优化代码
This commit is contained in:
h88782481
parent
202731df74
commit
96fbc4da80
15 changed files with 2768 additions and 1383 deletions
561
adapters/cc_anthropic_adapter.py
Normal file
561
adapters/cc_anthropic_adapter.py
Normal file
|
|
@ -0,0 +1,561 @@
|
|||
"""OpenAI Chat Completions ↔ Anthropic Messages 格式转换
|
||||
|
||||
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：
|
||||
- 请求方向：OpenAI Chat Completions → Anthropic Messages
|
||||
- 响应方向：Anthropic Messages → OpenAI Chat Completions
|
||||
- 流式方向：Anthropic SSE 事件 → OpenAI Chat Completions chunk
|
||||
|
||||
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：
|
||||
- system 消息上提
|
||||
- tool_calls / tool_use 双向映射
|
||||
- tool 消息 / tool_result 双向映射
|
||||
- 图片块转换
|
||||
- 思考内容与流式工具参数的时序保留
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
from utils.http import gen_id
|
||||
from utils.tool_fixer import fix_anthropic_tool_use, normalize_args, repair_str_replace_args
|
||||
|
||||
JsonDict = dict[str, Any]
|
||||
|
||||
|
||||
# Anthropic stop_reason → OpenAI finish_reason
|
||||
_STOP_REASON_MAP = {
|
||||
'end_turn': 'stop',
|
||||
'max_tokens': 'length',
|
||||
'tool_use': 'tool_calls',
|
||||
'stop_sequence': 'stop',
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 请求转换: CC → Messages
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def cc_to_messages_request(payload: JsonDict) -> JsonDict:
|
||||
"""将 OpenAI Chat Completions 请求转换为 Anthropic Messages 请求。
|
||||
|
||||
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
|
||||
Anthropic 世界：
|
||||
- `system` 消息提取到顶层 `system`
|
||||
- assistant 的 `tool_calls` 变成 `tool_use` 内容块
|
||||
- `tool` 角色消息变成 user 侧的 `tool_result` 内容块
|
||||
|
||||
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。
|
||||
"""
|
||||
messages = payload.get('messages', [])
|
||||
anthropic_messages: list[JsonDict] = []
|
||||
system_parts: list[str] = []
|
||||
|
||||
for message in messages:
|
||||
converted, system_text = _convert_request_message(message)
|
||||
if system_text is not None:
|
||||
system_parts.append(system_text)
|
||||
continue
|
||||
if converted is not None:
|
||||
anthropic_messages.append(converted)
|
||||
|
||||
anthropic_messages = _merge_same_role(anthropic_messages)
|
||||
return _build_messages_request(payload, anthropic_messages, system_parts)
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 非流式响应转换: Messages → CC
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def messages_to_cc_response(data: JsonDict, request_id: str | None = None) -> JsonDict:
|
||||
"""将 Anthropic Messages 非流式响应转换为 OpenAI CC 响应。"""
|
||||
request_id = request_id or gen_id('chatcmpl-')
|
||||
data = fix_anthropic_tool_use(data)
|
||||
|
||||
content_text, reasoning_text, tool_calls = _collect_response_parts(data.get('content', []))
|
||||
message = _build_cc_message(content_text, reasoning_text, tool_calls)
|
||||
usage = data.get('usage', {})
|
||||
|
||||
return {
|
||||
'id': request_id,
|
||||
'object': 'chat.completion',
|
||||
'model': data.get('model', 'claude'),
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'message': message,
|
||||
'finish_reason': _STOP_REASON_MAP.get(data.get('stop_reason', 'end_turn'), 'stop'),
|
||||
}],
|
||||
'usage': _build_cc_usage(
|
||||
input_tokens=usage.get('input_tokens', 0),
|
||||
output_tokens=usage.get('output_tokens', 0),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 流式响应转换: Anthropic SSE → CC chunks
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
class AnthropicStreamConverter:
|
||||
"""将 Anthropic SSE 事件逐个转换为 OpenAI Chat Completions chunk。
|
||||
|
||||
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
|
||||
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
|
||||
组装出连续的 `tool_calls` 增量。
|
||||
|
||||
这个类主要维护三类状态：
|
||||
1. 当前请求的 chunk ID
|
||||
2. 当前工具调用的索引位置
|
||||
3. 输入 / 输出令牌统计
|
||||
|
||||
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。
|
||||
"""
|
||||
|
||||
def __init__(self, request_id: str | None = None):
|
||||
self._id = request_id or gen_id('chatcmpl-')
|
||||
self._tool_index = -1
|
||||
self._input_tokens = 0
|
||||
self._output_tokens = 0
|
||||
|
||||
def process_event(self, event_type: str, event_data: JsonDict) -> list[str]:
|
||||
"""处理单个 Anthropic SSE 事件。
|
||||
|
||||
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
|
||||
字符串，交给上层直接作为 SSE data 发送给 Cursor。
|
||||
"""
|
||||
if event_type == 'message_start':
|
||||
return self._handle_message_start(event_data)
|
||||
if event_type == 'content_block_start':
|
||||
return self._handle_content_block_start(event_data)
|
||||
if event_type == 'content_block_delta':
|
||||
return self._handle_content_block_delta(event_data)
|
||||
if event_type == 'message_delta':
|
||||
return self._handle_message_delta(event_data)
|
||||
return []
|
||||
|
||||
def _handle_message_start(self, event_data: JsonDict) -> list[str]:
|
||||
"""处理消息开始事件，产出 assistant 角色起始 chunk。
|
||||
|
||||
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
|
||||
当前 assistant 消息。
|
||||
"""
|
||||
message = event_data.get('message', {})
|
||||
self._input_tokens = message.get('usage', {}).get('input_tokens', 0)
|
||||
|
||||
chunk = self._make_chunk(delta={'role': 'assistant', 'content': ''})
|
||||
if message.get('model'):
|
||||
chunk['model'] = message['model']
|
||||
return [self._dump_chunk(chunk)]
|
||||
|
||||
def _handle_content_block_start(self, event_data: JsonDict) -> list[str]:
|
||||
"""处理内容块开始事件。
|
||||
|
||||
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
|
||||
事件里；而 tool_use 需要先开一个空 arguments 的 tool_call 槽位。
|
||||
"""
|
||||
block = event_data.get('content_block', {})
|
||||
if block.get('type') != 'tool_use':
|
||||
return []
|
||||
|
||||
self._tool_index += 1
|
||||
return [self._dump_chunk(self._make_chunk(delta={
|
||||
'tool_calls': [{
|
||||
'index': self._tool_index,
|
||||
'id': block.get('id', gen_id('toolu_')),
|
||||
'type': 'function',
|
||||
'function': {
|
||||
'name': block.get('name', ''),
|
||||
'arguments': '',
|
||||
},
|
||||
}]
|
||||
}))]
|
||||
|
||||
def _handle_content_block_delta(self, event_data: JsonDict) -> list[str]:
|
||||
"""处理内容块增量事件。
|
||||
|
||||
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
|
||||
OpenAI chunk 里的 `content`、`reasoning_content` 和 `tool_calls.function.arguments`。
|
||||
"""
|
||||
delta = event_data.get('delta', {})
|
||||
delta_type = delta.get('type', '')
|
||||
|
||||
if delta_type == 'text_delta' and delta.get('text'):
|
||||
return [self._dump_chunk(self._make_chunk(delta={'content': delta['text']}))]
|
||||
|
||||
if delta_type == 'thinking_delta' and delta.get('thinking'):
|
||||
return [self._dump_chunk(self._make_chunk(delta={'reasoning_content': delta['thinking']}))]
|
||||
|
||||
if delta_type == 'input_json_delta' and delta.get('partial_json'):
|
||||
return [self._dump_chunk(self._make_chunk(delta={
|
||||
'tool_calls': [{
|
||||
'index': self._tool_index,
|
||||
'function': {'arguments': delta['partial_json']},
|
||||
}]
|
||||
}))]
|
||||
|
||||
return []
|
||||
|
||||
def _handle_message_delta(self, event_data: JsonDict) -> list[str]:
|
||||
"""处理消息收尾事件，补出 finish_reason 和 usage。
|
||||
|
||||
当 Anthropic 发出 `message_delta` 时，说明这一轮 assistant 输出已经收束，
|
||||
这里会统一生成最后一个带 usage 的收尾 chunk。
|
||||
"""
|
||||
delta = event_data.get('delta', {})
|
||||
usage = event_data.get('usage', {})
|
||||
self._output_tokens = usage.get('output_tokens', 0)
|
||||
|
||||
chunk = self._make_chunk(
|
||||
delta={},
|
||||
finish_reason=_STOP_REASON_MAP.get(delta.get('stop_reason', ''), 'stop'),
|
||||
)
|
||||
chunk['usage'] = _build_cc_usage(
|
||||
input_tokens=self._input_tokens,
|
||||
output_tokens=self._output_tokens,
|
||||
)
|
||||
return [self._dump_chunk(chunk)]
|
||||
|
||||
def _make_chunk(self, delta: JsonDict, finish_reason: str | None = None) -> JsonDict:
|
||||
"""构造标准 OpenAI Chat Completions chunk 对象。"""
|
||||
choice: JsonDict = {'index': 0, 'delta': delta}
|
||||
if finish_reason:
|
||||
choice['finish_reason'] = finish_reason
|
||||
return {
|
||||
'id': self._id,
|
||||
'object': 'chat.completion.chunk',
|
||||
'model': 'claude',
|
||||
'choices': [choice],
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _dump_chunk(chunk: JsonDict) -> str:
|
||||
"""统一序列化 chunk，方便上层直接写入 SSE data。"""
|
||||
return json.dumps(chunk)
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 请求转换辅助
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def _convert_request_message(message: Any) -> tuple[JsonDict | None, str | None]:
|
||||
"""将单条 OpenAI 消息转换为 Anthropic 消息或 system 文本。"""
|
||||
if not isinstance(message, dict):
|
||||
return None, None
|
||||
|
||||
role = message.get('role', '')
|
||||
content = message.get('content', '')
|
||||
|
||||
if role == 'system':
|
||||
return None, _flatten_text(content)
|
||||
if role == 'tool':
|
||||
return _convert_tool_role_message(message), None
|
||||
|
||||
anthropic_role = 'assistant' if role == 'assistant' else 'user'
|
||||
anthropic_content = _convert_content(message)
|
||||
|
||||
if role == 'assistant' and 'tool_calls' in message:
|
||||
anthropic_content = _append_tool_use_blocks(anthropic_content, message.get('tool_calls', []))
|
||||
|
||||
if not anthropic_content and anthropic_content != 0:
|
||||
return None, None
|
||||
return {'role': anthropic_role, 'content': anthropic_content}, None
|
||||
|
||||
|
||||
def _convert_tool_role_message(message: JsonDict) -> JsonDict | None:
|
||||
"""将 OpenAI 的 tool 角色消息转换为 Anthropic 的 tool_result 内容块。"""
|
||||
content = message.get('content', '')
|
||||
text = content if isinstance(content, str) else json.dumps(content, ensure_ascii=False)
|
||||
anthropic_content = [{
|
||||
'type': 'tool_result',
|
||||
'tool_use_id': message.get('tool_call_id', ''),
|
||||
'content': text,
|
||||
}]
|
||||
|
||||
if not anthropic_content:
|
||||
return None
|
||||
return {'role': 'user', 'content': anthropic_content}
|
||||
|
||||
|
||||
def _append_tool_use_blocks(content: Any, tool_calls: list[Any]) -> list[JsonDict]:
|
||||
"""把 OpenAI assistant.tool_calls 追加成 Anthropic tool_use 内容块。"""
|
||||
blocks = _to_blocks(content)
|
||||
for tool_call in tool_calls:
|
||||
if not isinstance(tool_call, dict):
|
||||
continue
|
||||
function_data = tool_call.get('function', {})
|
||||
blocks.append({
|
||||
'type': 'tool_use',
|
||||
'id': tool_call.get('id', gen_id('toolu_')),
|
||||
'name': function_data.get('name', ''),
|
||||
'input': _parse_tool_arguments(function_data.get('arguments', '{}')),
|
||||
})
|
||||
return blocks
|
||||
|
||||
|
||||
def _build_messages_request(
|
||||
payload: JsonDict,
|
||||
anthropic_messages: list[JsonDict],
|
||||
system_parts: list[str],
|
||||
) -> JsonDict:
|
||||
"""组装最终的 Anthropic Messages 请求体。"""
|
||||
result: JsonDict = {
|
||||
'model': payload.get('model', 'claude-sonnet-4-20250514'),
|
||||
'messages': anthropic_messages,
|
||||
# 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。
|
||||
'max_tokens': max(payload.get('max_tokens') or 8192, 8192),
|
||||
}
|
||||
|
||||
if system_parts:
|
||||
result['system'] = '\n\n'.join(system_parts)
|
||||
if 'tools' in payload:
|
||||
result['tools'] = _convert_tools(payload['tools'])
|
||||
|
||||
for key in ('temperature', 'top_p', 'stream'):
|
||||
if key in payload:
|
||||
result[key] = payload[key]
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 非流式响应转换辅助
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def _collect_response_parts(content_blocks: Any) -> tuple[str, str, list[JsonDict]]:
|
||||
"""从 Anthropic content 块中提取文本、思考内容和工具调用。"""
|
||||
content_text = ''
|
||||
reasoning_text = ''
|
||||
tool_calls: list[JsonDict] = []
|
||||
|
||||
if not isinstance(content_blocks, list):
|
||||
return content_text, reasoning_text, tool_calls
|
||||
|
||||
for block in content_blocks:
|
||||
if not isinstance(block, dict):
|
||||
continue
|
||||
|
||||
block_type = block.get('type', '')
|
||||
if block_type == 'text':
|
||||
content_text += block.get('text', '')
|
||||
elif block_type == 'thinking':
|
||||
reasoning_text += block.get('thinking', '')
|
||||
elif block_type == 'tool_use':
|
||||
tool_calls.append(_convert_tool_use_block(block, index=len(tool_calls)))
|
||||
|
||||
return content_text, reasoning_text, tool_calls
|
||||
|
||||
|
||||
def _convert_tool_use_block(block: JsonDict, *, index: int) -> JsonDict:
|
||||
"""将 Anthropic 的 tool_use 块转换为 OpenAI tool_call。"""
|
||||
tool_name = block.get('name', '')
|
||||
input_data = block.get('input', {})
|
||||
|
||||
if isinstance(input_data, dict):
|
||||
input_data = normalize_args(input_data)
|
||||
input_data = repair_str_replace_args(tool_name, input_data)
|
||||
arguments_text = json.dumps(input_data, ensure_ascii=False)
|
||||
else:
|
||||
arguments_text = str(input_data)
|
||||
|
||||
return {
|
||||
'index': index,
|
||||
'id': block.get('id', gen_id('toolu_')),
|
||||
'type': 'function',
|
||||
'function': {
|
||||
'name': tool_name,
|
||||
'arguments': arguments_text,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _build_cc_message(content_text: str, reasoning_text: str, tool_calls: list[JsonDict]) -> JsonDict:
|
||||
"""构造 OpenAI CC 响应中的 assistant message。"""
|
||||
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:
|
||||
"""将 Anthropic usage 字段映射为 OpenAI usage。"""
|
||||
return {
|
||||
'prompt_tokens': input_tokens,
|
||||
'completion_tokens': output_tokens,
|
||||
'total_tokens': input_tokens + output_tokens,
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 通用辅助
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def _parse_tool_arguments(arguments: Any) -> Any:
|
||||
"""将 tool_call.arguments 尽量解析为对象，供 Anthropic tool_use.input 使用。
|
||||
|
||||
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，
|
||||
后续上游会把它当普通文本而不是工具参数对象。
|
||||
"""
|
||||
if not isinstance(arguments, str):
|
||||
return arguments if arguments is not None else {}
|
||||
try:
|
||||
return json.loads(arguments)
|
||||
except json.JSONDecodeError:
|
||||
return {}
|
||||
|
||||
|
||||
def _flatten_text(content: Any) -> str:
|
||||
"""将 content 扁平化为纯文本，主要用于 system 消息上提。"""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if isinstance(content, list):
|
||||
parts: list[str] = []
|
||||
for part in content:
|
||||
if isinstance(part, str):
|
||||
parts.append(part)
|
||||
elif isinstance(part, dict) and part.get('type') == 'text':
|
||||
parts.append(part.get('text', ''))
|
||||
return '\n'.join(parts)
|
||||
return str(content)
|
||||
|
||||
|
||||
def _convert_content(message: JsonDict) -> Any:
|
||||
"""将 OpenAI 消息的 content 字段转换为 Anthropic 内容格式。"""
|
||||
content = message.get('content', '')
|
||||
if content is None:
|
||||
return ''
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if not isinstance(content, list):
|
||||
return str(content)
|
||||
|
||||
blocks: list[JsonDict] = []
|
||||
for part in content:
|
||||
converted = _convert_content_part(part)
|
||||
if converted is not None:
|
||||
blocks.append(converted)
|
||||
return blocks
|
||||
|
||||
|
||||
def _convert_content_part(part: Any) -> JsonDict | None:
|
||||
"""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:
|
||||
"""转换单个工具定义。"""
|
||||
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
|
||||
Loading…
Add table
Add a link
Reference in a new issue