chore: import upstream snapshot with attribution
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This commit is contained in:
@@ -0,0 +1,722 @@
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import { createLogger } from '@sim/logger'
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||||
import { getErrorMessage, toError } from '@sim/utils/errors'
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import OpenAI from 'openai'
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import type { ChatCompletionCreateParamsStreaming } from 'openai/resources/chat/completions'
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import { env } from '@/lib/core/config/env'
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import type { StreamingExecution } from '@/executor/types'
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import { MAX_TOOL_ITERATIONS } from '@/providers'
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import { formatMessagesForProvider } from '@/providers/attachments'
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import { createReadableStreamFromLiteLLMStream } from '@/providers/litellm/utils'
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import { getProviderDefaultModel, getProviderModels } from '@/providers/models'
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import { createStreamingExecution } from '@/providers/streaming-execution'
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import { adaptOpenAIChatToolSchema } from '@/providers/tool-schema-adapter'
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import { enrichLastModelSegmentFromChatCompletions } from '@/providers/trace-enrichment'
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import type {
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Message,
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ProviderConfig,
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ProviderRequest,
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ProviderResponse,
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TimeSegment,
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} from '@/providers/types'
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import { ProviderError } from '@/providers/types'
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import {
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calculateCost,
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enforceStrictSchema,
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prepareToolExecution,
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prepareToolsWithUsageControl,
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sumToolCosts,
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trackForcedToolUsage,
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} from '@/providers/utils'
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import { useProvidersStore } from '@/stores/providers'
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import { executeTool } from '@/tools'
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const logger = createLogger('LiteLLMProvider')
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const LITELLM_VERSION = '1.0.0'
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export const litellmProvider: ProviderConfig = {
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id: 'litellm',
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name: 'LiteLLM',
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description: 'LiteLLM proxy with OpenAI-compatible API',
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version: LITELLM_VERSION,
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models: getProviderModels('litellm'),
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defaultModel: getProviderDefaultModel('litellm'),
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async initialize() {
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if (typeof window !== 'undefined') {
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logger.info('Skipping LiteLLM initialization on client side to avoid CORS issues')
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return
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}
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const baseUrl = (env.LITELLM_BASE_URL || '').replace(/\/$/, '')
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if (!baseUrl) {
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logger.info('LITELLM_BASE_URL not configured, skipping initialization')
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return
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}
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try {
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const headers: Record<string, string> = {
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'Content-Type': 'application/json',
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}
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if (env.LITELLM_API_KEY) {
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headers.Authorization = `Bearer ${env.LITELLM_API_KEY}`
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}
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const response = await fetch(`${baseUrl}/v1/models`, { headers })
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if (!response.ok) {
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await response.text().catch(() => {})
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useProvidersStore.getState().setProviderModels('litellm', [])
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logger.warn('LiteLLM service is not available. The provider will be disabled.')
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return
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}
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const { vllmUpstreamResponseSchema } = await import('@/lib/api/contracts/providers')
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const data = vllmUpstreamResponseSchema.parse(await response.json())
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const models = data.data.map((model) => `litellm/${model.id}`)
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this.models = models
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useProvidersStore.getState().setProviderModels('litellm', models)
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logger.info(`Discovered ${models.length} LiteLLM model(s):`, { models })
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} catch (error) {
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logger.warn('LiteLLM model instantiation failed. The provider will be disabled.', {
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error: getErrorMessage(error, 'Unknown error'),
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})
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}
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},
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executeRequest: async (
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request: ProviderRequest
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): Promise<ProviderResponse | StreamingExecution> => {
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logger.info('Preparing LiteLLM request', {
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model: request.model,
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hasSystemPrompt: !!request.systemPrompt,
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hasMessages: !!request.messages?.length,
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hasTools: !!request.tools?.length,
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toolCount: request.tools?.length || 0,
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hasResponseFormat: !!request.responseFormat,
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stream: !!request.stream,
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})
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const baseUrl = (env.LITELLM_BASE_URL || '').replace(/\/$/, '')
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if (!baseUrl) {
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throw new Error('LITELLM_BASE_URL is required for LiteLLM provider')
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}
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const apiKey = request.apiKey || env.LITELLM_API_KEY || 'empty'
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const litellm = new OpenAI({
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apiKey,
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baseURL: `${baseUrl}/v1`,
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})
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const allMessages: Message[] = []
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if (request.systemPrompt) {
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allMessages.push({
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role: 'system',
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content: request.systemPrompt,
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})
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}
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if (request.context) {
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allMessages.push({
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role: 'user',
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content: request.context,
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})
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}
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if (request.messages) {
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allMessages.push(...request.messages)
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}
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const formattedMessages = formatMessagesForProvider(allMessages, 'litellm') as Message[]
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const tools = request.tools?.length
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? request.tools.map((tool) => adaptOpenAIChatToolSchema(tool))
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: undefined
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const payload: any = {
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model: request.model.replace(/^litellm\//, ''),
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messages: formattedMessages,
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}
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if (request.temperature !== undefined) payload.temperature = request.temperature
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if (request.maxTokens != null) payload.max_completion_tokens = request.maxTokens
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if (request.reasoningEffort !== undefined && request.reasoningEffort !== 'auto') {
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payload.reasoning_effort = request.reasoningEffort
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}
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const isStrictResponseFormat = request.responseFormat
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? request.responseFormat.strict !== false
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: false
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const responseFormatPayload = request.responseFormat
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? {
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type: 'json_schema' as const,
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json_schema: {
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name: request.responseFormat.name || 'response_schema',
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schema: isStrictResponseFormat
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? enforceStrictSchema(request.responseFormat.schema || request.responseFormat)
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: request.responseFormat.schema || request.responseFormat,
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strict: isStrictResponseFormat,
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},
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}
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: undefined
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let preparedTools: ReturnType<typeof prepareToolsWithUsageControl> | null = null
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let hasActiveTools = false
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if (tools?.length) {
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preparedTools = prepareToolsWithUsageControl(tools, request.tools, logger, 'litellm')
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const { tools: filteredTools, toolChoice } = preparedTools
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if (filteredTools?.length && toolChoice) {
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payload.tools = filteredTools
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payload.tool_choice = toolChoice
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hasActiveTools = true
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logger.info('LiteLLM request configuration:', {
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toolCount: filteredTools.length,
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toolChoice:
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typeof toolChoice === 'string'
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? toolChoice
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: toolChoice.type === 'function'
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? `force:${toolChoice.function.name}`
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: 'unknown',
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model: payload.model,
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})
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}
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}
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const deferResponseFormat = !!responseFormatPayload && hasActiveTools
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if (responseFormatPayload && !deferResponseFormat) {
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payload.response_format = responseFormatPayload
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logger.info('Added JSON schema response format to LiteLLM request')
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}
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const providerStartTime = Date.now()
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const providerStartTimeISO = new Date(providerStartTime).toISOString()
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try {
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if (request.stream && (!tools || tools.length === 0 || !hasActiveTools)) {
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logger.info('Using streaming response for LiteLLM request')
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const streamingParams: ChatCompletionCreateParamsStreaming = {
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...payload,
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stream: true,
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stream_options: { include_usage: true },
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}
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const streamResponse = await litellm.chat.completions.create(
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streamingParams,
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request.abortSignal ? { signal: request.abortSignal } : undefined
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)
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const streamingResult = createStreamingExecution({
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model: request.model,
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providerStartTime,
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providerStartTimeISO,
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timing: { kind: 'simple', segmentName: request.model },
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initialTokens: { input: 0, output: 0, total: 0 },
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initialCost: { input: 0, output: 0, total: 0 },
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isStreaming: true,
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createStream: ({ output, finalizeTiming }) =>
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createReadableStreamFromLiteLLMStream(streamResponse, (content, usage) => {
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let cleanContent = content
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if (cleanContent && request.responseFormat) {
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cleanContent = cleanContent.replace(/```json\n?|\n?```/g, '').trim()
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}
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output.content = cleanContent
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output.tokens = {
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input: usage.prompt_tokens,
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output: usage.completion_tokens,
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total: usage.total_tokens,
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}
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const costResult = calculateCost(
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request.model,
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usage.prompt_tokens,
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usage.completion_tokens
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)
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output.cost = {
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input: costResult.input,
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output: costResult.output,
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total: costResult.total,
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}
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finalizeTiming()
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}),
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})
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return streamingResult
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}
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const initialCallTime = Date.now()
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const originalToolChoice = payload.tool_choice
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const forcedTools = preparedTools?.forcedTools || []
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let usedForcedTools: string[] = []
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const checkForForcedToolUsage = (
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response: any,
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toolChoice: string | { type: string; function?: { name: string }; name?: string; any?: any }
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) => {
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if (typeof toolChoice === 'object' && response.choices[0]?.message?.tool_calls) {
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const toolCallsResponse = response.choices[0].message.tool_calls
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const result = trackForcedToolUsage(
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toolCallsResponse,
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toolChoice,
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logger,
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'litellm',
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forcedTools,
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usedForcedTools
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)
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hasUsedForcedTool = result.hasUsedForcedTool
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usedForcedTools = result.usedForcedTools
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}
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}
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let currentResponse = await litellm.chat.completions.create(
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payload,
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request.abortSignal ? { signal: request.abortSignal } : undefined
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)
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const firstResponseTime = Date.now() - initialCallTime
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let content = currentResponse.choices[0]?.message?.content || ''
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|
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if (content && request.responseFormat) {
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content = content.replace(/```json\n?|\n?```/g, '').trim()
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||||
}
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|
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const tokens = {
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input: currentResponse.usage?.prompt_tokens || 0,
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output: currentResponse.usage?.completion_tokens || 0,
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total: currentResponse.usage?.total_tokens || 0,
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||||
}
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const toolCalls = []
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const toolResults: Record<string, unknown>[] = []
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const currentMessages = [...formattedMessages]
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let iterationCount = 0
|
||||
|
||||
let modelTime = firstResponseTime
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let toolsTime = 0
|
||||
|
||||
let hasUsedForcedTool = false
|
||||
|
||||
const timeSegments: TimeSegment[] = [
|
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{
|
||||
type: 'model',
|
||||
name: request.model,
|
||||
startTime: initialCallTime,
|
||||
endTime: initialCallTime + firstResponseTime,
|
||||
duration: firstResponseTime,
|
||||
},
|
||||
]
|
||||
|
||||
checkForForcedToolUsage(currentResponse, originalToolChoice)
|
||||
|
||||
while (iterationCount < MAX_TOOL_ITERATIONS) {
|
||||
if (currentResponse.choices[0]?.message?.content) {
|
||||
content = currentResponse.choices[0].message.content
|
||||
if (request.responseFormat) {
|
||||
content = content.replace(/```json\n?|\n?```/g, '').trim()
|
||||
}
|
||||
}
|
||||
|
||||
const toolCallsInResponse = currentResponse.choices[0]?.message?.tool_calls
|
||||
|
||||
enrichLastModelSegmentFromChatCompletions(
|
||||
timeSegments,
|
||||
currentResponse,
|
||||
toolCallsInResponse,
|
||||
{ model: request.model, provider: 'litellm' }
|
||||
)
|
||||
|
||||
if (!toolCallsInResponse || toolCallsInResponse.length === 0) {
|
||||
break
|
||||
}
|
||||
|
||||
logger.info(
|
||||
`Processing ${toolCallsInResponse.length} tool calls (iteration ${iterationCount + 1}/${MAX_TOOL_ITERATIONS})`
|
||||
)
|
||||
|
||||
const toolsStartTime = Date.now()
|
||||
|
||||
const toolExecutionPromises = toolCallsInResponse.map(async (toolCall) => {
|
||||
const toolCallStartTime = Date.now()
|
||||
const toolName = toolCall.function.name
|
||||
|
||||
try {
|
||||
const toolArgs = toolCall.function.arguments
|
||||
? JSON.parse(toolCall.function.arguments)
|
||||
: {}
|
||||
const tool = request.tools?.find((t) => t.id === toolName)
|
||||
|
||||
if (!tool) return null
|
||||
|
||||
const { toolParams, executionParams } = prepareToolExecution(tool, toolArgs, request)
|
||||
const result = await executeTool(toolName, executionParams, {
|
||||
signal: request.abortSignal,
|
||||
})
|
||||
const toolCallEndTime = Date.now()
|
||||
|
||||
return {
|
||||
toolCall,
|
||||
toolName,
|
||||
toolParams,
|
||||
result,
|
||||
startTime: toolCallStartTime,
|
||||
endTime: toolCallEndTime,
|
||||
duration: toolCallEndTime - toolCallStartTime,
|
||||
}
|
||||
} catch (error) {
|
||||
const toolCallEndTime = Date.now()
|
||||
logger.error('Error processing tool call:', { error, toolName })
|
||||
|
||||
return {
|
||||
toolCall,
|
||||
toolName,
|
||||
toolParams: {},
|
||||
result: {
|
||||
success: false,
|
||||
output: undefined,
|
||||
error: getErrorMessage(error, 'Tool execution failed'),
|
||||
},
|
||||
startTime: toolCallStartTime,
|
||||
endTime: toolCallEndTime,
|
||||
duration: toolCallEndTime - toolCallStartTime,
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
const executionResults = await Promise.allSettled(toolExecutionPromises)
|
||||
|
||||
currentMessages.push({
|
||||
role: 'assistant',
|
||||
content: null,
|
||||
tool_calls: toolCallsInResponse.map((tc) => ({
|
||||
id: tc.id,
|
||||
type: 'function',
|
||||
function: {
|
||||
name: tc.function.name,
|
||||
arguments: tc.function.arguments,
|
||||
},
|
||||
})),
|
||||
})
|
||||
|
||||
const respondedToolCallIds = new Set<string>()
|
||||
|
||||
for (const settledResult of executionResults) {
|
||||
if (settledResult.status === 'rejected' || !settledResult.value) continue
|
||||
|
||||
const { toolCall, toolName, toolParams, result, startTime, endTime, duration } =
|
||||
settledResult.value
|
||||
|
||||
timeSegments.push({
|
||||
type: 'tool',
|
||||
name: toolName,
|
||||
startTime: startTime,
|
||||
endTime: endTime,
|
||||
duration: duration,
|
||||
toolCallId: toolCall.id,
|
||||
})
|
||||
|
||||
let resultContent: any
|
||||
if (result.success && result.output) {
|
||||
toolResults.push(result.output)
|
||||
resultContent = result.output
|
||||
} else {
|
||||
resultContent = {
|
||||
error: true,
|
||||
message: result.error || 'Tool execution failed',
|
||||
tool: toolName,
|
||||
}
|
||||
}
|
||||
|
||||
toolCalls.push({
|
||||
name: toolName,
|
||||
arguments: toolParams,
|
||||
startTime: new Date(startTime).toISOString(),
|
||||
endTime: new Date(endTime).toISOString(),
|
||||
duration: duration,
|
||||
result: resultContent,
|
||||
success: result.success,
|
||||
})
|
||||
|
||||
currentMessages.push({
|
||||
role: 'tool',
|
||||
tool_call_id: toolCall.id,
|
||||
name: toolName,
|
||||
content: JSON.stringify(resultContent),
|
||||
})
|
||||
respondedToolCallIds.add(toolCall.id)
|
||||
}
|
||||
|
||||
for (const tc of toolCallsInResponse) {
|
||||
if (respondedToolCallIds.has(tc.id)) continue
|
||||
currentMessages.push({
|
||||
role: 'tool',
|
||||
tool_call_id: tc.id,
|
||||
name: tc.function.name,
|
||||
content: JSON.stringify({
|
||||
error: true,
|
||||
message: `Tool "${tc.function.name}" is not available`,
|
||||
tool: tc.function.name,
|
||||
}),
|
||||
})
|
||||
}
|
||||
|
||||
const thisToolsTime = Date.now() - toolsStartTime
|
||||
toolsTime += thisToolsTime
|
||||
|
||||
const nextPayload = {
|
||||
...payload,
|
||||
messages: currentMessages,
|
||||
}
|
||||
|
||||
if (typeof originalToolChoice === 'object' && hasUsedForcedTool && forcedTools.length > 0) {
|
||||
const remainingTools = forcedTools.filter((tool) => !usedForcedTools.includes(tool))
|
||||
|
||||
if (remainingTools.length > 0) {
|
||||
nextPayload.tool_choice = {
|
||||
type: 'function',
|
||||
function: { name: remainingTools[0] },
|
||||
}
|
||||
logger.info(`Forcing next tool: ${remainingTools[0]}`)
|
||||
} else {
|
||||
nextPayload.tool_choice = 'auto'
|
||||
logger.info('All forced tools have been used, switching to auto tool_choice')
|
||||
}
|
||||
}
|
||||
|
||||
const nextModelStartTime = Date.now()
|
||||
|
||||
currentResponse = await litellm.chat.completions.create(
|
||||
nextPayload,
|
||||
request.abortSignal ? { signal: request.abortSignal } : undefined
|
||||
)
|
||||
|
||||
checkForForcedToolUsage(currentResponse, nextPayload.tool_choice)
|
||||
|
||||
const nextModelEndTime = Date.now()
|
||||
const thisModelTime = nextModelEndTime - nextModelStartTime
|
||||
|
||||
timeSegments.push({
|
||||
type: 'model',
|
||||
name: request.model,
|
||||
startTime: nextModelStartTime,
|
||||
endTime: nextModelEndTime,
|
||||
duration: thisModelTime,
|
||||
})
|
||||
|
||||
modelTime += thisModelTime
|
||||
|
||||
if (currentResponse.choices[0]?.message?.content) {
|
||||
content = currentResponse.choices[0].message.content
|
||||
if (request.responseFormat) {
|
||||
content = content.replace(/```json\n?|\n?```/g, '').trim()
|
||||
}
|
||||
}
|
||||
|
||||
if (currentResponse.usage) {
|
||||
tokens.input += currentResponse.usage.prompt_tokens || 0
|
||||
tokens.output += currentResponse.usage.completion_tokens || 0
|
||||
tokens.total += currentResponse.usage.total_tokens || 0
|
||||
}
|
||||
|
||||
iterationCount++
|
||||
}
|
||||
|
||||
if (iterationCount === MAX_TOOL_ITERATIONS) {
|
||||
enrichLastModelSegmentFromChatCompletions(
|
||||
timeSegments,
|
||||
currentResponse,
|
||||
currentResponse.choices[0]?.message?.tool_calls,
|
||||
{ model: request.model, provider: 'litellm' }
|
||||
)
|
||||
}
|
||||
|
||||
if (request.stream) {
|
||||
logger.info('Using streaming for final response after tool processing')
|
||||
|
||||
const accumulatedCost = calculateCost(request.model, tokens.input, tokens.output)
|
||||
|
||||
const streamingParams: ChatCompletionCreateParamsStreaming = {
|
||||
...payload,
|
||||
messages: currentMessages,
|
||||
tool_choice: 'none',
|
||||
stream: true,
|
||||
stream_options: { include_usage: true },
|
||||
}
|
||||
if (deferResponseFormat && responseFormatPayload) {
|
||||
streamingParams.response_format = responseFormatPayload
|
||||
streamingParams.parallel_tool_calls = false
|
||||
}
|
||||
const streamResponse = await litellm.chat.completions.create(
|
||||
streamingParams,
|
||||
request.abortSignal ? { signal: request.abortSignal } : undefined
|
||||
)
|
||||
|
||||
const streamingResult = createStreamingExecution({
|
||||
model: request.model,
|
||||
providerStartTime,
|
||||
providerStartTimeISO,
|
||||
timing: {
|
||||
kind: 'accumulated',
|
||||
modelTime,
|
||||
toolsTime,
|
||||
firstResponseTime,
|
||||
iterations: iterationCount + 1,
|
||||
timeSegments,
|
||||
},
|
||||
initialTokens: {
|
||||
input: tokens.input,
|
||||
output: tokens.output,
|
||||
total: tokens.total,
|
||||
},
|
||||
initialCost: {
|
||||
input: accumulatedCost.input,
|
||||
output: accumulatedCost.output,
|
||||
total: accumulatedCost.total,
|
||||
},
|
||||
toolCalls:
|
||||
toolCalls.length > 0
|
||||
? {
|
||||
list: toolCalls,
|
||||
count: toolCalls.length,
|
||||
}
|
||||
: undefined,
|
||||
isStreaming: true,
|
||||
createStream: ({ output }) =>
|
||||
createReadableStreamFromLiteLLMStream(streamResponse, (content, usage) => {
|
||||
let cleanContent = content
|
||||
if (cleanContent && request.responseFormat) {
|
||||
cleanContent = cleanContent.replace(/```json\n?|\n?```/g, '').trim()
|
||||
}
|
||||
|
||||
output.content = cleanContent
|
||||
output.tokens = {
|
||||
input: tokens.input + usage.prompt_tokens,
|
||||
output: tokens.output + usage.completion_tokens,
|
||||
total: tokens.total + usage.total_tokens,
|
||||
}
|
||||
|
||||
const streamCost = calculateCost(
|
||||
request.model,
|
||||
usage.prompt_tokens,
|
||||
usage.completion_tokens
|
||||
)
|
||||
const tc = sumToolCosts(toolResults)
|
||||
output.cost = {
|
||||
input: accumulatedCost.input + streamCost.input,
|
||||
output: accumulatedCost.output + streamCost.output,
|
||||
toolCost: tc || undefined,
|
||||
total: accumulatedCost.total + streamCost.total + tc,
|
||||
}
|
||||
}),
|
||||
})
|
||||
|
||||
return streamingResult
|
||||
}
|
||||
|
||||
if (deferResponseFormat && responseFormatPayload) {
|
||||
logger.info('Applying deferred JSON schema response format after tool processing')
|
||||
|
||||
const finalFormatStartTime = Date.now()
|
||||
const finalPayload: any = {
|
||||
...payload,
|
||||
messages: currentMessages,
|
||||
response_format: responseFormatPayload,
|
||||
tool_choice: 'none',
|
||||
parallel_tool_calls: false,
|
||||
}
|
||||
|
||||
currentResponse = await litellm.chat.completions.create(
|
||||
finalPayload,
|
||||
request.abortSignal ? { signal: request.abortSignal } : undefined
|
||||
)
|
||||
|
||||
const finalFormatEndTime = Date.now()
|
||||
timeSegments.push({
|
||||
type: 'model',
|
||||
name: request.model,
|
||||
startTime: finalFormatStartTime,
|
||||
endTime: finalFormatEndTime,
|
||||
duration: finalFormatEndTime - finalFormatStartTime,
|
||||
})
|
||||
modelTime += finalFormatEndTime - finalFormatStartTime
|
||||
|
||||
const formattedContent = currentResponse.choices[0]?.message?.content
|
||||
if (formattedContent) {
|
||||
content = formattedContent.replace(/```json\n?|\n?```/g, '').trim()
|
||||
}
|
||||
|
||||
if (currentResponse.usage) {
|
||||
tokens.input += currentResponse.usage.prompt_tokens || 0
|
||||
tokens.output += currentResponse.usage.completion_tokens || 0
|
||||
tokens.total += currentResponse.usage.total_tokens || 0
|
||||
}
|
||||
|
||||
enrichLastModelSegmentFromChatCompletions(
|
||||
timeSegments,
|
||||
currentResponse,
|
||||
currentResponse.choices[0]?.message?.tool_calls,
|
||||
{ model: request.model, provider: 'litellm' }
|
||||
)
|
||||
}
|
||||
|
||||
const providerEndTime = Date.now()
|
||||
const providerEndTimeISO = new Date(providerEndTime).toISOString()
|
||||
const totalDuration = providerEndTime - providerStartTime
|
||||
|
||||
return {
|
||||
content,
|
||||
model: request.model,
|
||||
tokens,
|
||||
toolCalls: toolCalls.length > 0 ? toolCalls : undefined,
|
||||
toolResults: toolResults.length > 0 ? toolResults : undefined,
|
||||
timing: {
|
||||
startTime: providerStartTimeISO,
|
||||
endTime: providerEndTimeISO,
|
||||
duration: totalDuration,
|
||||
modelTime: modelTime,
|
||||
toolsTime: toolsTime,
|
||||
firstResponseTime: firstResponseTime,
|
||||
iterations: iterationCount + 1,
|
||||
timeSegments: timeSegments,
|
||||
},
|
||||
}
|
||||
} catch (error) {
|
||||
const providerEndTime = Date.now()
|
||||
const providerEndTimeISO = new Date(providerEndTime).toISOString()
|
||||
const totalDuration = providerEndTime - providerStartTime
|
||||
|
||||
let errorMessage = toError(error).message
|
||||
let errorType: string | undefined
|
||||
let errorCode: string | number | undefined
|
||||
|
||||
if (error && typeof error === 'object' && 'error' in error) {
|
||||
const litellmError = error.error as any
|
||||
if (litellmError && typeof litellmError === 'object') {
|
||||
errorMessage = litellmError.message || errorMessage
|
||||
errorType = litellmError.type
|
||||
errorCode = litellmError.code
|
||||
}
|
||||
}
|
||||
|
||||
logger.error('Error in LiteLLM request:', {
|
||||
error: errorMessage,
|
||||
errorType,
|
||||
errorCode,
|
||||
duration: totalDuration,
|
||||
})
|
||||
|
||||
throw new ProviderError(errorMessage, {
|
||||
startTime: providerStartTimeISO,
|
||||
endTime: providerEndTimeISO,
|
||||
duration: totalDuration,
|
||||
})
|
||||
}
|
||||
},
|
||||
}
|
||||
Reference in New Issue
Block a user