasoner模型
|
||||
if pipeline_config['trigger'].get('misc', '').get('remove_think'):
|
||||
|
||||
pass
|
||||
else:
|
||||
if reasoning_content is not None :
|
||||
chatcmpl_message['content'] = '<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
|
||||
if reasoning_content is not None:
|
||||
chatcmpl_message['content'] = (
|
||||
'<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
|
||||
)
|
||||
|
||||
message = llm_entities.Message(**chatcmpl_message)
|
||||
|
||||
return message
|
||||
|
||||
|
||||
async def _make_msg_chunk(
|
||||
self,
|
||||
pipeline_config: dict[str, typing.Any],
|
||||
chat_completion: chat_completion.ChatCompletion,
|
||||
idx: int,
|
||||
) -> llm_entities.MessageChunk:
|
||||
|
||||
# 处理流式chunk和完整响应的差异
|
||||
# print(chat_completion.choices[0])
|
||||
if hasattr(chat_completion, 'choices'):
|
||||
@@ -98,7 +96,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
if 'role' not in delta or delta['role'] is None:
|
||||
delta['role'] = 'assistant'
|
||||
|
||||
|
||||
reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None
|
||||
|
||||
delta['content'] = '' if delta['content'] is None else delta['content']
|
||||
@@ -106,13 +103,13 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
|
||||
# deepseek的reasoner模型
|
||||
if pipeline_config['trigger'].get('misc', '').get('remove_think'):
|
||||
if reasoning_content is not None :
|
||||
if reasoning_content is not None:
|
||||
pass
|
||||
else:
|
||||
delta['content'] = delta['content']
|
||||
else:
|
||||
if reasoning_content is not None and idx == 0:
|
||||
delta['content'] += f'<think>\n{reasoning_content}'
|
||||
delta['content'] += f'<think>\n{reasoning_content}'
|
||||
elif reasoning_content is None:
|
||||
if self.is_content:
|
||||
delta['content'] = delta['content']
|
||||
@@ -122,7 +119,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
else:
|
||||
delta['content'] += reasoning_content
|
||||
|
||||
|
||||
message = llm_entities.MessageChunk(**delta)
|
||||
|
||||
return message
|
||||
@@ -135,9 +131,10 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
use_funcs: list[tools_entities.LLMFunction] = None,
|
||||
stream: bool = False,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
|
||||
) ->llm_entities.MessageChunk:
|
||||
self.client.api_key = use_model.token_mgr.get_token()
|
||||
|
||||
|
||||
args = {}
|
||||
args['model'] = use_model.model_entity.name
|
||||
|
||||
@@ -163,14 +160,14 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
|
||||
if stream:
|
||||
current_content = ''
|
||||
args["stream"] = True
|
||||
args['stream'] = True
|
||||
chunk_idx = 0
|
||||
self.is_content = False
|
||||
tool_calls_map: dict[str, llm_entities.ToolCall] = {}
|
||||
pipeline_config = query.pipeline_config
|
||||
async for chunk in self._req_stream(args, extra_body=extra_args):
|
||||
# 处理流式消息
|
||||
delta_message = await self._make_msg_chunk(pipeline_config,chunk,chunk_idx)
|
||||
delta_message = await self._make_msg_chunk(pipeline_config, chunk, chunk_idx)
|
||||
if delta_message.content:
|
||||
current_content += delta_message.content
|
||||
delta_message.content = current_content
|
||||
@@ -182,15 +179,13 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
id=tool_call.id,
|
||||
type=tool_call.type,
|
||||
function=llm_entities.FunctionCall(
|
||||
name=tool_call.function.name if tool_call.function else '',
|
||||
arguments=''
|
||||
name=tool_call.function.name if tool_call.function else '', arguments=''
|
||||
),
|
||||
)
|
||||
if tool_call.function and tool_call.function.arguments:
|
||||
# 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
|
||||
tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
|
||||
|
||||
|
||||
chunk_idx += 1
|
||||
chunk_choices = getattr(chunk, 'choices', None)
|
||||
if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
|
||||
@@ -198,11 +193,9 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
delta_message.content = current_content
|
||||
|
||||
if chunk_idx % 64 == 0 or delta_message.is_final:
|
||||
|
||||
yield delta_message
|
||||
# return
|
||||
|
||||
|
||||
async def _closure(
|
||||
self,
|
||||
query: core_entities.Query,
|
||||
@@ -211,7 +204,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
use_funcs: list[tools_entities.LLMFunction] = None,
|
||||
stream: bool = False,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
|
||||
) -> llm_entities.Message:
|
||||
self.client.api_key = use_model.token_mgr.get_token()
|
||||
|
||||
args = {}
|
||||
@@ -237,22 +230,15 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
|
||||
args['messages'] = messages
|
||||
|
||||
|
||||
|
||||
# 发送请求
|
||||
|
||||
resp = await self._req(args, extra_body=extra_args)
|
||||
# 处理请求结果
|
||||
pipeline_config = query.pipeline_config
|
||||
message = await self._make_msg(resp,pipeline_config)
|
||||
|
||||
message = await self._make_msg(resp, pipeline_config)
|
||||
|
||||
return message
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
async def invoke_llm(
|
||||
self,
|
||||
query: core_entities.Query,
|
||||
@@ -273,7 +259,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
req_messages.append(msg_dict)
|
||||
|
||||
try:
|
||||
|
||||
msg = await self._closure(
|
||||
query=query,
|
||||
req_messages=req_messages,
|
||||
@@ -334,7 +319,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
funcs: typing.List[tools_entities.LLMFunction] = None,
|
||||
stream: bool = False,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
) -> llm_entities.MessageChunk:
|
||||
) -> llm_entities.MessageChunk:
|
||||
req_messages = [] # req_messages 仅用于类内，外部同步由 query.messages 进行
|
||||
for m in messages:
|
||||
msg_dict = m.dict(exclude_none=True)
|
||||
|
||||
@@ -55,6 +55,6 @@ class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
raise errors.RequesterError('接口返回为空，请确定模型提供商服务是否正常')
|
||||
pipeline_config = query.pipeline_config
|
||||
# 处理请求结果
|
||||
message = await self._make_msg(resp,pipeline_config)
|
||||
message = await self._make_msg(resp, pipeline_config)
|
||||
|
||||
return message
|
||||
|
||||
@@ -185,8 +185,6 @@ class DashScopeAPIRunner(runner.RequestRunner):
|
||||
# 将参考资料替换到文本中
|
||||
pending_content = self._replace_references(pending_content, references_dict)
|
||||
|
||||
|
||||
|
||||
yield llm_entities.Message(
|
||||
role='assistant',
|
||||
content=pending_content,
|
||||
@@ -261,13 +259,11 @@ class DashScopeAPIRunner(runner.RequestRunner):
|
||||
role='assistant',
|
||||
content=pending_content,
|
||||
is_final=is_final,
|
||||
|
||||
)
|
||||
|
||||
# 保存当前会话的session_id用于下次对话的语境
|
||||
query.session.using_conversation.uuid = stream_output.get('session_id')
|
||||
|
||||
|
||||
else:
|
||||
for chunk in response:
|
||||
if chunk.get('status_code') != 200:
|
||||
|
||||
@@ -148,7 +148,6 @@ class DifyServiceAPIRunner(runner.RequestRunner):
|
||||
if mode == 'workflow':
|
||||
if chunk['event'] == 'node_finished':
|
||||
if not is_stream:
|
||||
|
||||
if chunk['data']['node_type'] == 'answer':
|
||||
yield llm_entities.Message(
|
||||
role='assistant',
|
||||
@@ -274,7 +273,6 @@ class DifyServiceAPIRunner(runner.RequestRunner):
|
||||
content=self._try_convert_thinking(pending_agent_message),
|
||||
)
|
||||
|
||||
|
||||
if chunk['event'] == 'agent_thought':
|
||||
if chunk['tool'] != '' and chunk['observation'] != '': # 工具调用结果，跳过
|
||||
continue
|
||||
|
||||
@@ -2,7 +2,6 @@ from __future__ import annotations
|
||||
|
||||
import json
|
||||
import copy
|
||||
from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE
|
||||
import typing
|
||||
from .. import runner
|
||||
from ...core import entities as core_entities
|
||||
@@ -30,11 +29,14 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
|
||||
class ToolCallTracker:
|
||||
"""工具调用追踪器"""
|
||||
|
||||
def __init__(self):
|
||||
self.active_calls: dict[str,dict] = {}
|
||||
self.active_calls: dict[str, dict] = {}
|
||||
self.completed_calls: list[llm_entities.ToolCall] = []
|
||||
|
||||
async def run(self, query: core_entities.Query) -> typing.AsyncGenerator[llm_entities.Message | llm_entities.MessageChunk, None]:
|
||||
async def run(
|
||||
self, query: core_entities.Query
|
||||
) -> typing.AsyncGenerator[llm_entities.Message | llm_entities.MessageChunk, None]:
|
||||
"""运行请求"""
|
||||
pending_tool_calls = []
|
||||
|
||||
@@ -89,16 +91,14 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
|
||||
is_stream = query.adapter.is_stream_output_supported()
|
||||
try:
|
||||
# print(await query.adapter.is_stream_output_supported())
|
||||
is_stream = await query.adapter.is_stream_output_supported()
|
||||
|
||||
except AttributeError:
|
||||
is_stream = False
|
||||
# while True:
|
||||
# pass
|
||||
|
||||
if not is_stream:
|
||||
# 非流式输出，直接请求
|
||||
# print(123)
|
||||
|
||||
msg = await query.use_llm_model.requester.invoke_llm(
|
||||
query,
|
||||
query.use_llm_model,
|
||||
@@ -108,7 +108,6 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
)
|
||||
yield msg
|
||||
final_msg = msg
|
||||
print(final_msg)
|
||||
else:
|
||||
# 流式输出，需要处理工具调用
|
||||
tool_calls_map: dict[str, llm_entities.ToolCall] = {}
|
||||
@@ -122,27 +121,26 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
):
|
||||
assert isinstance(msg, llm_entities.MessageChunk)
|
||||
yield msg
|
||||
# if msg.tool_calls:
|
||||
# for tool_call in msg.tool_calls:
|
||||
# if tool_call.id not in tool_calls_map:
|
||||
# tool_calls_map[tool_call.id] = llm_entities.ToolCall(
|
||||
# id=tool_call.id,
|
||||
# type=tool_call.type,
|
||||
# function=llm_entities.FunctionCall(
|
||||
# name=tool_call.function.name if tool_call.function else '',
|
||||
# arguments=''
|
||||
# ),
|
||||
# )
|
||||
# if tool_call.function and tool_call.function.arguments:
|
||||
# # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
|
||||
# tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
|
||||
if msg.tool_calls:
|
||||
for tool_call in msg.tool_calls:
|
||||
if tool_call.id not in tool_calls_map:
|
||||
tool_calls_map[tool_call.id] = llm_entities.ToolCall(
|
||||
id=tool_call.id,
|
||||
type=tool_call.type,
|
||||
function=llm_entities.FunctionCall(
|
||||
name=tool_call.function.name if tool_call.function else '',
|
||||
arguments=''
|
||||
),
|
||||
)
|
||||
if tool_call.function and tool_call.function.arguments:
|
||||
# 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
|
||||
tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
|
||||
final_msg = llm_entities.Message(
|
||||
role=msg.role,
|
||||
content=msg.all_content,
|
||||
tool_calls=list(tool_calls_map.values()),
|
||||
)
|
||||
|
||||
|
||||
pending_tool_calls = final_msg.tool_calls
|
||||
|
||||
req_messages.append(final_msg)
|
||||
@@ -193,8 +191,7 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
id=tool_call.id,
|
||||
type=tool_call.type,
|
||||
function=llm_entities.FunctionCall(
|
||||
name=tool_call.function.name if tool_call.function else '',
|
||||
arguments=''
|
||||
name=tool_call.function.name if tool_call.function else '', arguments=''
|
||||
),
|
||||
)
|
||||
if tool_call.function and tool_call.function.arguments:
|
||||
@@ -206,7 +203,6 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
tool_calls=list(tool_calls_map.values()),
|
||||
)
|
||||
else:
|
||||
print("非流式")
|
||||
# 处理完所有调用，再次请求
|
||||
msg = await query.use_llm_model.requester.invoke_llm(
|
||||
query,
|
||||
|
||||
Reference in New Issue
Block a user