"""OpenAI Chat Completions ↔ Gemini Contents 格式转换 将 CC 格式请求转换为 Gemini generateContent 格式， 并将 Gemini 响应转换回 CC 格式。仅支持出站方向（CC → Gemini → CC）。 """ from __future__ import annotations import json import logging from typing import Any, Iterator from adapters.helpers import ( build_cc_message, build_cc_response, build_cc_tool_call, build_cc_usage, extract_text, make_cc_chunk, parse_json_safe, ) from utils.http import gen_id JsonDict = dict[str, Any] logger = logging.getLogger(__name__) _FINISH_REASON_MAP = { 'STOP': 'stop', 'MAX_TOKENS': 'length', 'SAFETY': 'content_filter', 'RECITATION': 'content_filter', } # ═══════════════════════════════════════════════════════════ # 请求转换: CC → Gemini generateContent # ═══════════════════════════════════════════════════════════ def cc_to_gemini_request(payload: JsonDict) -> JsonDict: """将 CC 请求转换为 Gemini generateContent 请求。""" messages = payload.get('messages', []) system_parts: list[str] = [] contents: list[JsonDict] = [] for msg in messages: role = msg.get('role', '') if role in ('system', 'developer'): system_parts.append(extract_text(msg.get('content', ''))) continue converted = _convert_message(msg) if converted: contents.append(converted) contents = _merge_same_role(contents) result: JsonDict = { 'contents': contents, 'generationConfig': _build_generation_config(payload), } if system_parts: result['systemInstruction'] = { 'parts': [{'text': '\n\n'.join(system_parts)}], } tools = _convert_tools(payload.get('tools')) if tools: result['tools'] = tools return result # ═══════════════════════════════════════════════════════════ # 非流式响应转换: Gemini → CC # ═══════════════════════════════════════════════════════════ def gemini_to_cc_response(data: JsonDict, request_id: str | None = None) -> JsonDict: """将 Gemini generateContent 响应转换为 CC 响应。""" request_id = request_id or gen_id('chatcmpl-') candidates = data.get('candidates', []) candidate = candidates[0] if candidates else {} content_text, reasoning_text, tool_calls = _extract_parts( candidate.get('content', {}).get('parts', []) ) finish = candidate.get('finishReason', 'STOP') if tool_calls and finish == 'STOP': finish_reason = 'tool_calls' else: finish_reason = _FINISH_REASON_MAP.get(finish, 'stop') return build_cc_response( response_id=request_id, message=build_cc_message(content_text, reasoning_text, tool_calls), finish_reason=finish_reason, usage=_convert_usage(data.get('usageMetadata', {})), model=data.get('modelVersion', 'gemini'), ) # ═══════════════════════════════════════════════════════════ # 流式转换: Gemini SSE → CC chunks # ═══════════════════════════════════════════════════════════ class GeminiStreamConverter: """将 Gemini SSE chunk 逐个转换为 CC chunk。 Gemini 流式每个 SSE data 是一个完整的 GenerateContentResponse， 包含 candidates[0].content.parts。 """ def __init__(self, request_id: str | None = None): self._id = request_id or gen_id('chatcmpl-') self._tool_call_index = 0 self._started = False def process_chunk(self, data: JsonDict) -> list[JsonDict]: """处理一个 Gemini SSE chunk，返回 CC chunk 列表。""" results: list[JsonDict] = [] candidates = data.get('candidates', []) if not candidates: return results candidate = candidates[0] parts = candidate.get('content', {}).get('parts', []) if not self._started: self._started = True results.append(self._make_chunk({'role': 'assistant', 'content': ''})) for part in parts: if part.get('thought') and part.get('text'): results.append(self._make_chunk({'reasoning_content': part['text']})) elif 'text' in part and not part.get('thought'): results.append(self._make_chunk({'content': part['text']})) elif 'functionCall' in part: fc = part['functionCall'] results.append(self._make_chunk({'tool_calls': [{ 'index': self._tool_call_index, 'id': fc.get('id') or gen_id('call_'), 'type': 'function', 'function': { 'name': fc.get('name', ''), 'arguments': json.dumps(fc.get('args', {}), ensure_ascii=False), }, }]})) self._tool_call_index += 1 finish = candidate.get('finishReason') if finish: has_tools = self._tool_call_index > 0 if has_tools and finish == 'STOP': fr = 'tool_calls' else: fr = _FINISH_REASON_MAP.get(finish, 'stop') chunk = self._make_chunk({}, finish_reason=fr) usage_meta = data.get('usageMetadata') if usage_meta: chunk['usage'] = _convert_usage(usage_meta) results.append(chunk) return results def _make_chunk(self, delta: JsonDict, finish_reason: str | None = None) -> JsonDict: return make_cc_chunk(self._id, delta, finish_reason, model='gemini') # ═══════════════════════════════════════════════════════════ # 请求转换辅助 # ═══════════════════════════════════════════════════════════ def _convert_message(msg: JsonDict) -> JsonDict | None: """将单条 CC 消息转为 Gemini Content。""" role = msg.get('role', '') gemini_role = 'model' if role == 'assistant' else 'user' parts: list[JsonDict] = [] if role == 'tool': return { 'role': 'user', '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 {}), }, }], } if msg.get('reasoning_content'): parts.append({'text': msg['reasoning_content'], 'thought': True}) content = msg.get('content') if isinstance(content, str) and content: parts.append({'text': content}) elif isinstance(content, list): for block in content: if not isinstance(block, dict): continue if block.get('type') == 'text': parts.append({'text': block.get('text', '')}) elif block.get('type') == 'image_url': img = _convert_image_part(block) if img: parts.append(img) for tc in msg.get('tool_calls', []): func = tc.get('function', {}) parts.append({ 'functionCall': { 'name': func.get('name', ''), 'args': parse_json_safe(func.get('arguments', '{}'), fallback={}), }, }) if not parts: return None return {'role': gemini_role, 'parts': parts} def _convert_image_part(block: JsonDict) -> JsonDict | None: """将 OpenAI image_url 转为 Gemini inlineData。""" url_data = block.get('image_url', {}) url = url_data.get('url', '') if isinstance(url_data, dict) else str(url_data) if url.startswith('data:'): media_type, _, b64 = url.partition(';base64,') return {'inlineData': { 'mimeType': media_type.replace('data:', '') or 'image/png', 'data': b64, }} return None def _build_generation_config(payload: JsonDict) -> JsonDict: """从 CC payload 构建 Gemini generationConfig。""" config: JsonDict = {} if 'max_tokens' in payload: config['maxOutputTokens'] = payload['max_tokens'] elif 'max_completion_tokens' in payload: config['maxOutputTokens'] = payload['max_completion_tokens'] if 'temperature' in payload: config['temperature'] = payload['temperature'] if 'top_p' in payload: config['topP'] = payload['top_p'] stop = payload.get('stop') if stop: config['stopSequences'] = stop if isinstance(stop, list) else [stop] return config def _convert_tools(tools: Any) -> list[JsonDict] | None: """将 CC tools 转为 Gemini functionDeclarations。""" if not isinstance(tools, list) or not tools: return None declarations: list[JsonDict] = [] for tool in tools: if not isinstance(tool, dict): continue func = tool.get('function', tool) if tool.get('type') == 'function' else tool if 'name' not in func: continue decl: JsonDict = { 'name': func.get('name', ''), 'description': func.get('description', ''), } params = func.get('parameters') if params: decl['parameters'] = params declarations.append(decl) if not declarations: return None return [{'functionDeclarations': declarations}] # ═══════════════════════════════════════════════════════════ # 响应转换辅助 # ═══════════════════════════════════════════════════════════ def _extract_parts(parts: list[Any]) -> tuple[str, str, list[JsonDict]]: """从 Gemini parts 中提取文本、思考内容和工具调用。""" text = '' reasoning = '' tool_calls: list[JsonDict] = [] for part in parts: if not isinstance(part, dict): continue if part.get('thought') and 'text' in part: reasoning += part['text'] elif 'text' in part: 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), )) return text, reasoning, tool_calls def _convert_usage(meta: JsonDict) -> JsonDict: """将 Gemini usageMetadata 转为 CC usage。""" prompt = meta.get('promptTokenCount', 0) candidates = meta.get('candidatesTokenCount', 0) thoughts = meta.get('thoughtsTokenCount', 0) return build_cc_usage(prompt, candidates + thoughts) def _merge_same_role(contents: list[JsonDict]) -> list[JsonDict]: """合并相邻同角色的 Gemini contents。""" if not contents: return contents merged = [contents[0]] for c in contents[1:]: if c['role'] == merged[-1]['role']: merged[-1]['parts'].extend(c['parts']) else: merged.append(c) return merged # ═══════════════════════════════════════════════════════════ # 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 []