回退旧版本
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h88782481
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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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