diff --git a/src/renderer/src/aiCore/clients/gemini/GeminiAPIClient.ts b/src/renderer/src/aiCore/clients/gemini/GeminiAPIClient.ts index 0cafcc406..dc1bf08c2 100644 --- a/src/renderer/src/aiCore/clients/gemini/GeminiAPIClient.ts +++ b/src/renderer/src/aiCore/clients/gemini/GeminiAPIClient.ts @@ -561,6 +561,7 @@ export class GeminiAPIClient extends BaseApiClient< let isFirstThinkingChunk = true return () => ({ async transform(chunk: GeminiSdkRawChunk, controller: TransformStreamDefaultController) { + logger.silly('chunk', chunk) if (chunk.candidates && chunk.candidates.length > 0) { for (const candidate of chunk.candidates) { if (candidate.content) { diff --git a/src/renderer/src/aiCore/middleware/common/FinalChunkConsumerMiddleware.ts b/src/renderer/src/aiCore/middleware/common/FinalChunkConsumerMiddleware.ts index 0b563349c..cffbd4e11 100644 --- a/src/renderer/src/aiCore/middleware/common/FinalChunkConsumerMiddleware.ts +++ b/src/renderer/src/aiCore/middleware/common/FinalChunkConsumerMiddleware.ts @@ -64,6 +64,7 @@ const FinalChunkConsumerMiddleware: CompletionsMiddleware = try { while (true) { const { done, value: chunk } = await reader.read() + logger.silly('chunk', chunk) if (done) { logger.debug(`Input stream finished.`) break diff --git a/src/renderer/src/aiCore/middleware/core/McpToolChunkMiddleware.ts b/src/renderer/src/aiCore/middleware/core/McpToolChunkMiddleware.ts index 2642d8bef..5110564f5 100644 --- a/src/renderer/src/aiCore/middleware/core/McpToolChunkMiddleware.ts +++ b/src/renderer/src/aiCore/middleware/core/McpToolChunkMiddleware.ts @@ -1,8 +1,15 @@ import { loggerService } from '@logger' -import { MCPTool, MCPToolResponse, Model, ToolCallResponse } from '@renderer/types' +import { MCPCallToolResponse, MCPTool, MCPToolResponse, Model, ToolCallResponse } from '@renderer/types' import { ChunkType, MCPToolCreatedChunk } from '@renderer/types/chunk' import { SdkMessageParam, SdkRawOutput, SdkToolCall } from '@renderer/types/sdk' -import { parseAndCallTools } from '@renderer/utils/mcp-tools' +import { + callMCPTool, + getMcpServerByTool, + isToolAutoApproved, + parseToolUse, + upsertMCPToolResponse +} from '@renderer/utils/mcp-tools' +import { confirmSameNameTools, requestToolConfirmation, setToolIdToNameMapping } from '@renderer/utils/userConfirmation' import { CompletionsParams, CompletionsResult, GenericChunk } from '../schemas' import { CompletionsContext, CompletionsMiddleware } from '../types' @@ -369,4 +376,207 @@ function getCurrentReqMessages(ctx: CompletionsContext): SdkMessageParam[] { return ctx.apiClientInstance.extractMessagesFromSdkPayload(sdkPayload) } -export default McpToolChunkMiddleware +export async function parseAndCallTools( + tools: MCPToolResponse[], + allToolResponses: MCPToolResponse[], + onChunk: CompletionsParams['onChunk'], + convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined, + model: Model, + mcpTools?: MCPTool[], + abortSignal?: AbortSignal, + topicId?: CompletionsParams['topicId'] +): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }> + +export async function parseAndCallTools( + content: string, + allToolResponses: MCPToolResponse[], + onChunk: CompletionsParams['onChunk'], + convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined, + model: Model, + mcpTools?: MCPTool[], + abortSignal?: AbortSignal, + topicId?: CompletionsParams['topicId'] +): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }> + +export async function parseAndCallTools( + content: string | MCPToolResponse[], + allToolResponses: MCPToolResponse[], + onChunk: CompletionsParams['onChunk'], + convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined, + model: Model, + mcpTools?: MCPTool[], + abortSignal?: AbortSignal, + topicId?: CompletionsParams['topicId'] +): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }> { + const toolResults: R[] = [] + let curToolResponses: MCPToolResponse[] = [] + if (Array.isArray(content)) { + curToolResponses = content + } else { + // process tool use + curToolResponses = parseToolUse(content, mcpTools || [], 0) + } + if (!curToolResponses || curToolResponses.length === 0) { + return { toolResults, confirmedToolResponses: [] } + } + + for (const toolResponse of curToolResponses) { + upsertMCPToolResponse( + allToolResponses, + { + ...toolResponse, + status: 'pending' + }, + onChunk! + ) + } + + // 创建工具确认Promise映射，并立即处理每个确认 + const confirmedTools: MCPToolResponse[] = [] + const pendingPromises: Promise[] = [] + + curToolResponses.forEach((toolResponse) => { + const server = getMcpServerByTool(toolResponse.tool) + const isAutoApproveEnabled = isToolAutoApproved(toolResponse.tool, server) + let confirmationPromise: Promise + if (isAutoApproveEnabled) { + confirmationPromise = Promise.resolve(true) + } else { + setToolIdToNameMapping(toolResponse.id, toolResponse.tool.name) + + confirmationPromise = requestToolConfirmation(toolResponse.id, abortSignal).then((confirmed) => { + if (confirmed && server) { + // 自动确认其他同名的待确认工具 + confirmSameNameTools(toolResponse.tool.name) + } + return confirmed + }) + } + + const processingPromise = confirmationPromise + .then(async (confirmed) => { + if (confirmed) { + // 立即更新为invoking状态 + upsertMCPToolResponse( + allToolResponses, + { + ...toolResponse, + status: 'invoking' + }, + onChunk! + ) + + // 执行工具调用 + try { + const images: string[] = [] + const toolCallResponse = await callMCPTool(toolResponse, topicId, model.name) + + // 立即更新为done状态 + upsertMCPToolResponse( + allToolResponses, + { + ...toolResponse, + status: 'done', + response: toolCallResponse + }, + onChunk! + ) + + // 处理图片 + for (const content of toolCallResponse.content) { + if (content.type === 'image' && content.data) { + images.push(`data:${content.mimeType};base64,${content.data}`) + } + } + + if (images.length) { + onChunk?.({ + type: ChunkType.IMAGE_CREATED + }) + onChunk?.({ + type: ChunkType.IMAGE_COMPLETE, + image: { + type: 'base64', + images: images + } + }) + } + + // 转换消息并添加到结果 + const convertedMessage = convertToMessage(toolResponse, toolCallResponse, model) + if (convertedMessage) { + confirmedTools.push(toolResponse) + toolResults.push(convertedMessage) + } + } catch (error) { + logger.error(`Error executing tool ${toolResponse.id}:`, error) + // 更新为错误状态 + upsertMCPToolResponse( + allToolResponses, + { + ...toolResponse, + status: 'done', + response: { + isError: true, + content: [ + { + type: 'text', + text: `Error executing tool: ${error instanceof Error ? error.message : 'Unknown error'}` + } + ] + } + }, + onChunk! + ) + } + } else { + // 立即更新为cancelled状态 + upsertMCPToolResponse( + allToolResponses, + { + ...toolResponse, + status: 'cancelled', + response: { + isError: false, + content: [ + { + type: 'text', + text: 'Tool call cancelled by user.' + } + ] + } + }, + onChunk! + ) + } + }) + .catch((error) => { + logger.error(`Error waiting for tool confirmation ${toolResponse.id}:`, error) + // 立即更新为cancelled状态 + upsertMCPToolResponse( + allToolResponses, + { + ...toolResponse, + status: 'cancelled', + response: { + isError: true, + content: [ + { + type: 'text', + text: `Error in confirmation process: ${error instanceof Error ? error.message : 'Unknown error'}` + } + ] + } + }, + onChunk! + ) + }) + + pendingPromises.push(processingPromise) + }) + + // 等待所有工具处理完成（但每个工具的状态已经实时更新） + await Promise.all(pendingPromises) + + return { toolResults, confirmedToolResponses: confirmedTools } +} diff --git a/src/renderer/src/services/__tests__/ApiService.test.ts b/src/renderer/src/services/__tests__/ApiService.test.ts index 6fd37370d..2a7bf2958 100644 --- a/src/renderer/src/services/__tests__/ApiService.test.ts +++ b/src/renderer/src/services/__tests__/ApiService.test.ts @@ -6,8 +6,10 @@ import { ApiClientFactory } from '@renderer/aiCore/clients/ApiClientFactory' import { BaseApiClient } from '@renderer/aiCore/clients/BaseApiClient' import { GeminiAPIClient } from '@renderer/aiCore/clients/gemini/GeminiAPIClient' import { GenericChunk } from '@renderer/aiCore/middleware/schemas' -import { Assistant, Provider, WebSearchSource } from '@renderer/types' +import { isVisionModel } from '@renderer/config/models' +import { Assistant, MCPCallToolResponse, MCPToolResponse, Model, Provider, WebSearchSource } from '@renderer/types' import { + Chunk, ChunkType, LLMResponseCompleteChunk, LLMWebSearchCompleteChunk, @@ -15,7 +17,15 @@ import { TextStartChunk, ThinkingStartChunk } from '@renderer/types/chunk' -import { GeminiSdkRawChunk, OpenAISdkRawChunk, OpenAISdkRawContentSource } from '@renderer/types/sdk' +import { + GeminiSdkMessageParam, + GeminiSdkRawChunk, + GeminiSdkToolCall, + OpenAISdkRawChunk, + OpenAISdkRawContentSource +} from '@renderer/types/sdk' +import * as McpToolsModule from '@renderer/utils/mcp-tools' +import { mcpToolCallResponseToGeminiMessage } from '@renderer/utils/mcp-tools' import { cloneDeep } from 'lodash' import OpenAI from 'openai' import { ChatCompletionChunk } from 'openai/resources' @@ -610,11 +620,32 @@ const geminiToolUseChunks: GeminiSdkRawChunk[] = [ ], role: 'model' }, - finishReason: FinishReason.STOP, index: 0 } ], usageMetadata: {} + } as GeminiSdkRawChunk, + { + candidates: [ + { + content: { + parts: [ + { + functionCall: { + name: 'mcp-tool-1', + args: { + name: 'alice', + age: 13 + } + } as GeminiSdkToolCall + } + ], + role: 'model' + }, + finishReason: FinishReason.STOP + } + ], + usageMetadata: {} } as GeminiSdkRawChunk ] @@ -895,6 +926,7 @@ const mockOpenaiApiClient = { choice.delta && Object.keys(choice.delta).length > 0 && (!('content' in choice.delta) || + (choice.delta.tool_calls && choice.delta.tool_calls.length > 0) || (typeof choice.delta.content === 'string' && choice.delta.content !== '') || (typeof (choice.delta as any).reasoning_content === 'string' && (choice.delta as any).reasoning_content !== '') || @@ -1141,6 +1173,14 @@ mockGeminiThinkingApiClient.createCompletions = vi.fn().mockImplementation(() => const mockGeminiToolUseApiClient = cloneDeep(mockGeminiApiClient) mockGeminiToolUseApiClient.createCompletions = vi.fn().mockImplementation(() => geminiToolUseChunkGenerator()) +mockGeminiToolUseApiClient.convertMcpToolResponseToSdkMessageParam = vi + .fn() + .mockImplementation( + (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model): GeminiSdkMessageParam | undefined => { + // mcp使用tooluse + return mcpToolCallResponseToGeminiMessage(mcpToolResponse, resp, isVisionModel(model)) + } + ) const mockProvider = { id: 'gemini', @@ -1537,199 +1577,235 @@ describe('ApiService', () => { expect(filteredChunks).toEqual(expectedChunks) }) - // it('should extract tool use responses correctly', async () => { - // const mockCreate = vi.mocked(ApiClientFactory.create) - // mockCreate.mockReturnValue(mockGeminiToolUseApiClient as unknown as BaseApiClient) - // const AI = new AiProvider(mockProvider) - // const spy = vi.spyOn(McpToolsModule, 'callMCPTool') - // spy.mockResolvedValue({ - // content: [{ type: 'text', text: 'test' }], - // isError: false - // }) + it('should extract tool use responses correctly', async () => { + const mockCreate = vi.mocked(ApiClientFactory.create) + mockCreate.mockReturnValue(mockGeminiToolUseApiClient as unknown as BaseApiClient) + const AI = new AiProvider(mockProvider) - // const result = await AI.completions({ - // callType: 'test', - // messages: [], - // assistant: { - // id: '1', - // name: 'test', - // prompt: 'test', - // model: { - // id: 'gemini-2.5-pro', - // name: 'Gemini 2.5 Pro' - // }, - // settings: { - // toolUseMode: 'prompt' - // } - // } as Assistant, - // mcpTools: [ - // { - // id: 'mcp-tool-1', - // name: 'mcp-tool-1', - // serverId: 'mcp-server-1', - // serverName: 'mcp-server-1', - // description: 'mcp-tool-1', - // inputSchema: { - // type: 'object', - // title: 'mcp-tool-1', - // properties: { - // name: { type: 'string' }, - // age: { type: 'number' } - // }, - // description: 'print the name and age', - // required: ['name', 'age'] - // } - // } - // ], - // onChunk: mockOnChunk, - // enableReasoning: true, - // streamOutput: true - // }) + const mcpChunks: GenericChunk[] = [] + const firstResponseChunks: GenericChunk[] = [] - // expect(result).toBeDefined() - // expect(ApiClientFactory.create).toHaveBeenCalledWith(mockProvider) - // expect(result.stream).toBeDefined() + const spy = vi.spyOn(McpToolsModule, 'callMCPTool') + spy.mockResolvedValue({ + content: [{ type: 'text', text: 'test' }], + isError: false + }) - // const stream = result.stream! as ReadableStream - // const reader = stream.getReader() + const onChunk = vi.fn((chunk: Chunk) => { + mcpChunks.push(chunk) + }) - // const chunks: GenericChunk[] = [] + const result = await AI.completions({ + callType: 'test', + messages: [], + assistant: { + id: '1', + name: 'test', + prompt: 'test', + model: { + id: 'gemini-2.5-pro', + name: 'Gemini 2.5 Pro' + }, + settings: { + toolUseMode: 'prompt' + } + } as Assistant, + mcpTools: [ + { + id: 'mcp-tool-1', + name: 'mcp-tool-1', + serverId: 'mcp-server-1', + serverName: 'mcp-server-1', + description: 'mcp-tool-1', + inputSchema: { + type: 'object', + title: 'mcp-tool-1', + properties: { + name: { type: 'string' }, + age: { type: 'number' } + }, + description: 'print the name and age', + required: ['name', 'age'] + } + } + ], + onChunk, + enableReasoning: true, + streamOutput: true + }) - // while (true) { - // const { done, value } = await reader.read() - // if (done) break - // chunks.push(value) - // } + expect(result).toBeDefined() + expect(ApiClientFactory.create).toHaveBeenCalledWith(mockProvider) + expect(result.stream).toBeDefined() - // reader.releaseLock() + const stream = result.stream! as ReadableStream + const reader = stream.getReader() - // const filteredChunks = chunks.map((chunk) => { - // if (chunk.type === ChunkType.THINKING_DELTA || chunk.type === ChunkType.THINKING_COMPLETE) { - // delete (chunk as any).thinking_millsec - // return chunk - // } - // return chunk - // }) + while (true) { + const { done, value: chunk } = await reader.read() + if (done) break + firstResponseChunks.push(chunk) + } - // const expectedChunks: GenericChunk[] = [ - // { - // type: ChunkType.THINKING_START - // }, - // { - // type: ChunkType.THINKING_DELTA, - // text: '**Initiating File Retrieval**\n\nI\'ve determined that the `tool_mcp-tool-1` tool is suitable for this task. It seems the user intends to read a file, and this tool aligns with that objective. Currently, I\'m focusing on the necessary parameters. The `tool_mcp-tool-1` tool requires a `name` and `age`, which the user has helpfully provided: `{"name": "xxx", "age": 20}`. I\'m verifying the input.\n\n\n' - // }, - // { - // type: ChunkType.THINKING_COMPLETE, - // text: '**Initiating File Retrieval**\n\nI\'ve determined that the `tool_mcp-tool-1` tool is suitable for this task. It seems the user intends to read a file, and this tool aligns with that objective. Currently, I\'m focusing on the necessary parameters. The `tool_mcp-tool-1` tool requires a `name` and `age`, which the user has helpfully provided: `{"name": "xxx", "age": 20}`. I\'m verifying the input.\n\n\n' - // }, - // { - // type: ChunkType.TEXT_START - // }, - // { - // type: ChunkType.TEXT_DELTA, - // text: '好的，我将为您打印用户的' - // }, - // { - // type: ChunkType.TEXT_DELTA, - // text: '好的，我将为您打印用户的信息。\n' - // }, - // { - // type: ChunkType.TEXT_COMPLETE, - // text: '好的，我将为您打印用户的信息。\n' - // }, - // { - // type: ChunkType.MCP_TOOL_CREATED - // }, - // { - // type: ChunkType.MCP_TOOL_PENDING, - // responses: [ - // { - // id: 'mcp-tool-1', - // tool: { - // id: 'mcp-tool-1', - // serverId: 'mcp-server-1', - // serverName: 'mcp-server-1', - // name: 'mcp-tool-1', - // inputSchema: { - // type: 'object', - // title: 'mcp-tool-1', - // properties: { - // name: { type: 'string' }, - // age: { type: 'number' } - // }, - // description: 'print the name and age', - // required: ['name', 'age'] - // } - // }, - // arguments: { - // name: 'xxx', - // age: 20 - // }, - // status: 'pending' - // } - // ] - // }, - // { - // type: ChunkType.MCP_TOOL_IN_PROGRESS, - // responses: [ - // { - // id: 'mcp-tool-1', - // tool: { - // id: 'mcp-tool-1', - // serverId: 'mcp-server-1', - // serverName: 'mcp-server-1', - // name: 'mcp-tool-1', - // inputSchema: { - // type: 'object', - // title: 'mcp-tool-1', - // properties: { - // name: { type: 'string' }, - // age: { type: 'number' } - // }, - // description: 'print the name and age', - // required: ['name', 'age'] - // } - // }, - // arguments: { - // name: 'xxx', - // age: 20 - // }, - // status: 'invoking' - // } - // ] - // }, - // { - // type: ChunkType.MCP_TOOL_COMPLETE, - // responses: [ - // { - // id: 'mcp-tool-1', - // tool: { - // id: 'mcp-tool-1', - // serverId: 'mcp-server-1', - // serverName: 'mcp-server-1', - // name: 'mcp-tool-1', - // inputSchema: { - // type: 'object', - // title: 'mcp-tool-1', - // properties: { - // name: { type: 'string' }, - // age: { type: 'number' } - // }, - // description: 'print the name and age', - // required: ['name', 'age'] - // } - // }, - // arguments: { - // name: 'xxx', - // age: 20 - // }, - // status: 'done' - // } - // ] - // } - // ] + reader.releaseLock() - // expect(filteredChunks).toEqual(expectedChunks) - // }) + const filteredFirstResponseChunks = firstResponseChunks.map((chunk) => { + if (chunk.type === ChunkType.THINKING_DELTA || chunk.type === ChunkType.THINKING_COMPLETE) { + delete (chunk as any).thinking_millsec + return chunk + } + return chunk + }) + + const expectedFirstResponseChunks: GenericChunk[] = [ + { + type: ChunkType.THINKING_START + }, + { + type: ChunkType.THINKING_DELTA, + text: '**Initiating File Retrieval**\n\nI\'ve determined that the `tool_mcp-tool-1` tool is suitable for this task. It seems the user intends to read a file, and this tool aligns with that objective. Currently, I\'m focusing on the necessary parameters. The `tool_mcp-tool-1` tool requires a `name` and `age`, which the user has helpfully provided: `{"name": "xxx", "age": 20}`. I\'m verifying the input.\n\n\n' + }, + { + type: ChunkType.THINKING_COMPLETE, + text: '**Initiating File Retrieval**\n\nI\'ve determined that the `tool_mcp-tool-1` tool is suitable for this task. It seems the user intends to read a file, and this tool aligns with that objective. Currently, I\'m focusing on the necessary parameters. The `tool_mcp-tool-1` tool requires a `name` and `age`, which the user has helpfully provided: `{"name": "xxx", "age": 20}`. I\'m verifying the input.\n\n\n' + }, + { + type: ChunkType.TEXT_START + }, + { + type: ChunkType.TEXT_DELTA, + text: '好的，我将为您打印用户的' + }, + { + type: ChunkType.TEXT_DELTA, + text: '好的，我将为您打印用户的信息。\n' + }, + { + type: ChunkType.TEXT_COMPLETE, + text: '好的，我将为您打印用户的信息。\n' + }, + { + type: ChunkType.LLM_RESPONSE_COMPLETE, + response: { + usage: { + completion_tokens: 0, + prompt_tokens: 0, + total_tokens: 0 + } + } + } + ] + + const expectedMcpResponseChunks: GenericChunk[] = [ + { + type: ChunkType.MCP_TOOL_PENDING, + responses: [ + { + id: 'mcp-tool-1-0', + tool: { + description: 'mcp-tool-1', + id: 'mcp-tool-1', + serverId: 'mcp-server-1', + serverName: 'mcp-server-1', + name: 'mcp-tool-1', + inputSchema: { + type: 'object', + title: 'mcp-tool-1', + properties: { + name: { type: 'string' }, + age: { type: 'number' } + }, + description: 'print the name and age', + required: ['name', 'age'] + } + }, + toolUseId: 'mcp-tool-1', + arguments: { + name: 'xxx', + age: 20 + }, + status: 'pending' + } + ] + }, + { + type: ChunkType.MCP_TOOL_IN_PROGRESS, + responses: [ + { + id: 'mcp-tool-1-0', + response: undefined, + tool: { + description: 'mcp-tool-1', + id: 'mcp-tool-1', + serverId: 'mcp-server-1', + serverName: 'mcp-server-1', + name: 'mcp-tool-1', + inputSchema: { + type: 'object', + title: 'mcp-tool-1', + properties: { + name: { type: 'string' }, + age: { type: 'number' } + }, + description: 'print the name and age', + required: ['name', 'age'] + } + }, + toolUseId: 'mcp-tool-1', + arguments: { + name: 'xxx', + age: 20 + }, + status: 'invoking' + } + ] + }, + { + type: ChunkType.MCP_TOOL_COMPLETE, + responses: [ + { + id: 'mcp-tool-1-0', + tool: { + description: 'mcp-tool-1', + id: 'mcp-tool-1', + serverId: 'mcp-server-1', + serverName: 'mcp-server-1', + name: 'mcp-tool-1', + inputSchema: { + type: 'object', + title: 'mcp-tool-1', + properties: { + name: { type: 'string' }, + age: { type: 'number' } + }, + description: 'print the name and age', + required: ['name', 'age'] + } + }, + response: { + content: [ + { + text: 'test', + type: 'text' + } + ], + isError: false + }, + toolUseId: 'mcp-tool-1', + arguments: { + name: 'xxx', + age: 20 + }, + status: 'done' + } + ] + }, + { + type: ChunkType.LLM_RESPONSE_CREATED + } + ] + + expect(filteredFirstResponseChunks).toEqual(expectedFirstResponseChunks) + expect(mcpChunks).toEqual(expectedMcpResponseChunks) + }) }) diff --git a/src/renderer/src/utils/mcp-tools.ts b/src/renderer/src/utils/mcp-tools.ts index 71409241d..095aacaeb 100644 --- a/src/renderer/src/utils/mcp-tools.ts +++ b/src/renderer/src/utils/mcp-tools.ts @@ -27,9 +27,6 @@ import { ChatCompletionTool } from 'openai/resources' -import { CompletionsParams } from '../aiCore/middleware/schemas' -import { confirmSameNameTools, requestToolConfirmation, setToolIdToNameMapping } from './userConfirmation' - const logger = loggerService.withContext('Utils:MCPTools') const MCP_AUTO_INSTALL_SERVER_NAME = '@cherry/mcp-auto-install' @@ -534,211 +531,6 @@ export function parseToolUse(content: string, mcpTools: MCPTool[], startIdx: num return tools } -export async function parseAndCallTools( - tools: MCPToolResponse[], - allToolResponses: MCPToolResponse[], - onChunk: CompletionsParams['onChunk'], - convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined, - model: Model, - mcpTools?: MCPTool[], - abortSignal?: AbortSignal, - topicId?: CompletionsParams['topicId'] -): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }> - -export async function parseAndCallTools( - content: string, - allToolResponses: MCPToolResponse[], - onChunk: CompletionsParams['onChunk'], - convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined, - model: Model, - mcpTools?: MCPTool[], - abortSignal?: AbortSignal, - topicId?: CompletionsParams['topicId'] -): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }> - -export async function parseAndCallTools( - content: string | MCPToolResponse[], - allToolResponses: MCPToolResponse[], - onChunk: CompletionsParams['onChunk'], - convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined, - model: Model, - mcpTools?: MCPTool[], - abortSignal?: AbortSignal, - topicId?: CompletionsParams['topicId'] -): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }> { - const toolResults: R[] = [] - let curToolResponses: MCPToolResponse[] = [] - if (Array.isArray(content)) { - curToolResponses = content - } else { - // process tool use - curToolResponses = parseToolUse(content, mcpTools || [], 0) - } - if (!curToolResponses || curToolResponses.length === 0) { - return { toolResults, confirmedToolResponses: [] } - } - - for (const toolResponse of curToolResponses) { - upsertMCPToolResponse( - allToolResponses, - { - ...toolResponse, - status: 'pending' - }, - onChunk! - ) - } - - // 创建工具确认Promise映射，并立即处理每个确认 - const confirmedTools: MCPToolResponse[] = [] - const pendingPromises: Promise[] = [] - - curToolResponses.forEach((toolResponse) => { - const server = getMcpServerByTool(toolResponse.tool) - const isAutoApproveEnabled = isToolAutoApproved(toolResponse.tool, server) - let confirmationPromise: Promise - if (isAutoApproveEnabled) { - confirmationPromise = Promise.resolve(true) - } else { - setToolIdToNameMapping(toolResponse.id, toolResponse.tool.name) - - confirmationPromise = requestToolConfirmation(toolResponse.id, abortSignal).then((confirmed) => { - if (confirmed && server) { - // 自动确认其他同名的待确认工具 - confirmSameNameTools(toolResponse.tool.name) - } - return confirmed - }) - } - - const processingPromise = confirmationPromise - .then(async (confirmed) => { - if (confirmed) { - // 立即更新为invoking状态 - upsertMCPToolResponse( - allToolResponses, - { - ...toolResponse, - status: 'invoking' - }, - onChunk! - ) - - // 执行工具调用 - try { - const images: string[] = [] - const toolCallResponse = await callMCPTool(toolResponse, topicId, model.name) - - // 立即更新为done状态 - upsertMCPToolResponse( - allToolResponses, - { - ...toolResponse, - status: 'done', - response: toolCallResponse - }, - onChunk! - ) - - // 处理图片 - for (const content of toolCallResponse.content) { - if (content.type === 'image' && content.data) { - images.push(`data:${content.mimeType};base64,${content.data}`) - } - } - - if (images.length) { - onChunk?.({ - type: ChunkType.IMAGE_CREATED - }) - onChunk?.({ - type: ChunkType.IMAGE_COMPLETE, - image: { - type: 'base64', - images: images - } - }) - } - - // 转换消息并添加到结果 - const convertedMessage = convertToMessage(toolResponse, toolCallResponse, model) - if (convertedMessage) { - confirmedTools.push(toolResponse) - toolResults.push(convertedMessage) - } - } catch (error) { - logger.error(`Error executing tool ${toolResponse.id}:`, error) - // 更新为错误状态 - upsertMCPToolResponse( - allToolResponses, - { - ...toolResponse, - status: 'done', - response: { - isError: true, - content: [ - { - type: 'text', - text: `Error executing tool: ${error instanceof Error ? error.message : 'Unknown error'}` - } - ] - } - }, - onChunk! - ) - } - } else { - // 立即更新为cancelled状态 - upsertMCPToolResponse( - allToolResponses, - { - ...toolResponse, - status: 'cancelled', - response: { - isError: false, - content: [ - { - type: 'text', - text: 'Tool call cancelled by user.' - } - ] - } - }, - onChunk! - ) - } - }) - .catch((error) => { - logger.error(`Error waiting for tool confirmation ${toolResponse.id}:`, error) - // 立即更新为cancelled状态 - upsertMCPToolResponse( - allToolResponses, - { - ...toolResponse, - status: 'cancelled', - response: { - isError: true, - content: [ - { - type: 'text', - text: `Error in confirmation process: ${error instanceof Error ? error.message : 'Unknown error'}` - } - ] - } - }, - onChunk! - ) - }) - - pendingPromises.push(processingPromise) - }) - - // 等待所有工具处理完成（但每个工具的状态已经实时更新） - await Promise.all(pendingPromises) - - return { toolResults, confirmedToolResponses: confirmedTools } -} - export function mcpToolCallResponseToOpenAICompatibleMessage( mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse,