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add dingtalk file support, fix video/voice file to bailian (#1683)

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* add dingtalk file support, fix video/voice file to bailian

* fix bailian files conversation

* 更新 bailianchatcmpl.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update libs/dingtalk_api/api.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* chore: bump langbot-plugin version to 0.1.3b1

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Junyan Qin <rockchinq@gmail.com>
This commit is contained in:
Bruce
2025-09-29 16:33:42 +08:00
committed by GitHub
parent 72d701df3e
commit ea42579374
7 changed files with 262 additions and 2 deletions
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213
pkg/provider/modelmgr/requesters/bailianchatcmpl.py
View File

@@ -1,9 +1,14 @@
from __future__ import annotations
import typing
import dashscope
import openai
from . import modelscopechatcmpl
from .. import requester
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
import langbot_plugin.api.entities.builtin.provider.message as provider_message
class BailianChatCompletions(modelscopechatcmpl.ModelScopeChatCompletions):
@@ -15,3 +20,211 @@ class BailianChatCompletions(modelscopechatcmpl.ModelScopeChatCompletions):
'base_url': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
'timeout': 120,
}
async def _closure_stream(
self,
query: pipeline_query.Query,
req_messages: list[dict],
use_model: requester.RuntimeLLMModel,
use_funcs: list[resource_tool.LLMTool] = None,
extra_args: dict[str, typing.Any] = {},
remove_think: bool = False,
) -> provider_message.Message | typing.AsyncGenerator[provider_message.MessageChunk, None]:
self.client.api_key = use_model.token_mgr.get_token()
args = {}
args['model'] = use_model.model_entity.name
if use_funcs:
tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
if tools:
args['tools'] = tools
# 设置此次请求中的messages
messages = req_messages.copy()
is_use_dashscope_call = False # 是否使用阿里原生库调用
is_enable_multi_model = True # 是否支持多轮对话
use_time_num = 0 # 模型已调用次数，防止存在多文件时重复调用
use_time_ids = [] # 已调用的ID列表
message_id = 0 # 记录消息序号
for msg in messages:
# print(msg)
if 'content' in msg and isinstance(msg['content'], list):
for me in msg['content']:
if me['type'] == 'image_base64':
me['image_url'] = {'url': me['image_base64']}
me['type'] = 'image_url'
del me['image_base64']
elif me['type'] == 'file_url' and '.' in me.get('file_name', ''):
# 1. 视频文件推理
# https://bailian.console.aliyun.com/?tab=doc#/doc/?type=model&url=2845871
file_type = me.get('file_name').lower().split('.')[-1]
if file_type in ['mp4', 'avi', 'mkv', 'mov', 'flv', 'wmv']:
me['type'] = 'video_url'
me['video_url'] = {'url': me['file_url']}
del me['file_url']
del me['file_name']
use_time_num +=1
use_time_ids.append(message_id)
is_enable_multi_model = False
# 2. 语音文件识别, 无法通过openai的audio字段传递，暂时不支持
# https://bailian.console.aliyun.com/?tab=doc#/doc/?type=model&url=2979031
elif file_type in ['aac', 'amr', 'aiff', 'flac', 'm4a',
'mp3', 'mpeg', 'ogg', 'opus', 'wav', 'webm', 'wma']:
me['audio'] = me['file_url']
me['type'] = 'audio'
del me['file_url']
del me['type']
del me['file_name']
is_use_dashscope_call = True
use_time_num +=1
use_time_ids.append(message_id)
is_enable_multi_model = False
message_id += 1
# 使用列表推导式，保留不在 use_time_ids[:-1] 中的元素，仅保留最后一个多媒体消息
if not is_enable_multi_model and use_time_num > 1:
messages = [msg for idx, msg in enumerate(messages) if idx not in use_time_ids[:-1]]
if not is_enable_multi_model:
messages = [msg for msg in messages if 'resp_message_id' not in msg]
args['messages'] = messages
args['stream'] = True
# 流式处理状态
# tool_calls_map: dict[str, provider_message.ToolCall] = {}
chunk_idx = 0
thinking_started = False
thinking_ended = False
role = 'assistant' # 默认角色
if is_use_dashscope_call:
response = dashscope.MultiModalConversation.call(
# 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx"
api_key=use_model.token_mgr.get_token(),
model=use_model.model_entity.name,
messages=messages,
result_format="message",
asr_options={
# "language": "zh", # 可选，若已知音频的语种，可通过该参数指定待识别语种，以提升识别准确率
"enable_lid": True,
"enable_itn": False
},
stream=True
)
content_length_list = []
previous_length = 0 # 记录上一次的内容长度
for res in response:
chunk = res["output"]
# 解析 chunk 数据
if hasattr(chunk, 'choices') and chunk.choices:
choice = chunk.choices[0]
delta_content = choice["message"].content[0]["text"]
finish_reason = choice["finish_reason"]
content_length_list.append(len(delta_content))
else:
delta_content = ""
finish_reason = None
# 跳过空的第一个 chunk（只有 role 没有内容）
if chunk_idx == 0 and not delta_content:
chunk_idx += 1
continue
# 检查 content_length_list 是否有足够的数据
if len(content_length_list) >= 2:
now_content = delta_content[previous_length: content_length_list[-1]]
previous_length = content_length_list[-1] # 更新上一次的长度
else:
now_content = delta_content # 第一次循环时直接使用 delta_content
previous_length = len(delta_content) # 更新上一次的长度
# 构建 MessageChunk - 只包含增量内容
chunk_data = {
'role': role,
'content': now_content if now_content else None,
'is_final': bool(finish_reason) and finish_reason != "null",
}
# 移除 None 值
chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
yield provider_message.MessageChunk(**chunk_data)
chunk_idx += 1
else:
async for chunk in self._req_stream(args, extra_body=extra_args):
# 解析 chunk 数据
if hasattr(chunk, 'choices') and chunk.choices:
choice = chunk.choices[0]
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
finish_reason = getattr(choice, 'finish_reason', None)
else:
delta = {}
finish_reason = None
# 从第一个 chunk 获取 role，后续使用这个 role
if 'role' in delta and delta['role']:
role = delta['role']
# 获取增量内容
delta_content = delta.get('content', '')
reasoning_content = delta.get('reasoning_content', '')
# 处理 reasoning_content
if reasoning_content:
# accumulated_reasoning += reasoning_content
# 如果设置了 remove_think，跳过 reasoning_content
if remove_think:
chunk_idx += 1
continue
# 第一次出现 reasoning_content，添加 <think> 开始标签
if not thinking_started:
thinking_started = True
delta_content = '<think>\n' + reasoning_content
else:
# 继续输出 reasoning_content
delta_content = reasoning_content
elif thinking_started and not thinking_ended and delta_content:
# reasoning_content 结束，normal content 开始，添加 </think> 结束标签
thinking_ended = True
delta_content = '\n</think>\n' + delta_content
# 处理工具调用增量
if delta.get('tool_calls'):
for tool_call in delta['tool_calls']:
if tool_call['id'] != '':
tool_id = tool_call['id']
if tool_call['function']['name'] is not None:
tool_name = tool_call['function']['name']
if tool_call['type'] is None:
tool_call['type'] = 'function'
tool_call['id'] = tool_id
tool_call['function']['name'] = tool_name
tool_call['function']['arguments'] = (
'' if tool_call['function']['arguments'] is None else tool_call['function']['arguments']
)
# 跳过空的第一个 chunk（只有 role 没有内容）
if chunk_idx == 0 and not delta_content and not reasoning_content and not delta.get('tool_calls'):
chunk_idx += 1
continue
# 构建 MessageChunk - 只包含增量内容
chunk_data = {
'role': role,
'content': delta_content if delta_content else None,
'tool_calls': delta.get('tool_calls'),
'is_final': bool(finish_reason),
}
# 移除 None 值
chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
yield provider_message.MessageChunk(**chunk_data)
chunk_idx += 1
# return