@coursebuilder/core/dist/providers/openai.cjs.map

Core package for Course Builder

{"version":3,"sources":["../../src/providers/openai.ts"],"sourcesContent":["import { createOpenAI } from '@ai-sdk/openai'\nimport { streamText, type CoreMessage } from 'ai'\n\nimport { AIOutput } from '../types'\n\nexport const STREAM_COMPLETE = `\\\\ok`\n\n/**\n * PartyKit chunk publisher that buffers and sends chunks at intervals\n * to maintain the expected streaming behavior for the UI\n */\nclass PartyKitChunkPublisher {\n\trequestId: string\n\tinterval = 250\n\tbuffer: {\n\t\tcontents: string\n\t\tsignal?: Promise<unknown>\n\t}\n\tpartyUrl: string\n\n\tconstructor(requestId: string, partyUrlBase: string) {\n\t\tthis.requestId = requestId\n\t\tthis.buffer = {\n\t\t\tcontents: '',\n\t\t}\n\t\tthis.partyUrl = `${partyUrlBase}/party/${requestId}`\n\t}\n\n\tasync publishMessage(message: string) {\n\t\tawait this.sendToPartyKit(message, this.requestId, this.partyUrl)\n\t}\n\n\tasync appendToBufferAndPublish(text: string) {\n\t\tlet resolve = (_val?: any) => {}\n\t\tthis.buffer.contents += text\n\n\t\tif (this.buffer.signal) {\n\t\t\t// Already enqueued.\n\t\t\treturn\n\t\t}\n\n\t\tthis.buffer.signal = new Promise((r) => {\n\t\t\tresolve = r\n\t\t})\n\n\t\tsetTimeout(() => {\n\t\t\tif (this.buffer.contents.length === 0) {\n\t\t\t\tresolve()\n\t\t\t\treturn\n\t\t\t}\n\t\t\tthis.sendToPartyKit(this.buffer.contents, this.requestId, this.partyUrl)\n\t\t\tresolve()\n\t\t\tthis.buffer = {\n\t\t\t\tcontents: '',\n\t\t\t}\n\t\t}, this.interval)\n\t}\n\n\tasync waitForBuffer() {\n\t\tawait this.buffer.signal\n\t}\n\n\tprivate async sendToPartyKit(\n\t\tbody: string,\n\t\trequestId: string,\n\t\tpartyUrl: string,\n\t) {\n\t\treturn await fetch(partyUrl, {\n\t\t\tmethod: 'POST',\n\t\t\tbody: JSON.stringify({\n\t\t\t\tbody,\n\t\t\t\trequestId,\n\t\t\t\tname: 'ai.message',\n\t\t\t}),\n\t\t}).catch((e) => {\n\t\t\tconsole.error('Failed to send chunk to PartyKit:', e)\n\t\t})\n\t}\n}\n\nexport interface LlmProviderConfig {\n\tid: string\n\tname: string\n\ttype: string\n\toptions: LlmProviderConsumerConfig\n\tapiKey: string\n\tpartyUrlBase: string\n\tbaseUrl?: string\n\tdefaultModel?: string\n\tcreateChatCompletion: (\n\t\toptions: CreateChatCompletionOptions,\n\t) => Promise<AIOutput | null>\n}\n\nexport type LlmProviderConsumerConfig = Omit<\n\tPartial<LlmProviderConfig>,\n\t'options' | 'type'\n> & {\n\tapiKey: string\n\tpartyUrlBase: string\n\tbaseUrl?: string\n\tdefaultModel?: string\n}\n\nexport type CreateChatCompletionOptions = {\n\tmessages: CoreMessage[]\n\tchatId: string\n\tmodel: string\n}\n\nexport default function OpenAIProvider(\n\toptions: LlmProviderConsumerConfig,\n): LlmProviderConfig {\n\tconst client = createOpenAI({\n\t\tapiKey: options.apiKey,\n\t\t...(options.baseUrl && {\n\t\t\tbaseURL: options.baseUrl,\n\t\t}),\n\t})\n\n\treturn {\n\t\tid: 'openai',\n\t\tname: 'OpenAI',\n\t\ttype: 'llm',\n\t\toptions,\n\t\t...options,\n\t\tcreateChatCompletion: async (\n\t\t\tcreateChatOptions: CreateChatCompletionOptions,\n\t\t) => {\n\t\t\ttry {\n\t\t\t\tconst modelName =\n\t\t\t\t\tcreateChatOptions.model || options.defaultModel || 'gpt-4o'\n\n\t\t\t\t// Create PartyKit publisher with buffering behavior\n\t\t\t\tconst publisher = new PartyKitChunkPublisher(\n\t\t\t\t\tcreateChatOptions.chatId,\n\t\t\t\t\toptions.partyUrlBase,\n\t\t\t\t)\n\n\t\t\t\tconst result = await streamText({\n\t\t\t\t\tmodel: client(modelName),\n\t\t\t\t\tmessages: createChatOptions.messages,\n\t\t\t\t\tonChunk: async ({ chunk }) => {\n\t\t\t\t\t\tif (chunk.type === 'text-delta') {\n\t\t\t\t\t\t\t// Use the buffered publisher to maintain expected streaming behavior\n\t\t\t\t\t\t\tawait publisher.appendToBufferAndPublish(chunk.textDelta)\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t})\n\n\t\t\t\t// We need to consume the stream to make result.text resolve\n\t\t\t\t// Since we're already handling chunks in onChunk, we can consume textStream to completion\n\t\t\t\tlet fullText = ''\n\t\t\t\tfor await (const textPart of result.textStream) {\n\t\t\t\t\tfullText += textPart\n\t\t\t\t}\n\n\t\t\t\t// Wait for any remaining buffered content to be sent\n\t\t\t\tawait publisher.waitForBuffer()\n\n\t\t\t\t// Send completion signal using the expected format\n\t\t\t\tawait publisher.publishMessage(STREAM_COMPLETE)\n\n\t\t\t\treturn {\n\t\t\t\t\trole: 'assistant',\n\t\t\t\t\tcontent: fullText,\n\t\t\t\t}\n\t\t\t} catch (error) {\n\t\t\t\tconsole.error('OpenAI streaming error:', error)\n\t\t\t\tthrow error\n\t\t\t}\n\t\t},\n\t} as const\n}\n\nexport const MockOpenAIProvider: LlmProviderConfig = {\n\tid: 'mock-openai' as const,\n\tname: 'Mock OpenAI',\n\ttype: 'llm',\n\toptions: {\n\t\tapiKey: 'mock-api-key',\n\t\tpartyUrlBase: 'mock-callback-url',\n\t},\n\tapiKey: 'mock-api-key',\n\tpartyUrlBase: 'mock-callback-url',\n\tcreateChatCompletion: () => 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