702 lines
26 KiB
Python
702 lines
26 KiB
Python
"""OpenAI Chat Completions ↔ Anthropic Messages 格式转换
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这个模块是项目里最核心的协议桥之一，负责在两套主流对话协议之间做双向适配：
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- 请求方向：OpenAI Chat Completions → Anthropic Messages
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- 响应方向：Anthropic Messages → OpenAI Chat Completions
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- 流式方向：Anthropic SSE 事件 → OpenAI Chat Completions chunk
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这里的代码看起来会比普通字段映射更重，是因为它不仅要做字段重命名，还要处理：
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- system 消息上提
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- tool_calls / tool_use 双向映射
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- tool 消息 / tool_result 双向映射
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- 图片块转换
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- 思考内容与流式工具参数的时序保留
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"""
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from __future__ import annotations
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import json
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from typing import Any
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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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'tool_use': 'tool_calls',
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'stop_sequence': 'stop',
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}
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# ═══════════════════════════════════════════════════════════
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# 请求转换: CC → Messages
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# ═══════════════════════════════════════════════════════════
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def cc_to_messages_request(payload: JsonDict) -> JsonDict:
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"""将 OpenAI Chat Completions 请求转换为 Anthropic Messages 请求。
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这一步不是简单替换字段名，而是主动把 OpenAI 世界中的几类特殊语义映射到
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Anthropic 世界：
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- `system` 消息提取到顶层 `system`
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- assistant 的 `tool_calls` 变成 `tool_use` 内容块
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- `tool` 角色消息变成 user 侧的 `tool_result` 内容块
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另外，这里会把相邻同角色消息做合并，因为 Anthropic 对消息角色交替的要求更严格。
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"""
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messages = payload.get('messages', [])
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anthropic_messages: list[JsonDict] = []
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system_parts: list[str] = []
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for message in messages:
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converted, system_text = _convert_request_message(message)
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if system_text is not None:
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system_parts.append(system_text)
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continue
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if converted is not None:
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anthropic_messages.append(converted)
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anthropic_messages = _merge_same_role(anthropic_messages)
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result = _build_messages_request(payload, anthropic_messages, system_parts)
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optimize_cache_control(result)
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return result
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# ═══════════════════════════════════════════════════════════
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# 非流式响应转换: Messages → CC
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# ═══════════════════════════════════════════════════════════
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def messages_to_cc_response(data: JsonDict, request_id: str | None = None) -> JsonDict:
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"""将 Anthropic Messages 非流式响应转换为 OpenAI CC 响应。"""
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request_id = request_id or gen_id('chatcmpl-')
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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 {
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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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}
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# ═══════════════════════════════════════════════════════════
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# 流式响应转换: Anthropic SSE → CC chunks
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# ═══════════════════════════════════════════════════════════
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class AnthropicStreamConverter:
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"""将 Anthropic SSE 事件逐个转换为 OpenAI Chat Completions chunk。
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之所以做成有状态转换器，而不是单纯的函数映射，是因为 Anthropic 的流式工具调用
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会把名字、参数、结束信号拆散在多个事件中，而 OpenAI chunk 语义要求我们按顺序
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组装出连续的 `tool_calls` 增量。
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这个类主要维护三类状态：
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1. 当前请求的 chunk ID
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2. 当前工具调用的索引位置
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3. 输入 / 输出令牌统计
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最终目标是把 Anthropic 的事件流稳定映射成 Cursor 能直接消费的 CC chunk 流。
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"""
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def __init__(self, request_id: str | None = None):
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"""初始化流式转换所需的请求标识、工具索引和 usage 状态。"""
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self._id = request_id or gen_id('chatcmpl-')
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self._tool_index = -1
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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[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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return self._handle_content_block_start(event_data)
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if event_type == 'content_block_delta':
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return self._handle_content_block_delta(event_data)
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if event_type == 'message_delta':
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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[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 [self._dump_chunk(chunk)]
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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._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': {
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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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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._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._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._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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return []
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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 = 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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)
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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 [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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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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# 请求转换辅助
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# ═══════════════════════════════════════════════════════════
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def _convert_request_message(message: Any) -> tuple[JsonDict | None, str | None]:
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"""将单条 OpenAI 消息转换为 Anthropic 消息或 system 文本。"""
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if not isinstance(message, dict):
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return None, None
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role = message.get('role', '')
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content = message.get('content', '')
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if role == 'system':
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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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anthropic_role = 'assistant' if role == 'assistant' else 'user'
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anthropic_content = _convert_content(message)
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if role == 'assistant' and 'tool_calls' in message:
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anthropic_content = _append_tool_use_blocks(anthropic_content, message.get('tool_calls', []))
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if not anthropic_content and anthropic_content != 0:
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return None, None
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return {'role': anthropic_role, 'content': anthropic_content}, None
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def _convert_tool_role_message(message: JsonDict) -> JsonDict | None:
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"""将 OpenAI 的 tool 角色消息转换为 Anthropic 的 tool_result 内容块。"""
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content = message.get('content', '')
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text = content if isinstance(content, str) else json.dumps(content, ensure_ascii=False)
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anthropic_content = [{
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'type': 'tool_result',
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'tool_use_id': message.get('tool_call_id', ''),
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'content': text,
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}]
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if not anthropic_content:
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return None
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return {'role': 'user', 'content': anthropic_content}
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def _append_tool_use_blocks(content: Any, tool_calls: list[Any]) -> list[JsonDict]:
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"""把 OpenAI assistant.tool_calls 追加成 Anthropic tool_use 内容块。"""
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blocks = _to_blocks(content)
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for tool_call in tool_calls:
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if not isinstance(tool_call, dict):
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continue
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function_data = tool_call.get('function', {})
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blocks.append({
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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_tool_arguments(function_data.get('arguments', '{}')),
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})
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return blocks
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def _build_messages_request(
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payload: JsonDict,
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anthropic_messages: list[JsonDict],
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system_parts: list[str],
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) -> JsonDict:
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"""组装最终的 Anthropic Messages 请求体。"""
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result: JsonDict = {
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'model': payload.get('model', 'claude-sonnet-4-20250514'),
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'messages': anthropic_messages,
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# 沿用项目当前策略：未设置或设置过小都兜底到 8192，避免上游因默认值过小过早截断。
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'max_tokens': max(payload.get('max_tokens') or 8192, 8192),
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}
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if system_parts:
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result['system'] = '\n\n'.join(system_parts)
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if 'tools' in payload:
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result['tools'] = _convert_tools(payload['tools'])
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for key in ('temperature', 'top_p', 'stream'):
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if key in payload:
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result[key] = payload[key]
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return result
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# ═══════════════════════════════════════════════════════════
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# 非流式响应转换辅助
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# ═══════════════════════════════════════════════════════════
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def _collect_response_parts(content_blocks: Any) -> tuple[str, str, list[JsonDict]]:
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"""从 Anthropic content 块中提取文本、思考内容和工具调用。"""
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content_text = ''
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reasoning_text = ''
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tool_calls: list[JsonDict] = []
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if not isinstance(content_blocks, list):
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return content_text, reasoning_text, tool_calls
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for block in content_blocks:
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if not isinstance(block, dict):
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continue
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block_type = block.get('type', '')
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if block_type == 'text':
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content_text += block.get('text', '')
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elif block_type == 'thinking':
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reasoning_text += block.get('thinking', '')
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elif block_type == 'tool_use':
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tool_calls.append(_convert_tool_use_block(block, index=len(tool_calls)))
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return content_text, reasoning_text, tool_calls
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def _convert_tool_use_block(block: JsonDict, *, index: int) -> JsonDict:
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"""将 Anthropic 的 tool_use 块转换为 OpenAI tool_call。"""
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tool_name = block.get('name', '')
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input_data = block.get('input', {})
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if isinstance(input_data, dict):
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input_data = normalize_args(input_data)
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input_data = repair_str_replace_args(tool_name, input_data)
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arguments_text = json.dumps(input_data, ensure_ascii=False)
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else:
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arguments_text = str(input_data)
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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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# 通用辅助
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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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if content is None:
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return ''
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if isinstance(content, str):
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return content
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if not isinstance(content, list):
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return str(content)
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blocks: list[JsonDict] = []
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for part in content:
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converted = _convert_content_part(part)
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if converted is not None:
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blocks.append(converted)
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return blocks
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def _convert_content_part(part: Any) -> JsonDict | None:
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"""将单个 OpenAI content part 转为 Anthropic block。"""
|
||
if isinstance(part, str):
|
||
return {'type': 'text', 'text': part}
|
||
if not isinstance(part, dict):
|
||
return None
|
||
|
||
part_type = part.get('type', '')
|
||
if part_type == 'text':
|
||
return {'type': 'text', 'text': part.get('text', '')}
|
||
if part_type == 'image_url':
|
||
return _convert_image(part)
|
||
if part_type in ('tool_use', 'tool_result'):
|
||
return part
|
||
return None
|
||
|
||
|
||
def _convert_image(part: JsonDict) -> JsonDict:
|
||
"""将 OpenAI image_url 格式转换为 Anthropic image 格式。"""
|
||
url_data = part.get('image_url', {})
|
||
url = url_data.get('url', '') if isinstance(url_data, dict) else str(url_data)
|
||
|
||
if url.startswith('data:'):
|
||
media_type, _, base64_data = url.partition(';base64,')
|
||
return {
|
||
'type': 'image',
|
||
'source': {
|
||
'type': 'base64',
|
||
'media_type': media_type.replace('data:', '') or 'image/png',
|
||
'data': base64_data,
|
||
},
|
||
}
|
||
|
||
return {
|
||
'type': 'image',
|
||
'source': {
|
||
'type': 'url',
|
||
'url': url,
|
||
},
|
||
}
|
||
|
||
|
||
def _convert_tools(tools: Any) -> list[JsonDict]:
|
||
"""将 OpenAI tools 转为 Anthropic tools 格式。
|
||
|
||
这里兼容两种常见输入：
|
||
- 标准 OpenAI `{"type": "function", "function": {...}}`
|
||
- Cursor 常见的扁平工具格式 `{"name": ..., "input_schema": ...}`
|
||
"""
|
||
if not isinstance(tools, list):
|
||
return []
|
||
|
||
result: list[JsonDict] = []
|
||
for tool in tools:
|
||
converted = _convert_tool_definition(tool)
|
||
if converted is not None:
|
||
result.append(converted)
|
||
return result
|
||
|
||
|
||
def _convert_tool_definition(tool: Any) -> JsonDict | None:
|
||
"""将 OpenAI 或 Anthropic 风格的工具定义统一转换为 Anthropic `tools` 项。"""
|
||
if not isinstance(tool, dict):
|
||
return None
|
||
|
||
if tool.get('type') == 'function' and 'function' in tool:
|
||
function_data = tool['function']
|
||
return {
|
||
'name': function_data.get('name', ''),
|
||
'description': function_data.get('description', ''),
|
||
'input_schema': function_data.get('parameters', {'type': 'object', 'properties': {}}),
|
||
}
|
||
|
||
if 'name' in tool and 'input_schema' in tool:
|
||
return {
|
||
'name': tool.get('name', ''),
|
||
'description': tool.get('description', ''),
|
||
'input_schema': tool.get('input_schema', {'type': 'object', 'properties': {}}),
|
||
}
|
||
|
||
return None
|
||
|
||
|
||
def _to_blocks(content: Any) -> list[JsonDict]:
|
||
"""将内容统一转换成 block 列表。"""
|
||
if isinstance(content, str):
|
||
return [{'type': 'text', 'text': content}] if content else []
|
||
if isinstance(content, list):
|
||
return list(content)
|
||
return [{'type': 'text', 'text': str(content)}] if content else []
|
||
|
||
|
||
def _merge_same_role(messages: list[JsonDict]) -> list[JsonDict]:
|
||
"""合并相邻同角色消息。
|
||
|
||
Anthropic 要求消息角色严格交替，而 OpenAI/调用方不一定遵守这一点。
|
||
这里仅合并“相邻同角色”消息，以最小改动满足 Anthropic 约束，同时尽量保留
|
||
原本的消息顺序和内容块排列。
|
||
"""
|
||
if not messages:
|
||
return messages
|
||
|
||
merged = [messages[0]]
|
||
for message in messages[1:]:
|
||
if message['role'] == merged[-1]['role']:
|
||
previous_blocks = _to_blocks(merged[-1]['content'])
|
||
current_blocks = _to_blocks(message['content'])
|
||
merged[-1]['content'] = previous_blocks + current_blocks
|
||
else:
|
||
merged.append(message)
|
||
return merged
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════
|
||
# Anthropic cache_control 优化
|
||
# ═══════════════════════════════════════════════════════════
|
||
|
||
_MAX_BREAKPOINTS = 4
|
||
_BLOCK_WINDOW = 20
|
||
_EPHEMERAL = {'type': 'ephemeral'}
|
||
|
||
|
||
def optimize_cache_control(request: JsonDict) -> None:
|
||
"""自动设置最优的 Anthropic cache_control 断点。
|
||
|
||
算法移植自 CursorProxy 的 ensure_cache_control.go：
|
||
1. 归一化所有消息 content 为数组格式
|
||
2. 清空所有已有 cache_control
|
||
3. 注入结构锚点（tools 末尾 + system 末尾）
|
||
4. 注入消息锚点（最后一个可缓存块 + 窗口边界）
|
||
5. 总断点数不超过 4 个
|
||
"""
|
||
_normalize_message_contents(request)
|
||
_clear_all_cache_controls(request)
|
||
|
||
structural = _inject_structural_anchors(request)
|
||
remaining = _MAX_BREAKPOINTS - structural
|
||
if remaining <= 0:
|
||
return
|
||
|
||
refs = _collect_cacheable_block_refs(request)
|
||
if not refs:
|
||
return
|
||
|
||
desired = 1 if len(refs) < _BLOCK_WINDOW else 2
|
||
anchors = min(desired, remaining)
|
||
|
||
if anchors >= 1 and refs:
|
||
refs[-1]['cache_control'] = _EPHEMERAL
|
||
|
||
if anchors >= 2 and len(refs) > 1:
|
||
target = len(refs) - _BLOCK_WINDOW
|
||
idx = _pick_window_anchor(refs, target)
|
||
if idx is not None and idx != len(refs) - 1:
|
||
refs[idx]['cache_control'] = _EPHEMERAL
|
||
|
||
|
||
def _normalize_message_contents(request: JsonDict) -> None:
|
||
"""将所有消息的 content 统一转为数组格式。"""
|
||
for msg in request.get('messages', []):
|
||
content = msg.get('content')
|
||
if isinstance(content, str):
|
||
msg['content'] = [{'type': 'text', 'text': content}]
|
||
elif content is None:
|
||
msg['content'] = []
|
||
|
||
|
||
def _clear_all_cache_controls(request: JsonDict) -> None:
|
||
"""清空所有已有的 cache_control 字段。"""
|
||
for tool in request.get('tools', []):
|
||
tool.pop('cache_control', None)
|
||
|
||
system = request.get('system')
|
||
if isinstance(system, list):
|
||
for block in system:
|
||
if isinstance(block, dict):
|
||
block.pop('cache_control', None)
|
||
|
||
for msg in request.get('messages', []):
|
||
content = msg.get('content')
|
||
if isinstance(content, list):
|
||
for block in content:
|
||
if isinstance(block, dict):
|
||
block.pop('cache_control', None)
|
||
|
||
|
||
def _inject_structural_anchors(request: JsonDict) -> int:
|
||
"""在 tools 末尾和 system 末尾注入结构锚点，返回注入数量。"""
|
||
count = 0
|
||
|
||
tools = request.get('tools')
|
||
if tools and isinstance(tools, list):
|
||
tools[-1]['cache_control'] = _EPHEMERAL
|
||
count += 1
|
||
|
||
system = request.get('system')
|
||
if isinstance(system, list) and system:
|
||
last = system[-1]
|
||
if isinstance(last, dict):
|
||
last['cache_control'] = _EPHEMERAL
|
||
count += 1
|
||
elif isinstance(system, str) and system:
|
||
request['system'] = [
|
||
{'type': 'text', 'text': system, 'cache_control': _EPHEMERAL}
|
||
]
|
||
count += 1
|
||
|
||
return count
|
||
|
||
|
||
def _is_cacheable_block(block: Any) -> bool:
|
||
"""判断一个内容块是否可以设置 cache_control。"""
|
||
if not isinstance(block, dict):
|
||
return False
|
||
block_type = block.get('type', '')
|
||
if block_type in ('thinking', 'redacted_thinking'):
|
||
return False
|
||
if block_type == 'text' and not block.get('text'):
|
||
return False
|
||
return True
|
||
|
||
|
||
def _collect_cacheable_block_refs(request: JsonDict) -> list[JsonDict]:
|
||
"""收集所有消息中可缓存块的引用列表。"""
|
||
refs: list[JsonDict] = []
|
||
for msg in request.get('messages', []):
|
||
content = msg.get('content')
|
||
if not isinstance(content, list):
|
||
continue
|
||
for block in content:
|
||
if _is_cacheable_block(block):
|
||
refs.append(block)
|
||
return refs
|
||
|
||
|
||
def _pick_window_anchor(refs: list[JsonDict], target: int) -> int | None:
|
||
"""在目标位置附近选择一个窗口锚点，优先左侧。"""
|
||
if target < 0:
|
||
target = 0
|
||
if target >= len(refs):
|
||
return None
|
||
|
||
for i in range(target, -1, -1):
|
||
if 'cache_control' not in refs[i]:
|
||
return i
|
||
for i in range(target + 1, len(refs)):
|
||
if 'cache_control' not in refs[i]:
|
||
return i
|
||
return None
|