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type { LogLevel, LogTransport } from \"@/types\"\n\nexport default class ProviderLogger {\n private _transports: LogTransport[] = []\n private _prefix: string\n private _severity: LogLevel\n\n constructor(prefix?: string, severity: LogLevel = \"info\") {\n this._prefix = prefix ?? \"\"\n this._severity = severity\n }\n\n public log(logLevel: LogLevel, message: string | Error) {\n switch (logLevel) {\n case \"debug\":\n if (this._severity === \"debug\") {\n this._logToTransports(\"debug\", this._prefix, message)\n }\n break\n case \"info\":\n if (this._severity === \"debug\" || this._severity === \"info\") {\n this._logToTransports(\"info\", this._prefix, message)\n }\n break\n case \"warn\":\n if (this._severity === \"debug\" || this._severity === \"info\" || this._severity === \"warn\") {\n this._logToTransports(\"warn\", this._prefix, message)\n }\n break\n case \"error\":\n this._logToTransports(\"error\", this._prefix, message)\n break\n }\n }\n\n public debug(message: string) {\n this._logToTransports(\"debug\", this._prefix, message)\n }\n\n public info(message: string) {\n this._logToTransports(\"info\", this._prefix, message)\n }\n\n public warn(message: string) {\n this._logToTransports(\"warn\", this._prefix, message)\n }\n\n public error(message: string | Error) {\n this._logToTransports(\"error\", this._prefix, message)\n }\n\n public addTransport(transport: LogTransport) {\n this._transports.push(transport)\n\n return () => {\n this._transports.splice(this._transports.indexOf(transport), 1)\n }\n }\n\n private _logToTransports(level: LogLevel, prefix: string, message: string | Error) {\n const timestamp = new Date().toTimeString().split(\" \")[0]\n\n for (const transport of this._transports) {\n transport(level, message, timestamp, prefix)\n }\n }\n}\n","import { LogTransport } from \"@/types\"\n\nexport const consoleTransport: LogTransport = (level, message, timestamp, prefix) => {\n const log = {\n [\"debug\"]: console.debug,\n [\"info\"]: console.info,\n [\"warn\"]: console.warn,\n [\"error\"]: console.error\n }[level]\n\n log(`LLM-CLIENT--${prefix} ${timestamp}: ${message}`)\n}\n","import ProviderLogger from \"@/logger\"\nimport { consoleTransport } from \"@/logger/transports/console\"\nimport {\n AnthropicChatCompletionParams,\n AnthropicChatCompletionParamsNonStream,\n AnthropicChatCompletionParamsStream,\n ExtendedCompletionAnthropic,\n ExtendedCompletionChunkAnthropic,\n LogLevel,\n OpenAILikeClient\n} from \"@/types\"\nimport Anthropic from \"@anthropic-ai/sdk\"\nimport OpenAI, { ClientOptions } from \"openai\"\n\n/**\n * AnthropicProvider is a class that provides an interface for interacting with the Anthropic API.\n * It implements the OpenAILikeClient interface and allows users to create chat completions using\n * the Anthropic API.\n */\nexport class AnthropicProvider extends Anthropic implements OpenAILikeClient<\"anthropic\"> {\n public apiKey: string\n public logLevel: LogLevel = (process.env?.[\"LOG_LEVEL\"] as LogLevel) ?? \"info\"\n private logger: ProviderLogger\n\n /**\n * Constructs a new instance of the AnthropicProvider class.\n * @param opts - An optional ClientOptions object containing the API key.\n */\n constructor(\n opts?: ClientOptions & {\n logLevel?: LogLevel\n }\n ) {\n const apiKey = opts?.apiKey ?? process.env?.[\"ANTHROPIC_API_KEY\"] ?? null\n\n if (!apiKey) {\n throw new Error(\n \"API key is required for AnthropicProvider - please provide it in the constructor or set it as an environment variable named ANTHROPIC_API_KEY.\"\n )\n }\n\n super({ apiKey })\n\n this.logLevel = opts?.logLevel ?? this.logLevel\n this.apiKey = apiKey\n this.logger = new ProviderLogger(\"GEMINI-CLIENT\")\n this.logger.addTransport(consoleTransport)\n }\n [key: string]: unknown\n\n /**\n * Transforms the Anthropic API response into an ExtendedCompletionAnthropic or ExtendedCompletionChunkAnthropic object.\n * @param result - The Anthropic API response.\n * @param stream - An optional parameter indicating whether the response is a stream.\n * @returns A Promise that resolves to an ExtendedCompletionAnthropic or ExtendedCompletionChunkAnthropic object.\n */\n private async transformResponse(\n result: Anthropic.Messages.Message,\n { stream }: { stream?: boolean } = {}\n ): Promise {\n if (!result.id) throw new Error(\"Response id is undefined\")\n this.logger.log(\"debug\", `Response: ${result}`)\n\n result.content.forEach(content => {\n content.type === \"tool_use\"\n ? this.logger.log(\"debug\", `JSON Summary: ${JSON.stringify(content.input, null, 2)}`)\n : this.logger.log(\n \"debug\",\n `No JSON summary found in the response. \n ${JSON.stringify(content, null, 2)}`\n )\n })\n\n const transformedResponse = {\n id: result.id,\n originResponse: result,\n model: result.model,\n usage: {\n prompt_tokens: result.usage.input_tokens,\n completion_tokens: result.usage.output_tokens,\n total_tokens: result.usage.input_tokens + result.usage.output_tokens\n }\n }\n\n if (!stream) {\n const toolUseBlocks = result.content.filter(\n block => block.type === \"tool_use\"\n ) as Anthropic.ToolUseBlock[]\n\n const resultTextBlocks = result.content.filter(\n block => block.type === \"text\"\n ) as Anthropic.TextBlock[]\n\n const tool_calls = toolUseBlocks.map(block => ({\n id: block.id,\n type: \"function\",\n function: {\n name: block.name,\n arguments: JSON.stringify(block.input)\n }\n }))\n\n const content = resultTextBlocks.map(choice => choice.text).join(\"\")\n\n return {\n ...transformedResponse,\n object: \"chat.completion\",\n choices: [\n {\n message: {\n role: \"assistant\",\n content,\n ...(tool_calls?.length ? { tool_calls } : {})\n },\n finish_reason: tool_calls?.length ? \"tool_calls\" : \"stop\",\n index: 0,\n logprobs: null\n } as OpenAI.ChatCompletion.Choice\n ]\n }\n }\n\n return transformedResponse\n }\n\n /**\n * Transforms the OpenAI chat completion parameters into Anthropic chat completion parameters.\n * @param params - The OpenAI chat completion parameters.\n * @returns The transformed Anthropic chat completion parameters.\n */\n private transformParamsRegular(\n params: AnthropicChatCompletionParams\n ): Anthropic.Messages.MessageCreateParams {\n let tools: Anthropic.Tool[] = []\n\n const systemMessages = params.messages.filter(message => message.role === \"system\")\n\n const messages = params.messages.filter(\n message => message.role === \"user\" || message.role === \"assistant\"\n ) as Anthropic.MessageParam[]\n\n const system = systemMessages?.length\n ? systemMessages.map(message => message.content).join(\"\\n\")\n : \"\"\n\n if (systemMessages.length) {\n console.warn(\n \"Anthropic does not support system messages - concatenating them all into a single 'system' property.\"\n )\n }\n\n if (!params.max_tokens) {\n throw new Error(\"max_tokens is required\")\n }\n\n if (\"tools\" in params && Array.isArray(params.tools) && params.tools.length > 0) {\n tools = params.tools.map(tool => ({\n name: tool.function.name ?? \"\",\n description: tool.function.description ?? \"\",\n input_schema: {\n type: \"object\",\n ...tool.function.parameters\n }\n }))\n }\n\n return {\n model: params.model,\n tools,\n system: system?.length ? system : undefined,\n messages,\n max_tokens: params.max_tokens,\n stop_sequences: params.stop\n ? Array.isArray(params.stop)\n ? params.stop\n : [params.stop]\n : undefined,\n temperature: params.temperature ?? undefined,\n top_p: params.top_p ?? undefined,\n top_k: params.n ?? undefined,\n stream: params?.stream ?? false,\n tool_choice:\n \"tool_choice\" in params &&\n typeof params.tool_choice === \"object\" &&\n \"function\" in params.tool_choice\n ? {\n type: \"tool\",\n name: params.tool_choice.function.name\n }\n : undefined\n }\n }\n\n /**\n * Streams the chat completion response from the Anthropic API.\n * @param response - The Response object from the Anthropic API.\n * @returns An asynchronous iterable of ExtendedCompletionChunkAnthropic objects.\n */\n private async *streamChatCompletion(\n response: AsyncIterable\n ): AsyncIterable {\n let finalChatCompletion: ExtendedCompletionChunkAnthropic | null = null\n\n for await (const event of response) {\n switch (event.type) {\n case \"message_start\":\n this.logger.log(\"debug\", `Message start: ${event}`)\n finalChatCompletion = {\n id: event.message.id,\n object: \"chat.completion.chunk\",\n created: Date.now(),\n model: event.message.model,\n choices: [\n {\n index: 0,\n delta: { role: \"assistant\" },\n finish_reason: null\n }\n ],\n usage: {\n prompt_tokens: event.message.usage.input_tokens,\n completion_tokens: 0,\n total_tokens: event.message.usage.input_tokens\n },\n originResponse: event.message\n }\n\n yield finalChatCompletion\n break\n\n case \"content_block_start\":\n this.logger.log(\"debug\", `Content block start: ${event}`)\n break\n\n case \"content_block_delta\":\n if (finalChatCompletion && finalChatCompletion.choices) {\n if (event.delta.type === \"text_delta\") {\n finalChatCompletion.choices[0].delta = {\n content: event.delta.text,\n role: \"assistant\"\n }\n } else if (event.delta.type === \"input_json_delta\") {\n finalChatCompletion.choices[0].delta = {\n content: event.delta.partial_json,\n role: \"assistant\"\n }\n }\n\n yield finalChatCompletion\n }\n break\n\n case \"content_block_stop\":\n this.logger.log(\"debug\", `Content block stop: ${event}`)\n break\n\n case \"message_delta\":\n if (finalChatCompletion && finalChatCompletion.usage) {\n finalChatCompletion.usage.completion_tokens = event.usage?.output_tokens || 0\n finalChatCompletion.usage.total_tokens =\n finalChatCompletion.usage.prompt_tokens + finalChatCompletion.usage.completion_tokens\n }\n break\n\n case \"message_stop\":\n this.logger.log(\"debug\", `Message stop: ${event}`)\n if (finalChatCompletion && finalChatCompletion.choices) {\n finalChatCompletion.choices[0].finish_reason = \"stop\"\n finalChatCompletion.choices[0].delta = {\n content: null,\n role: \"assistant\"\n }\n yield finalChatCompletion\n }\n break\n\n default:\n this.logger.log(\"warn\", `Unknown event type: ${event}`)\n }\n }\n }\n /**\n * Creates a chat completion using the Anthropic API.\n * @param params - The chat completion parameters.\n * @returns A Promise that resolves to an ExtendedCompletionAnthropic object or an asynchronous iterable of ExtendedCompletionChunkAnthropic objects if streaming is enabled.\n */\n public async create(\n params: AnthropicChatCompletionParamsStream\n ): Promise>\n\n public async create(\n params: AnthropicChatCompletionParamsNonStream\n ): Promise\n\n public async create(\n params: AnthropicChatCompletionParams\n ): Promise | ExtendedCompletionAnthropic> {\n try {\n const anthropicParams = this.transformParamsRegular(params)\n\n if (params.stream) {\n this.logger.log(\"debug\", \"Starting streaming completion response\")\n\n const messageStream = await this.messages.stream({\n ...anthropicParams\n })\n\n return this.streamChatCompletion(messageStream)\n } else {\n const result = await this.messages.create({\n ...anthropicParams,\n stream: false\n })\n\n const transformedResult = await this.transformResponse(result)\n\n return transformedResult as ExtendedCompletionAnthropic\n }\n } catch (error) {\n this.logger.error(new Error(\"Error in Anthropic API request:\", { cause: error }))\n throw error\n }\n }\n\n public chat = {\n completions: {\n create: this.create.bind(this)\n }\n }\n}\n","import ProviderLogger from \"@/logger\"\nimport { consoleTransport } from \"@/logger/transports/console\"\nimport {\n ExtendedCompletionChunkGoogle,\n ExtendedCompletionGoogle,\n GoogleCacheCreateParams,\n GoogleChatCompletionParams,\n GoogleChatCompletionParamsNonStream,\n GoogleChatCompletionParamsStream,\n LogLevel,\n OpenAILikeClient\n} from \"@/types\"\nimport {\n ChatSession,\n Content,\n DynamicRetrievalMode,\n EnhancedGenerateContentResponse,\n FunctionCallingMode,\n FunctionDeclaration,\n GenerationConfig,\n GoogleGenerativeAI,\n GoogleGenerativeAIError,\n GroundingMetadata,\n SafetySetting,\n StartChatParams,\n Tool,\n ToolConfig\n} from \"@google/generative-ai\"\nimport { CachedContentUpdateParams, GoogleAICacheManager } from \"@google/generative-ai/server\"\nimport { ClientOptions } from \"openai\"\n\ninterface ExtendedAdditionalProperties {\n cacheName?: string\n sessionId?: string\n safetySettings?: SafetySetting[]\n modelGenerationConfig?: GenerationConfig\n}\n\ninterface ModelConfig {\n model: string\n safetySettings?: SafetySetting[]\n generationConfig?: GenerationConfig\n tools?: Tool[]\n systemInstruction?: string | Content | undefined\n}\ninterface GroundingMetadataExtended extends GroundingMetadata {\n webSearchQueries: string[]\n groundingChunks?: Array<{\n web?: {\n uri: string\n title: string\n }\n }>\n searchEntryPoint?: {\n renderedContent: string\n }\n groundingSupports?: Array<{\n segment: {\n startIndex?: number\n endIndex?: number\n text: string\n }\n groundingChunkIndices: number[]\n confidenceScores: number[]\n }>\n}\n\ninterface ExtendedChoice {\n index: number\n message: {\n role: string\n content: string\n tool_calls?: Array<{\n index: number\n id: string\n function: {\n name: string\n arguments: string\n }\n type: string\n }>\n }\n finish_reason: string | null\n logprobs?: null\n grounding_metadata?: {\n search_queries: string[]\n sources: Array<{\n url: string\n title: string\n }>\n search_suggestion_html: string | undefined\n }\n}\n\nexport class GoogleProvider extends GoogleGenerativeAI implements OpenAILikeClient<\"google\"> {\n public apiKey: string\n public logLevel: LogLevel = (process.env?.[\"LOG_LEVEL\"] as LogLevel) ?? \"info\"\n private googleCacheManager\n private logger: ProviderLogger\n private activeChatSessions: Map = new Map()\n\n constructor(\n opts?: ClientOptions & {\n logLevel?: LogLevel\n groundingThreshold?: number\n }\n ) {\n const apiKey = opts?.apiKey ?? process.env?.[\"GEMINI_API_KEY\"] ?? null\n\n if (!apiKey) {\n throw new Error(\n \"API key is required for GeminiProvider - please provide it in the constructor or set it as an environment variable named GEMINI_API_KEY.\"\n )\n }\n\n super(apiKey)\n\n this.logLevel = opts?.logLevel ?? this.logLevel\n this.apiKey = apiKey\n this.googleCacheManager = new GoogleAICacheManager(apiKey)\n this.logger = new ProviderLogger(\"GEMINI-CLIENT\")\n this.logger.addTransport(consoleTransport)\n }\n\n private cleanSchema(schema: Record): Record {\n const { additionalProperties, _additionalProperties, ...rest } = schema\n\n this.logger.log(\n this.logLevel,\n `Removing unsupported 'additionalProperties' from schema - ${JSON.stringify(\n additionalProperties ?? {}\n )}`\n )\n\n if (rest[\"properties\"] && typeof rest[\"properties\"] === \"object\") {\n rest[\"properties\"] = Object.entries(rest[\"properties\"]).reduce(\n (acc, [key, value]) => {\n acc[key] = typeof value === \"object\" && value !== null ? this.cleanSchema(value) : value\n return acc\n },\n {} as Record\n )\n }\n\n if (rest[\"items\"] && typeof rest[\"items\"] === \"object\" && rest[\"items\"] !== null) {\n rest[\"items\"] = this.cleanSchema(rest[\"items\"] as Record)\n }\n\n return rest\n }\n\n /**\n * Creates a generation config from the provided parameters\n */\n private createGenerationConfig(params: GoogleChatCompletionParams): GenerationConfig {\n return {\n temperature: params.temperature ?? undefined,\n topP: params.top_p ?? undefined,\n topK: params.n ?? undefined,\n maxOutputTokens: params.max_tokens ?? undefined,\n stopSequences: params.stop\n ? Array.isArray(params.stop)\n ? params.stop\n : [params.stop]\n : undefined,\n candidateCount: params.n ?? undefined\n }\n }\n\n /**\n * Transforms messages into Google's chat history format\n */\n private transformHistory(messages: GoogleChatCompletionParams[\"messages\"]): Content[] {\n return messages.map(message => ({\n role: message.role === \"assistant\" ? \"model\" : \"user\",\n parts: [{ text: message.content.toString() }]\n }))\n }\n\n private getModelConfig(params: GoogleChatCompletionParams) {\n const additionalProps = params.additionalProperties as ExtendedAdditionalProperties | undefined\n\n const modelConfig: ModelConfig = {\n model: params?.model,\n safetySettings: additionalProps?.safetySettings,\n generationConfig: additionalProps?.modelGenerationConfig\n }\n\n if (params.groundingThreshold !== undefined) {\n modelConfig.tools = [\n {\n googleSearchRetrieval: {\n dynamicRetrievalConfig: {\n mode: DynamicRetrievalMode.MODE_DYNAMIC,\n dynamicThreshold: params.groundingThreshold\n }\n }\n }\n ]\n }\n\n if (params.systemInstruction) {\n modelConfig.systemInstruction = params.systemInstruction\n }\n\n return modelConfig\n }\n\n /**\n * Gets or creates a chat session\n */\n private async getChatSession(params: GoogleChatCompletionParams): Promise {\n const additionalProps = params.additionalProperties as ExtendedAdditionalProperties | undefined\n const sessionId = additionalProps?.sessionId\n\n if (sessionId && this.activeChatSessions.has(sessionId)) {\n return this.activeChatSessions.get(sessionId)!\n }\n\n const generationConfig = this.createGenerationConfig(params)\n const history = this.transformHistory(params.messages)\n\n let generativeModel\n\n if (additionalProps?.cacheName) {\n const cache = await this.googleCacheManager.get(additionalProps.cacheName)\n generativeModel = this.getGenerativeModelFromCachedContent(cache)\n } else {\n generativeModel = this.getGenerativeModel(this.getModelConfig(params))\n }\n\n const chatParams: StartChatParams = {\n generationConfig,\n history\n }\n\n if (params.tools?.length) {\n const functionDeclarations = params.tools.map(tool => ({\n name: tool.function.name ?? \"\",\n description: tool.function.description ?? \"\",\n parameters: {\n type: \"object\",\n ...(tool.function.parameters ? this.cleanSchema(tool.function.parameters) : {})\n }\n })) as FunctionDeclaration[]\n\n const toolChoice = params.tool_choice as\n | { type: \"function\"; function: { name: string } }\n | undefined\n\n chatParams.tools = [\n {\n functionDeclarations\n }\n ] as Tool[]\n\n if (toolChoice?.type === \"function\") {\n chatParams.toolConfig = {\n functionCallingConfig: {\n mode: FunctionCallingMode.ANY,\n allowedFunctionNames: [toolChoice.function.name]\n }\n } satisfies ToolConfig\n }\n }\n\n const chatSession = generativeModel.startChat(chatParams)\n\n if (sessionId) {\n this.activeChatSessions.set(sessionId, chatSession)\n }\n\n return chatSession\n }\n\n /**\n * Transforms the Google API response into an ExtendedCompletionGoogle or ExtendedCompletionChunkGoogle object.\n */\n private transformResponse(\n responseDataChunk: EnhancedGenerateContentResponse,\n params: GoogleChatCompletionParams\n ): ExtendedCompletionGoogle | ExtendedCompletionChunkGoogle {\n const responseText = responseDataChunk.text()\n const toolCalls =\n responseDataChunk.candidates?.[0]?.content?.parts?.flatMap(part =>\n part.functionCall\n ? [\n {\n index: 0,\n id: `call_${Math.random().toString(36).slice(2)}`,\n function: {\n name: part.functionCall.name,\n arguments: JSON.stringify(part.functionCall.args)\n },\n type: \"function\"\n }\n ]\n : []\n ) ?? []\n\n const groundingMetadata = responseDataChunk.candidates?.[0]?.groundingMetadata as\n | GroundingMetadataExtended\n | undefined\n\n let contentWithGrounding = responseText\n let sources: Array<{ url: string; title: string }> = []\n\n if (groundingMetadata) {\n sources =\n groundingMetadata.groundingChunks?.map(chunk => ({\n url: chunk.web?.uri ?? \"\",\n title: chunk.web?.title ?? \"\"\n })) ?? []\n\n if (groundingMetadata.groundingSupports?.length) {\n contentWithGrounding += \"\\n\\n**Grounded Segments**\\n\"\n groundingMetadata.groundingSupports.forEach(support => {\n const sourceIndices = support.groundingChunkIndices\n const sourceTitles = sourceIndices\n .map(index => sources[index]?.title)\n .filter(Boolean)\n .join(\", \")\n const avgConfidence =\n support.confidenceScores.reduce((a, b) => a + b, 0) / support.confidenceScores.length\n contentWithGrounding += `> \"${support.segment.text}\"\\n`\n contentWithGrounding += `> Sources: ${sourceTitles} (Confidence: ${(avgConfidence * 100).toFixed(1)}%)\\n\\n`\n })\n }\n\n if (sources.length > 0) {\n contentWithGrounding += \"\\n**Grounding Sources**\\n\"\n sources.forEach(source => {\n contentWithGrounding += `- [${source.title}](${source.url})\\n`\n })\n }\n }\n\n const responseDataBase = {\n id: `chatcmpl-${Math.random().toString(36).slice(2)}`,\n object: params.stream ? \"chat.completion.chunk\" : \"chat.completion\",\n created: Date.now(),\n model: params.model,\n system_fingerprint: undefined,\n originResponse: responseDataChunk,\n choices: [\n {\n index: 0,\n message: {\n role: \"assistant\",\n content: toolCalls.length ? \"\" : contentWithGrounding,\n ...(toolCalls.length ? { tool_calls: toolCalls } : {})\n },\n ...(groundingMetadata\n ? {\n grounding_metadata: {\n search_queries: groundingMetadata.webSearchQueries,\n sources: sources,\n search_suggestion_html: groundingMetadata.searchEntryPoint?.renderedContent,\n supports: groundingMetadata.groundingSupports?.map(support => ({\n text: support.segment.text,\n sources: support.groundingChunkIndices.map(index => sources[index]),\n confidence: support.confidenceScores\n }))\n }\n }\n : {}),\n finish_reason: responseDataChunk.candidates?.[0]?.finishReason?.toLowerCase() ?? null,\n logprobs: null\n } as ExtendedChoice\n ]\n }\n\n if (params.stream) {\n return {\n ...responseDataBase,\n choices: [\n {\n ...responseDataBase.choices[0],\n delta: responseDataBase.choices[0].message\n }\n ]\n } as ExtendedCompletionChunkGoogle\n }\n\n return responseDataBase as ExtendedCompletionGoogle\n }\n\n /**\n * Streams the chat completion response from the Google API.\n */\n private async *streamChatCompletion(\n stream: AsyncIterable,\n params: GoogleChatCompletionParams\n ): AsyncIterable {\n for await (const chunk of stream) {\n yield this.transformResponse(chunk, params) as ExtendedCompletionChunkGoogle\n }\n }\n\n /**\n * Creates a chat completion using the Google AI API.\n */\n public async create(\n params: GoogleChatCompletionParamsStream\n ): Promise>\n\n public async create(\n params: GoogleChatCompletionParamsNonStream\n ): Promise\n\n public async create(\n params: GoogleChatCompletionParams\n ): Promise> {\n try {\n if (!params?.model || !params?.messages?.length) {\n throw new Error(\"model and messages are required\")\n }\n\n const chatSession = await this.getChatSession(params)\n const lastMessage = params.messages[params.messages.length - 1]\n\n if (params?.stream) {\n this.logger.log(this.logLevel, \"Starting streaming completion response\")\n const result = await chatSession.sendMessageStream(lastMessage.content.toString())\n return this.streamChatCompletion(result.stream, params)\n } else {\n const result = await chatSession.sendMessage(lastMessage.content.toString())\n if (!result?.response) {\n throw new Error(\"Chat response failed\")\n }\n\n const transformedResult = this.transformResponse(result.response, params)\n transformedResult.model = params.model\n return transformedResult as ExtendedCompletionGoogle\n }\n } catch (error) {\n this.logger.log(this.logLevel, new Error(\"Error in Google API request:\", { cause: error }))\n throw error\n }\n }\n\n /**\n * Add content to the Google AI cache manager\n */\n public async createCacheManager(params: GoogleCacheCreateParams) {\n const contents = this.transformHistory(params.messages)\n\n try {\n return await this.googleCacheManager.create({\n ttlSeconds: params.ttlSeconds,\n model: params.model,\n contents\n })\n } catch (err: unknown) {\n let error = err as Error\n\n if (err instanceof GoogleGenerativeAIError) {\n error = new Error(\n \"Failed to create Gemini cache manager, ensure your API key supports caching (i.e. pay-as-you-go)\"\n )\n error.stack = (err as Error).stack\n }\n\n this.logger.log(this.logLevel, error)\n\n throw err\n }\n }\n\n public chat = {\n completions: {\n create: this.create.bind(this)\n }\n }\n\n /** Interface for Google AI Cache Manager */\n public cacheManager = {\n create: this.createCacheManager.bind(this),\n get: async (cacheName: string) => {\n return await this.googleCacheManager.get(cacheName)\n },\n list: async () => {\n return await this.googleCacheManager.list()\n },\n update: async (cacheName: string, params: GoogleCacheCreateParams) => {\n const contents = this.transformHistory(params.messages)\n return await this.googleCacheManager.update(cacheName, {\n cachedContent: {\n contents\n }\n } as CachedContentUpdateParams)\n },\n delete: async (cacheName: string) => {\n return await this.googleCacheManager.delete(cacheName)\n }\n }\n}\n","import { ClientOptions, OpenAI } from \"openai\"\n\nexport class OpenAIProvider extends OpenAI {\n constructor(opts?: ClientOptions) {\n super(opts)\n }\n}\n","import { AnthropicProvider } from \"@/providers/anthropic\"\nimport { GoogleProvider } from \"@/providers/google\"\nimport { OpenAIProvider } from \"@/providers/openai\"\nimport { OpenAILikeClient, Providers } from \"@/types\"\nimport { ClientOptions } from \"openai\"\n\nexport class LLMClient

{\n private providerInstance: OpenAILikeClient

\n\n constructor(\n opts: ClientOptions & {\n provider: P\n }\n ) {\n switch (opts?.provider) {\n case \"anthropic\":\n this.providerInstance = new AnthropicProvider(opts) as unknown as OpenAILikeClient

\n break\n case \"google\":\n this.providerInstance = new GoogleProvider(opts) as unknown as OpenAILikeClient

\n break\n case \"openai\":\n default:\n this.providerInstance = new OpenAIProvider(opts) as OpenAILikeClient

\n }\n\n const proxyHandler: ProxyHandler> = {\n get: (target, prop, receiver) => {\n if (prop in target) {\n return Reflect.get(target, prop, receiver)\n }\n }\n }\n\n this.providerInstance = new Proxy(this.providerInstance, proxyHandler) as OpenAILikeClient

\n }\n\n public getProviderInstance(): OpenAILikeClient

{\n return this.providerInstance\n }\n}\n\nexport function createLLMClient

(\n opts: ClientOptions & {\n provider: P\n logLevel?: string\n } = { provider: \"openai\" as P }\n): OpenAILikeClient

{\n const client = new LLMClient

(opts)\n return client.getProviderInstance()\n}\n"]}