重构代码
This commit is contained in:
h88782481
parent
56faf4fcf1
commit
70361242ab
9 changed files with 1195 additions and 1579 deletions
|
|
@ -15,8 +15,18 @@ from __future__ import annotations
|
|||
|
||||
import json
|
||||
from dataclasses import dataclass
|
||||
from typing import Any
|
||||
from typing import Any, Iterator
|
||||
|
||||
from adapters.helpers import (
|
||||
build_cc_message,
|
||||
build_cc_response,
|
||||
build_cc_tool_call,
|
||||
build_cc_usage,
|
||||
extract_text,
|
||||
make_cc_chunk,
|
||||
stringify_content,
|
||||
)
|
||||
from adapters.unified import UnifiedUsage
|
||||
from utils.http import gen_id
|
||||
|
||||
JsonDict = dict[str, Any]
|
||||
|
|
@ -85,7 +95,7 @@ def cc_to_responses(cc_resp: JsonDict, model: str = '') -> JsonDict:
|
|||
'status': _response_status_from_finish_reason(finish_reason),
|
||||
'model': model or cc_resp.get('model', ''),
|
||||
'output': _build_responses_output(message),
|
||||
'usage': _build_responses_usage(cc_resp.get('usage', {})),
|
||||
'usage': UnifiedUsage.from_cc_dict(cc_resp.get('usage', {})).to_responses_dict(),
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -94,31 +104,18 @@ def responses_to_cc_response(response_data: JsonDict, model: str = '') -> JsonDi
|
|||
output_items = response_data.get('output', [])
|
||||
content_text, reasoning_text, tool_calls = _collect_cc_parts_from_responses_output(output_items)
|
||||
finish_reason = _cc_finish_reason_from_responses(response_data, tool_calls)
|
||||
message = {
|
||||
'role': 'assistant',
|
||||
'content': content_text or None,
|
||||
}
|
||||
if reasoning_text:
|
||||
message['reasoning_content'] = reasoning_text
|
||||
if tool_calls:
|
||||
message['tool_calls'] = tool_calls
|
||||
|
||||
usage = response_data.get('usage', {})
|
||||
return {
|
||||
'id': response_data.get('id', gen_id('chatcmpl-')),
|
||||
'object': 'chat.completion',
|
||||
'model': model or response_data.get('model', ''),
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'message': message,
|
||||
'finish_reason': finish_reason,
|
||||
}],
|
||||
'usage': {
|
||||
'prompt_tokens': usage.get('input_tokens', 0),
|
||||
'completion_tokens': usage.get('output_tokens', 0),
|
||||
'total_tokens': usage.get('total_tokens', 0),
|
||||
},
|
||||
}
|
||||
|
||||
return build_cc_response(
|
||||
response_id=response_data.get('id', gen_id('chatcmpl-')),
|
||||
message=build_cc_message(content_text, reasoning_text, tool_calls),
|
||||
finish_reason=finish_reason,
|
||||
usage=build_cc_usage(
|
||||
input_tokens=usage.get('input_tokens', 0),
|
||||
output_tokens=usage.get('output_tokens', 0),
|
||||
),
|
||||
model=model or response_data.get('model', ''),
|
||||
)
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
|
@ -658,15 +655,7 @@ class ResponsesToCCStreamConverter:
|
|||
|
||||
def _make_chunk(self, delta: JsonDict, finish_reason: str | None = None) -> JsonDict:
|
||||
"""构造标准 Chat Completions chunk。"""
|
||||
choice: JsonDict = {'index': 0, 'delta': delta}
|
||||
if finish_reason:
|
||||
choice['finish_reason'] = finish_reason
|
||||
return {
|
||||
'id': self._id,
|
||||
'object': 'chat.completion.chunk',
|
||||
'model': self._model,
|
||||
'choices': [choice],
|
||||
}
|
||||
return make_cc_chunk(self._id, delta, finish_reason, model=self._model)
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
|
@ -715,7 +704,7 @@ def _append_responses_input_item(
|
|||
content = message.get('content')
|
||||
|
||||
if role == 'system':
|
||||
text = _content_to_text(content)
|
||||
text = extract_text(content)
|
||||
if text:
|
||||
instructions.append(text)
|
||||
return
|
||||
|
|
@ -724,11 +713,11 @@ def _append_responses_input_item(
|
|||
input_items.append({
|
||||
'type': 'function_call_output',
|
||||
'call_id': message.get('tool_call_id', ''),
|
||||
'output': _stringify_output(content),
|
||||
'output': stringify_content(content),
|
||||
})
|
||||
return
|
||||
|
||||
text = _content_to_text(content)
|
||||
text = extract_text(content)
|
||||
has_tool_calls = bool(message.get('tool_calls'))
|
||||
|
||||
if role == 'assistant' and has_tool_calls:
|
||||
|
|
@ -771,7 +760,7 @@ def _convert_input_items(items: list[Any], messages: list[JsonDict]) -> None:
|
|||
if role and not item_type:
|
||||
msg: JsonDict = {
|
||||
'role': role,
|
||||
'content': _normalize_simple_content(item.get('content', '')),
|
||||
'content': extract_text(item.get('content', '')),
|
||||
}
|
||||
if role == 'assistant' and pending_reasoning:
|
||||
msg['reasoning_content'] = pending_reasoning
|
||||
|
|
@ -810,7 +799,7 @@ def _append_message_item(items: list[Any], *, start: int, messages: list[JsonDic
|
|||
"""将一个 message 项及其后续连续 function_call 项合并成一条消息。"""
|
||||
item = items[start]
|
||||
role = item.get('role', 'assistant')
|
||||
content = _extract_text(item.get('content', []))
|
||||
content = extract_text(item.get('content', []))
|
||||
message: JsonDict = {'role': role, 'content': content or ''}
|
||||
|
||||
if role == 'assistant':
|
||||
|
|
@ -828,7 +817,11 @@ def _append_message_item(items: list[Any], *, start: int, messages: list[JsonDic
|
|||
|
||||
def _append_function_call_item(item: JsonDict, messages: list[JsonDict]) -> None:
|
||||
"""将独立的 Responses `function_call` 项挂接到最近的 assistant 消息上。"""
|
||||
tool_call = _build_cc_tool_call(item)
|
||||
tool_call = build_cc_tool_call(
|
||||
call_id=item.get('call_id') or gen_id('call_'),
|
||||
name=item.get('name', ''),
|
||||
arguments=item.get('arguments', '{}'),
|
||||
)
|
||||
|
||||
if messages and messages[-1]['role'] == 'assistant':
|
||||
messages[-1].setdefault('tool_calls', []).append(tool_call)
|
||||
|
|
@ -851,12 +844,6 @@ def _convert_function_call_output_item(item: JsonDict) -> JsonDict:
|
|||
}
|
||||
|
||||
|
||||
def _normalize_simple_content(content: Any) -> str:
|
||||
"""将简单 content 载荷规范化为纯文本字符串。"""
|
||||
if isinstance(content, list):
|
||||
return _extract_text(content) or ''
|
||||
return str(content) if content is not None else ''
|
||||
|
||||
|
||||
def _collect_function_calls(items: list[Any], start: int) -> tuple[list[JsonDict], int]:
|
||||
"""收集从指定位置开始连续出现的 `function_call` 项。"""
|
||||
|
|
@ -865,24 +852,17 @@ def _collect_function_calls(items: list[Any], start: int) -> tuple[list[JsonDict
|
|||
while index < len(items):
|
||||
next_item = items[index]
|
||||
if isinstance(next_item, dict) and next_item.get('type') == 'function_call':
|
||||
tool_calls.append(_build_cc_tool_call(next_item))
|
||||
tool_calls.append(build_cc_tool_call(
|
||||
call_id=next_item.get('call_id') or gen_id('call_'),
|
||||
name=next_item.get('name', ''),
|
||||
arguments=next_item.get('arguments', '{}'),
|
||||
))
|
||||
index += 1
|
||||
else:
|
||||
break
|
||||
return tool_calls, index - start
|
||||
|
||||
|
||||
def _build_cc_tool_call(item: JsonDict) -> JsonDict:
|
||||
"""将单个 Responses `function_call` 项转换为 CC `tool_call` 结构。"""
|
||||
return {
|
||||
'id': item.get('call_id') or gen_id('call_'),
|
||||
'type': 'function',
|
||||
'function': {
|
||||
'name': item.get('name', ''),
|
||||
'arguments': item.get('arguments', '{}'),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 非流式响应转换辅助
|
||||
|
|
@ -936,14 +916,6 @@ def _make_function_call_output_item(tool_call: JsonDict) -> JsonDict:
|
|||
}
|
||||
|
||||
|
||||
def _build_responses_usage(usage: JsonDict) -> JsonDict:
|
||||
"""将 Chat Completions 的 usage 字段映射为 Responses usage 结构。"""
|
||||
return {
|
||||
'input_tokens': usage.get('prompt_tokens', 0),
|
||||
'output_tokens': usage.get('completion_tokens', 0),
|
||||
'total_tokens': usage.get('total_tokens', 0),
|
||||
}
|
||||
|
||||
|
||||
def _collect_cc_parts_from_responses_output(output_items: Any) -> tuple[str, str, list[JsonDict]]:
|
||||
"""从 Responses `output` 中提取文本、思考摘要和工具调用。"""
|
||||
|
|
@ -959,11 +931,16 @@ def _collect_cc_parts_from_responses_output(output_items: Any) -> tuple[str, str
|
|||
continue
|
||||
item_type = item.get('type', '')
|
||||
if item_type == 'message':
|
||||
content_text += _extract_text(item.get('content', []))
|
||||
content_text += extract_text(item.get('content', []))
|
||||
elif item_type == 'reasoning':
|
||||
reasoning_text += _extract_reasoning_text(item)
|
||||
elif item_type == 'function_call':
|
||||
tool_calls.append(_build_cc_tool_call_from_responses_output(item, index=len(tool_calls)))
|
||||
tool_calls.append(build_cc_tool_call(
|
||||
call_id=item.get('call_id') or gen_id('call_'),
|
||||
name=item.get('name', ''),
|
||||
arguments=item.get('arguments', '{}'),
|
||||
index=len(tool_calls),
|
||||
))
|
||||
|
||||
return content_text, reasoning_text, tool_calls
|
||||
|
||||
|
|
@ -980,18 +957,6 @@ def _extract_reasoning_text(item: JsonDict) -> str:
|
|||
return ''.join(texts)
|
||||
|
||||
|
||||
def _build_cc_tool_call_from_responses_output(item: JsonDict, *, index: int) -> JsonDict:
|
||||
"""将 Responses `function_call` 输出项转换为 CC `tool_call`。"""
|
||||
return {
|
||||
'index': index,
|
||||
'id': item.get('call_id') or gen_id('call_'),
|
||||
'type': 'function',
|
||||
'function': {
|
||||
'name': item.get('name', ''),
|
||||
'arguments': item.get('arguments', '{}'),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _cc_finish_reason_from_responses(response_data: JsonDict, tool_calls: list[JsonDict]) -> str:
|
||||
"""根据 Responses 完成状态推断聊天补全的 finish_reason。"""
|
||||
|
|
@ -1017,57 +982,7 @@ def _map_anthropic_stop_reason(stop_reason: str) -> str:
|
|||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def _extract_text(content: Any) -> str:
|
||||
"""从多种内容块结构中提取并拼接纯文本。"""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if not isinstance(content, list):
|
||||
return str(content) if content else ''
|
||||
|
||||
texts: list[str] = []
|
||||
for part in content:
|
||||
if isinstance(part, str):
|
||||
texts.append(part)
|
||||
elif isinstance(part, dict):
|
||||
part_type = part.get('type', '')
|
||||
if part_type in ('output_text', 'input_text', 'text'):
|
||||
texts.append(part.get('text', ''))
|
||||
elif part_type == 'refusal':
|
||||
texts.append(part.get('refusal', ''))
|
||||
return '\n'.join(texts) if texts else ''
|
||||
|
||||
|
||||
def _content_to_text(content: Any) -> str:
|
||||
"""将任意 content 载荷转换为单个字符串。"""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if isinstance(content, list):
|
||||
return _extract_text(content)
|
||||
return str(content) if content is not None else ''
|
||||
|
||||
|
||||
def _content_to_responses_parts(content: Any, role: str = 'user') -> list[JsonDict]:
|
||||
"""将普通消息内容转换为 Responses 内容块数组。
|
||||
|
||||
assistant 消息使用 output_text，其他角色使用 input_text。
|
||||
"""
|
||||
if isinstance(content, list):
|
||||
text = _extract_text(content)
|
||||
else:
|
||||
text = _content_to_text(content)
|
||||
if not text:
|
||||
return []
|
||||
part_type = 'output_text' if role == 'assistant' else 'input_text'
|
||||
return [{'type': part_type, 'text': text}]
|
||||
|
||||
|
||||
def _stringify_output(content: Any) -> str:
|
||||
"""将工具输出统一序列化为字符串，便于放入 `function_call_output`。"""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if content is None:
|
||||
return ''
|
||||
return json.dumps(content, ensure_ascii=False) if not isinstance(content, str) else content
|
||||
|
||||
|
||||
def _build_responses_function_call_item(tool_call: JsonDict) -> JsonDict:
|
||||
|
|
@ -1081,6 +996,165 @@ def _build_responses_function_call_item(tool_call: JsonDict) -> JsonDict:
|
|||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# OutboundTransformer 实现: Responses
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
class ResponsesOutbound:
|
||||
"""OpenAI Responses 后端的出站转换器。
|
||||
|
||||
将 CC 格式转换为 Responses 格式并处理响应。
|
||||
"""
|
||||
|
||||
def build_request(self, payload: JsonDict) -> JsonDict:
|
||||
return cc_to_responses_request(payload)
|
||||
|
||||
def build_url(self, ctx) -> str:
|
||||
return f'{ctx.target_url.rstrip("/")}/v1/responses'
|
||||
|
||||
def build_headers(self, ctx) -> dict[str, str]:
|
||||
from utils.http import build_openai_headers
|
||||
return build_openai_headers(ctx.api_key)
|
||||
|
||||
def parse_response(self, raw: JsonDict) -> JsonDict:
|
||||
return responses_to_cc_response(raw)
|
||||
|
||||
def create_stream_processor(self) -> ResponsesStreamProcessorForCC:
|
||||
return ResponsesStreamProcessorForCC()
|
||||
|
||||
|
||||
class ResponsesStreamProcessorForCC:
|
||||
"""Responses SSE → CC chunk 流式处理器。
|
||||
|
||||
用于 /v1/chat/completions -> /v1/responses 的桥接路径。
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self._converter = ResponsesToCCStreamConverter()
|
||||
|
||||
def iter_events(self, response) -> Iterator:
|
||||
from utils.http import iter_responses_sse
|
||||
yield from iter_responses_sse(response)
|
||||
|
||||
def process_event(self, event: tuple) -> list[JsonDict]:
|
||||
event_type, event_data = event
|
||||
return self._converter.process_event(event_type, event_data)
|
||||
|
||||
def extract_usage(self, event: tuple) -> JsonDict | None:
|
||||
from adapters.unified import extract_responses_usage
|
||||
event_type, event_data = event
|
||||
extracted = extract_responses_usage(event_data)
|
||||
if extracted:
|
||||
return {
|
||||
'prompt_tokens': extracted.get('input_tokens', 0),
|
||||
'completion_tokens': extracted.get('output_tokens', 0),
|
||||
'total_tokens': extracted.get('total_tokens', 0),
|
||||
}
|
||||
return None
|
||||
|
||||
def finalize(self) -> list[JsonDict]:
|
||||
return []
|
||||
|
||||
|
||||
class ResponsesNativeOutbound:
|
||||
"""Responses 后端原生透传的出站转换器。
|
||||
|
||||
当 /v1/responses → /v1/responses 时直接透传，不经过 CC 中间格式。
|
||||
"""
|
||||
|
||||
def build_request(self, payload: JsonDict) -> JsonDict:
|
||||
return payload
|
||||
|
||||
def build_url(self, ctx) -> str:
|
||||
return f'{ctx.target_url.rstrip("/")}/v1/responses'
|
||||
|
||||
def build_headers(self, ctx) -> dict[str, str]:
|
||||
from utils.http import build_openai_headers
|
||||
return build_openai_headers(ctx.api_key)
|
||||
|
||||
def parse_response(self, raw: JsonDict) -> JsonDict:
|
||||
return raw
|
||||
|
||||
def create_stream_processor(self) -> ResponsesNativeStreamProcessor:
|
||||
return ResponsesNativeStreamProcessor()
|
||||
|
||||
|
||||
class ResponsesNativeStreamProcessor:
|
||||
"""Responses 原生 SSE 透传流式处理器。
|
||||
|
||||
上游就是 Responses 格式，只需透传事件并做轻量模型名改写。
|
||||
每个事件作为 SSE 字符串直接返回。
|
||||
"""
|
||||
|
||||
def iter_events(self, response) -> Iterator:
|
||||
from utils.http import iter_responses_sse
|
||||
yield from iter_responses_sse(response)
|
||||
|
||||
def process_event(self, event: tuple) -> list[JsonDict]:
|
||||
event_type, event_data = event
|
||||
return [{'_sse_event_type': event_type, **event_data}]
|
||||
|
||||
def extract_usage(self, event: tuple) -> JsonDict | None:
|
||||
from adapters.unified import extract_responses_usage
|
||||
_, event_data = event
|
||||
return extract_responses_usage(event_data)
|
||||
|
||||
def finalize(self) -> list[JsonDict]:
|
||||
return []
|
||||
|
||||
|
||||
class AnthropicOutboundForResponses:
|
||||
"""Anthropic 后端的出站转换器（用于 /v1/responses 路由）。
|
||||
|
||||
流式处理直接将 Anthropic SSE → Responses SSE，
|
||||
跳过 CC 中间态以保留原始时序。
|
||||
"""
|
||||
|
||||
def build_request(self, payload: JsonDict) -> JsonDict:
|
||||
from adapters.cc_anthropic_adapter import cc_to_messages_request
|
||||
return cc_to_messages_request(payload)
|
||||
|
||||
def build_url(self, ctx) -> str:
|
||||
return f'{ctx.target_url.rstrip("/")}/v1/messages'
|
||||
|
||||
def build_headers(self, ctx) -> dict[str, str]:
|
||||
from utils.http import build_anthropic_headers
|
||||
return build_anthropic_headers(ctx.api_key)
|
||||
|
||||
def parse_response(self, raw: JsonDict) -> JsonDict:
|
||||
from adapters.cc_anthropic_adapter import messages_to_cc_response
|
||||
return messages_to_cc_response(raw)
|
||||
|
||||
def create_stream_processor(self) -> AnthropicToResponsesStreamProcessor:
|
||||
return AnthropicToResponsesStreamProcessor()
|
||||
|
||||
|
||||
class AnthropicToResponsesStreamProcessor:
|
||||
"""Anthropic SSE → Responses SSE 直接转换的流式处理器。
|
||||
|
||||
跳过 CC 中间态，直接将 Anthropic 事件映射为 Responses 事件。
|
||||
返回的 chunk 是 SSE 字符串。
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self._converter = ResponsesStreamConverter()
|
||||
|
||||
def iter_events(self, response) -> Iterator:
|
||||
from utils.http import iter_anthropic_sse
|
||||
yield from iter_anthropic_sse(response)
|
||||
|
||||
def process_event(self, event: tuple) -> list[str]:
|
||||
event_type, event_data = event
|
||||
return self._converter.process_anthropic_event(event_type, event_data)
|
||||
|
||||
def extract_usage(self, event: tuple) -> JsonDict | None:
|
||||
return None
|
||||
|
||||
def finalize(self) -> list[str]:
|
||||
return self._converter.finalize()
|
||||
|
||||
|
||||
def _convert_cc_tools_to_responses(tools: Any) -> list[JsonDict]:
|
||||
"""将聊天补全风格的工具定义转换为 Responses `tools` 列表。"""
|
||||
if not isinstance(tools, list):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue