742e21a727
- Introduced LLM Provider declarations in plugin manifests, allowing plugins to specify their LLM capabilities. - Implemented validation for LLM Provider declarations to prevent duplicates and conflicts. - Enhanced the PluginRunner to handle LLM Provider invocation requests, enabling plugins to interact with LLM Providers seamlessly. - Added a ClientRegistry to manage LLM Provider registrations and ensure no conflicts arise between different plugins. - Created a PluginLLMClient to facilitate communication with LLM Providers through the plugin runtime. - Developed tests to ensure proper registration and conflict handling of LLM Providers.
192 lines
7.5 KiB
Python
192 lines
7.5 KiB
Python
from typing import Any, Dict, List
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from src.config.model_configs import APIProvider
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from src.llm_models.exceptions import RespParseException
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from src.llm_models.model_client.base_client import (
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APIResponse,
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AudioTranscriptionRequest,
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BaseClient,
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EmbeddingRequest,
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ResponseRequest,
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UsageRecord,
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)
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from src.llm_models.request_snapshot import (
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deserialize_tool_calls_snapshot,
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serialize_audio_request_snapshot,
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serialize_embedding_request_snapshot,
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serialize_response_request_snapshot,
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)
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class PluginLLMClient(BaseClient):
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"""通过插件 Runner RPC 调用第三方 LLM Provider 的客户端代理。"""
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def __init__(
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self,
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api_provider: APIProvider,
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supervisor: Any,
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plugin_id: str,
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client_type: str,
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) -> None:
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"""初始化插件 LLM Provider 客户端代理。
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Args:
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api_provider: 当前 API Provider 配置。
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supervisor: 拥有目标插件的 Supervisor。
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plugin_id: 目标插件 ID。
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client_type: 目标客户端类型。
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"""
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super().__init__(api_provider)
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self._supervisor = supervisor
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self._plugin_id = plugin_id
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self._client_type = client_type
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async def get_response(self, request: ResponseRequest) -> APIResponse:
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"""获取对话响应。
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Args:
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request: 统一响应请求对象。
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Returns:
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APIResponse: 统一响应对象。
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Raises:
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RespParseException: 插件返回内容无法转换为统一响应时抛出。
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"""
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if request.stream_response_handler is not None or request.async_response_parser is not None:
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raise RespParseException(message="插件 LLM Provider 暂不支持 Host 侧自定义流式处理器或响应解析器")
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payload = serialize_response_request_snapshot(request)
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result = await self._invoke_provider("response", payload)
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return self._build_api_response(result, request.model_info.name, request.model_info.api_provider)
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async def get_embedding(self, request: EmbeddingRequest) -> APIResponse:
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"""获取文本嵌入。
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Args:
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request: 统一嵌入请求对象。
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Returns:
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APIResponse: 嵌入响应。
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"""
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result = await self._invoke_provider("embedding", serialize_embedding_request_snapshot(request))
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return self._build_api_response(result, request.model_info.name, request.model_info.api_provider)
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async def get_audio_transcriptions(self, request: AudioTranscriptionRequest) -> APIResponse:
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"""获取音频转录。
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Args:
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request: 统一音频转录请求对象。
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Returns:
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APIResponse: 音频转录响应。
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"""
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result = await self._invoke_provider("audio_transcription", serialize_audio_request_snapshot(request))
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return self._build_api_response(result, request.model_info.name, request.model_info.api_provider)
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def get_support_image_formats(self) -> List[str]:
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"""获取支持的图片格式。
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Returns:
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List[str]: 插件 Provider 默认接收的图片格式列表。
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"""
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return ["jpeg", "jpg", "png", "webp"]
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def _build_api_provider_snapshot(self) -> Dict[str, Any]:
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"""构建可传给插件 Provider 的 API Provider 配置快照。
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Returns:
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Dict[str, Any]: 包含认证信息的 API Provider 配置字典。
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"""
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return self.api_provider.model_dump(mode="json")
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async def _invoke_provider(self, operation: str, request_payload: Dict[str, Any]) -> Dict[str, Any]:
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"""调用插件 Provider。
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Args:
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operation: 请求操作类型。
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request_payload: 已序列化的内部请求。
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Returns:
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Dict[str, Any]: 插件返回的响应字典。
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Raises:
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RespParseException: 插件调用失败或返回格式不合法时抛出。
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"""
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try:
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response = await self._supervisor.invoke_llm_provider(
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plugin_id=self._plugin_id,
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client_type=self._client_type,
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operation=operation,
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request={
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**request_payload,
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"api_provider": self._build_api_provider_snapshot(),
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},
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timeout_ms=max(1000, int(self.api_provider.timeout) * 1000),
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)
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except Exception as exc:
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raise RespParseException(message=f"插件 LLM Provider RPC 调用失败: {exc}") from exc
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if response.error:
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raise RespParseException(message=str(response.error.get("message", "插件 LLM Provider 调用失败")))
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payload = response.payload if isinstance(response.payload, dict) else {}
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success = bool(payload.get("success", False))
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result = payload.get("result")
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if not success:
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raise RespParseException(message=str(result or "插件 LLM Provider 返回失败"))
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if not isinstance(result, dict):
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raise RespParseException(message="插件 LLM Provider 返回值必须是字典")
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return result
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@staticmethod
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def _build_usage_record(raw_usage: Any, model_name: str, provider_name: str) -> UsageRecord | None:
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"""从插件返回值恢复使用量记录。
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Args:
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raw_usage: 插件返回的使用量字段。
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model_name: 当前模型名称。
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provider_name: 当前 Provider 名称。
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Returns:
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UsageRecord | None: 可挂载到 APIResponse 的使用量记录；缺失时返回 ``None``。
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"""
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if not isinstance(raw_usage, dict):
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return None
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return UsageRecord(
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model_name=str(raw_usage.get("model_name") or model_name),
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provider_name=str(raw_usage.get("provider_name") or provider_name),
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prompt_tokens=int(raw_usage.get("prompt_tokens") or 0),
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completion_tokens=int(raw_usage.get("completion_tokens") or 0),
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total_tokens=int(raw_usage.get("total_tokens") or 0),
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prompt_cache_hit_tokens=int(raw_usage.get("prompt_cache_hit_tokens") or 0),
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prompt_cache_miss_tokens=int(raw_usage.get("prompt_cache_miss_tokens") or 0),
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)
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@staticmethod
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def _build_api_response(result: Dict[str, Any], model_name: str, provider_name: str) -> APIResponse:
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"""从插件返回值恢复统一 APIResponse。
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Args:
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result: 插件返回的响应字典。
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model_name: 当前模型名称。
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provider_name: 当前 Provider 名称。
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Returns:
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APIResponse: 统一响应对象。
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"""
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raw_embedding = result.get("embedding")
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embedding = [float(item) for item in raw_embedding] if isinstance(raw_embedding, list) else None
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content = result.get("content")
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if not isinstance(content, str):
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content = result.get("response")
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reasoning_content = result.get("reasoning_content")
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if not isinstance(reasoning_content, str):
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reasoning_content = result.get("reasoning")
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return APIResponse(
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content=content if isinstance(content, str) else None,
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reasoning_content=reasoning_content if isinstance(reasoning_content, str) else None,
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tool_calls=deserialize_tool_calls_snapshot(result.get("tool_calls")) or None,
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embedding=embedding,
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usage=PluginLLMClient._build_usage_record(result.get("usage"), model_name, provider_name),
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raw_data=result.get("raw_data", result),
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)
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