回退旧版本
This commit is contained in:
h88782481
parent
cd577d17c3
commit
a8f5ada8e1
9 changed files with 1582 additions and 1216 deletions
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@ -18,21 +18,13 @@ from __future__ import annotations
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import json
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from typing import Any
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from adapters.helpers import (
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build_cc_message,
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build_cc_response,
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build_cc_tool_call,
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build_cc_usage,
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extract_text,
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make_cc_chunk,
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parse_json_safe,
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stringify_content,
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)
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from utils.http import gen_id
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from utils.tool_fixer import fix_anthropic_tool_use, normalize_args, repair_str_replace_args
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JsonDict = dict[str, Any]
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# Anthropic stop_reason → OpenAI finish_reason
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_STOP_REASON_MAP = {
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'end_turn': 'stop',
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'max_tokens': 'length',
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@ -86,18 +78,23 @@ def messages_to_cc_response(data: JsonDict, request_id: str | None = None) -> Js
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data = fix_anthropic_tool_use(data)
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content_text, reasoning_text, tool_calls = _collect_response_parts(data.get('content', []))
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message = _build_cc_message(content_text, reasoning_text, tool_calls)
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usage = data.get('usage', {})
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return build_cc_response(
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response_id=request_id,
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message=build_cc_message(content_text, reasoning_text, tool_calls),
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finish_reason=_STOP_REASON_MAP.get(data.get('stop_reason', 'end_turn'), 'stop'),
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usage=build_cc_usage(
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return {
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'id': request_id,
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'object': 'chat.completion',
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'model': data.get('model', 'claude'),
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'choices': [{
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'index': 0,
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'message': message,
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'finish_reason': _STOP_REASON_MAP.get(data.get('stop_reason', 'end_turn'), 'stop'),
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}],
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'usage': _build_cc_usage(
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input_tokens=usage.get('input_tokens', 0),
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output_tokens=usage.get('output_tokens', 0),
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),
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model=data.get('model', 'claude'),
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)
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}
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# ═══════════════════════════════════════════════════════════
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@ -127,8 +124,12 @@ class AnthropicStreamConverter:
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self._input_tokens = 0
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self._output_tokens = 0
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def process_event(self, event_type: str, event_data: JsonDict) -> list[JsonDict]:
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"""处理单个 Anthropic SSE 事件，返回 CC chunk dict 列表。"""
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def process_event(self, event_type: str, event_data: JsonDict) -> list[str]:
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"""处理单个 Anthropic SSE 事件。
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调用方会按事件顺序不断喂入 event/data，这里根据事件类型拆成一个或多个 CC chunk
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字符串，交给上层直接作为 SSE data 发送给 Cursor。
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"""
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if event_type == 'message_start':
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return self._handle_message_start(event_data)
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if event_type == 'content_block_start':
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@ -139,64 +140,104 @@ class AnthropicStreamConverter:
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return self._handle_message_delta(event_data)
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return []
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def _handle_message_start(self, event_data: JsonDict) -> list[JsonDict]:
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def _handle_message_start(self, event_data: JsonDict) -> list[str]:
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"""处理消息开始事件，产出 assistant 角色起始 chunk。
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这个起始 chunk 很重要，因为 Cursor 侧通常会依赖首个带 role 的 chunk 来初始化
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当前 assistant 消息。
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"""
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message = event_data.get('message', {})
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self._input_tokens = message.get('usage', {}).get('input_tokens', 0)
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chunk = self._make_chunk(delta={'role': 'assistant', 'content': ''})
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if message.get('model'):
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chunk['model'] = message['model']
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return [chunk]
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return [self._dump_chunk(chunk)]
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def _handle_content_block_start(self, event_data: JsonDict) -> list[JsonDict]:
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def _handle_content_block_start(self, event_data: JsonDict) -> list[str]:
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"""处理内容块开始事件。
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目前这里只需要显式处理 `tool_use`，因为文本和 thinking 的真正内容都在后续 delta
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事件里；而 tool_use 需要先开一个空 arguments 的 tool_call 槽位。
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"""
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block = event_data.get('content_block', {})
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if block.get('type') != 'tool_use':
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return []
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self._tool_index += 1
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return [self._make_chunk(delta={
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return [self._dump_chunk(self._make_chunk(delta={
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'tool_calls': [{
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'index': self._tool_index,
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'id': block.get('id', gen_id('toolu_')),
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'type': 'function',
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'function': {'name': block.get('name', ''), 'arguments': ''},
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'function': {
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'name': block.get('name', ''),
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'arguments': '',
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},
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}]
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})]
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}))]
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def _handle_content_block_delta(self, event_data: JsonDict) -> list[JsonDict]:
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def _handle_content_block_delta(self, event_data: JsonDict) -> list[str]:
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"""处理内容块增量事件。
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Anthropic 会把文本、思考内容、工具参数拆成不同 delta 类型，这里要分别映射成
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OpenAI chunk 里的 `content`、`reasoning_content` 和 `tool_calls.function.arguments`。
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"""
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delta = event_data.get('delta', {})
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delta_type = delta.get('type', '')
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if delta_type == 'text_delta' and delta.get('text'):
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return [self._make_chunk(delta={'content': delta['text']})]
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return [self._dump_chunk(self._make_chunk(delta={'content': delta['text']}))]
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if delta_type == 'thinking_delta' and delta.get('thinking'):
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return [self._make_chunk(delta={'reasoning_content': delta['thinking']})]
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return [self._dump_chunk(self._make_chunk(delta={'reasoning_content': delta['thinking']}))]
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if delta_type == 'input_json_delta' and delta.get('partial_json'):
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return [self._make_chunk(delta={
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return [self._dump_chunk(self._make_chunk(delta={
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'tool_calls': [{
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'index': self._tool_index,
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'function': {'arguments': delta['partial_json']},
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}]
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})]
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}))]
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return []
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def _handle_message_delta(self, event_data: JsonDict) -> list[JsonDict]:
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def _handle_message_delta(self, event_data: JsonDict) -> list[str]:
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"""处理消息收尾事件，补出 finish_reason 和 usage。
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当 Anthropic 发出 `message_delta` 时，说明这一轮 assistant 输出已经收束，
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这里会统一生成最后一个带 usage 的收尾 chunk。
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"""
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delta = event_data.get('delta', {})
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usage = event_data.get('usage', {})
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self._output_tokens = usage.get('output_tokens', 0)
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chunk = make_cc_chunk(
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self._id,
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chunk = self._make_chunk(
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delta={},
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finish_reason=_STOP_REASON_MAP.get(delta.get('stop_reason', ''), 'stop'),
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model='claude',
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)
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chunk['usage'] = build_cc_usage(
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chunk['usage'] = _build_cc_usage(
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input_tokens=self._input_tokens,
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output_tokens=self._output_tokens,
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)
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return [chunk]
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return [self._dump_chunk(chunk)]
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def _make_chunk(self, delta: JsonDict, finish_reason: str | None = None) -> JsonDict:
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"""构造标准 OpenAI Chat Completions chunk 对象。"""
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return make_cc_chunk(self._id, delta, finish_reason, model='claude')
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choice: JsonDict = {'index': 0, 'delta': delta}
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if finish_reason:
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choice['finish_reason'] = finish_reason
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return {
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'id': self._id,
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'object': 'chat.completion.chunk',
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'model': 'claude',
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'choices': [choice],
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}
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@staticmethod
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def _dump_chunk(chunk: JsonDict) -> str:
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"""统一序列化 chunk，方便上层直接写入 SSE data。"""
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return json.dumps(chunk)
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# ═══════════════════════════════════════════════════════════
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@ -213,7 +254,7 @@ def _convert_request_message(message: Any) -> tuple[JsonDict | None, str | None]
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content = message.get('content', '')
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if role == 'system':
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return None, extract_text(content)
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return None, _flatten_text(content)
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if role == 'tool':
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return _convert_tool_role_message(message), None
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@ -260,7 +301,7 @@ def _append_tool_use_blocks(content: Any, tool_calls: list[Any]) -> list[JsonDic
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'type': 'tool_use',
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'id': tool_call.get('id', gen_id('toolu_')),
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'name': function_data.get('name', ''),
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'input': parse_json_safe(function_data.get('arguments', '{}')),
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'input': _parse_tool_arguments(function_data.get('arguments', '{}')),
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})
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return blocks
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@ -331,12 +372,37 @@ def _convert_tool_use_block(block: JsonDict, *, index: int) -> JsonDict:
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else:
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arguments_text = str(input_data)
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return build_cc_tool_call(
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call_id=block.get('id', gen_id('toolu_')),
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name=tool_name,
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arguments=arguments_text,
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index=index,
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)
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return {
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'index': index,
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'id': block.get('id', gen_id('toolu_')),
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'type': 'function',
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'function': {
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'name': tool_name,
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'arguments': arguments_text,
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},
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}
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def _build_cc_message(content_text: str, reasoning_text: str, tool_calls: list[JsonDict]) -> JsonDict:
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"""构造 OpenAI CC 响应中的 assistant message。"""
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message: JsonDict = {
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'role': 'assistant',
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'content': content_text or None,
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}
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if reasoning_text:
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message['reasoning_content'] = reasoning_text
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if tool_calls:
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message['tool_calls'] = tool_calls
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return message
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def _build_cc_usage(*, input_tokens: int, output_tokens: int) -> JsonDict:
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"""将 Anthropic usage 字段映射为 OpenAI usage。"""
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return {
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'prompt_tokens': input_tokens,
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'completion_tokens': output_tokens,
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'total_tokens': input_tokens + output_tokens,
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}
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# ═══════════════════════════════════════════════════════════
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@ -344,6 +410,35 @@ def _convert_tool_use_block(block: JsonDict, *, index: int) -> JsonDict:
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# ═══════════════════════════════════════════════════════════
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def _parse_tool_arguments(arguments: Any) -> Any:
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"""将 tool_call.arguments 尽量解析为对象，供 Anthropic tool_use.input 使用。
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Anthropic 的 `tool_use.input` 天然期望对象结构；如果这里直接保留原始字符串，
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后续上游会把它当普通文本而不是工具参数对象。
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"""
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if not isinstance(arguments, str):
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return arguments if arguments is not None else {}
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try:
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return json.loads(arguments)
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except json.JSONDecodeError:
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return {}
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def _flatten_text(content: Any) -> str:
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"""将 content 扁平化为纯文本，主要用于 system 消息上提。"""
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts: list[str] = []
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for part in content:
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if isinstance(part, str):
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parts.append(part)
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elif isinstance(part, dict) and part.get('type') == 'text':
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parts.append(part.get('text', ''))
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return '\n'.join(parts)
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return str(content)
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def _convert_content(message: JsonDict) -> Any:
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"""将 OpenAI 消息的 content 字段转换为 Anthropic 内容格式。"""
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content = message.get('content', '')
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@ -613,78 +708,3 @@ def _pick_window_anchor(refs: list[JsonDict], target: int) -> int | None:
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if 'cache_control' not in refs[i]:
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return i
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return None
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# ═══════════════════════════════════════════════════════════
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# OutboundTransformer 实现: Anthropic Messages
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# ═══════════════════════════════════════════════════════════
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class AnthropicOutbound:
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"""Anthropic Messages 后端的出站转换器。
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将 CC 格式转换为 Anthropic Messages 格式并处理响应。
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"""
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def build_request(self, payload: JsonDict) -> JsonDict:
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return cc_to_messages_request(payload)
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def build_url(self, ctx) -> str:
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return f'{ctx.target_url.rstrip("/")}/v1/messages'
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def build_headers(self, ctx) -> dict[str, str]:
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from utils.http import build_anthropic_headers
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return build_anthropic_headers(ctx.api_key)
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def parse_response(self, raw: JsonDict) -> JsonDict:
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return messages_to_cc_response(raw)
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def create_stream_processor(self) -> AnthropicStreamProcessor:
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return AnthropicStreamProcessor()
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class AnthropicStreamProcessor:
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"""Anthropic SSE 流式处理器。
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包装 iter_anthropic_sse + AnthropicStreamConverter，
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将 Anthropic 事件流转换为 CC chunk。
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"""
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def __init__(self):
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self._converter = AnthropicStreamConverter()
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self._input_tokens = 0
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self._output_tokens = 0
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def iter_events(self, response) -> Iterator:
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from utils.http import iter_anthropic_sse
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yield from iter_anthropic_sse(response)
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def process_event(self, event: tuple) -> list[JsonDict]:
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event_type, event_data = event
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return self._converter.process_event(event_type, event_data)
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def extract_usage(self, event: tuple) -> JsonDict | None:
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event_type, event_data = event
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if event_type == 'message_start':
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message_usage = event_data.get('message', {}).get('usage', {})
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if isinstance(message_usage, dict):
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self._input_tokens = message_usage.get('input_tokens', 0)
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return {
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'prompt_tokens': self._input_tokens,
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'completion_tokens': 0,
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'total_tokens': self._input_tokens,
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}
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elif event_type == 'message_delta':
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delta_usage = event_data.get('usage', {})
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if isinstance(delta_usage, dict):
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completion = delta_usage.get('output_tokens', 0)
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self._output_tokens = completion
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return {
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'prompt_tokens': self._input_tokens,
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'completion_tokens': completion,
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'total_tokens': self._input_tokens + completion,
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}
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return None
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def finalize(self) -> list[JsonDict]:
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return []
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@ -8,17 +8,8 @@ from __future__ import annotations
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import json
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import logging
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from typing import Any, Iterator
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from typing import Any
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from adapters.helpers import (
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build_cc_message,
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build_cc_response,
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build_cc_tool_call,
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build_cc_usage,
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extract_text,
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make_cc_chunk,
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parse_json_safe,
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)
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from utils.http import gen_id
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JsonDict = dict[str, Any]
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@ -47,7 +38,7 @@ def cc_to_gemini_request(payload: JsonDict) -> JsonDict:
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for msg in messages:
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role = msg.get('role', '')
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if role in ('system', 'developer'):
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system_parts.append(extract_text(msg.get('content', '')))
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system_parts.append(_flatten_text(msg.get('content', '')))
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continue
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converted = _convert_message(msg)
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if converted:
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@ -93,13 +84,21 @@ def gemini_to_cc_response(data: JsonDict, request_id: str | None = None) -> Json
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else:
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finish_reason = _FINISH_REASON_MAP.get(finish, 'stop')
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return build_cc_response(
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response_id=request_id,
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message=build_cc_message(content_text, reasoning_text, tool_calls),
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finish_reason=finish_reason,
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usage=_convert_usage(data.get('usageMetadata', {})),
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model=data.get('modelVersion', 'gemini'),
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)
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message: JsonDict = {'role': 'assistant', 'content': content_text or None}
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if reasoning_text:
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message['reasoning_content'] = reasoning_text
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if tool_calls:
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message['tool_calls'] = tool_calls
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usage = _convert_usage(data.get('usageMetadata', {}))
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return {
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'id': request_id,
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'object': 'chat.completion',
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'model': data.get('modelVersion', 'gemini'),
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'choices': [{'index': 0, 'message': message, 'finish_reason': finish_reason}],
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'usage': usage,
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}
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# ═══════════════════════════════════════════════════════════
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@ -167,7 +166,15 @@ class GeminiStreamConverter:
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return results
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def _make_chunk(self, delta: JsonDict, finish_reason: str | None = None) -> JsonDict:
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return make_cc_chunk(self._id, delta, finish_reason, model='gemini')
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choice: JsonDict = {'index': 0, 'delta': delta}
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||||
if finish_reason:
|
||||
choice['finish_reason'] = finish_reason
|
||||
return {
|
||||
'id': self._id,
|
||||
'object': 'chat.completion.chunk',
|
||||
'model': 'gemini',
|
||||
'choices': [choice],
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
|
@ -187,7 +194,7 @@ def _convert_message(msg: JsonDict) -> JsonDict | None:
|
|||
'parts': [{
|
||||
'functionResponse': {
|
||||
'name': msg.get('name', msg.get('tool_call_id', '')),
|
||||
'response': parse_json_safe(msg.get('content', ''), fallback={'result': msg.get('content', '')} if msg.get('content', '') else {}),
|
||||
'response': _parse_json_safe(msg.get('content', '')),
|
||||
},
|
||||
}],
|
||||
}
|
||||
|
|
@ -214,7 +221,7 @@ def _convert_message(msg: JsonDict) -> JsonDict | None:
|
|||
parts.append({
|
||||
'functionCall': {
|
||||
'name': func.get('name', ''),
|
||||
'args': parse_json_safe(func.get('arguments', '{}'), fallback={}),
|
||||
'args': _parse_json_safe(func.get('arguments', '{}')),
|
||||
},
|
||||
})
|
||||
|
||||
|
|
@ -297,12 +304,15 @@ def _extract_parts(parts: list[Any]) -> tuple[str, str, list[JsonDict]]:
|
|||
text += part['text']
|
||||
elif 'functionCall' in part:
|
||||
fc = part['functionCall']
|
||||
tool_calls.append(build_cc_tool_call(
|
||||
call_id=fc.get('id') or gen_id('call_'),
|
||||
name=fc.get('name', ''),
|
||||
arguments=json.dumps(fc.get('args', {}), ensure_ascii=False),
|
||||
index=len(tool_calls),
|
||||
))
|
||||
tool_calls.append({
|
||||
'index': len(tool_calls),
|
||||
'id': fc.get('id') or gen_id('call_'),
|
||||
'type': 'function',
|
||||
'function': {
|
||||
'name': fc.get('name', ''),
|
||||
'arguments': json.dumps(fc.get('args', {}), ensure_ascii=False),
|
||||
},
|
||||
})
|
||||
|
||||
return text, reasoning, tool_calls
|
||||
|
||||
|
|
@ -312,7 +322,12 @@ def _convert_usage(meta: JsonDict) -> JsonDict:
|
|||
prompt = meta.get('promptTokenCount', 0)
|
||||
candidates = meta.get('candidatesTokenCount', 0)
|
||||
thoughts = meta.get('thoughtsTokenCount', 0)
|
||||
return build_cc_usage(prompt, candidates + thoughts)
|
||||
completion = candidates + thoughts
|
||||
return {
|
||||
'prompt_tokens': prompt,
|
||||
'completion_tokens': completion,
|
||||
'total_tokens': prompt + completion,
|
||||
}
|
||||
|
||||
|
||||
def _merge_same_role(contents: list[JsonDict]) -> list[JsonDict]:
|
||||
|
|
@ -328,65 +343,21 @@ def _merge_same_role(contents: list[JsonDict]) -> list[JsonDict]:
|
|||
return merged
|
||||
|
||||
|
||||
def _flatten_text(content: Any) -> str:
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if isinstance(content, list):
|
||||
return '\n'.join(
|
||||
p.get('text', '') if isinstance(p, dict) else str(p)
|
||||
for p in content
|
||||
)
|
||||
return str(content)
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# OutboundTransformer 实现: Gemini Contents
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
class GeminiOutbound:
|
||||
"""Gemini Contents 后端的出站转换器。
|
||||
|
||||
将 CC 格式转换为 Gemini generateContent 格式并处理响应。
|
||||
"""
|
||||
|
||||
def build_request(self, payload: JsonDict) -> JsonDict:
|
||||
return cc_to_gemini_request(payload)
|
||||
|
||||
def build_url(self, ctx) -> str:
|
||||
base = ctx.target_url.rstrip('/')
|
||||
model = ctx.upstream_model
|
||||
if ctx.is_stream:
|
||||
return f'{base}/v1/models/{model}:streamGenerateContent?alt=sse'
|
||||
return f'{base}/v1/models/{model}:generateContent'
|
||||
|
||||
def build_headers(self, ctx) -> dict[str, str]:
|
||||
from utils.http import build_gemini_headers
|
||||
return build_gemini_headers(ctx.api_key)
|
||||
|
||||
def parse_response(self, raw: JsonDict) -> JsonDict:
|
||||
return gemini_to_cc_response(raw)
|
||||
|
||||
def create_stream_processor(self) -> GeminiStreamProcessor:
|
||||
return GeminiStreamProcessor()
|
||||
|
||||
|
||||
class GeminiStreamProcessor:
|
||||
"""Gemini SSE 流式处理器。
|
||||
|
||||
包装 iter_gemini_sse + GeminiStreamConverter。
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self._converter = GeminiStreamConverter()
|
||||
|
||||
def iter_events(self, response) -> Iterator:
|
||||
from utils.http import iter_gemini_sse
|
||||
yield from iter_gemini_sse(response)
|
||||
|
||||
def process_event(self, event: JsonDict) -> list[JsonDict]:
|
||||
return self._converter.process_chunk(event)
|
||||
|
||||
def extract_usage(self, event: JsonDict) -> JsonDict | None:
|
||||
usage_meta = event.get('usageMetadata') if isinstance(event, dict) else None
|
||||
if isinstance(usage_meta, dict):
|
||||
return {
|
||||
'prompt_tokens': usage_meta.get('promptTokenCount', 0),
|
||||
'completion_tokens': usage_meta.get('candidatesTokenCount', 0),
|
||||
'total_tokens': usage_meta.get('totalTokenCount', 0),
|
||||
}
|
||||
return None
|
||||
|
||||
def finalize(self) -> list[JsonDict]:
|
||||
return []
|
||||
def _parse_json_safe(text: Any) -> Any:
|
||||
if not isinstance(text, str):
|
||||
return text if text is not None else {}
|
||||
try:
|
||||
return json.loads(text)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
return {'result': text} if text else {}
|
||||
|
|
|
|||
|
|
@ -1,155 +0,0 @@
|
|||
"""适配器公共辅助函数
|
||||
|
||||
收敛多个适配器都在重复实现的 CC 格式构建逻辑：
|
||||
- CC 消息/Usage/Tool Call/Stream Chunk 的标准构造
|
||||
- 内容扁平化、JSON 安全解析、工具输出序列化
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
from utils.http import gen_id
|
||||
|
||||
JsonDict = dict[str, Any]
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# CC 格式标准构造
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def build_cc_message(
|
||||
content_text: str,
|
||||
reasoning_text: str = '',
|
||||
tool_calls: list[JsonDict] | None = None,
|
||||
) -> JsonDict:
|
||||
"""构造标准的 CC assistant 消息。"""
|
||||
message: JsonDict = {
|
||||
'role': 'assistant',
|
||||
'content': content_text or None,
|
||||
}
|
||||
if reasoning_text:
|
||||
message['reasoning_content'] = reasoning_text
|
||||
if tool_calls:
|
||||
message['tool_calls'] = tool_calls
|
||||
return message
|
||||
|
||||
|
||||
def build_cc_usage(input_tokens: int, output_tokens: int) -> JsonDict:
|
||||
"""构造标准的 CC usage 字典。"""
|
||||
return {
|
||||
'prompt_tokens': input_tokens,
|
||||
'completion_tokens': output_tokens,
|
||||
'total_tokens': input_tokens + output_tokens,
|
||||
}
|
||||
|
||||
|
||||
def build_cc_tool_call(
|
||||
call_id: str,
|
||||
name: str,
|
||||
arguments: str,
|
||||
*,
|
||||
index: int | None = None,
|
||||
) -> JsonDict:
|
||||
"""构造标准的 CC tool_call 结构。"""
|
||||
tc: JsonDict = {
|
||||
'id': call_id or gen_id('call_'),
|
||||
'type': 'function',
|
||||
'function': {
|
||||
'name': name,
|
||||
'arguments': arguments,
|
||||
},
|
||||
}
|
||||
if index is not None:
|
||||
tc['index'] = index
|
||||
return tc
|
||||
|
||||
|
||||
def make_cc_chunk(
|
||||
chunk_id: str,
|
||||
delta: JsonDict,
|
||||
finish_reason: str | None = None,
|
||||
model: str = '',
|
||||
) -> JsonDict:
|
||||
"""构造标准的 CC 流式 chunk。"""
|
||||
choice: JsonDict = {'index': 0, 'delta': delta}
|
||||
if finish_reason:
|
||||
choice['finish_reason'] = finish_reason
|
||||
return {
|
||||
'id': chunk_id,
|
||||
'object': 'chat.completion.chunk',
|
||||
'model': model,
|
||||
'choices': [choice],
|
||||
}
|
||||
|
||||
|
||||
def build_cc_response(
|
||||
response_id: str,
|
||||
message: JsonDict,
|
||||
finish_reason: str,
|
||||
usage: JsonDict,
|
||||
model: str = '',
|
||||
) -> JsonDict:
|
||||
"""构造标准的 CC 非流式响应。"""
|
||||
return {
|
||||
'id': response_id,
|
||||
'object': 'chat.completion',
|
||||
'model': model,
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'message': message,
|
||||
'finish_reason': finish_reason,
|
||||
}],
|
||||
'usage': usage,
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 通用文本/JSON 处理
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def extract_text(content: Any) -> str:
|
||||
"""从多种内容格式中提取并拼接纯文本。
|
||||
|
||||
支持字符串、内容块列表（OpenAI/Anthropic/Responses 风格）。
|
||||
"""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if not isinstance(content, list):
|
||||
return str(content) if content is not None else ''
|
||||
|
||||
parts: list[str] = []
|
||||
for part in content:
|
||||
if isinstance(part, str):
|
||||
parts.append(part)
|
||||
elif isinstance(part, dict):
|
||||
part_type = part.get('type', '')
|
||||
if part_type in ('text', 'output_text', 'input_text'):
|
||||
parts.append(part.get('text', ''))
|
||||
elif part_type == 'refusal':
|
||||
parts.append(part.get('refusal', ''))
|
||||
elif 'text' in part and not part_type:
|
||||
parts.append(part['text'])
|
||||
return '\n'.join(parts) if parts else ''
|
||||
|
||||
|
||||
def parse_json_safe(text: Any, fallback: Any = None) -> Any:
|
||||
"""安全解析 JSON，失败时返回 fallback。"""
|
||||
if not isinstance(text, str):
|
||||
return text if text is not None else (fallback if fallback is not None else {})
|
||||
try:
|
||||
return json.loads(text)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
return fallback if fallback is not None else {}
|
||||
|
||||
|
||||
def stringify_content(content: Any) -> str:
|
||||
"""将任意内容序列化为字符串。"""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if content is None:
|
||||
return ''
|
||||
return json.dumps(content, ensure_ascii=False)
|
||||
|
|
@ -13,7 +13,7 @@ from __future__ import annotations
|
|||
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, Iterator
|
||||
from typing import Any
|
||||
|
||||
from utils.http import gen_id
|
||||
from utils.think_tag import extract_from_text
|
||||
|
|
@ -423,60 +423,3 @@ def _rewrite_function_call_finish_reason(choice: JsonDict) -> None:
|
|||
"""将旧版 finish_reason=function_call 升级为 tool_calls。"""
|
||||
if choice.get('finish_reason') == 'function_call':
|
||||
choice['finish_reason'] = 'tool_calls'
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# OutboundTransformer 实现: OpenAI Chat
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
class OpenAIChatOutbound:
|
||||
"""OpenAI Chat Completions 后端的出站转换器。
|
||||
|
||||
由于 CC 本身就是 OpenAI Chat 格式，请求/响应转换主要做兼容性修复。
|
||||
"""
|
||||
|
||||
def build_request(self, payload: JsonDict) -> JsonDict:
|
||||
return normalize_request(payload)
|
||||
|
||||
def build_url(self, ctx) -> str:
|
||||
return f'{ctx.target_url.rstrip("/")}/v1/chat/completions'
|
||||
|
||||
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 fix_response(raw)
|
||||
|
||||
def create_stream_processor(self) -> OpenAIChatStreamProcessor:
|
||||
return OpenAIChatStreamProcessor()
|
||||
|
||||
|
||||
class OpenAIChatStreamProcessor:
|
||||
"""OpenAI Chat SSE 流式处理器。
|
||||
|
||||
包装 iter_openai_sse + fix_stream_chunk + ThinkTagExtractor。
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
from utils.think_tag import ThinkTagExtractor
|
||||
self._think_extractor = ThinkTagExtractor()
|
||||
|
||||
def iter_events(self, response) -> Iterator:
|
||||
from utils.http import iter_openai_sse
|
||||
for chunk in iter_openai_sse(response):
|
||||
if chunk is None:
|
||||
return
|
||||
yield chunk
|
||||
|
||||
def process_event(self, event: JsonDict) -> list[JsonDict]:
|
||||
chunk = fix_stream_chunk(event)
|
||||
return list(self._think_extractor.process_chunk(chunk))
|
||||
|
||||
def extract_usage(self, event: JsonDict) -> JsonDict | None:
|
||||
return event.get('usage')
|
||||
|
||||
def finalize(self) -> list[JsonDict]:
|
||||
close_chunk = self._think_extractor.finalize()
|
||||
return [close_chunk] if close_chunk else []
|
||||
|
|
|
|||
|
|
@ -15,18 +15,8 @@ from __future__ import annotations
|
|||
|
||||
import json
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Iterator
|
||||
from typing import Any
|
||||
|
||||
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]
|
||||
|
|
@ -95,7 +85,7 @@ 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': UnifiedUsage.from_cc_dict(cc_resp.get('usage', {})).to_responses_dict(),
|
||||
'usage': _build_responses_usage(cc_resp.get('usage', {})),
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -104,18 +94,31 @@ 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)
|
||||
usage = response_data.get('usage', {})
|
||||
message = {
|
||||
'role': 'assistant',
|
||||
'content': content_text or None,
|
||||
}
|
||||
if reasoning_text:
|
||||
message['reasoning_content'] = reasoning_text
|
||||
if tool_calls:
|
||||
message['tool_calls'] = tool_calls
|
||||
|
||||
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', ''),
|
||||
)
|
||||
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),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
|
@ -655,7 +658,15 @@ class ResponsesToCCStreamConverter:
|
|||
|
||||
def _make_chunk(self, delta: JsonDict, finish_reason: str | None = None) -> JsonDict:
|
||||
"""构造标准 Chat Completions chunk。"""
|
||||
return make_cc_chunk(self._id, delta, finish_reason, model=self._model)
|
||||
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],
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
|
@ -704,7 +715,7 @@ def _append_responses_input_item(
|
|||
content = message.get('content')
|
||||
|
||||
if role == 'system':
|
||||
text = extract_text(content)
|
||||
text = _content_to_text(content)
|
||||
if text:
|
||||
instructions.append(text)
|
||||
return
|
||||
|
|
@ -713,11 +724,11 @@ def _append_responses_input_item(
|
|||
input_items.append({
|
||||
'type': 'function_call_output',
|
||||
'call_id': message.get('tool_call_id', ''),
|
||||
'output': stringify_content(content),
|
||||
'output': _stringify_output(content),
|
||||
})
|
||||
return
|
||||
|
||||
text = extract_text(content)
|
||||
text = _content_to_text(content)
|
||||
has_tool_calls = bool(message.get('tool_calls'))
|
||||
|
||||
if role == 'assistant' and has_tool_calls:
|
||||
|
|
@ -760,7 +771,7 @@ def _convert_input_items(items: list[Any], messages: list[JsonDict]) -> None:
|
|||
if role and not item_type:
|
||||
msg: JsonDict = {
|
||||
'role': role,
|
||||
'content': extract_text(item.get('content', '')),
|
||||
'content': _normalize_simple_content(item.get('content', '')),
|
||||
}
|
||||
if role == 'assistant' and pending_reasoning:
|
||||
msg['reasoning_content'] = pending_reasoning
|
||||
|
|
@ -799,7 +810,7 @@ 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':
|
||||
|
|
@ -817,11 +828,7 @@ 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(
|
||||
call_id=item.get('call_id') or gen_id('call_'),
|
||||
name=item.get('name', ''),
|
||||
arguments=item.get('arguments', '{}'),
|
||||
)
|
||||
tool_call = _build_cc_tool_call(item)
|
||||
|
||||
if messages and messages[-1]['role'] == 'assistant':
|
||||
messages[-1].setdefault('tool_calls', []).append(tool_call)
|
||||
|
|
@ -844,6 +851,12 @@ 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。"""
|
||||
|
|
@ -852,17 +865,24 @@ 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(
|
||||
call_id=next_item.get('call_id') or gen_id('call_'),
|
||||
name=next_item.get('name', ''),
|
||||
arguments=next_item.get('arguments', '{}'),
|
||||
))
|
||||
tool_calls.append(_build_cc_tool_call(next_item))
|
||||
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
|
||||
|
|
@ -916,6 +936,14 @@ 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。"""
|
||||
|
|
@ -931,16 +959,11 @@ 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(
|
||||
call_id=item.get('call_id') or gen_id('call_'),
|
||||
name=item.get('name', ''),
|
||||
arguments=item.get('arguments', '{}'),
|
||||
index=len(tool_calls),
|
||||
))
|
||||
tool_calls.append(_build_cc_tool_call_from_responses_output(item, index=len(tool_calls)))
|
||||
|
||||
return content_text, reasoning_text, tool_calls
|
||||
|
||||
|
|
@ -957,6 +980,18 @@ 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。"""
|
||||
|
|
@ -982,7 +1017,57 @@ 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:
|
||||
|
|
@ -996,165 +1081,6 @@ 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):
|
||||
|
|
|
|||
|
|
@ -1,354 +0,0 @@
|
|||
"""统一中间格式与转换器接口
|
||||
|
||||
定义项目中所有 API 格式共用的中间表示和转换器协议：
|
||||
- UnifiedRequest / UnifiedResponse: 统一的请求/响应数据结构
|
||||
- InboundTransformer / OutboundTransformer: 入站/出站转换器接口
|
||||
- StreamProcessor: 流式事件处理器接口
|
||||
- ClientFormatter: 客户端响应格式化接口
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any, Iterator, Protocol
|
||||
|
||||
from flask import Response, jsonify
|
||||
|
||||
import settings
|
||||
from utils.http import forward_request, gen_id, sse_response
|
||||
from utils.request_logger import (
|
||||
append_client_event,
|
||||
append_upstream_event,
|
||||
attach_client_response,
|
||||
attach_error,
|
||||
attach_upstream_request,
|
||||
attach_upstream_response,
|
||||
finalize_turn,
|
||||
set_stream_summary,
|
||||
)
|
||||
from utils.usage_tracker import usage_tracker
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
JsonDict = dict[str, Any]
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 统一数据模型
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
@dataclass
|
||||
class UnifiedUsage:
|
||||
"""标准化的令牌用量统计。"""
|
||||
|
||||
input_tokens: int = 0
|
||||
output_tokens: int = 0
|
||||
total_tokens: int = 0
|
||||
|
||||
def to_cc_dict(self) -> JsonDict:
|
||||
return {
|
||||
'prompt_tokens': self.input_tokens,
|
||||
'completion_tokens': self.output_tokens,
|
||||
'total_tokens': self.total_tokens,
|
||||
}
|
||||
|
||||
def to_responses_dict(self) -> JsonDict:
|
||||
return {
|
||||
'input_tokens': self.input_tokens,
|
||||
'output_tokens': self.output_tokens,
|
||||
'total_tokens': self.total_tokens,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_cc_dict(cls, d: JsonDict) -> UnifiedUsage:
|
||||
return cls(
|
||||
input_tokens=d.get('prompt_tokens', 0),
|
||||
output_tokens=d.get('completion_tokens', 0),
|
||||
total_tokens=d.get('total_tokens', 0),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_responses_dict(cls, d: JsonDict) -> UnifiedUsage:
|
||||
return cls(
|
||||
input_tokens=d.get('input_tokens', 0),
|
||||
output_tokens=d.get('output_tokens', 0),
|
||||
total_tokens=d.get('total_tokens', 0),
|
||||
)
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 转换器接口
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
class OutboundTransformer(Protocol):
|
||||
"""出站转换器：将 CC 中间格式转换为上游后端格式。
|
||||
|
||||
所有后端（OpenAI Chat / Responses / Anthropic / Gemini）各实现一套，
|
||||
内部复用各自现有的适配器函数。
|
||||
"""
|
||||
|
||||
def build_request(self, payload: JsonDict) -> JsonDict:
|
||||
"""将 CC 格式请求体转换为上游格式请求体。"""
|
||||
...
|
||||
|
||||
def build_url(self, ctx: Any) -> str:
|
||||
"""根据路由上下文构建上游请求 URL。"""
|
||||
...
|
||||
|
||||
def build_headers(self, ctx: Any) -> JsonDict:
|
||||
"""根据路由上下文构建上游请求头。"""
|
||||
...
|
||||
|
||||
def parse_response(self, raw: JsonDict) -> JsonDict:
|
||||
"""将上游非流式响应转换回 CC 格式。"""
|
||||
...
|
||||
|
||||
def create_stream_processor(self) -> StreamProcessor:
|
||||
"""创建该后端对应的流式事件处理器。"""
|
||||
...
|
||||
|
||||
|
||||
class StreamProcessor(Protocol):
|
||||
"""流式事件处理器接口。
|
||||
|
||||
每个后端的 SSE 格式不同，StreamProcessor 封装了具体的迭代与转换逻辑，
|
||||
让通用流式处理器不必关心后端差异。
|
||||
"""
|
||||
|
||||
def iter_events(self, response: Any) -> Iterator:
|
||||
"""从上游 HTTP 响应中迭代原始事件。"""
|
||||
...
|
||||
|
||||
def process_event(self, event: Any) -> list:
|
||||
"""将单个上游事件转换为输出项列表。
|
||||
|
||||
返回值通常是 list[JsonDict]（CC chunk），
|
||||
但 Anthropic→Responses 路径返回 list[str]（SSE 字符串）。
|
||||
"""
|
||||
...
|
||||
|
||||
def extract_usage(self, event: Any) -> JsonDict | None:
|
||||
"""从上游事件中提取用量信息（如果有的话）。"""
|
||||
...
|
||||
|
||||
def finalize(self) -> list:
|
||||
"""流结束时产出的收尾项。"""
|
||||
...
|
||||
|
||||
|
||||
class ClientFormatter(Protocol):
|
||||
"""客户端响应格式化器。
|
||||
|
||||
根据客户端期望的 API 格式（CC 或 Responses），将通用的处理结果
|
||||
格式化为最终返回给客户端的形态。
|
||||
"""
|
||||
|
||||
def format_response(self, cc_response: JsonDict, model: str) -> JsonDict:
|
||||
"""格式化非流式响应。"""
|
||||
...
|
||||
|
||||
def wrap_stream_item(self, item: Any) -> str:
|
||||
"""将单个流式输出项包装为 SSE 字符串。"""
|
||||
...
|
||||
|
||||
def format_error(self, message: str) -> str:
|
||||
"""构造流式错误消息。"""
|
||||
...
|
||||
|
||||
def format_done(self) -> str | None:
|
||||
"""构造流结束标记（CC 返回 [DONE]，Responses 返回 None）。"""
|
||||
...
|
||||
|
||||
def start_events(self) -> list[str]:
|
||||
"""流开始前的初始事件（Responses 返回 response.created）。"""
|
||||
...
|
||||
|
||||
@property
|
||||
def usage_input_key(self) -> str:
|
||||
"""usage 中输入令牌的字段名。"""
|
||||
...
|
||||
|
||||
@property
|
||||
def usage_output_key(self) -> str:
|
||||
"""usage 中输出令牌的字段名。"""
|
||||
...
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 通用请求/响应处理器
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def _dbg(message: str) -> None:
|
||||
if settings.get_debug_mode() in ('simple', 'verbose'):
|
||||
logger.info('[通用调试] %s', message)
|
||||
|
||||
|
||||
def extract_responses_usage(event_data: JsonDict) -> JsonDict | None:
|
||||
"""从原生 Responses 事件中提取 usage（公共辅助）。"""
|
||||
if not isinstance(event_data, dict):
|
||||
return None
|
||||
usage = event_data.get('usage')
|
||||
if isinstance(usage, dict):
|
||||
return usage
|
||||
response_obj = event_data.get('response')
|
||||
if isinstance(response_obj, dict):
|
||||
nested_usage = response_obj.get('usage')
|
||||
if isinstance(nested_usage, dict):
|
||||
return nested_usage
|
||||
return None
|
||||
|
||||
|
||||
def handle_non_stream(
|
||||
ctx: Any,
|
||||
outbound: OutboundTransformer,
|
||||
client_fmt: ClientFormatter,
|
||||
payload: JsonDict,
|
||||
turn: JsonDict | None,
|
||||
) -> Response:
|
||||
"""通用非流式处理器。
|
||||
|
||||
替代 chat.py 和 responses.py 中的 8 个 _handle_xxx_non_stream 函数。
|
||||
"""
|
||||
from routes.common import apply_body_modifications, apply_header_modifications, log_usage
|
||||
|
||||
upstream_payload = outbound.build_request(payload)
|
||||
url = outbound.build_url(ctx)
|
||||
headers = outbound.build_headers(ctx)
|
||||
upstream_payload = apply_body_modifications(upstream_payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
upstream_payload['stream'] = False
|
||||
attach_upstream_request(turn, upstream_payload, headers)
|
||||
resp, err = forward_request(url, headers, upstream_payload)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': 'upstream request failed'})
|
||||
finalize_turn(turn)
|
||||
return err
|
||||
|
||||
raw = resp.json()
|
||||
attach_upstream_response(turn, raw)
|
||||
_dbg('上游原始响应=' + json.dumps(raw, ensure_ascii=False, default=str)[:1000])
|
||||
|
||||
cc_response = outbound.parse_response(raw)
|
||||
result = client_fmt.format_response(cc_response, ctx.client_model)
|
||||
|
||||
_dbg('格式化后响应=' + json.dumps(result, ensure_ascii=False, default=str)[:1000])
|
||||
usage_data = result.get('usage', {})
|
||||
log_usage('通用', usage_data, input_key=client_fmt.usage_input_key, output_key=client_fmt.usage_output_key)
|
||||
usage_tracker.record(
|
||||
ctx.client_model,
|
||||
usage_data,
|
||||
input_key=client_fmt.usage_input_key,
|
||||
output_key=client_fmt.usage_output_key,
|
||||
)
|
||||
attach_client_response(turn, result)
|
||||
finalize_turn(turn, usage=usage_data)
|
||||
return jsonify(result)
|
||||
|
||||
|
||||
def handle_stream(
|
||||
ctx: Any,
|
||||
outbound: OutboundTransformer,
|
||||
client_fmt: ClientFormatter,
|
||||
payload: JsonDict,
|
||||
turn: JsonDict | None,
|
||||
) -> Response:
|
||||
"""通用流式处理器。
|
||||
|
||||
替代 chat.py 和 responses.py 中的 8 个 _handle_xxx_stream 函数。
|
||||
"""
|
||||
from routes.common import apply_body_modifications, apply_header_modifications
|
||||
|
||||
upstream_payload = outbound.build_request(payload)
|
||||
url = outbound.build_url(ctx)
|
||||
headers = outbound.build_headers(ctx)
|
||||
upstream_payload = apply_body_modifications(upstream_payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
upstream_payload['stream'] = True
|
||||
processor = outbound.create_stream_processor()
|
||||
|
||||
def generate():
|
||||
for start_evt in client_fmt.start_events():
|
||||
yield start_evt
|
||||
|
||||
attach_upstream_request(turn, upstream_payload, headers)
|
||||
resp, err = forward_request(url, headers, upstream_payload, stream=True)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': str(err)})
|
||||
set_stream_summary(turn, {'status': 'error'})
|
||||
finalize_turn(turn)
|
||||
yield client_fmt.format_error(str(err))
|
||||
return
|
||||
|
||||
event_count = 0
|
||||
client_items: list[str] = []
|
||||
last_usage: JsonDict | None = None
|
||||
|
||||
for event in processor.iter_events(resp):
|
||||
append_upstream_event(turn, {'type': 'upstream_event', 'data': event})
|
||||
|
||||
extracted = processor.extract_usage(event)
|
||||
if extracted is not None:
|
||||
last_usage = extracted
|
||||
|
||||
if event_count < 10:
|
||||
_dbg(
|
||||
f'上游事件#{event_count}='
|
||||
+ json.dumps(event, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
|
||||
for chunk in processor.process_event(event):
|
||||
if isinstance(chunk, dict):
|
||||
chunk['model'] = ctx.client_model
|
||||
wrapped = client_fmt.wrap_stream_item(chunk)
|
||||
client_items.append(wrapped)
|
||||
append_client_event(turn, {'type': 'stream_item', 'data': chunk})
|
||||
if event_count < 10:
|
||||
_dbg(
|
||||
f'返回片段#{event_count}='
|
||||
+ json.dumps(chunk, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
yield wrapped
|
||||
|
||||
event_count += 1
|
||||
|
||||
for chunk in processor.finalize():
|
||||
if isinstance(chunk, dict):
|
||||
chunk['model'] = ctx.client_model
|
||||
wrapped = client_fmt.wrap_stream_item(chunk)
|
||||
client_items.append(wrapped)
|
||||
append_client_event(turn, {'type': 'stream_item', 'data': chunk})
|
||||
yield wrapped
|
||||
|
||||
done = client_fmt.format_done()
|
||||
if done:
|
||||
append_client_event(turn, {'type': 'done'})
|
||||
yield done
|
||||
|
||||
_dbg(f'流式响应结束，共 {event_count} 个事件')
|
||||
usage_tracker.record(
|
||||
ctx.client_model,
|
||||
last_usage,
|
||||
input_key=client_fmt.usage_input_key,
|
||||
output_key=client_fmt.usage_output_key,
|
||||
)
|
||||
set_stream_summary(turn, {
|
||||
'event_count': event_count,
|
||||
'client_item_count': len(client_items),
|
||||
'usage': last_usage,
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'event_count': len(client_items),
|
||||
'usage': last_usage,
|
||||
})
|
||||
finalize_turn(turn, usage=last_usage)
|
||||
|
||||
return sse_response(generate())
|
||||
683
routes/chat.py
683
routes/chat.py
|
|
@ -1,7 +1,8 @@
|
|||
"""路由: /v1/chat/completions
|
||||
|
||||
处理 Cursor 发来的 OpenAI Chat Completions 格式请求。
|
||||
根据模型映射的后端类型，通过统一的出站转换器转发到不同后端。
|
||||
根据模型映射的后端类型，转发到 OpenAI 兼容接口、Anthropic Messages 接口，
|
||||
或原生 OpenAI Responses 接口。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
|
@ -10,34 +11,103 @@ import json
|
|||
import logging
|
||||
from typing import Any
|
||||
|
||||
import settings
|
||||
from flask import Blueprint, jsonify, request
|
||||
|
||||
from adapters.openai_compat_fixer import normalize_request
|
||||
from adapters.responses_cc_adapter import responses_to_cc
|
||||
from adapters.unified import handle_non_stream, handle_stream
|
||||
from routes.common import (
|
||||
CCClientFormatter,
|
||||
build_route_context,
|
||||
get_outbound,
|
||||
inject_instructions_cc,
|
||||
log_route_context,
|
||||
should_inject_thinking,
|
||||
from adapters.cc_anthropic_adapter import (
|
||||
AnthropicStreamConverter,
|
||||
cc_to_messages_request,
|
||||
messages_to_cc_response,
|
||||
)
|
||||
from utils.request_logger import start_turn
|
||||
from adapters.cc_gemini_adapter import (
|
||||
GeminiStreamConverter,
|
||||
cc_to_gemini_request,
|
||||
gemini_to_cc_response,
|
||||
)
|
||||
from adapters.openai_compat_fixer import fix_response, fix_stream_chunk, normalize_request
|
||||
from adapters.responses_cc_adapter import (
|
||||
ResponsesToCCStreamConverter,
|
||||
cc_to_responses_request,
|
||||
responses_to_cc,
|
||||
responses_to_cc_response,
|
||||
)
|
||||
from config import Config
|
||||
from routes.common import (
|
||||
RouteContext,
|
||||
apply_body_modifications,
|
||||
apply_header_modifications,
|
||||
build_anthropic_target,
|
||||
build_gemini_target,
|
||||
build_openai_target,
|
||||
build_responses_target,
|
||||
build_route_context,
|
||||
chat_error_chunk,
|
||||
inject_instructions_anthropic,
|
||||
inject_instructions_cc,
|
||||
inject_instructions_responses,
|
||||
log_route_context,
|
||||
log_usage,
|
||||
sse_data_message,
|
||||
)
|
||||
from utils.http import (
|
||||
forward_request,
|
||||
gen_id,
|
||||
iter_anthropic_sse,
|
||||
iter_gemini_sse,
|
||||
iter_openai_sse,
|
||||
iter_responses_sse,
|
||||
sse_response,
|
||||
)
|
||||
from utils.request_logger import (
|
||||
append_client_event,
|
||||
append_upstream_event,
|
||||
attach_client_response,
|
||||
attach_error,
|
||||
attach_upstream_request,
|
||||
attach_upstream_response,
|
||||
finalize_turn,
|
||||
set_stream_summary,
|
||||
start_turn,
|
||||
)
|
||||
from utils.think_tag import ThinkTagExtractor
|
||||
from utils.thinking_cache import thinking_cache
|
||||
from utils.usage_tracker import usage_tracker
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
bp = Blueprint('chat', __name__)
|
||||
|
||||
|
||||
def _dbg(message: str) -> None:
|
||||
"""仅在调试模式下输出详细日志。"""
|
||||
if settings.get_debug_mode() in ('simple', 'verbose'):
|
||||
logger.info('[聊天补全调试] %s', message)
|
||||
|
||||
|
||||
def _extract_responses_usage(event_data: dict[str, Any]) -> dict[str, Any] | None:
|
||||
"""从原生 Responses 事件中提取 usage。
|
||||
|
||||
`/v1/chat/completions -> /v1/responses` 的桥接流式路径也需要读取 usage，
|
||||
因此在本文件保留一个本地辅助函数，避免依赖其他路由模块的私有实现。
|
||||
"""
|
||||
if not isinstance(event_data, dict):
|
||||
return None
|
||||
usage = event_data.get('usage')
|
||||
if isinstance(usage, dict):
|
||||
return usage
|
||||
response_obj = event_data.get('response')
|
||||
if isinstance(response_obj, dict):
|
||||
nested_usage = response_obj.get('usage')
|
||||
if isinstance(nested_usage, dict):
|
||||
return nested_usage
|
||||
return None
|
||||
|
||||
|
||||
@bp.route('/v1/chat/completions', methods=['POST'])
|
||||
def chat_completions():
|
||||
"""处理聊天补全请求并按模型映射分发到不同后端。"""
|
||||
original_payload = request.get_json(force=True)
|
||||
payload, message_count = _normalize_chat_payload(
|
||||
json.loads(json.dumps(original_payload, ensure_ascii=False, default=str))
|
||||
)
|
||||
payload, message_count = _normalize_chat_payload(json.loads(json.dumps(original_payload, ensure_ascii=False, default=str)))
|
||||
|
||||
client_model = payload.get('model', 'unknown')
|
||||
is_stream = payload.get('stream', False)
|
||||
|
|
@ -57,39 +127,23 @@ def chat_completions():
|
|||
log_route_context('聊天补全', ctx, extra=f'消息数={message_count}')
|
||||
_log_messages(payload)
|
||||
|
||||
payload['model'] = ctx.upstream_model
|
||||
payload = normalize_request(payload)
|
||||
if should_inject_thinking(ctx.backend):
|
||||
if ctx.backend != 'responses':
|
||||
payload['messages'] = thinking_cache.inject(payload.get('messages', []))
|
||||
payload = inject_instructions_cc(payload, ctx.custom_instructions, ctx.instructions_position)
|
||||
|
||||
outbound = get_outbound(ctx.backend)
|
||||
client_fmt = CCClientFormatter()
|
||||
|
||||
if ctx.is_stream:
|
||||
result = handle_stream(ctx, outbound, client_fmt, payload, turn)
|
||||
else:
|
||||
result = handle_non_stream(ctx, outbound, client_fmt, payload, turn)
|
||||
|
||||
if not ctx.is_stream and isinstance(result, tuple):
|
||||
response_data = result
|
||||
elif hasattr(result, 'json'):
|
||||
try:
|
||||
response_data = result.get_json(silent=True) or {}
|
||||
except Exception:
|
||||
response_data = {}
|
||||
else:
|
||||
response_data = {}
|
||||
|
||||
_try_cache_thinking(response_data)
|
||||
return result
|
||||
if ctx.backend == 'openai':
|
||||
return _handle_openai_backend(ctx, payload, turn)
|
||||
if ctx.backend == 'responses':
|
||||
return _handle_responses_backend(ctx, payload, turn)
|
||||
if ctx.backend == 'gemini':
|
||||
return _handle_gemini_backend(ctx, payload, turn)
|
||||
return _handle_anthropic_backend(ctx, payload, turn)
|
||||
|
||||
|
||||
def _normalize_chat_payload(payload: dict[str, Any]) -> tuple[dict[str, Any], int]:
|
||||
"""整理聊天补全入口的请求体。
|
||||
|
||||
当 Cursor 或调用方把 Responses 格式误发到 `/v1/chat/completions` 时，
|
||||
先降级转换成 Chat Completions，再进入统一主流程。
|
||||
这里保留了一层兼容逻辑：当 Cursor 或调用方把 Responses 格式误发到
|
||||
`/v1/chat/completions` 时，先降级转换成 Chat Completions，再进入统一主流程。
|
||||
"""
|
||||
message_count = len(payload.get('messages', []))
|
||||
|
||||
|
|
@ -103,11 +157,548 @@ def _normalize_chat_payload(payload: dict[str, Any]) -> tuple[dict[str, Any], in
|
|||
return payload, message_count
|
||||
|
||||
|
||||
def _try_cache_thinking(response_data: dict[str, Any]) -> None:
|
||||
"""尝试从非流式响应中缓存思维链内容。"""
|
||||
if not isinstance(response_data, dict):
|
||||
def _handle_openai_backend(ctx: RouteContext, payload: dict[str, Any], turn: dict[str, Any]):
|
||||
"""处理走 OpenAI 兼容后端的聊天补全请求。"""
|
||||
_dbg(
|
||||
'原始请求字段=' + str(list(payload.keys())) + ' '
|
||||
+ '附加字段='
|
||||
+ json.dumps(
|
||||
{k: v for k, v in payload.items() if k != 'messages'},
|
||||
ensure_ascii=False,
|
||||
default=str,
|
||||
)[:500]
|
||||
)
|
||||
|
||||
payload = normalize_request(payload, ctx.upstream_model)
|
||||
payload = inject_instructions_cc(payload, ctx.custom_instructions, ctx.instructions_position)
|
||||
_dbg(
|
||||
f'标准化完成：模型={payload.get("model")} '
|
||||
f'工具数={len(payload.get("tools", []))}'
|
||||
)
|
||||
|
||||
url, headers = build_openai_target(ctx)
|
||||
payload = apply_body_modifications(payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
if ctx.is_stream:
|
||||
return _handle_openai_stream(ctx, payload, url, headers, turn)
|
||||
return _handle_openai_non_stream(ctx, payload, url, headers, turn)
|
||||
|
||||
|
||||
def _handle_openai_non_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any],
|
||||
):
|
||||
"""处理 OpenAI 兼容后端的非流式返回。"""
|
||||
payload['stream'] = False
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': 'upstream request failed'})
|
||||
finalize_turn(turn)
|
||||
return err
|
||||
|
||||
raw = resp.json()
|
||||
attach_upstream_response(turn, raw)
|
||||
_dbg('上游原始响应=' + json.dumps(raw, ensure_ascii=False, default=str)[:1000])
|
||||
|
||||
data = fix_response(raw)
|
||||
return _finalize_chat_response(ctx, data, turn=turn, debug_label='修复后响应')
|
||||
|
||||
|
||||
def _handle_openai_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any],
|
||||
):
|
||||
"""处理 OpenAI 兼容后端的流式返回。"""
|
||||
payload['stream'] = True
|
||||
|
||||
def generate():
|
||||
"""消费上游 OpenAI SSE，并逐段产出给 Cursor 的聊天补全流。"""
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload, stream=True)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': str(err)})
|
||||
set_stream_summary(turn, {'status': 'error'})
|
||||
finalize_turn(turn)
|
||||
yield chat_error_chunk(str(err))
|
||||
return
|
||||
for choice in response_data.get('choices', []):
|
||||
|
||||
think_extractor = ThinkTagExtractor()
|
||||
chunk_count = 0
|
||||
last_usage = None
|
||||
client_chunks: list[dict[str, Any]] = []
|
||||
|
||||
for chunk in iter_openai_sse(resp):
|
||||
if chunk is None:
|
||||
_dbg(f'流式响应结束，共 {chunk_count} 个数据片段')
|
||||
close_chunk = think_extractor.finalize()
|
||||
if close_chunk:
|
||||
client_chunks.append(close_chunk)
|
||||
append_client_event(turn, {'type': 'chat_chunk', 'data': close_chunk})
|
||||
yield sse_data_message(close_chunk)
|
||||
append_client_event(turn, {'type': 'done'})
|
||||
yield sse_data_message('[DONE]')
|
||||
usage_tracker.record(ctx.client_model, last_usage)
|
||||
set_stream_summary(turn, {
|
||||
'chunk_count': chunk_count,
|
||||
'client_chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'chat.completion.stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
})
|
||||
finalize_turn(turn, usage=last_usage)
|
||||
return
|
||||
|
||||
append_upstream_event(turn, {'type': 'openai_chunk', 'data': chunk})
|
||||
if chunk.get('usage'):
|
||||
last_usage = chunk['usage']
|
||||
|
||||
if chunk_count < 10:
|
||||
_dbg(
|
||||
f'上游原始片段#{chunk_count}='
|
||||
+ json.dumps(chunk, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
|
||||
chunk = fix_stream_chunk(chunk)
|
||||
chunk['model'] = ctx.client_model
|
||||
|
||||
for out in think_extractor.process_chunk(chunk):
|
||||
client_chunks.append(out)
|
||||
append_client_event(turn, {'type': 'chat_chunk', 'data': out})
|
||||
if chunk_count < 10:
|
||||
_dbg(
|
||||
f'返回片段#{chunk_count}='
|
||||
+ json.dumps(out, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
yield sse_data_message(out)
|
||||
|
||||
chunk_count += 1
|
||||
|
||||
usage_tracker.record(ctx.client_model, last_usage)
|
||||
set_stream_summary(turn, {
|
||||
'chunk_count': chunk_count,
|
||||
'client_chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
'ended_without_done': True,
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'chat.completion.stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
})
|
||||
finalize_turn(turn, usage=last_usage)
|
||||
|
||||
return sse_response(generate())
|
||||
|
||||
|
||||
def _handle_responses_backend(ctx: RouteContext, payload: dict[str, Any], turn: dict[str, Any] | None):
|
||||
"""处理走原生 Responses 后端的聊天补全请求。
|
||||
|
||||
当上游只支持 `/v1/responses` 时，需要先把聊天补全请求转换为 Responses 请求，
|
||||
返回时再转换回聊天补全协议。
|
||||
"""
|
||||
responses_payload = cc_to_responses_request(payload)
|
||||
responses_payload['model'] = ctx.upstream_model
|
||||
responses_payload = inject_instructions_responses(responses_payload, ctx.custom_instructions, ctx.instructions_position)
|
||||
_dbg(
|
||||
'已转换为 Responses 请求：字段=' + str(list(responses_payload.keys()))
|
||||
+ f' 输入项数={len(responses_payload.get("input", []))}'
|
||||
)
|
||||
|
||||
url, headers = build_responses_target(ctx)
|
||||
responses_payload = apply_body_modifications(responses_payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
if ctx.is_stream:
|
||||
return _handle_responses_stream(ctx, responses_payload, url, headers, turn)
|
||||
return _handle_responses_non_stream(ctx, responses_payload, url, headers, turn)
|
||||
|
||||
|
||||
def _handle_responses_non_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理原生 Responses 后端的非流式返回。"""
|
||||
payload['stream'] = False
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': 'upstream request failed'})
|
||||
finalize_turn(turn)
|
||||
return err
|
||||
|
||||
raw = resp.json()
|
||||
attach_upstream_response(turn, raw)
|
||||
_dbg('上游原始响应=' + json.dumps(raw, ensure_ascii=False, default=str)[:1000])
|
||||
|
||||
data = responses_to_cc_response(raw, ctx.client_model)
|
||||
return _finalize_chat_response(ctx, data, turn=turn, debug_label='Responses 转回聊天补全后')
|
||||
|
||||
|
||||
def _handle_responses_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理原生 Responses 后端的流式返回。"""
|
||||
payload['stream'] = True
|
||||
converter = ResponsesToCCStreamConverter(model=ctx.client_model)
|
||||
|
||||
def generate():
|
||||
"""消费上游 Responses 事件，并实时转换成聊天补全 chunk。"""
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload, stream=True)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': str(err)})
|
||||
set_stream_summary(turn, {'status': 'error'})
|
||||
finalize_turn(turn)
|
||||
yield chat_error_chunk(str(err))
|
||||
return
|
||||
|
||||
event_count = 0
|
||||
client_chunks: list[Any] = []
|
||||
last_usage: dict[str, Any] | None = None
|
||||
for event_type, event_data in iter_responses_sse(resp):
|
||||
append_upstream_event(turn, {'type': event_type, 'data': event_data})
|
||||
extracted_usage = _extract_responses_usage(event_data)
|
||||
if extracted_usage:
|
||||
last_usage = {
|
||||
'prompt_tokens': extracted_usage.get('input_tokens', 0),
|
||||
'completion_tokens': extracted_usage.get('output_tokens', 0),
|
||||
'total_tokens': extracted_usage.get('total_tokens', 0),
|
||||
}
|
||||
if event_count < 10:
|
||||
_dbg(
|
||||
f'上游事件#{event_count} 类型={event_type} 数据='
|
||||
+ json.dumps(event_data, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
|
||||
for chunk in converter.process_event(event_type, event_data):
|
||||
client_chunks.append(chunk)
|
||||
append_client_event(turn, {'type': 'chat_chunk', 'data': chunk})
|
||||
if isinstance(chunk, dict) and isinstance(chunk.get('usage'), dict):
|
||||
last_usage = chunk['usage']
|
||||
if event_count < 10:
|
||||
_dbg(
|
||||
f'返回片段#{event_count}='
|
||||
+ json.dumps(chunk, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
yield sse_data_message(chunk)
|
||||
|
||||
event_count += 1
|
||||
|
||||
_dbg(f'流式响应结束，共 {event_count} 个事件')
|
||||
append_client_event(turn, {'type': 'done'})
|
||||
yield sse_data_message('[DONE]')
|
||||
usage_tracker.record(ctx.client_model, last_usage)
|
||||
set_stream_summary(turn, {
|
||||
'event_count': event_count,
|
||||
'client_chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'chat.completion.stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
})
|
||||
finalize_turn(turn, usage=last_usage)
|
||||
|
||||
return sse_response(generate())
|
||||
|
||||
|
||||
def _handle_gemini_backend(ctx: RouteContext, payload: dict[str, Any], turn: dict[str, Any] | None):
|
||||
"""处理走 Gemini Contents 后端的聊天补全请求。"""
|
||||
payload = inject_instructions_cc(payload, ctx.custom_instructions, ctx.instructions_position)
|
||||
gemini_payload = cc_to_gemini_request(payload)
|
||||
_dbg(
|
||||
'已转换为 Gemini 请求：字段=' + str(list(gemini_payload.keys()))
|
||||
+ f' 内容数={len(gemini_payload.get("contents", []))}'
|
||||
)
|
||||
|
||||
url, headers = build_gemini_target(ctx, stream=ctx.is_stream)
|
||||
gemini_payload = apply_body_modifications(gemini_payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
if ctx.is_stream:
|
||||
return _handle_gemini_stream(ctx, gemini_payload, url, headers, turn)
|
||||
return _handle_gemini_non_stream(ctx, gemini_payload, url, headers, turn)
|
||||
|
||||
|
||||
def _handle_gemini_non_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理 Gemini 后端的非流式返回。"""
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': 'upstream request failed'})
|
||||
finalize_turn(turn)
|
||||
return err
|
||||
|
||||
raw = resp.json()
|
||||
attach_upstream_response(turn, raw)
|
||||
_dbg('上游原始响应=' + json.dumps(raw, ensure_ascii=False, default=str)[:1000])
|
||||
|
||||
data = gemini_to_cc_response(raw)
|
||||
return _finalize_chat_response(ctx, data, turn=turn, debug_label='Gemini 转回聊天补全后')
|
||||
|
||||
|
||||
def _handle_gemini_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理 Gemini 后端的流式返回。"""
|
||||
converter = GeminiStreamConverter()
|
||||
|
||||
def generate():
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload, stream=True)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': str(err)})
|
||||
set_stream_summary(turn, {'status': 'error'})
|
||||
finalize_turn(turn)
|
||||
yield chat_error_chunk(str(err))
|
||||
return
|
||||
|
||||
chunk_count = 0
|
||||
client_chunks: list[Any] = []
|
||||
last_usage: dict[str, Any] | None = None
|
||||
for gemini_chunk in iter_gemini_sse(resp):
|
||||
append_upstream_event(turn, {'type': 'gemini_chunk', 'data': gemini_chunk})
|
||||
usage_meta = gemini_chunk.get('usageMetadata') if isinstance(gemini_chunk, dict) else None
|
||||
if isinstance(usage_meta, dict):
|
||||
last_usage = {
|
||||
'prompt_tokens': usage_meta.get('promptTokenCount', 0),
|
||||
'completion_tokens': usage_meta.get('candidatesTokenCount', 0),
|
||||
'total_tokens': usage_meta.get('totalTokenCount', 0),
|
||||
}
|
||||
if chunk_count < 10:
|
||||
_dbg(
|
||||
f'上游 Gemini 片段#{chunk_count}='
|
||||
+ json.dumps(gemini_chunk, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
|
||||
for cc_chunk in converter.process_chunk(gemini_chunk):
|
||||
cc_chunk['model'] = ctx.client_model
|
||||
client_chunks.append(cc_chunk)
|
||||
append_client_event(turn, {'type': 'chat_chunk', 'data': cc_chunk})
|
||||
if isinstance(cc_chunk, dict) and isinstance(cc_chunk.get('usage'), dict):
|
||||
last_usage = cc_chunk['usage']
|
||||
if chunk_count < 10:
|
||||
_dbg(
|
||||
f'返回片段#{chunk_count}='
|
||||
+ json.dumps(cc_chunk, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
yield sse_data_message(cc_chunk)
|
||||
|
||||
chunk_count += 1
|
||||
|
||||
_dbg(f'流式响应结束，共 {chunk_count} 个数据片段')
|
||||
append_client_event(turn, {'type': 'done'})
|
||||
yield sse_data_message('[DONE]')
|
||||
usage_tracker.record(ctx.client_model, last_usage)
|
||||
set_stream_summary(turn, {
|
||||
'chunk_count': chunk_count,
|
||||
'client_chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'chat.completion.stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
})
|
||||
finalize_turn(turn, usage=last_usage)
|
||||
|
||||
return sse_response(generate())
|
||||
|
||||
|
||||
def _handle_anthropic_backend(ctx: RouteContext, payload: dict[str, Any], turn: dict[str, Any] | None):
|
||||
"""处理走 Anthropic Messages 后端的聊天补全请求。"""
|
||||
payload['model'] = ctx.upstream_model
|
||||
anthropic_payload = cc_to_messages_request(payload)
|
||||
anthropic_payload = inject_instructions_anthropic(anthropic_payload, ctx.custom_instructions, ctx.instructions_position)
|
||||
_dbg(
|
||||
'已转换为 Messages 请求：字段=' + str(list(anthropic_payload.keys()))
|
||||
+ f' 消息数={len(anthropic_payload.get("messages", []))}'
|
||||
)
|
||||
|
||||
url, headers = build_anthropic_target(ctx)
|
||||
anthropic_payload = apply_body_modifications(anthropic_payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
if ctx.is_stream:
|
||||
return _handle_anthropic_stream(ctx, anthropic_payload, url, headers, turn)
|
||||
return _handle_anthropic_non_stream(ctx, anthropic_payload, url, headers, turn)
|
||||
|
||||
|
||||
def _handle_anthropic_non_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理 Anthropic 后端的非流式返回。"""
|
||||
payload['stream'] = False
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': 'upstream request failed'})
|
||||
finalize_turn(turn)
|
||||
return err
|
||||
|
||||
raw = resp.json()
|
||||
attach_upstream_response(turn, raw)
|
||||
_dbg('上游原始响应=' + json.dumps(raw, ensure_ascii=False, default=str)[:1000])
|
||||
|
||||
data = messages_to_cc_response(raw)
|
||||
return _finalize_chat_response(ctx, data, turn=turn, debug_label='Messages 转回聊天补全后')
|
||||
|
||||
|
||||
def _handle_anthropic_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理 Anthropic 后端的流式返回。
|
||||
|
||||
这里仍然保留独立的事件级转换器，而不是先落成完整响应再回放，
|
||||
是为了尽量保持 Cursor 端的流式体验和工具调用时序。
|
||||
"""
|
||||
payload['stream'] = True
|
||||
converter = AnthropicStreamConverter()
|
||||
|
||||
def generate():
|
||||
"""消费上游 Anthropic 事件流，并逐步映射为聊天补全 SSE。"""
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload, stream=True)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': str(err)})
|
||||
set_stream_summary(turn, {'status': 'error'})
|
||||
finalize_turn(turn)
|
||||
yield chat_error_chunk(str(err))
|
||||
return
|
||||
|
||||
event_count = 0
|
||||
client_chunks: list[Any] = []
|
||||
last_usage: dict[str, Any] | None = None
|
||||
for event_type, event_data in iter_anthropic_sse(resp):
|
||||
append_upstream_event(turn, {'type': event_type, 'data': event_data})
|
||||
if event_type == 'message_start':
|
||||
message_usage = event_data.get('message', {}).get('usage', {})
|
||||
if isinstance(message_usage, dict):
|
||||
last_usage = {
|
||||
'prompt_tokens': message_usage.get('input_tokens', 0),
|
||||
'completion_tokens': 0,
|
||||
'total_tokens': message_usage.get('input_tokens', 0),
|
||||
}
|
||||
elif event_type == 'message_delta':
|
||||
delta_usage = event_data.get('usage', {})
|
||||
if isinstance(delta_usage, dict):
|
||||
prompt_tokens = 0
|
||||
if isinstance(last_usage, dict):
|
||||
prompt_tokens = last_usage.get('prompt_tokens', 0)
|
||||
completion_tokens = delta_usage.get('output_tokens', 0)
|
||||
last_usage = {
|
||||
'prompt_tokens': prompt_tokens,
|
||||
'completion_tokens': completion_tokens,
|
||||
'total_tokens': prompt_tokens + completion_tokens,
|
||||
}
|
||||
if event_count < 10:
|
||||
_dbg(
|
||||
f'上游事件#{event_count} 类型={event_type} 数据='
|
||||
+ json.dumps(event_data, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
|
||||
for chunk_str in converter.process_event(event_type, event_data):
|
||||
try:
|
||||
chunk_obj = json.loads(chunk_str)
|
||||
chunk_obj['model'] = ctx.client_model
|
||||
if isinstance(chunk_obj.get('usage'), dict):
|
||||
last_usage = chunk_obj['usage']
|
||||
chunk_str = json.dumps(chunk_obj, ensure_ascii=False)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
client_chunks.append(chunk_str)
|
||||
append_client_event(turn, {'type': 'chat_chunk', 'data': chunk_str})
|
||||
if event_count < 10:
|
||||
_dbg(f'返回片段#{event_count}={chunk_str[:500]}')
|
||||
yield sse_data_message(chunk_str)
|
||||
|
||||
event_count += 1
|
||||
|
||||
_dbg(f'流式响应结束，共 {event_count} 个事件')
|
||||
append_client_event(turn, {'type': 'done'})
|
||||
yield sse_data_message('[DONE]')
|
||||
usage_tracker.record(ctx.client_model, last_usage)
|
||||
set_stream_summary(turn, {
|
||||
'event_count': event_count,
|
||||
'client_chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'chat.completion.stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'chunk_count': len(client_chunks),
|
||||
'usage': last_usage,
|
||||
})
|
||||
finalize_turn(turn, usage=last_usage)
|
||||
|
||||
return sse_response(generate())
|
||||
|
||||
|
||||
def _finalize_chat_response(
|
||||
ctx: RouteContext,
|
||||
data: dict[str, Any],
|
||||
*,
|
||||
turn: dict[str, Any] | None,
|
||||
debug_label: str,
|
||||
):
|
||||
"""统一收尾非流式聊天补全响应。
|
||||
|
||||
三条后端链路最终都会回到 Chat Completions 格式，因此这里集中做：
|
||||
- 回填给 Cursor 展示的模型名
|
||||
- 输出统一调试日志
|
||||
- 输出统一令牌统计日志
|
||||
"""
|
||||
data['model'] = ctx.client_model
|
||||
_dbg(debug_label + '=' + json.dumps(data, ensure_ascii=False, default=str)[:1000])
|
||||
log_usage('聊天补全', data.get('usage', {}), input_key='prompt_tokens', output_key='completion_tokens')
|
||||
|
||||
usage_tracker.record(ctx.client_model, data.get('usage'))
|
||||
attach_client_response(turn, data)
|
||||
finalize_turn(turn, usage=data.get('usage'))
|
||||
|
||||
for choice in data.get('choices', []):
|
||||
msg = choice.get('message', {})
|
||||
if msg.get('reasoning_content'):
|
||||
thinking_cache.store_from_response(
|
||||
|
|
@ -116,6 +707,8 @@ def _try_cache_thinking(response_data: dict[str, Any]) -> None:
|
|||
)
|
||||
break
|
||||
|
||||
return jsonify(data)
|
||||
|
||||
|
||||
def _log_messages(payload: dict[str, Any]) -> None:
|
||||
"""记录消息摘要，方便排查请求形态是否符合预期。"""
|
||||
|
|
|
|||
193
routes/common.py
193
routes/common.py
|
|
@ -12,6 +12,7 @@ import logging
|
|||
from typing import Any
|
||||
|
||||
import settings
|
||||
from utils.http import build_anthropic_headers, build_gemini_headers, build_openai_headers
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
|
@ -54,6 +55,42 @@ def build_route_context(client_model: str, is_stream: bool) -> RouteContext:
|
|||
)
|
||||
|
||||
|
||||
def build_openai_target(ctx: RouteContext) -> tuple[str, dict[str, str]]:
|
||||
"""根据路由上下文生成 OpenAI 兼容后端的地址和请求头。"""
|
||||
url = f'{ctx.target_url.rstrip("/")}/v1/chat/completions'
|
||||
headers = build_openai_headers(ctx.api_key)
|
||||
return url, headers
|
||||
|
||||
|
||||
def build_responses_target(ctx: RouteContext) -> tuple[str, dict[str, str]]:
|
||||
"""根据路由上下文生成 OpenAI Responses 后端的地址和请求头。"""
|
||||
url = f'{ctx.target_url.rstrip("/")}/v1/responses'
|
||||
headers = build_openai_headers(ctx.api_key)
|
||||
return url, headers
|
||||
|
||||
|
||||
def build_anthropic_target(ctx: RouteContext) -> tuple[str, dict[str, str]]:
|
||||
"""根据路由上下文生成 Anthropic 后端的地址和请求头。"""
|
||||
url = f'{ctx.target_url.rstrip("/")}/v1/messages'
|
||||
headers = build_anthropic_headers(ctx.api_key)
|
||||
return url, headers
|
||||
|
||||
|
||||
def build_gemini_target(ctx: RouteContext, stream: bool = False) -> tuple[str, dict[str, str]]:
|
||||
"""根据路由上下文生成 Gemini 后端的地址和请求头。
|
||||
|
||||
Gemini URL 格式: {base}/v1/models/{model}:generateContent
|
||||
流式: {base}/v1/models/{model}:streamGenerateContent?alt=sse
|
||||
"""
|
||||
base = ctx.target_url.rstrip('/')
|
||||
model = ctx.upstream_model
|
||||
if stream:
|
||||
url = f'{base}/v1/models/{model}:streamGenerateContent?alt=sse'
|
||||
else:
|
||||
url = f'{base}/v1/models/{model}:generateContent'
|
||||
headers = build_gemini_headers(ctx.api_key)
|
||||
return url, headers
|
||||
|
||||
|
||||
def log_route_context(route_name: str, ctx: RouteContext, *, extra: str = '') -> None:
|
||||
"""统一输出路由级日志，避免不同入口的日志格式逐渐漂移。"""
|
||||
|
|
@ -100,6 +137,11 @@ def sse_event_message(event_type: str, data: Any) -> str:
|
|||
return f'event: {event_type}\ndata: {payload}\n\n'
|
||||
|
||||
|
||||
def chat_error_chunk(message: str, error_type: str = 'upstream_error') -> str:
|
||||
"""构造聊天补全流式接口使用的错误消息。"""
|
||||
return sse_data_message({'error': {'message': message, 'type': error_type}})
|
||||
|
||||
|
||||
def responses_error_event(message: str) -> str:
|
||||
"""构造 Responses 流式接口使用的错误事件。"""
|
||||
return sse_event_message('error', {'error': message})
|
||||
|
|
@ -173,20 +215,6 @@ def inject_instructions_anthropic(payload: dict[str, Any], instructions: str, po
|
|||
return payload
|
||||
|
||||
|
||||
def should_inject_thinking(backend: str) -> bool:
|
||||
"""判断当前后端是否需要注入历史 thinking。
|
||||
|
||||
仅对明确能消费历史 reasoning/thinking 的后端启用：
|
||||
- anthropic
|
||||
- gemini
|
||||
- responses
|
||||
|
||||
OpenAI Chat 兼容后端通常不接受 `reasoning_content` 历史字段，
|
||||
若注入会导致上游报错，因此显式排除。
|
||||
"""
|
||||
return backend in ('anthropic', 'gemini', 'responses')
|
||||
|
||||
|
||||
# ─── Body / Header 修改 ──────────────────────────
|
||||
|
||||
|
||||
|
|
@ -220,140 +248,3 @@ def apply_header_modifications(headers: dict[str, str], modifications: dict[str,
|
|||
headers[key] = str(value)
|
||||
logger.info('已应用 header_modifications: %s', list(modifications.keys()))
|
||||
return headers
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 后端注册表 + ClientFormatter 实现
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def get_outbound(backend: str):
|
||||
"""根据后端类型获取对应的 OutboundTransformer 实例。"""
|
||||
from adapters.cc_anthropic_adapter import AnthropicOutbound
|
||||
from adapters.cc_gemini_adapter import GeminiOutbound
|
||||
from adapters.openai_compat_fixer import OpenAIChatOutbound
|
||||
from adapters.responses_cc_adapter import ResponsesOutbound
|
||||
|
||||
registry = {
|
||||
'openai': OpenAIChatOutbound,
|
||||
'anthropic': AnthropicOutbound,
|
||||
'gemini': GeminiOutbound,
|
||||
'responses': ResponsesOutbound,
|
||||
}
|
||||
cls = registry.get(backend, OpenAIChatOutbound)
|
||||
return cls()
|
||||
|
||||
|
||||
class CCClientFormatter:
|
||||
"""Chat Completions 客户端格式化器。
|
||||
|
||||
将通用处理结果格式化为 OpenAI Chat Completions 格式，
|
||||
供 /v1/chat/completions 端点使用。
|
||||
"""
|
||||
|
||||
def format_response(self, cc_response: dict[str, Any], model: str) -> dict[str, Any]:
|
||||
cc_response['model'] = model
|
||||
return cc_response
|
||||
|
||||
def wrap_stream_item(self, item: Any) -> str:
|
||||
payload = item if isinstance(item, str) else json.dumps(item, ensure_ascii=False)
|
||||
return f'data: {payload}\n\n'
|
||||
|
||||
def format_error(self, message: str) -> str:
|
||||
return sse_data_message({'error': {'message': message, 'type': 'upstream_error'}})
|
||||
|
||||
def format_done(self) -> str | None:
|
||||
return sse_data_message('[DONE]')
|
||||
|
||||
def start_events(self) -> list[str]:
|
||||
return []
|
||||
|
||||
@property
|
||||
def usage_input_key(self) -> str:
|
||||
return 'prompt_tokens'
|
||||
|
||||
@property
|
||||
def usage_output_key(self) -> str:
|
||||
return 'completion_tokens'
|
||||
|
||||
|
||||
class ResponsesClientFormatter:
|
||||
"""Responses API 客户端格式化器。
|
||||
|
||||
将通用处理结果格式化为 OpenAI Responses 格式，
|
||||
供 /v1/responses 端点使用。
|
||||
|
||||
流式场景使用 ResponsesStreamConverter 做 CC chunk → Responses SSE 转换。
|
||||
"""
|
||||
|
||||
def __init__(self, model: str = ''):
|
||||
from adapters.responses_cc_adapter import ResponsesStreamConverter, cc_to_responses
|
||||
self._model = model
|
||||
self._converter = ResponsesStreamConverter(model=model)
|
||||
self._cc_to_responses = cc_to_responses
|
||||
|
||||
def format_response(self, cc_response: dict[str, Any], model: str) -> dict[str, Any]:
|
||||
return self._cc_to_responses(cc_response, model)
|
||||
|
||||
def wrap_stream_item(self, item: Any) -> str:
|
||||
if isinstance(item, str):
|
||||
return item
|
||||
events = self._converter.process_cc_chunk(item)
|
||||
return ''.join(events)
|
||||
|
||||
def format_error(self, message: str) -> str:
|
||||
return responses_error_event(message)
|
||||
|
||||
def format_done(self) -> str | None:
|
||||
events = self._converter.finalize()
|
||||
return ''.join(events) if events else None
|
||||
|
||||
def start_events(self) -> list[str]:
|
||||
return self._converter.start_events()
|
||||
|
||||
@property
|
||||
def usage_input_key(self) -> str:
|
||||
return 'input_tokens'
|
||||
|
||||
@property
|
||||
def usage_output_key(self) -> str:
|
||||
return 'output_tokens'
|
||||
|
||||
|
||||
class ResponsesPassthroughFormatter:
|
||||
"""Responses 透传格式化器。
|
||||
|
||||
当后端本身就是 Responses 格式时使用，做轻量模型名改写。
|
||||
"""
|
||||
|
||||
def __init__(self, model: str = ''):
|
||||
self._model = model
|
||||
|
||||
def format_response(self, response_data: dict[str, Any], model: str) -> dict[str, Any]:
|
||||
response_data['model'] = model
|
||||
return response_data
|
||||
|
||||
def wrap_stream_item(self, item: Any) -> str:
|
||||
if isinstance(item, str):
|
||||
return item
|
||||
event_type = item.pop('_sse_event_type', None)
|
||||
if event_type:
|
||||
return f'event: {event_type}\ndata: {json.dumps(item, ensure_ascii=False)}\n\n'
|
||||
return f'data: {json.dumps(item, ensure_ascii=False)}\n\n'
|
||||
|
||||
def format_error(self, message: str) -> str:
|
||||
return responses_error_event(message)
|
||||
|
||||
def format_done(self) -> str | None:
|
||||
return None
|
||||
|
||||
def start_events(self) -> list[str]:
|
||||
return []
|
||||
|
||||
@property
|
||||
def usage_input_key(self) -> str:
|
||||
return 'input_tokens'
|
||||
|
||||
@property
|
||||
def usage_output_key(self) -> str:
|
||||
return 'output_tokens'
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
"""路由: /v1/responses
|
||||
|
||||
处理 Cursor 对 GPT、Claude-Opus 等模型发出的 Responses API 请求。
|
||||
请求先转换为 Chat Completions 中间表示，再通过统一出站转换器分发。
|
||||
请求会先转换为 Chat Completions 中间表示，再按后端类型分发，最后转换回 Responses 格式。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
|
@ -13,31 +13,62 @@ from typing import Any
|
|||
import settings
|
||||
from flask import Blueprint, jsonify, request
|
||||
|
||||
from adapters.openai_compat_fixer import normalize_request
|
||||
from adapters.responses_cc_adapter import (
|
||||
AnthropicOutboundForResponses,
|
||||
ResponsesNativeOutbound,
|
||||
responses_to_cc,
|
||||
)
|
||||
from adapters.unified import handle_non_stream, handle_stream
|
||||
from adapters.cc_anthropic_adapter import cc_to_messages_request, messages_to_cc_response
|
||||
from adapters.cc_gemini_adapter import GeminiStreamConverter, cc_to_gemini_request, gemini_to_cc_response
|
||||
from adapters.openai_compat_fixer import fix_response, fix_stream_chunk, normalize_request
|
||||
from adapters.responses_cc_adapter import ResponsesStreamConverter, cc_to_responses, responses_to_cc
|
||||
from config import Config
|
||||
from routes.common import (
|
||||
ResponsesClientFormatter,
|
||||
ResponsesPassthroughFormatter,
|
||||
RouteContext,
|
||||
apply_body_modifications,
|
||||
apply_header_modifications,
|
||||
build_anthropic_target,
|
||||
build_gemini_target,
|
||||
build_openai_target,
|
||||
build_responses_target,
|
||||
build_route_context,
|
||||
get_outbound,
|
||||
inject_instructions_anthropic,
|
||||
inject_instructions_cc,
|
||||
inject_instructions_responses,
|
||||
log_route_context,
|
||||
should_inject_thinking,
|
||||
log_usage,
|
||||
responses_error_event,
|
||||
)
|
||||
from utils.request_logger import start_turn
|
||||
from utils.http import (
|
||||
forward_request,
|
||||
gen_id,
|
||||
iter_anthropic_sse,
|
||||
iter_gemini_sse,
|
||||
iter_openai_sse,
|
||||
iter_responses_sse,
|
||||
sse_response,
|
||||
)
|
||||
from utils.request_logger import (
|
||||
append_client_event,
|
||||
append_upstream_event,
|
||||
attach_client_response,
|
||||
attach_error,
|
||||
attach_upstream_request,
|
||||
attach_upstream_response,
|
||||
finalize_turn,
|
||||
set_stream_summary,
|
||||
start_turn,
|
||||
)
|
||||
from utils.think_tag import ThinkTagExtractor
|
||||
from utils.thinking_cache import thinking_cache
|
||||
from utils.usage_tracker import usage_tracker
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
bp = Blueprint('responses', __name__)
|
||||
|
||||
|
||||
def _dbg(message: str) -> None:
|
||||
"""仅在调试模式下输出详细日志。"""
|
||||
if settings.get_debug_mode() in ('simple', 'verbose'):
|
||||
logger.info('[响应生成调试] %s', message)
|
||||
|
||||
|
||||
@bp.route('/v1/responses', methods=['POST'])
|
||||
def responses_endpoint():
|
||||
"""处理 Responses 请求并按模型映射分发。"""
|
||||
|
|
@ -59,43 +90,543 @@ def responses_endpoint():
|
|||
)
|
||||
log_route_context('响应生成', ctx)
|
||||
|
||||
if ctx.backend == 'responses':
|
||||
return _handle_native_responses(ctx, payload, turn)
|
||||
|
||||
cc_payload = _build_cc_payload(payload, ctx)
|
||||
|
||||
if ctx.backend == 'anthropic':
|
||||
outbound = AnthropicOutboundForResponses()
|
||||
else:
|
||||
outbound = get_outbound(ctx.backend)
|
||||
if ctx.backend == 'openai':
|
||||
return _handle_openai_backend(ctx, cc_payload, turn)
|
||||
if ctx.backend == 'responses':
|
||||
return _handle_responses_backend(ctx, payload, turn)
|
||||
if ctx.backend == 'gemini':
|
||||
return _handle_gemini_backend(ctx, cc_payload, turn)
|
||||
return _handle_anthropic_backend(ctx, cc_payload, turn)
|
||||
|
||||
client_fmt = ResponsesClientFormatter(model=ctx.client_model)
|
||||
|
||||
def _build_cc_payload(payload: dict[str, Any], ctx: RouteContext) -> dict[str, Any]:
|
||||
"""将 Responses 请求统一降级为 Chat Completions 中间表示。
|
||||
|
||||
这样后续无论走 OpenAI 兼容后端还是 Anthropic 后端，都能复用一套
|
||||
中间协议，避免在路由层同时维护两套完全不同的请求编排逻辑。
|
||||
"""
|
||||
cc_payload = responses_to_cc(payload)
|
||||
cc_payload['model'] = ctx.upstream_model
|
||||
cc_payload['messages'] = thinking_cache.inject(cc_payload.get('messages', []))
|
||||
cc_payload = inject_instructions_cc(cc_payload, ctx.custom_instructions, ctx.instructions_position)
|
||||
_dbg(
|
||||
'已转换为聊天补全中间表示：字段=' + str(list(cc_payload.keys()))
|
||||
+ f' 消息数={len(cc_payload.get("messages", []))}'
|
||||
)
|
||||
return cc_payload
|
||||
|
||||
|
||||
def _handle_openai_backend(ctx: RouteContext, cc_payload: dict[str, Any], turn: dict[str, Any]):
|
||||
"""处理走 OpenAI 兼容后端的 Responses 请求。"""
|
||||
cc_payload = normalize_request(cc_payload)
|
||||
_dbg(
|
||||
f'标准化完成：模型={cc_payload.get("model")} '
|
||||
f'工具数={len(cc_payload.get("tools", []))}'
|
||||
)
|
||||
|
||||
url, headers = build_openai_target(ctx)
|
||||
cc_payload = apply_body_modifications(cc_payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
if ctx.is_stream:
|
||||
return handle_stream(ctx, outbound, client_fmt, cc_payload, turn)
|
||||
return handle_non_stream(ctx, outbound, client_fmt, cc_payload, turn)
|
||||
return _handle_openai_stream(ctx, cc_payload, url, headers, turn)
|
||||
return _handle_openai_non_stream(ctx, cc_payload, url, headers, turn)
|
||||
|
||||
|
||||
def _handle_native_responses(ctx, payload: dict[str, Any], turn: dict[str, Any]):
|
||||
"""处理走原生 Responses 后端的请求（直接透传）。"""
|
||||
def _handle_openai_non_stream(
|
||||
ctx: RouteContext,
|
||||
cc_payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any],
|
||||
):
|
||||
"""处理 OpenAI 兼容后端的非流式 Responses 返回。"""
|
||||
cc_payload['stream'] = False
|
||||
attach_upstream_request(turn, cc_payload, headers)
|
||||
resp, err = forward_request(url, headers, cc_payload)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': 'upstream request failed'})
|
||||
finalize_turn(turn)
|
||||
return err
|
||||
|
||||
raw = resp.json()
|
||||
attach_upstream_response(turn, raw)
|
||||
_dbg('上游原始响应=' + json.dumps(raw, ensure_ascii=False, default=str)[:1000])
|
||||
|
||||
fixed = fix_response(raw)
|
||||
response_data = cc_to_responses(fixed, ctx.client_model)
|
||||
return _finalize_responses_response(
|
||||
response_data,
|
||||
client_model=ctx.client_model,
|
||||
turn=turn,
|
||||
debug_label='转换为 Responses 后',
|
||||
)
|
||||
|
||||
|
||||
def _handle_openai_stream(
|
||||
ctx: RouteContext,
|
||||
cc_payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理 OpenAI 兼容后端的流式 Responses 返回。"""
|
||||
cc_payload['stream'] = True
|
||||
converter = ResponsesStreamConverter(model=ctx.client_model)
|
||||
|
||||
def generate():
|
||||
"""消费 OpenAI 聊天补全流，并实时改写为 Responses SSE。"""
|
||||
yield from converter.start_events()
|
||||
|
||||
attach_upstream_request(turn, cc_payload, headers)
|
||||
resp, err = forward_request(url, headers, cc_payload, stream=True)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': str(err)})
|
||||
set_stream_summary(turn, {'status': 'error'})
|
||||
finalize_turn(turn)
|
||||
yield responses_error_event(str(err))
|
||||
return
|
||||
|
||||
think_extractor = ThinkTagExtractor()
|
||||
chunk_count = 0
|
||||
client_events: list[str] = []
|
||||
|
||||
for chunk in iter_openai_sse(resp):
|
||||
if chunk is None:
|
||||
_dbg(f'流式响应结束，共 {chunk_count} 个数据片段')
|
||||
finalized_events = converter.finalize()
|
||||
for item in finalized_events:
|
||||
client_events.append(item)
|
||||
append_client_event(turn, {'type': 'responses_event', 'data': item})
|
||||
yield item
|
||||
usage_tracker.record(ctx.client_model)
|
||||
set_stream_summary(turn, {
|
||||
'chunk_count': chunk_count,
|
||||
'client_event_count': len(client_events),
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'responses.stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'event_count': len(client_events),
|
||||
})
|
||||
finalize_turn(turn)
|
||||
return
|
||||
|
||||
append_upstream_event(turn, {'type': 'openai_chunk', 'data': chunk})
|
||||
if chunk_count < 10:
|
||||
_dbg(
|
||||
f'上游原始片段#{chunk_count}='
|
||||
+ json.dumps(chunk, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
|
||||
chunk = fix_stream_chunk(chunk)
|
||||
for out in think_extractor.process_chunk(chunk):
|
||||
for evt in converter.process_cc_chunk(out):
|
||||
client_events.append(evt)
|
||||
append_client_event(turn, {'type': 'responses_event', 'data': evt})
|
||||
if chunk_count < 10:
|
||||
_dbg(
|
||||
f'转换后片段#{chunk_count}='
|
||||
+ json.dumps(out, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
yield evt
|
||||
|
||||
chunk_count += 1
|
||||
|
||||
return sse_response(generate())
|
||||
|
||||
|
||||
def _handle_responses_backend(ctx: RouteContext, payload: dict[str, Any], turn: dict[str, Any] | None):
|
||||
"""处理走原生 Responses 后端的请求。
|
||||
|
||||
当中转站本身就只支持 `/v1/responses` 时，不需要再绕到聊天补全中间协议，
|
||||
直接转发原生 Responses 请求即可。
|
||||
"""
|
||||
payload = dict(payload)
|
||||
payload['model'] = ctx.upstream_model
|
||||
payload = inject_instructions_responses(payload, ctx.custom_instructions, ctx.instructions_position)
|
||||
|
||||
outbound = ResponsesNativeOutbound()
|
||||
client_fmt = ResponsesPassthroughFormatter(model=ctx.client_model)
|
||||
url, headers = build_responses_target(ctx)
|
||||
payload = apply_body_modifications(payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
if ctx.is_stream:
|
||||
return handle_stream(ctx, outbound, client_fmt, payload, turn)
|
||||
return handle_non_stream(ctx, outbound, client_fmt, payload, turn)
|
||||
return _handle_responses_stream(ctx, payload, url, headers, turn)
|
||||
return _handle_responses_non_stream(ctx, payload, url, headers, turn)
|
||||
|
||||
|
||||
def _build_cc_payload(payload: dict[str, Any], ctx) -> dict[str, Any]:
|
||||
"""将 Responses 请求统一降级为 Chat Completions 中间表示。"""
|
||||
cc_payload = responses_to_cc(payload)
|
||||
cc_payload['model'] = ctx.upstream_model
|
||||
cc_payload = normalize_request(cc_payload)
|
||||
if should_inject_thinking(ctx.backend):
|
||||
cc_payload['messages'] = thinking_cache.inject(cc_payload.get('messages', []))
|
||||
cc_payload = inject_instructions_cc(cc_payload, ctx.custom_instructions, ctx.instructions_position)
|
||||
return cc_payload
|
||||
def _handle_responses_non_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理原生 Responses 后端的非流式返回。"""
|
||||
payload['stream'] = False
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': 'upstream request failed'})
|
||||
finalize_turn(turn)
|
||||
return err
|
||||
|
||||
response_data = resp.json()
|
||||
attach_upstream_response(turn, response_data)
|
||||
response_data['model'] = ctx.client_model
|
||||
return _finalize_responses_response(
|
||||
response_data,
|
||||
client_model=ctx.client_model,
|
||||
turn=turn,
|
||||
debug_label='原生 Responses 返回后',
|
||||
)
|
||||
|
||||
|
||||
def _handle_responses_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理原生 Responses 后端的流式返回。"""
|
||||
payload['stream'] = True
|
||||
converter = ResponsesStreamConverter(model=ctx.client_model)
|
||||
|
||||
def generate():
|
||||
"""透传上游原生 Responses 流，并做轻量模型名改写。"""
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload, stream=True)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': str(err)})
|
||||
set_stream_summary(turn, {'status': 'error'})
|
||||
finalize_turn(turn)
|
||||
yield responses_error_event(str(err))
|
||||
return
|
||||
|
||||
event_count = 0
|
||||
client_events: list[str] = []
|
||||
last_usage: dict[str, Any] | None = None
|
||||
for event_type, event_data in iter_responses_sse(resp):
|
||||
append_upstream_event(turn, {'type': event_type, 'data': event_data})
|
||||
extracted_usage = _extract_responses_usage(event_data)
|
||||
if extracted_usage:
|
||||
last_usage = extracted_usage
|
||||
if event_count < 10:
|
||||
_dbg(
|
||||
f'上游事件#{event_count} 类型={event_type} 数据='
|
||||
+ json.dumps(event_data, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
produced = converter.process_responses_event(event_type, event_data)
|
||||
for evt in produced:
|
||||
client_events.append(evt)
|
||||
append_client_event(turn, {'type': 'responses_event', 'data': evt})
|
||||
yield evt
|
||||
event_count += 1
|
||||
|
||||
_dbg(f'流式响应结束，共 {event_count} 个事件')
|
||||
usage_tracker.record(
|
||||
ctx.client_model,
|
||||
last_usage,
|
||||
input_key='input_tokens',
|
||||
output_key='output_tokens',
|
||||
)
|
||||
set_stream_summary(turn, {
|
||||
'event_count': event_count,
|
||||
'client_event_count': len(client_events),
|
||||
'usage': last_usage,
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'responses.stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'event_count': len(client_events),
|
||||
'usage': last_usage,
|
||||
})
|
||||
finalize_turn(turn, usage=last_usage)
|
||||
|
||||
return sse_response(generate())
|
||||
|
||||
|
||||
def _extract_responses_usage(event_data: dict[str, Any]) -> dict[str, Any] | None:
|
||||
"""从原生 Responses 事件中提取 usage。
|
||||
|
||||
原生 `/v1/responses` 流式通常会在 `response.completed` 事件里携带 usage，
|
||||
也可能直接挂在顶层 `usage` 字段。这里统一做兼容提取，供统计与日志复用。
|
||||
"""
|
||||
if not isinstance(event_data, dict):
|
||||
return None
|
||||
usage = event_data.get('usage')
|
||||
if isinstance(usage, dict):
|
||||
return usage
|
||||
response_obj = event_data.get('response')
|
||||
if isinstance(response_obj, dict):
|
||||
nested_usage = response_obj.get('usage')
|
||||
if isinstance(nested_usage, dict):
|
||||
return nested_usage
|
||||
return None
|
||||
|
||||
|
||||
def _handle_gemini_backend(ctx: RouteContext, cc_payload: dict[str, Any], turn: dict[str, Any] | None):
|
||||
"""处理走 Gemini Contents 后端的 Responses 请求。"""
|
||||
gemini_payload = cc_to_gemini_request(cc_payload)
|
||||
_dbg(
|
||||
'已转换为 Gemini 请求：字段=' + str(list(gemini_payload.keys()))
|
||||
+ f' 内容数={len(gemini_payload.get("contents", []))}'
|
||||
)
|
||||
|
||||
url, headers = build_gemini_target(ctx, stream=ctx.is_stream)
|
||||
gemini_payload = apply_body_modifications(gemini_payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
if ctx.is_stream:
|
||||
return _handle_gemini_stream(ctx, gemini_payload, url, headers, turn)
|
||||
return _handle_gemini_non_stream(ctx, gemini_payload, url, headers, turn)
|
||||
|
||||
|
||||
def _handle_gemini_non_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理 Gemini 后端的非流式 Responses 返回。"""
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': 'upstream request failed'})
|
||||
finalize_turn(turn)
|
||||
return err
|
||||
|
||||
raw = resp.json()
|
||||
attach_upstream_response(turn, raw)
|
||||
_dbg('上游原始响应=' + json.dumps(raw, ensure_ascii=False, default=str)[:1000])
|
||||
|
||||
cc_data = gemini_to_cc_response(raw)
|
||||
response_data = cc_to_responses(cc_data, ctx.client_model)
|
||||
return _finalize_responses_response(
|
||||
response_data,
|
||||
client_model=ctx.client_model,
|
||||
turn=turn,
|
||||
debug_label='Gemini 转回 Responses 后',
|
||||
)
|
||||
|
||||
|
||||
def _handle_gemini_stream(
|
||||
ctx: RouteContext,
|
||||
payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理 Gemini 后端的流式 Responses 返回。"""
|
||||
converter = ResponsesStreamConverter(model=ctx.client_model)
|
||||
gemini_converter = GeminiStreamConverter()
|
||||
|
||||
def generate():
|
||||
yield from converter.start_events()
|
||||
|
||||
attach_upstream_request(turn, payload, headers)
|
||||
resp, err = forward_request(url, headers, payload, stream=True)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': str(err)})
|
||||
set_stream_summary(turn, {'status': 'error'})
|
||||
finalize_turn(turn)
|
||||
yield responses_error_event(str(err))
|
||||
return
|
||||
|
||||
chunk_count = 0
|
||||
client_events: list[str] = []
|
||||
last_usage: dict[str, Any] | None = None
|
||||
for gemini_chunk in iter_gemini_sse(resp):
|
||||
append_upstream_event(turn, {'type': 'gemini_chunk', 'data': gemini_chunk})
|
||||
usage_meta = gemini_chunk.get('usageMetadata') if isinstance(gemini_chunk, dict) else None
|
||||
if isinstance(usage_meta, dict):
|
||||
last_usage = {
|
||||
'input_tokens': usage_meta.get('promptTokenCount', 0),
|
||||
'output_tokens': usage_meta.get('candidatesTokenCount', 0),
|
||||
'total_tokens': usage_meta.get('totalTokenCount', 0),
|
||||
}
|
||||
if chunk_count < 10:
|
||||
_dbg(
|
||||
f'上游 Gemini 片段#{chunk_count}='
|
||||
+ json.dumps(gemini_chunk, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
|
||||
for cc_chunk in gemini_converter.process_chunk(gemini_chunk):
|
||||
for evt in converter.process_cc_chunk(cc_chunk):
|
||||
client_events.append(evt)
|
||||
append_client_event(turn, {'type': 'responses_event', 'data': evt})
|
||||
yield evt
|
||||
|
||||
chunk_count += 1
|
||||
|
||||
_dbg(f'流式响应结束，共 {chunk_count} 个数据片段')
|
||||
finalized_events = converter.finalize()
|
||||
for evt in finalized_events:
|
||||
client_events.append(evt)
|
||||
append_client_event(turn, {'type': 'responses_event', 'data': evt})
|
||||
yield evt
|
||||
usage_tracker.record(
|
||||
ctx.client_model,
|
||||
last_usage,
|
||||
input_key='input_tokens',
|
||||
output_key='output_tokens',
|
||||
)
|
||||
set_stream_summary(turn, {
|
||||
'chunk_count': chunk_count,
|
||||
'client_event_count': len(client_events),
|
||||
'usage': last_usage,
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'responses.stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'event_count': len(client_events),
|
||||
'usage': last_usage,
|
||||
})
|
||||
finalize_turn(turn, usage=last_usage)
|
||||
|
||||
return sse_response(generate())
|
||||
|
||||
|
||||
def _handle_anthropic_backend(ctx: RouteContext, cc_payload: dict[str, Any], turn: dict[str, Any] | None):
|
||||
"""处理走 Anthropic 后端的 Responses 请求。"""
|
||||
anthropic_payload = cc_to_messages_request(cc_payload)
|
||||
_dbg(
|
||||
'已转换为 Messages 请求：字段=' + str(list(anthropic_payload.keys()))
|
||||
+ f' 消息数={len(anthropic_payload.get("messages", []))}'
|
||||
)
|
||||
|
||||
url, headers = build_anthropic_target(ctx)
|
||||
anthropic_payload = apply_body_modifications(anthropic_payload, ctx.body_modifications)
|
||||
headers = apply_header_modifications(headers, ctx.header_modifications)
|
||||
|
||||
if ctx.is_stream:
|
||||
return _handle_anthropic_stream(ctx, anthropic_payload, url, headers, turn)
|
||||
return _handle_anthropic_non_stream(ctx, anthropic_payload, url, headers, turn)
|
||||
|
||||
|
||||
def _handle_anthropic_non_stream(
|
||||
ctx: RouteContext,
|
||||
anthropic_payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理 Anthropic 后端的非流式 Responses 返回。"""
|
||||
anthropic_payload['stream'] = False
|
||||
attach_upstream_request(turn, anthropic_payload, headers)
|
||||
resp, err = forward_request(url, headers, anthropic_payload)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': 'upstream request failed'})
|
||||
finalize_turn(turn)
|
||||
return err
|
||||
|
||||
raw = resp.json()
|
||||
attach_upstream_response(turn, raw)
|
||||
_dbg('上游原始响应=' + json.dumps(raw, ensure_ascii=False, default=str)[:1000])
|
||||
|
||||
cc_data = messages_to_cc_response(raw)
|
||||
response_data = cc_to_responses(cc_data, ctx.client_model)
|
||||
return _finalize_responses_response(
|
||||
response_data,
|
||||
client_model=ctx.client_model,
|
||||
turn=turn,
|
||||
debug_label='Messages 转回 Responses 后',
|
||||
)
|
||||
|
||||
|
||||
def _handle_anthropic_stream(
|
||||
ctx: RouteContext,
|
||||
anthropic_payload: dict[str, Any],
|
||||
url: str,
|
||||
headers: dict[str, str],
|
||||
turn: dict[str, Any] | None,
|
||||
):
|
||||
"""处理 Anthropic 后端的流式 Responses 返回。
|
||||
|
||||
这里直接将 Anthropic SSE 事件映射到 Responses SSE，故意跳过 CC 流式中间态，
|
||||
这样可以减少一次事件重组，降低流式转换复杂度，也更容易保留原始时序。
|
||||
"""
|
||||
anthropic_payload['stream'] = True
|
||||
converter = ResponsesStreamConverter(model=ctx.client_model)
|
||||
|
||||
def generate():
|
||||
"""消费 Anthropic SSE，并直接映射为 Responses 事件序列。"""
|
||||
yield from converter.start_events()
|
||||
|
||||
attach_upstream_request(turn, anthropic_payload, headers)
|
||||
resp, err = forward_request(url, headers, anthropic_payload, stream=True)
|
||||
if err:
|
||||
attach_error(turn, {'stage': 'forward_request', 'message': str(err)})
|
||||
set_stream_summary(turn, {'status': 'error'})
|
||||
finalize_turn(turn)
|
||||
yield responses_error_event(str(err))
|
||||
return
|
||||
|
||||
event_count = 0
|
||||
client_events: list[str] = []
|
||||
for event_type, event_data in iter_anthropic_sse(resp):
|
||||
append_upstream_event(turn, {'type': event_type, 'data': event_data})
|
||||
if event_count < 10:
|
||||
_dbg(
|
||||
f'上游事件#{event_count} 类型={event_type} 数据='
|
||||
+ json.dumps(event_data, ensure_ascii=False, default=str)[:500]
|
||||
)
|
||||
|
||||
produced = converter.process_anthropic_event(event_type, event_data)
|
||||
for evt in produced:
|
||||
client_events.append(evt)
|
||||
append_client_event(turn, {'type': 'responses_event', 'data': evt})
|
||||
yield evt
|
||||
event_count += 1
|
||||
|
||||
_dbg(f'流式响应结束，共 {event_count} 个事件')
|
||||
finalized_events = converter.finalize()
|
||||
for evt in finalized_events:
|
||||
client_events.append(evt)
|
||||
append_client_event(turn, {'type': 'responses_event', 'data': evt})
|
||||
yield evt
|
||||
usage_tracker.record(ctx.client_model)
|
||||
set_stream_summary(turn, {
|
||||
'event_count': event_count,
|
||||
'client_event_count': len(client_events),
|
||||
})
|
||||
attach_client_response(turn, {
|
||||
'type': 'responses.stream.summary',
|
||||
'model': ctx.client_model,
|
||||
'event_count': len(client_events),
|
||||
})
|
||||
finalize_turn(turn)
|
||||
|
||||
return sse_response(generate())
|
||||
|
||||
|
||||
def _finalize_responses_response(
|
||||
response_data: dict[str, Any],
|
||||
*,
|
||||
client_model: str,
|
||||
turn: dict[str, Any],
|
||||
debug_label: str,
|
||||
):
|
||||
"""统一收尾非流式 Responses 响应。
|
||||
|
||||
两条转换链路和一条原生 Responses 链路最终都会回到 Responses 对象，因此这里集中
|
||||
处理调试日志、回填展示模型名以及 usage 日志。
|
||||
"""
|
||||
response_data['model'] = response_data.get('model') or ''
|
||||
_dbg(debug_label + '=' + json.dumps(response_data, ensure_ascii=False, default=str)[:1000])
|
||||
log_usage('响应生成', response_data.get('usage', {}), input_key='input_tokens', output_key='output_tokens')
|
||||
|
||||
usage_tracker.record(
|
||||
client_model,
|
||||
response_data.get('usage'),
|
||||
input_key='input_tokens',
|
||||
output_key='output_tokens',
|
||||
)
|
||||
|
||||
attach_client_response(turn, response_data)
|
||||
finalize_turn(turn, usage=response_data.get('usage'))
|
||||
|
||||
return jsonify(response_data)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue