feat: add support for ModelScope API #1490
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获得帮助Original review guide in EnglishReviewer's GuideThis PR adds end-to-end ModelScope API support by registering a new adapter in the LLM registry and implementing a dedicated ModelScopeAdapter (with its own config), complete with custom request construction, streaming/non-streaming logic, and response parsing. Class diagram for ModelScopeAdapter integrationclassDiagram
class OpenAIConfig {
}
class ModelScopeConfig {
api_base: str = "https://api-inference.modelscope.cn/v1"
}
OpenAIConfig <|-- ModelScopeConfig
class OpenAIAdapterChatBase {
}
class ModelScopeAdapter {
+chat(req: LLMChatRequest) LLMChatResponse
+_handle_streaming_request(api_url: str, headers: dict, model: str, req: LLMChatRequest) LLMChatResponse
+_handle_non_streaming_request(api_url: str, headers: dict, data: dict, model: str) LLMChatResponse
+_parse_response(response_data: dict, model: str) LLMChatResponse
}
OpenAIAdapterChatBase <|-- ModelScopeAdapter
class LLMChatRequest {
}
class LLMChatResponse {
}
class Usage {
}
class Message {
}
class LLMChatContentPartType {
}
class LLMChatTextContent {
}
class LLMToolCallContent {
}
ModelScopeAdapter --> ModelScopeConfig
ModelScopeAdapter --> LLMChatRequest
ModelScopeAdapter --> LLMChatResponse
LLMChatResponse --> Usage
LLMChatResponse --> Message
Message --> LLMChatContentPartType
LLMChatContentPartType <|-- LLMChatTextContent
LLMChatContentPartType <|-- LLMToolCallContent
File-Level Changes
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Hey @yrk111222 -
AI Agents
:
##
### 1
<location> `kirara_ai/plugins/llm_preset_adapters/modelscope_adapter.py:45` </location>
<code_context>
+ }
+
+ #
+ data = {
+ "messages": asyncio.run(convert_llm_chat_message_to_openai_message(req.messages, self.media_manager)),
+ "model": req.model,
</code_context>
<issue_to_address>
asyncio.run
asyncio.run，Event LoopWeb Server Notebookasyncio.run
</issue_to_address>
### 2
<location> `kirara_ai/plugins/llm_preset_adapters/modelscope_adapter.py:97` </location>
<code_context>
+ role: Optional[str] = None
+ usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
+
+ for line in response.iter_lines():
+ if not line:
+ continue
+
</code_context>
<issue_to_address>
，
</issue_to_address>
<suggested_fix>
<<<<<<< SEARCH
response = requests.post(api_url, json=data, headers=headers, stream=True)
response.raise_for_status()
#
full_content = ""
tool_calls = []
finish_reason: Optional[str] = None
role: Optional[str] = None
usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
for line in response.iter_lines():
if not line:
continue
=======
import socket
from requests.exceptions import Timeout, RequestException
response = requests.post(api_url, json=data, headers=headers, stream=True, timeout=30)
response.raise_for_status()
#
full_content = ""
tool_calls = []
finish_reason: Optional[str] = None
role: Optional[str] = None
usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
max_iterations = 10000 #
iteration = 0
try:
for line in response.iter_lines():
iteration += 1
if iteration > max_iterations:
logger.error("Streaming response exceeded maximum allowed iterations (%d), aborting.", max_iterations)
break
if not line:
continue
except (Timeout, RequestException, socket.timeout) as e:
logger.error("Error occurred during streaming response: %s", str(e))
#
raise
>>>>>>> REPLACE
</suggested_fix>
### 3
<location> `kirara_ai/plugins/llm_preset_adapters/modelscope_adapter.py:172` </location>
<code_context>
+
+ def _handle_non_streaming_request(self, api_url: str, headers: dict, data: dict, model: str) -> LLMChatResponse:
+ """""""
+ response = requests.post(api_url, json=data, headers=headers)
+
+ try:
</code_context>
<issue_to_address>
requests.post
API
</issue_to_address>
<suggested_fix>
<<<<<<< SEARCH
response = requests.post(api_url, json=data, headers=headers)
=======
response = requests.post(api_url, json=data, headers=headers, timeout=10)
>>>>>>> REPLACE
</suggested_fix>
### 4
<location> `kirara_ai/plugins/llm_preset_adapters/modelscope_adapter.py:77` </location>
<code_context>
+ else:
+ return self._handle_streaming_request(api_url, headers, req.model, req)
+
+ def _handle_streaming_request(self, api_url: str, headers: dict, model: str, req:LLMChatRequest) -> LLMChatResponse:
+ """Qwen/QwQ-32B"""
+ data = {
</code_context>
<issue_to_address>
response-building logichelper methods
SSE/response‐building logicDRY
1) **SSE→JSON**
```python
def _sse_parser(self, resp) -> Iterator[dict]:
for line in resp.iter_lines():
if not line or not line.startswith(b"data: "):
continue
data = line[6:].decode("utf-8")
if data == "[DONE]":
return
try:
yield json.loads(data)
except json.JSONDecodeError:
logger.warning(f"Bad SSE JSON: {data}")
```
In `_handle_streaming_request`
```python
for chunk in self._sse_parser(response):
# existing “choices = chunk.get…” logic
```
2) **tool-call → LLMToolCallContent list**
```python
def _make_tool_contents(self, raw_calls: List[dict]) -> List[LLMToolCallContent]:
return [
LLMToolCallContent(
id=c["id"],
name=c["function"]["name"],
parameters=json.loads(c["function"].get("arguments", "{}"))
)
for c in raw_calls
]
```
```python
content = [
LLMToolCallContent(…)
for call in tool_calls
]
```
with
```python
content = self._make_tool_contents(tool_calls)
```
3) **LLMChatResponse builder**
```python
def _build_response(
self,
model: str,
usage: dict,
content_parts: List[LLMChatContentPartType],
role: str,
finish_reason: str
) -> LLMChatResponse:
return LLMChatResponse(
model=model,
usage=Usage(**{
k: usage.get(k, 0) for k in ("prompt_tokens","completion_tokens","total_tokens")
}),
message=Message(
content=content_parts,
role=role,
tool_calls=pick_tool_calls(content_parts),
finish_reason=finish_reason
)
)
```
`_handle_streaming_request``_parse_response`
```python
return self._build_response(model, usage, content_parts, role or "assistant", finish_reason or "")
```
—80%
</issue_to_address>！👍👎
Original comment in English
Hey @yrk111222 - I've reviewed your changes and they look great!
Prompt for AI Agents
Please address the comments from this code review:
## Individual Comments
### Comment 1
<location> `kirara_ai/plugins/llm_preset_adapters/modelscope_adapter.py:45` </location>
<code_context>
+ }
+
+ # 构建请求数据
+ data = {
+ "messages": asyncio.run(convert_llm_chat_message_to_openai_message(req.messages, self.media_manager)),
+ "model": req.model,
</code_context>
<issue_to_address>
Potential blocking on asyncio.run in synchronous context.
Refactor to avoid asyncio.run, as it can fail if an event loop is already running (e.g., in web servers or notebooks). Ensure this method is only called when no event loop exists.
</issue_to_address>
### Comment 2
<location> `kirara_ai/plugins/llm_preset_adapters/modelscope_adapter.py:97` </location>
<code_context>
+ role: Optional[str] = None
+ usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
+
+ for line in response.iter_lines():
+ if not line:
+ continue
+
</code_context>
<issue_to_address>
No timeout or error handling for streaming response.
Without timeout or error handling, the loop may hang if the server becomes unresponsive. Please add timeout handling or limit the number of iterations.
</issue_to_address>
<suggested_fix>
<<<<<<< SEARCH
response = requests.post(api_url, json=data, headers=headers, stream=True)
response.raise_for_status()
# 处理流式响应
full_content = ""
tool_calls = []
finish_reason: Optional[str] = None
role: Optional[str] = None
usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
for line in response.iter_lines():
if not line:
continue
=======
import socket
from requests.exceptions import Timeout, RequestException
response = requests.post(api_url, json=data, headers=headers, stream=True, timeout=30)
response.raise_for_status()
# 处理流式响应
full_content = ""
tool_calls = []
finish_reason: Optional[str] = None
role: Optional[str] = None
usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
max_iterations = 10000 # 防止死循环
iteration = 0
try:
for line in response.iter_lines():
iteration += 1
if iteration > max_iterations:
logger.error("Streaming response exceeded maximum allowed iterations (%d), aborting.", max_iterations)
break
if not line:
continue
except (Timeout, RequestException, socket.timeout) as e:
logger.error("Error occurred during streaming response: %s", str(e))
# 可以根据需要抛出异常或返回部分内容
raise
>>>>>>> REPLACE
</suggested_fix>
### Comment 3
<location> `kirara_ai/plugins/llm_preset_adapters/modelscope_adapter.py:172` </location>
<code_context>
+
+ def _handle_non_streaming_request(self, api_url: str, headers: dict, data: dict, model: str) -> LLMChatResponse:
+ """处理非流式请求"""
+ response = requests.post(api_url, json=data, headers=headers)
+
+ try:
</code_context>
<issue_to_address>
No timeout specified for requests.post.
Setting a timeout prevents the request from hanging indefinitely if the API does not respond.
</issue_to_address>
<suggested_fix>
<<<<<<< SEARCH
response = requests.post(api_url, json=data, headers=headers)
=======
response = requests.post(api_url, json=data, headers=headers, timeout=10)
>>>>>>> REPLACE
</suggested_fix>
### Comment 4
<location> `kirara_ai/plugins/llm_preset_adapters/modelscope_adapter.py:77` </location>
<code_context>
+ else:
+ return self._handle_streaming_request(api_url, headers, req.model, req)
+
+ def _handle_streaming_request(self, api_url: str, headers: dict, model: str, req:LLMChatRequest) -> LLMChatResponse:
+ """处理流式请求，当模型为Qwen/QwQ-32B时"""
+ data = {
</code_context>
<issue_to_address>
Consider extracting repeated parsing and response-building logic into helper methods to reduce code duplication.
Here are three small, focused extractions you can make to collapse the duplicated parsing/response‐building logic. None of these changes revert the behavior—you’ll still parse SSE vs. JSON the same way—but you DRY out:
1) **Extract SSE→JSON chunks into a helper**
```python
def _sse_parser(self, resp) -> Iterator[dict]:
for line in resp.iter_lines():
if not line or not line.startswith(b"data: "):
continue
data = line[6:].decode("utf-8")
if data == "[DONE]":
return
try:
yield json.loads(data)
except json.JSONDecodeError:
logger.warning(f"Bad SSE JSON: {data}")
```
In `_handle_streaming_request` replace your loop with:
```python
for chunk in self._sse_parser(response):
# existing “choices = chunk.get…” logic
```
2) **Extract tool-call → LLMToolCallContent list**
```python
def _make_tool_contents(self, raw_calls: List[dict]) -> List[LLMToolCallContent]:
return [
LLMToolCallContent(
id=c["id"],
name=c["function"]["name"],
parameters=json.loads(c["function"].get("arguments", "{}"))
)
for c in raw_calls
]
```
Then in both methods replace
```python
content = [
LLMToolCallContent(…)
for call in tool_calls
]
```
with
```python
content = self._make_tool_contents(tool_calls)
```
3) **Pull out common LLMChatResponse builder**
```python
def _build_response(
self,
model: str,
usage: dict,
content_parts: List[LLMChatContentPartType],
role: str,
finish_reason: str
) -> LLMChatResponse:
return LLMChatResponse(
model=model,
usage=Usage(**{
k: usage.get(k, 0) for k in ("prompt_tokens","completion_tokens","total_tokens")
}),
message=Message(
content=content_parts,
role=role,
tool_calls=pick_tool_calls(content_parts),
finish_reason=finish_reason
)
)
```
Then at end of both `_handle_streaming_request` and `_parse_response` you simply do:
```python
return self._build_response(model, usage, content_parts, role or "assistant", finish_reason or "")
```
—these three helpers remove > 80% of your copy-paste while preserving every feature.
</issue_to_address>Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
| } | ||
|
|
||
| # 构建请求数据 | ||
| data = { |
There was a problem hiding this comment.
issue (bug_risk): asyncio.run
asyncio.run，
asyncio.run，Web Server Notebook
Original comment in English
issue (bug_risk): Potential blocking on asyncio.run in synchronous context.
Refactor to avoid asyncio.run, as it can fail if an event loop is already running (e.g., in web servers or notebooks). Ensure this method is only called when no event loop exists.
Comment on lines
87
to
99
| response = requests.post(api_url, json=data, headers=headers, stream=True) | ||
| response.raise_for_status() | ||
|
|
||
| # 处理流式响应 | ||
| full_content = "" | ||
| tool_calls = [] | ||
| finish_reason: Optional[str] = None | ||
| role: Optional[str] = None | ||
| usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} | ||
|
|
||
| for line in response.iter_lines(): | ||
| if not line: | ||
| continue |
There was a problem hiding this comment.
suggestion (bug_risk):
，
，
Suggested change
| response = requests.post(api_url, json=data, headers=headers, stream=True) | |
| response.raise_for_status() | |
| # 处理流式响应 | |
| full_content = "" | |
| tool_calls = [] | |
| finish_reason: Optional[str] = None | |
| role: Optional[str] = None | |
| usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} | |
| for line in response.iter_lines(): | |
| if not line: | |
| continue | |
| import socket | |
| from requests.exceptions import Timeout, RequestException | |
| response = requests.post(api_url, json=data, headers=headers, stream=True, timeout=30) | |
| response.raise_for_status() | |
| # | |
| full_content = "" | |
| tool_calls = [] | |
| finish_reason: Optional[str] = None | |
| role: Optional[str] = None | |
| usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} | |
| max_iterations = 10000 # | |
| iteration = 0 | |
| try: | |
| for line in response.iter_lines(): | |
| iteration += 1 | |
| if iteration > max_iterations: | |
| logger.error("Streaming response exceeded maximum allowed iterations (%d), aborting.", max_iterations) | |
| break | |
| if not line: | |
| continue | |
| except (Timeout, RequestException, socket.timeout) as e: | |
| logger.error("Error occurred during streaming response: %s", str(e)) | |
| # | |
| raise |
Original comment in English
suggestion (bug_risk): No timeout or error handling for streaming response.
Without timeout or error handling, the loop may hang if the server becomes unresponsive. Please add timeout handling or limit the number of iterations.
Suggested change
| response = requests.post(api_url, json=data, headers=headers, stream=True) | |
| response.raise_for_status() | |
| # 处理流式响应 | |
| full_content = "" | |
| tool_calls = [] | |
| finish_reason: Optional[str] = None | |
| role: Optional[str] = None | |
| usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} | |
| for line in response.iter_lines(): | |
| if not line: | |
| continue | |
| import socket | |
| from requests.exceptions import Timeout, RequestException | |
| response = requests.post(api_url, json=data, headers=headers, stream=True, timeout=30) | |
| response.raise_for_status() | |
| # 处理流式响应 | |
| full_content = "" | |
| tool_calls = [] | |
| finish_reason: Optional[str] = None | |
| role: Optional[str] = None | |
| usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} | |
| max_iterations = 10000 # 防止死循环 | |
| iteration = 0 | |
| try: | |
| for line in response.iter_lines(): | |
| iteration += 1 | |
| if iteration > max_iterations: | |
| logger.error("Streaming response exceeded maximum allowed iterations (%d), aborting.", max_iterations) | |
| break | |
| if not line: | |
| continue | |
| except (Timeout, RequestException, socket.timeout) as e: | |
| logger.error("Error occurred during streaming response: %s", str(e)) | |
| # 可以根据需要抛出异常或返回部分内容 | |
| raise |
|
|
||
| def _handle_non_streaming_request(self, api_url: str, headers: dict, data: dict, model: str) -> LLMChatResponse: | ||
| """处理非流式请求""" | ||
| response = requests.post(api_url, json=data, headers=headers) |
There was a problem hiding this comment.
suggestion (bug_risk): requests.post
API
Suggested change
| response = requests.post(api_url, json=data, headers=headers) | |
| response = requests.post(api_url, json=data, headers=headers, timeout=10) |
Original comment in English
suggestion (bug_risk): No timeout specified for requests.post.
Setting a timeout prevents the request from hanging indefinitely if the API does not respond.
Suggested change
| response = requests.post(api_url, json=data, headers=headers) | |
| response = requests.post(api_url, json=data, headers=headers, timeout=10) |
| else: | ||
| return self._handle_streaming_request(api_url, headers, req.model, req) | ||
|
|
||
| def _handle_streaming_request(self, api_url: str, headers: dict, model: str, req:LLMChatRequest) -> LLMChatResponse: |
There was a problem hiding this comment.
issue (complexity):
/response-building logichelper methods.
SSE/response‐building logic
- SSE→JSON
def _sse_parser(self, resp) -> Iterator[dict]:
for line in resp.iter_lines():
if not line or not line.startswith(b"data: "):
continue
data = line[6:].decode("utf-8")
if data == "[DONE]":
return
try:
yield json.loads(data)
except json.JSONDecodeError:
logger.warning(f"Bad SSE JSON: {data}")In _handle_streaming_request
for chunk in self._sse_parser(response):
# existing “choices = chunk.get…” logic- tool-call → LLMToolCallContent list
def _make_tool_contents(self, raw_calls: List[dict]) -> List[LLMToolCallContent]:
return [
LLMToolCallContent(
id=c["id"],
name=c["function"]["name"],
parameters=json.loads(c["function"].get("arguments", "{}"))
)
for c in raw_calls
]content = [
LLMToolCallContent(…)
for call in tool_calls
]with
content = self._make_tool_contents(tool_calls)- LLMChatResponse builder
def _build_response(
self,
model: str,
usage: dict,
content_parts: List[LLMChatContentPartType],
role: str,
finish_reason: str
) -> LLMChatResponse:
return LLMChatResponse(
model=model,
usage=Usage(**{
k: usage.get(k, 0) for k in ("prompt_tokens","completion_tokens","total_tokens")
}),
message=Message(
content=content_parts,
role=role,
tool_calls=pick_tool_calls(content_parts),
finish_reason=finish_reason
)
)_handle_streaming_request``_parse_response
return self._build_response(model, usage, content_parts, role or "assistant", finish_reason or "")—80%
Original comment in English
issue (complexity): Consider extracting repeated parsing and response-building logic into helper methods to reduce code duplication.
Here are three small, focused extractions you can make to collapse the duplicated parsing/response‐building logic. None of these changes revert the behavior—you’ll still parse SSE vs. JSON the same way—but you DRY out:
- Extract SSE→JSON chunks into a helper
def _sse_parser(self, resp) -> Iterator[dict]:
for line in resp.iter_lines():
if not line or not line.startswith(b"data: "):
continue
data = line[6:].decode("utf-8")
if data == "[DONE]":
return
try:
yield json.loads(data)
except json.JSONDecodeError:
logger.warning(f"Bad SSE JSON: {data}")In _handle_streaming_request replace your loop with:
for chunk in self._sse_parser(response):
# existing “choices = chunk.get…” logic- Extract tool-call → LLMToolCallContent list
def _make_tool_contents(self, raw_calls: List[dict]) -> List[LLMToolCallContent]:
return [
LLMToolCallContent(
id=c["id"],
name=c["function"]["name"],
parameters=json.loads(c["function"].get("arguments", "{}"))
)
for c in raw_calls
]Then in both methods replace
content = [
LLMToolCallContent(…)
for call in tool_calls
]with
content = self._make_tool_contents(tool_calls)- Pull out common LLMChatResponse builder
def _build_response(
self,
model: str,
usage: dict,
content_parts: List[LLMChatContentPartType],
role: str,
finish_reason: str
) -> LLMChatResponse:
return LLMChatResponse(
model=model,
usage=Usage(**{
k: usage.get(k, 0) for k in ("prompt_tokens","completion_tokens","total_tokens")
}),
message=Message(
content=content_parts,
role=role,
tool_calls=pick_tool_calls(content_parts),
finish_reason=finish_reason
)
)Then at end of both _handle_streaming_request and _parse_response you simply do:
return self._build_response(model, usage, content_parts, role or "assistant", finish_reason or "")—these three helpers remove > 80% of your copy-paste while preserving every feature.
|
|
||
| # 处理工具调用或普通响应 | ||
| content: list[LLMChatContentPartType] = [] | ||
| if tool_calls := message.get("tool_calls", None): |
There was a problem hiding this comment.
suggestion (code-quality): Replace dict.get(x, None) with dict.get(x) (remove-none-from-default-get)
Suggested change
| if tool_calls := message.get("tool_calls", None): | |
| if tool_calls := message.get("tool_calls"): |
getkey
None``None
Original comment in English
suggestion (code-quality): Replace dict.get(x, None) with dict.get(x) (remove-none-from-default-get)
Suggested change
| if tool_calls := message.get("tool_calls", None): | |
| if tool_calls := message.get("tool_calls"): |
Explanation
When using a dictionary'sget method you can specify a default to return ifthe key is not found. This defaults to
None, so it is unnecessary to specifyNone if this is the required behaviour. Removing the unnecessary argumentmakes the code slightly shorter and clearer.
| } | ||
|
|
||
| # 构建请求数据 | ||
| data = { |
There was a problem hiding this comment.
issue (code-quality): (merge-dict-assign)
Original comment in English
issue (code-quality): Merge dictionary assignment with declaration (merge-dict-assign)
| else: | ||
| return self._handle_streaming_request(api_url, headers, req.model, req) | ||
|
|
||
| def _handle_streaming_request(self, api_url: str, headers: dict, model: str, req:LLMChatRequest) -> LLMChatResponse: |
There was a problem hiding this comment.
issue (code-quality):
- (
use-named-expression) - ModelScopeAdapter._handle_streaming_request - 20% (
low-code-quality)
25%。
- 10
- 。
Original comment in English
issue (code-quality): We've found these issues:
- Use named expression to simplify assignment and conditional (
use-named-expression) - Low code quality found in ModelScopeAdapter._handle_streaming_request - 20% (
low-code-quality)
Explanation
The quality score for this function is below the quality threshold of 25%.
This score is a combination of the method length, cognitive complexity and working memory.
How can you solve this?
It might be worth refactoring this function to make it shorter and more readable.
- Reduce the function length by extracting pieces of functionality out into
their own functions. This is the most important thing you can do - ideally a
function should be less than 10 lines. - Reduce nesting, perhaps by introducing guard clauses to return early.
- Ensure that variables are tightly scoped, so that code using related concepts
sits together within the function rather than being scattered.
![github-actions[bot]](https://avatars.githubusercontent.com/in/15368?s=60&v=4){kind=small}
| logger.debug(f"Sending request to ModelScope API: {api_url}") | ||
| logger.debug(f"Request data: {json.dumps(data, indent=2, ensure_ascii=False)}") | ||
| if req.model not in ["Qwen/QwQ-32B","deepseek-ai/DeepSeek-R1-0528"]: | ||
| return self._handle_non_streaming_request(api_url, headers, data, req.model) |
There was a problem hiding this comment.
MyPy 类型错误: Argument 4 to "_handle_non_streaming_request" of "ModelScopeAdapter" has incompatible type "str | None"; expected "str" (arg-type)
详细信息请参考 mypy 文档。
| "stream": True, | ||
| "messages": [{ | ||
| "role": "user", | ||
| "content": req.messages[0].content[0].text |
There was a problem hiding this comment.
MyPy 类型错误: Item "LLMChatImageContent" of "LLMChatTextContent | LLMChatImageContent | LLMToolCallContent | LLMToolResultContent" has no attribute "text" (union-attr)
详细信息请参考 mypy 文档。
| "stream": True, | ||
| "messages": [{ | ||
| "role": "user", | ||
| "content": req.messages[0].content[0].text |
There was a problem hiding this comment.
MyPy 类型错误: Item "LLMToolCallContent" of "LLMChatTextContent | LLMChatImageContent | LLMToolCallContent | LLMToolResultContent" has no attribute "text" (union-attr)
详细信息请参考 mypy 文档。
| "stream": True, | ||
| "messages": [{ | ||
| "role": "user", | ||
| "content": req.messages[0].content[0].text |
There was a problem hiding this comment.
MyPy 类型错误: Item "LLMToolResultContent" of "LLMChatTextContent | LLMChatImageContent | LLMToolCallContent | LLMToolResultContent" has no attribute "text" (union-attr)
详细信息请参考 mypy 文档。
| ), | ||
| message=Message( | ||
| content=content_parts, | ||
| role=role or "assistant", |
There was a problem hiding this comment.
MyPy 类型错误: Argument "role" to "Message" has incompatible type "str"; expected "Literal['user', 'assistant', 'system', 'tool']" (arg-type)
详细信息请参考 mypy 文档。
| logger.debug(f"Sending request to ModelScope API: {api_url}") | ||
| logger.debug(f"Request data: {json.dumps(data, indent=2, ensure_ascii=False)}") | ||
| if req.model not in ["Qwen/QwQ-32B","deepseek-ai/DeepSeek-R1-0528"]: | ||
| return self._handle_non_streaming_request(api_url, headers, data, req.model) |
There was a problem hiding this comment.
MyPy 类型错误: Argument 4 to "_handle_non_streaming_request" of "ModelScopeAdapter" has incompatible type "str | None"; expected "str" (arg-type)
详细信息请参考 mypy 文档。
| "stream": True, | ||
| "messages": [{ | ||
| "role": "user", | ||
| "content": req.messages[0].content[0].text |
There was a problem hiding this comment.
MyPy 类型错误: Item "LLMChatImageContent" of "LLMChatTextContent | LLMChatImageContent | LLMToolCallContent | LLMToolResultContent" has no attribute "text" (union-attr)
详细信息请参考 mypy 文档。
| "stream": True, | ||
| "messages": [{ | ||
| "role": "user", | ||
| "content": req.messages[0].content[0].text |
There was a problem hiding this comment.
MyPy 类型错误: Item "LLMToolCallContent" of "LLMChatTextContent | LLMChatImageContent | LLMToolCallContent | LLMToolResultContent" has no attribute "text" (union-attr)
详细信息请参考 mypy 文档。
| "stream": True, | ||
| "messages": [{ | ||
| "role": "user", | ||
| "content": req.messages[0].content[0].text |
There was a problem hiding this comment.
MyPy 类型错误: Item "LLMToolResultContent" of "LLMChatTextContent | LLMChatImageContent | LLMToolCallContent | LLMToolResultContent" has no attribute "text" (union-attr)
详细信息请参考 mypy 文档。
| ), | ||
| message=Message( | ||
| content=content_parts, | ||
| role=role or "assistant", |
There was a problem hiding this comment.
MyPy 类型错误: Argument "role" to "Message" has incompatible type "str"; expected "Literal['user', 'assistant', 'system', 'tool']" (arg-type)
详细信息请参考 mypy 文档。
| logger.debug(f"Sending request to ModelScope API: {api_url}") | ||
| logger.debug(f"Request data: {json.dumps(data, indent=2, ensure_ascii=False)}") | ||
| if req.model not in ["Qwen/QwQ-32B","deepseek-ai/DeepSeek-R1-0528"]: | ||
| return self._handle_non_streaming_request(api_url, headers, data, req.model) |
There was a problem hiding this comment.
MyPy 类型错误: Argument 4 to "_handle_non_streaming_request" of "ModelScopeAdapter" has incompatible type "str | None"; expected "str" (arg-type)
详细信息请参考 mypy 文档。
| "stream": True, | ||
| "messages": [{ | ||
| "role": "user", | ||
| "content": req.messages[0].content[0].text |
There was a problem hiding this comment.
MyPy 类型错误: Item "LLMChatImageContent" of "LLMChatTextContent | LLMChatImageContent | LLMToolCallContent | LLMToolResultContent" has no attribute "text" (union-attr)
详细信息请参考 mypy 文档。
| "stream": True, | ||
| "messages": [{ | ||
| "role": "user", | ||
| "content": req.messages[0].content[0].text |
There was a problem hiding this comment.
MyPy 类型错误: Item "LLMToolCallContent" of "LLMChatTextContent | LLMChatImageContent | LLMToolCallContent | LLMToolResultContent" has no attribute "text" (union-attr)
详细信息请参考 mypy 文档。
| "stream": True, | ||
| "messages": [{ | ||
| "role": "user", | ||
| "content": req.messages[0].content[0].text |
There was a problem hiding this comment.
MyPy 类型错误: Item "LLMToolResultContent" of "LLMChatTextContent | LLMChatImageContent | LLMToolCallContent | LLMToolResultContent" has no attribute "text" (union-attr)
详细信息请参考 mypy 文档。
| ), | ||
| message=Message( | ||
| content=content_parts, | ||
| role=data["messages"][0].get("role", "assistant"), |
There was a problem hiding this comment.
MyPy 类型错误: Value of type "object" is not indexable (index)
详细信息请参考 mypy 文档。
| logger.debug(f"Sending request to ModelScope API: {api_url}") | ||
| logger.debug(f"Request data: {json.dumps(data, indent=2, ensure_ascii=False)}") | ||
| if req.model not in ["Qwen/QwQ-32B","deepseek-ai/DeepSeek-R1-0528"]: | ||
| return self._handle_non_streaming_request(api_url, headers, data, req.model) |
There was a problem hiding this comment.
MyPy 类型错误: Argument 4 to "_handle_non_streaming_request" of "ModelScopeAdapter" has incompatible type "str | None"; expected "str" (arg-type)
详细信息请参考 mypy 文档。
| # 只返回最终内容 | ||
| content_parts = [LLMChatTextContent(text=full_content)] | ||
|
|
||
| first_message = data["messages"][0] if data.get("messages") and len(data["messages"]) > 0 else {} |
There was a problem hiding this comment.
MyPy 类型错误: Value of type "object" is not indexable (index)
详细信息请参考 mypy 文档。
| # 只返回最终内容 | ||
| content_parts = [LLMChatTextContent(text=full_content)] | ||
|
|
||
| first_message = data["messages"][0] if data.get("messages") and len(data["messages"]) > 0 else {} |
There was a problem hiding this comment.
MyPy 类型错误: Argument 1 to "len" has incompatible type "object"; expected "Sized" (arg-type)
详细信息请参考 mypy 文档。
| ), | ||
| message=Message( | ||
| content=content_parts, | ||
| role=data["messages"][0].get("role", "assistant"), |
There was a problem hiding this comment.
MyPy 类型错误: Value of type "object" is not indexable (index)
详细信息请参考 mypy 文档。
| ), | ||
| message=Message( | ||
| content=content_parts, | ||
| role=role_value, |
There was a problem hiding this comment.
MyPy 类型错误: Argument "role" to "Message" has incompatible type "str"; expected "Literal['user', 'assistant', 'system', 'tool']" (arg-type)
详细信息请参考 mypy 文档。
| if "role" in delta and delta["role"]: | ||
| role_str = delta["role"] | ||
| if role_str in {'user', 'assistant', 'system', 'tool'}: | ||
| role_value = cast(Literal['user', 'assistant', 'system', 'tool'], role_str) |
There was a problem hiding this comment.
MyPy 类型错误: Name "Literal" is not defined (name-defined)
详细信息请参考 mypy 文档。
| content_parts = [LLMChatTextContent(text=full_content)] | ||
|
|
||
| # 构建响应时确保使用有效的角色 | ||
| valid_role: Literal['user', 'assistant', 'system', 'tool'] = role_value or "assistant" |
There was a problem hiding this comment.
MyPy 类型错误: Name "Literal" is not defined (name-defined)
详细信息请参考 mypy 文档。
MyPy 类型检查通过 ✅PR 修改的代码行通过了类型检查。 |
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实现功能
实现对魔搭社区ModelScope API的接入，可以通过修改配置应用魔搭社区丰富的模型。获取魔搭社区的免费API-Key具体可以查看文档。
注意
记得在使用之前绑定阿里云账号，具体绑定操作可以查看链接。
好的，这是翻译成中文的 pull request 总结：
Sourcery 总结
添加对 ModelScope API 的支持，通过引入一个新的适配器和配置，将其注册到 LLMPresetAdaptersPlugin 中，并处理带有工具调用解析的流式和非流式聊天补全。
新特性：
Original summary in English
Summary by Sourcery
Add support for ModelScope API by introducing a new adapter and configuration, registering it in the LLMPresetAdaptersPlugin, and handling both streaming and non-streaming chat completions with tool call parsing.
New Features: