kaiyuan/chatgpt-on-wechat
1
0
Fork 0
mirror of https://github.com/zhayujie/chatgpt-on-wechat.git synced 2026-06-02 00:57:41 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #2721 from yrk111222/feat/modelscope-update

Browse Source 
Feat/modelscope update
This commit is contained in:
zhayujie
2026-03-30 11:39:50 +08:00
committed by GitHub
parent fa149cf4aa 294e380288
commit 61732aecfc
4 changed files with 764 additions and 157 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
bridge/agent_bridge.py
View File

@@ -115,6 +115,8 @@ class AgentLLMModel(LLMModel):
return const.QWEN_DASHSCOPE
if model_name in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
return const.MOONSHOT
if conf().get("bot_type") == "modelscope":
return const.MODELSCOPE
for prefix, btype in self._MODEL_PREFIX_MAP:
if model_name.startswith(prefix):
return btype

7
channel/web/web_channel.py
View File

@@ -624,6 +624,13 @@ class ConfigHandler:
"api_base_default": "https://api.deepseek.com/v1",
"models": [const.DEEPSEEK_CHAT, const.DEEPSEEK_REASONER],
}),
("modelscope", {
"label": "ModelScope",
"api_key_field": "modelscope_api_key",
"api_base_key": None,
"api_base_default": None,
"models": [const.QWEN3_5_27B, const.QWEN3_235B_A22B_INSTRUCT_2507],
}),
("linkai", {
"label": "LinkAI",
"api_key_field": "linkai_api_key",

17
common/const.py
View File

@@ -124,6 +124,10 @@ DOUBAO_SEED_2_PRO = "doubao-seed-2-0-pro-260215"
DOUBAO_SEED_2_LITE = "doubao-seed-2-0-lite-260215"
DOUBAO_SEED_2_MINI = "doubao-seed-2-0-mini-260215"
# ModelScope(魔搭社区)
QWEN3_235B_A22B_INSTRUCT_2507 = "Qwen/Qwen3-235B-A22B-Instruct-2507"
QWEN3_5_27B = "Qwen/Qwen3.5-27B"
# 其他模型
WEN_XIN = "wenxin"
WEN_XIN_4 = "wenxin-4"
@@ -135,11 +139,14 @@ MODELSCOPE = "modelscope"
GITEE_AI_MODEL_LIST = ["Yi-34B-Chat", "InternVL2-8B", "deepseek-coder-33B-instruct", "InternVL2.5-26B", "Qwen2-VL-72B", "Qwen2.5-32B-Instruct", "glm-4-9b-chat", "codegeex4-all-9b", "Qwen2.5-Coder-32B-Instruct", "Qwen2.5-72B-Instruct", "Qwen2.5-7B-Instruct", "Qwen2-72B-Instruct", "Qwen2-7B-Instruct", "code-raccoon-v1", "Qwen2.5-14B-Instruct"]
MODELSCOPE_MODEL_LIST = ["LLM-Research/c4ai-command-r-plus-08-2024","mistralai/Mistral-Small-Instruct-2409","mistralai/Ministral-8B-Instruct-2410","mistralai/Mistral-Large-Instruct-2407",
"Qwen/Qwen2.5-Coder-32B-Instruct","Qwen/Qwen2.5-Coder-14B-Instruct","Qwen/Qwen2.5-Coder-7B-Instruct","Qwen/Qwen2.5-72B-Instruct","Qwen/Qwen2.5-32B-Instruct","Qwen/Qwen2.5-14B-Instruct","Qwen/Qwen2.5-7B-Instruct","Qwen/QwQ-32B-Preview",
"LLM-Research/Llama-3.3-70B-Instruct","opencompass/CompassJudger-1-32B-Instruct","Qwen/QVQ-72B-Preview","LLM-Research/Meta-Llama-3.1-405B-Instruct","LLM-Research/Meta-Llama-3.1-8B-Instruct","Qwen/Qwen2-VL-7B-Instruct","LLM-Research/Meta-Llama-3.1-70B-Instruct",
"Qwen/Qwen2.5-14B-Instruct-1M","Qwen/Qwen2.5-7B-Instruct-1M","Qwen/Qwen2.5-VL-3B-Instruct","Qwen/Qwen2.5-VL-7B-Instruct","Qwen/Qwen2.5-VL-72B-Instruct","deepseek-ai/DeepSeek-R1-Distill-Llama-70B","deepseek-ai/DeepSeek-R1-Distill-Llama-8B","deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
"deepseek-ai/DeepSeek-R1-Distill-Qwen-14B","deepseek-ai/DeepSeek-R1-Distill-Qwen-7B","deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B","deepseek-ai/DeepSeek-R1","deepseek-ai/DeepSeek-V3","Qwen/QwQ-32B"]
MODELSCOPE_MODEL_LIST = ["deepseek-ai/DeepSeek-R1-0528", "deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "deepseek-ai/DeepSeek-V3.2", "LLM-Research/c4ai-command-r-plus-08-2024", "LLM-Research/Llama-4-Maverick-17B-128E-Instruct", "meituan-longcat/LongCat-Flash-Lite", "MiniMax/MiniMax-M1-80k", "MiniMax/MiniMax-M2.5", "mistralai/Ministral-8B-Instruct-2410",
"mistralai/Mistral-Large-Instruct-2407", "mistralai/Mistral-Small-Instruct-2409", "moonshotai/Kimi-K2.5", "MusePublic/Qwen-Image-Edit", "opencompass/CompassJudger-1-32B-Instruct", "OpenGVLab/InternVL3_5-241B-A28B",
"Qwen/QVQ-72B-Preview", "Qwen/Qwen-Image-Edit", "Qwen/Qwen3-0.6B", "Qwen/Qwen3-1.7B", "Qwen/Qwen3-14B", "Qwen/Qwen3-235B-A22B", "Qwen/Qwen3-235B-A22B-Instruct-2507", "Qwen/Qwen3-235B-A22B-Thinking-2507", "Qwen/Qwen3-30B-A3B", "Qwen/Qwen3-30B-A3B-Thinking-2507",
"Qwen/Qwen3-32B", "Qwen/Qwen3-4B", "Qwen/Qwen3-8B", "Qwen/Qwen3-Coder-30B-A3B-Instruct", "Qwen/Qwen3-Coder-480B-A35B-Instruct", "Qwen/Qwen3-Next-80B-A3B-Instruct", "Qwen/Qwen3-Next-80B-A3B-Thinking", "Qwen/Qwen3-VL-235B-A22B-Instruct", "Qwen/Qwen3-VL-8B-Instruct",
"Qwen/Qwen3-VL-8B-Thinking", "Qwen/Qwen3.5-122B-A10B", "Qwen/Qwen3.5-27B", "Qwen/Qwen3.5-35B-A3B", "Qwen/Qwen3.5-397B-A17B", "Qwen/QwQ-32B", "Qwen/QwQ-32B-Preview", "Shanghai_AI_Laboratory/Intern-S1", "Shanghai_AI_Laboratory/Intern-S1-mini",
"stepfun-ai/Step-3.5-Flash", "XiaomiMiMo/MiMo-V2-Flash", "ZhipuAI/GLM-4.7-Flash", "ZhipuAI/GLM-5"]
MODEL_LIST = [
# Claude

875
models/modelscope/modelscope_bot.py
View File

@@ -1,8 +1,9 @@
# encoding:utf-8
import time
import json
import openai
import time
import requests
from models.bot import Bot
from models.session_manager import SessionManager
from bridge.context import ContextType
@@ -10,39 +11,39 @@ from bridge.reply import Reply, ReplyType
from common.log import logger
from config import conf, load_config
from .modelscope_session import ModelScopeSession
import requests
# ModelScope对话模型API
class ModelScopeBot(Bot):
def __init__(self):
super().__init__()
self.sessions = SessionManager(ModelScopeSession, model=conf().get("model") or "Qwen/Qwen2.5-7B-Instruct")
model = conf().get("model") or "Qwen/Qwen2.5-7B-Instruct"
model = conf().get("model") or "Qwen/Qwen3.5-27B"
if model == "modelscope":
model = "Qwen/Qwen2.5-7B-Instruct"
model = "Qwen/Qwen3.5-27B"
self.sessions = SessionManager(ModelScopeSession, model=model)
self.args = {
"model": model, # 对话模型的名称
"temperature": conf().get("temperature", 0.3), # 如果设置，值域须为 [0, 1] 我们推荐 0.3，以达到较合适的效果。
"top_p": conf().get("top_p", 1.0), # 使用默认值
"model": model,
"temperature": conf().get("temperature", 0.3),
"top_p": conf().get("top_p", 1.0),
}
self.api_key = conf().get("modelscope_api_key")
self.base_url = conf().get("modelscope_base_url", "https://api-inference.modelscope.cn/v1")
if self.base_url.endswith("/chat/completions"):
self.base_url = self.base_url.rsplit("/chat/completions", 1)[0]
if self.base_url.endswith("/"):
self.base_url = self.base_url.rstrip("/")
@property
def api_key(self):
return conf().get("modelscope_api_key")
# Cache context for Agent mode usage
self._last_context = None
logger.info("[MODELSCOPE] base_url configured as: {}".format(self.base_url))
@property
def base_url(self):
return conf().get("modelscope_base_url", "https://api-inference.modelscope.cn/v1/chat/completions")
"""
需要获取ModelScope支持API-inference的模型名称列表，请到魔搭社区官网模型中心查看 https://modelscope.cn/models?filter=inference_type&page=1。
或者使用命令 curl https://api-inference.modelscope.cn/v1/models 对模型列表和ID进行获取。查看commend/const.py文件也可以获取模型列表。
获取ModelScope的免费API Key，请到魔搭社区官网用户中心查看获取方式 https://modelscope.cn/docs/model-service/API-Inference/intro。
"""
def reply(self, query, context=None):
# acquire reply content
# Cache context for Agent mode usage
self._last_context = context
if context.type == ContextType.TEXT:
logger.info("[MODELSCOPE_AI] query={}".format(query))
logger.info("[MODELSCOPE] query={}".format(query))
session_id = context["session_id"]
reply = None
@@ -56,102 +57,99 @@ class ModelScopeBot(Bot):
elif query == "#更新配置":
load_config()
reply = Reply(ReplyType.INFO, "配置已更新")
if reply:
return reply
session = self.sessions.session_query(query, session_id)
logger.debug("[MODELSCOPE_AI] session query={}".format(session.messages))
logger.debug("[MODELSCOPE] session query={}".format(session.messages))
model = context.get("modelscope_model")
new_args = self.args.copy()
if model:
new_args["model"] = model
if new_args["model"] == "Qwen/QwQ-32B":
model_name = new_args["model"]
# Unified judgment for thinking model
if self._is_thinking_model(model_name):
new_args["enable_thinking"] = True
reply_content = self.reply_text_stream(session, args=new_args)
else:
reply_content = self.reply_text(session, args=new_args)
logger.debug(
"[MODELSCOPE_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
"[MODELSCOPE] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
session.messages,
session_id,
reply_content["content"],
reply_content["completion_tokens"],
)
)
if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
# 只有当 content 为空且 completion_tokens 为 0 时才标记为错误
if len(reply_content["content"]) == 0:
reply = Reply(ReplyType.ERROR, reply_content["content"])
else:
reply = Reply(ReplyType.TEXT, reply_content["content"])
elif reply_content["completion_tokens"] > 0:
self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
self.sessions.session_reply(
reply_content["content"],
session_id,
reply_content["total_tokens"]
)
reply = Reply(ReplyType.TEXT, reply_content["content"])
else:
reply = Reply(ReplyType.ERROR, reply_content["content"])
logger.debug("[MODELSCOPE_AI] reply {} used 0 tokens.".format(reply_content))
return reply
elif context.type == ContextType.IMAGE_CREATE:
ok, retstring = self.create_img(query, 0)
reply = None
if ok:
reply = Reply(ReplyType.IMAGE_URL, retstring)
else:
reply = Reply(ReplyType.ERROR, retstring)
return reply
else:
reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
logger.debug("[MODELSCOPE] reply {} used 0 tokens.".format(reply_content))
return reply
def reply_text(self, session: ModelScopeSession, args=None, retry_count=0) -> dict:
"""
call openai's ChatCompletion to get the answer
:param session: a conversation session
:param session_id: session id
:param retry_count: retry count
:return: {}
"""
elif context.type == ContextType.IMAGE_CREATE:
ok, retstring = self.create_img(query, 0)
return Reply(ReplyType.IMAGE_URL, retstring) if ok else Reply(ReplyType.ERROR, retstring)
else:
return Reply(ReplyType.ERROR, "Bot 不支持处理{}类型的消息".format(context.type))
def reply_text(self, session, args=None, retry_count=0):
try:
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer " + self.api_key
}
body = args
body["messages"] = session.messages
body = args.copy() if args else {}
body["messages"] = self._convert_messages_for_modelscope(session.messages)
body["stream"] = False
res = requests.post(
self.base_url,
"{}/chat/completions".format(self.base_url),
headers=headers,
data=json.dumps(body)
json=body,
timeout=120
)
if res.status_code == 200:
response = res.json()
return {
"total_tokens": response["usage"]["total_tokens"],
"completion_tokens": response["usage"]["completion_tokens"],
"content": response["choices"][0]["message"]["content"]
"total_tokens": response.get("usage", {}).get("total_tokens", 0),
"completion_tokens": response.get("usage", {}).get("completion_tokens", 0),
"content": response["choices"][0]["message"]["content"] if response.get("choices") else ""
}
else:
response = res.json()
if "errors" in response:
error = response.get("errors")
elif "error" in response:
error = response.get("error")
else:
error = "Unknown error"
logger.error(f"[MODELSCOPE_AI] chat failed, status_code={res.status_code}, "
f"msg={error.get('message')}, type={error.get('type')}")
error = response.get("error", response.get("errors", {}))
logger.error(
"[MODELSCOPE] chat failed, status_code={}, msg={}".format(
res.status_code,
error.get('message') if isinstance(error, dict) else error
)
)
result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
need_retry = False
if res.status_code >= 500:
# server error, need retry
logger.warn(f"[MODELSCOPE_AI] do retry, times={retry_count}")
logger.warn("[MODELSCOPE] do retry, times={}".format(retry_count))
need_retry = retry_count < 2
elif res.status_code == 401:
result["content"] = "授权失败，请检查API Key是否正确"
result["content"] = "授权失败，请检查 API Key 是否正确"
elif res.status_code == 429:
result["content"] = "请求过于频繁，请稍后再试"
need_retry = retry_count < 2
@@ -163,6 +161,7 @@ class ModelScopeBot(Bot):
return self.reply_text(session, args, retry_count + 1)
else:
return result
except Exception as e:
logger.exception(e)
need_retry = retry_count < 2
@@ -172,111 +171,703 @@ class ModelScopeBot(Bot):
else:
return result
def reply_text_stream(self, session: ModelScopeSession, args=None, retry_count=0) -> dict:
"""
call ModelScope's ChatCompletion to get the answer with stream response
:param session: a conversation session
:param session_id: session id
:param retry_count: retry count
:return: {}
"""
def reply_text_stream(self, session, args=None):
try:
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer " + self.api_key
}
body = args
body["messages"] = session.messages
body["stream"] = True # 启用流式响应
body = args.copy() if args else {}
body["messages"] = self._convert_messages_for_modelscope(session.messages)
body["stream"] = True
res = requests.post(
self.base_url,
"{}/chat/completions".format(self.base_url),
headers=headers,
data=json.dumps(body),
stream=True
json=body,
stream=True,
timeout=120
)
if res.status_code == 200:
content = ""
total_tokens = completion_tokens = 0
finish_reason = None
for line in res.iter_lines():
if line:
if not line:
continue
decoded_line = line.decode('utf-8')
if decoded_line.startswith("data: "):
if not decoded_line.startswith("data: "):
continue
data_str = decoded_line[6:]
if data_str.strip() == "[DONE]":
break
try:
json_data = json.loads(decoded_line[6:])
delta_content = json_data.get("choices", [{}])[0].get("delta", {}).get("content", "")
if delta_content:
content += delta_content
except json.JSONDecodeError as e:
pass
json_data = json.loads(data_str)
if "usage" in json_data:
total_tokens = json_data["usage"].get("total_tokens", 0)
completion_tokens = json_data["usage"].get("completion_tokens", 0)
delta = json_data.get("choices", [{}])[0].get("delta", {})
if delta and delta.get("content"):
content += delta["content"]
choice = json_data.get("choices", [{}])[0]
if choice.get("finish_reason"):
finish_reason = choice["finish_reason"]
except json.JSONDecodeError:
continue
if finish_reason is None and content:
finish_reason = "stop"
return {
"total_tokens": 1, # 流式响应通常不返回token使用情况
"completion_tokens": 1,
"total_tokens": total_tokens,
"completion_tokens": completion_tokens,
"content": content
}
else:
response = res.json()
if "errors" in response:
error = response.get("errors")
elif "error" in response:
error = response.get("error")
else:
error = "Unknown error"
logger.error(f"[MODELSCOPE_AI] chat failed, status_code={res.status_code}, "
f"msg={error.get('message')}, type={error.get('type')}")
return {"completion_tokens": 0, "content": "请求失败"}
result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
need_retry = False
if res.status_code >= 500:
# server error, need retry
logger.warn(f"[MODELSCOPE_AI] do retry, times={retry_count}")
need_retry = retry_count < 2
elif res.status_code == 401:
result["content"] = "授权失败，请检查API Key是否正确"
elif res.status_code == 429:
result["content"] = "请求过于频繁，请稍后再试"
need_retry = retry_count < 2
else:
need_retry = False
if need_retry:
time.sleep(3)
return self.reply_text_stream(session, args, retry_count + 1)
else:
return result
except Exception as e:
logger.exception(e)
need_retry = retry_count < 2
result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
if need_retry:
return self.reply_text_stream(session, args, retry_count + 1)
else:
return result
def create_img(self, query, retry_count=0):
return {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
def create_img(self, query):
try:
logger.info("[ModelScopeImage] image_query={}".format(query))
headers = {
"Content-Type": "application/json; charset=utf-8", # 明确指定编码
"Authorization": f"Bearer {self.api_key}"
create_headers = {
"Authorization": "Bearer " + self.api_key,
"Content-Type": "application/json; charset=utf-8",
"X-ModelScope-Async-Mode": "true"
}
payload = {
"prompt": query, # required
"n": 1,
"model": conf().get("text_to_image"),
"prompt": query,
"n": 1,
}
url = "https://api-inference.modelscope.cn/v1/images/generations"
# 手动序列化并保留中文（禁用 ASCII 转义）
json_payload = json.dumps(payload, ensure_ascii=False).encode('utf-8')
logger.debug("[ModelScopeImage] model={}".format(payload["model"]))
# 使用 data 参数发送原始字符串（requests 会自动处理编码）
res = requests.post(url, headers=headers, data=json_payload)
res = requests.post(
"{}/images/generations".format(self.base_url),
headers=create_headers,
data=json.dumps(payload, ensure_ascii=False).encode('utf-8'),
timeout=120
)
response_data = res.json()
image_url = response_data['images'][0]['url']
logger.info("[ModelScopeImage] image_url={}".format(image_url))
logger.debug("[ModelScopeImage] create task status={}".format(res.status_code))
logger.debug("[ModelScopeImage] create task response={}".format(res.text))
if res.status_code != 200:
logger.error("[ModelScopeImage] create task failed: {}".format(res.text))
return False, "创建画图任务失败：{}".format(res.status_code)
task_data = res.json()
task_id = task_data.get("task_id")
if not task_id:
logger.error("[ModelScopeImage] No task_id in response: {}".format(task_data))
return False, "创建画图任务失败：未返回 task_id"
logger.info("[ModelScopeImage] task_id={}".format(task_id))
max_wait_times = 60
wait_interval = 5
for i in range(max_wait_times):
time.sleep(wait_interval)
poll_headers = {
"Authorization": "Bearer " + self.api_key,
"X-ModelScope-Task-Type": "image_generation"
}
poll_url = "{}/tasks/{}".format(self.base_url, task_id)
logger.debug("[ModelScopeImage] poll {} URL: {}".format(i+1, poll_url))
logger.debug("[ModelScopeImage] poll headers: {}".format(poll_headers))
task_res = requests.get(
poll_url,
headers=poll_headers,
timeout=30
)
logger.debug("[ModelScopeImage] poll {} status={}".format(i+1, task_res.status_code))
logger.debug("[ModelScopeImage] poll response={}".format(task_res.text))
if task_res.status_code != 200:
logger.error("[ModelScopeImage] poll task error: {}".format(task_res.text))
continue
data = task_res.json()
task_status = data.get("task_status")
logger.debug("[ModelScopeImage] task_status={}".format(task_status))
if task_status == "SUCCEED":
output_images = data.get("output_images", [])
if output_images and len(output_images) > 0:
image_url = output_images[0]
logger.info("[ModelScopeImage] image generated successfully: {}".format(image_url))
return True, image_url
else:
logger.error("[ModelScopeImage] No output_images in success response: {}".format(data))
return False, "画图成功但未返回图片 URL"
elif task_status == "FAILED":
error_msg = "未知错误"
if "errors" in data:
error_msg = data["errors"].get("message", "未知错误")
elif "message" in data:
error_msg = data["message"]
logger.error("[ModelScopeImage] task failed: {}".format(data))
return False, "画图任务失败：{}".format(error_msg)
elif task_status == "CANCELED":
logger.error("[ModelScopeImage] task canceled: {}".format(data))
return False, "画图任务已取消"
logger.debug("[ModelScopeImage] waiting for task to complete...")
logger.error("[ModelScopeImage] task timeout after {} seconds".format(max_wait_times * wait_interval))
return False, "画图超时，请稍后再试"
except Exception as e:
logger.error(format(e))
logger.error("[ModelScopeImage] error: {}".format(format(e)))
return False, "画图出现问题，请休息一下再问我吧"
# ==================== Agent Mode Support ====================
def _detect_image_intent(self, message):
"""Detect whether the message has drawing intention (keyword detection)"""
if not message:
return False
message_lower = message.lower()
image_keywords = ["画", "图片", "图像", "生成图", "photo", "image", "draw", "paint", "generate"]
if any(keyword in message_lower for keyword in image_keywords):
logger.info("[MODELSCOPE] Image intent detected by keyword: {}".format(message[:50]))
return True
return False
def _is_thinking_model(self, model_name):
"""
Determine whether it is a thinking model.
A thinking model requires: 1) enabling the enable_thinking parameter, and 2) using streaming responses.
"""
if not model_name:
return False
model_name_lower = model_name.lower()
if "thinking" in model_name_lower or "think" in model_name_lower:
return True
if model_name in ["Qwen/QwQ-32B", ]:
return True
return False
def call_with_tools(self, messages, tools=None, stream=False, **kwargs):
"""
Call ModelScope API with tool call support.
Also check ContextType and keywords; if either matches, trigger drawing.
"""
try:
# Check the IMAGE_CREATE type from the cached context
context = getattr(self, '_last_context', None)
# If the context type is IMAGE_CREATE, directly call create_img
if context and hasattr(context, 'type') and context.type == ContextType.IMAGE_CREATE:
logger.info("[MODELSCOPE] IMAGE_CREATE context detected, calling create_img directly")
query = getattr(context, 'content', '')
if query:
ok, result = self.create_img(query)
if ok:
logger.info("[MODELSCOPE] Image generated: {}".format(result))
if stream:
return self._create_image_stream_response(result)
else:
return self._create_image_response(result)
else:
logger.error("[MODELSCOPE] Image generation failed: {}".format(result))
error_content = "画图失败：{}".format(result)
if stream:
return self._create_error_stream_response(error_content)
else:
return self._create_error_response(error_content)
# Extract message content
last_message = ""
if messages and len(messages) > 0:
last_msg = messages[-1]
if isinstance(last_msg, dict):
content = last_msg.get("content", "")
if isinstance(content, list):
text_parts = []
for block in content:
if isinstance(block, dict):
if block.get("type") == "text":
text_parts.append(block.get("text", ""))
last_message = " ".join(text_parts)
else:
last_message = content
elif isinstance(last_msg, str):
last_message = last_msg
if not isinstance(last_message, str):
last_message = str(last_message)
logger.debug("[MODELSCOPE] Extracted message: {}".format(last_message[:100]))
# Keyword detection
has_image_intent = self._detect_image_intent(last_message)
if has_image_intent:
logger.info("[MODELSCOPE] Image intent detected by keyword, calling create_img directly")
ok, result = self.create_img(last_message)
if ok:
logger.info("[MODELSCOPE] Image generated: {}".format(result))
if stream:
return self._create_image_stream_response(result)
else:
return self._create_image_response(result)
else:
logger.error("[MODELSCOPE] Image generation failed: {}".format(result))
error_content = "画图失败：{}".format(result)
if stream:
return self._create_error_stream_response(error_content)
else:
return self._create_error_response(error_content)
# No drawing intent, proceed with normal tool call flow
session_id = kwargs.get('session_id', 'default_session')
session = self.sessions.session_query("", session_id)
session.messages = messages
args = self.args.copy()
args.update(kwargs)
# Unified judgment for thinking model
model_name = args.get("model", self.args.get("model", ""))
if self._is_thinking_model(model_name):
args["enable_thinking"] = True
if tools:
args["tools"] = self._convert_tools_to_openai_format(tools)
args["tool_choice"] = "auto"
logger.debug(
"[MODELSCOPE] call_with_tools: model={}, tools={}, stream={}, enable_thinking={}".format(
args.get('model'),
len(tools) if tools else 0,
stream,
args.get('enable_thinking')
)
)
if stream:
return self._handle_stream_response(session, args)
else:
return self._handle_sync_response(session, args)
except Exception as e:
logger.error("[MODELSCOPE] call_with_tools error: {}".format(e))
error_msg = "{}".format(e)
def error_generator():
yield {"error": True, "message": error_msg, "status_code": 500}
return error_generator()
def _handle_sync_response(self, session, args):
result = self.reply_text(session, args)
content = result.get("content", "")
tool_calls = result.get("tool_calls")
if tool_calls:
for tool_call in tool_calls:
tool_name = tool_call.get("function", {}).get("name", "")
if tool_name in ["create_image", "generate_image"]:
try:
tool_args = json.loads(tool_call.get("function", {}).get("arguments", "{}"))
prompt = tool_args.get("prompt", "")
ok, image_url = self.create_img(prompt)
if ok:
result["tool_execution_result"] = {"image_url": image_url, "success": True}
else:
result["tool_execution_result"] = {"error": image_url, "success": False}
except Exception as e:
logger.error("[MODELSCOPE] Sync tool execution error: {}".format(e))
return {
"choices": [{
"message": {
"role": "assistant",
"content": content,
"tool_calls": tool_calls
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 0,
"completion_tokens": result.get("completion_tokens", 0),
"total_tokens": result.get("total_tokens", 0)
},
"model": args.get("model", self.args.get("model"))
}
def _handle_stream_response(self, session, args):
try:
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer " + self.api_key
}
body = args.copy()
body["messages"] = self._convert_messages_for_modelscope(session.messages)
body["stream"] = True
response = requests.post(
"{}/chat/completions".format(self.base_url),
headers=headers,
json=body,
stream=True,
timeout=120
)
if response.status_code != 200:
yield {"error": True, "message": response.text, "status_code": response.status_code}
return
current_tool_calls = {}
finish_reason = None
for line in response.iter_lines():
if not line:
continue
line = line.decode("utf-8")
if not line.startswith("data: ") or line[6:].strip() == "[DONE]":
continue
try:
chunk = json.loads(line[6:])
if chunk.get("error"):
yield {"error": True, "message": str(chunk["error"]), "status_code": 500}
return
choices = chunk.get("choices")
if not choices or len(choices) == 0:
continue
choice = choices[0]
if not choice:
continue
delta = choice.get("delta")
if not delta:
continue
if delta.get("reasoning_content"):
continue
tool_call_chunks = delta.get("tool_calls")
if tool_call_chunks:
cleaned_chunks = []
for tool_call_chunk in tool_call_chunks:
if not tool_call_chunk:
continue
index = tool_call_chunk.get("index", 0)
func_info = tool_call_chunk.get("function") or {}
if index not in current_tool_calls:
current_tool_calls[index] = {
"id": tool_call_chunk.get("id") or "",
"name": func_info.get("name") or "",
"arguments": ""
}
logger.debug("[MODELSCOPE] tool_call start: {}".format(func_info.get('name')))
args_str = func_info.get("arguments")
if args_str:
current_tool_calls[index]["arguments"] += (
args_str if isinstance(args_str, str) else str(args_str)
)
cleaned_chunk = {
"index": index,
"id": tool_call_chunk.get("id") or "call_{}".format(index),
"type": "function",
"function": {
"name": func_info.get("name") or current_tool_calls[index].get("name", ""),
"arguments": func_info.get("arguments") or ""
}
}
cleaned_chunks.append(cleaned_chunk)
if cleaned_chunks:
yield {
"choices": [{
"index": 0,
"delta": {
"role": "assistant",
"tool_calls": cleaned_chunks
}
}]
}
continue
content = delta.get("content")
if content:
logger.debug("[MODELSCOPE] stream content: {}...".format(content[:50]))
yield_chunk = {
"choices": [{
"index": 0,
"delta": {
"role": delta.get("role"),
"content": content
}
}]
}
if choice.get("finish_reason"):
finish_reason = choice["finish_reason"]
yield_chunk["choices"][0]["finish_reason"] = finish_reason
yield yield_chunk
except json.JSONDecodeError:
continue
except Exception as e:
logger.error("[MODELSCOPE] chunk process error: {}".format(e))
continue
logger.debug(
"[MODELSCOPE] stream completed: has_tool_calls={}, finish_reason={}".format(
len(current_tool_calls) > 0,
finish_reason
)
)
if current_tool_calls:
logger.debug("[MODELSCOPE] tool_calls collected: {}".format(list(current_tool_calls.values())))
for idx, tool_call in current_tool_calls.items():
tool_name = tool_call.get("name", "")
tool_args_str = tool_call.get("arguments", "{}")
if tool_name in ["create_image", "generate_image"]:
try:
tool_args = json.loads(tool_args_str) if tool_args_str else {}
prompt = tool_args.get("prompt", "")
logger.info("[MODELSCOPE] Executing image tool directly: {}".format(prompt[:50]))
ok, result = self.create_img(prompt)
if ok:
logger.info("[MODELSCOPE] Image generated: {}".format(result))
yield {
"choices": [{
"index": 0,
"delta": {
"role": "tool",
"content": json.dumps({"image_url": result, "success": True})
},
"tool_call_id": tool_call.get("id", "")
}]
}
else:
logger.error("[MODELSCOPE] Image generation failed: {}".format(result))
yield {
"choices": [{
"index": 0,
"delta": {
"role": "tool",
"content": json.dumps({"error": result, "success": False})
},
"tool_call_id": tool_call.get("id", "")
}]
}
except Exception as e:
logger.error("[MODELSCOPE] Image tool execution error: {}".format(e))
yield {
"choices": [{
"index": 0,
"delta": {
"role": "tool",
"content": json.dumps({"error": str(e), "success": False})
},
"tool_call_id": tool_call.get("id", "")
}]
}
yield {
"choices": [{
"index": 0,
"delta": {},
"finish_reason": finish_reason or "stop"
}]
}
except Exception as e:
logger.error("[MODELSCOPE] stream tool call error: {}".format(e))
error_msg = "{}".format(e)
def error_generator():
yield {"error": True, "message": error_msg, "status_code": 500}
return error_generator()
# ==================== Format Conversion ====================
def _convert_messages_for_modelscope(self, messages):
if not messages:
return []
converted = []
for msg in messages:
role = msg.get("role")
content = msg.get("content")
if isinstance(content, str):
converted.append(msg)
continue
if isinstance(content, list):
new_content = []
for block in content:
if not isinstance(block, dict):
new_content.append(block)
continue
block_type = block.get("type")
if block_type == "tool_result":
tool_content = block.get("content", "")
if not isinstance(tool_content, str):
tool_content = json.dumps(tool_content, ensure_ascii=False)
new_content.append({
"type": "text",
"text": "[工具执行结果]: {}".format(tool_content)
})
elif block_type == "tool_use":
tool_name = block.get("name", "unknown")
tool_input = block.get("input", {})
if not isinstance(tool_input, str):
tool_input = json.dumps(tool_input, ensure_ascii=False)
new_content.append({
"type": "text",
"text": "[工具调用]: {}({})".format(tool_name, tool_input)
})
else:
new_content.append(block)
converted.append({"role": role, "content": new_content})
else:
converted.append(msg)
return converted
def _convert_tools_to_openai_format(self, tools):
if not tools:
return None
converted = []
for tool in tools:
if "type" in tool and tool["type"] == "function":
converted.append(tool)
else:
converted.append({
"type": "function",
"function": {
"name": tool.get("name"),
"description": tool.get("description"),
"parameters": tool.get("input_schema", {})
}
})
return converted
def _create_image_response(self, image_url):
return {
"choices": [{
"message": {
"role": "assistant",
"content": "已为您生成图片：{}".format(image_url),
"tool_calls": None
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 0,
"completion_tokens": 0,
"total_tokens": 0
},
"model": self.args.get("model")
}
def _create_image_stream_response(self, image_url):
content = "已为您生成图片：{}".format(image_url)
yield {
"choices": [{
"index": 0,
"delta": {"role": "assistant"}
}]
}
chunk_size = 10
for i in range(0, len(content), chunk_size):
chunk = content[i:i+chunk_size]
yield {
"choices": [{
"index": 0,
"delta": {"content": chunk}
}]
}
yield {
"choices": [{
"index": 0,
"delta": {},
"finish_reason": "stop"
}]
}
def _create_error_response(self, error_msg):
return {
"choices": [{
"message": {
"role": "assistant",
"content": error_msg,
"tool_calls": None
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 0,
"completion_tokens": 0,
"total_tokens": 0
},
"model": self.args.get("model")
}
def _create_error_stream_response(self, error_msg):
yield {
"choices": [{
"index": 0,
"delta": {"role": "assistant"}
}]
}
chunk_size = 10
for i in range(0, len(error_msg), chunk_size):
chunk = error_msg[i:i+chunk_size]
yield {
"choices": [{
"index": 0,
"delta": {"content": chunk}
}]
}
yield {
"choices": [{
"index": 0,
"delta": {},
"finish_reason": "stop"
}]
}