@copilotkit/runtime/dist/chunk-IIXJVVTV.mjs.map

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{"version":3,"sources":["../src/service-adapters/langchain/langserve.ts","../src/service-adapters/openai/openai-adapter.ts","../src/service-adapters/openai/utils.ts","../src/service-adapters/langchain/utils.ts","../src/service-adapters/langchain/langchain-adapter.ts","../src/service-adapters/google/google-genai-adapter.ts","../src/service-adapters/openai/openai-assistant-adapter.ts","../src/service-adapters/unify/unify-adapter.ts","../src/service-adapters/groq/groq-adapter.ts","../src/service-adapters/anthropic/anthropic-adapter.ts","../src/service-adapters/anthropic/utils.ts","../src/service-adapters/experimental/ollama/ollama-adapter.ts","../src/service-adapters/bedrock/bedrock-adapter.ts","../src/service-adapters/empty/empty-adapter.ts"],"sourcesContent":["import { Parameter, Action } from \"@copilotkit/shared\";\nimport { RemoteRunnable } from \"langchain/runnables/remote\";\n\nexport interface RemoteChainParameters {\n  name: string;\n  description: string;\n  chainUrl: string;\n  parameters?: Parameter[];\n  parameterType?: \"single\" | \"multi\";\n}\n\nexport class RemoteChain {\n  name: string;\n  description: string;\n  chainUrl: string;\n  parameters?: Parameter[];\n  parameterType: \"single\" | \"multi\";\n\n  constructor(options: RemoteChainParameters) {\n    this.name = options.name;\n    this.description = options.description;\n    this.chainUrl = options.chainUrl;\n    this.parameters = options.parameters;\n    this.parameterType = options.parameterType || \"multi\";\n  }\n\n  async toAction(): Promise<Action<any>> {\n    if (!this.parameters) {\n      await this.inferLangServeParameters();\n    }\n\n    return {\n      name: this.name,\n      description: this.description,\n      parameters: this.parameters!,\n      handler: async (args: any) => {\n        const runnable = new RemoteRunnable({ url: this.chainUrl });\n        let input: any;\n        if (this.parameterType === \"single\") {\n          input = args[Object.keys(args)[0]];\n        } else {\n          input = args;\n        }\n        return await runnable.invoke(input);\n      },\n    };\n  }\n\n  async inferLangServeParameters() {\n    const supportedTypes = [\"string\", \"number\", \"boolean\"];\n\n    let schemaUrl = this.chainUrl.replace(/\\/+$/, \"\") + \"/input_schema\";\n    let schema = await fetch(schemaUrl)\n      .then((res) => res.json())\n      .catch(() => {\n        throw new Error(\"Failed to fetch langserve schema at \" + schemaUrl);\n      });\n    // for now, don't use json schema, just do a simple conversion\n\n    if (supportedTypes.includes(schema.type)) {\n      this.parameterType = \"single\";\n      this.parameters = [\n        {\n          name: \"input\",\n          type: schema.type,\n          description: \"The input to the chain\",\n        },\n      ];\n    } else if (schema.type === \"object\") {\n      this.parameterType = \"multi\";\n      this.parameters = Object.keys(schema.properties).map((key) => {\n        let property = schema.properties[key];\n        if (!supportedTypes.includes(property.type)) {\n          throw new Error(\"Unsupported schema type\");\n        }\n        return {\n          name: key,\n          type: property.type,\n          description: property.description || \"\",\n          required: schema.required?.includes(key) || false,\n        };\n      });\n    } else {\n      throw new Error(\"Unsupported schema type\");\n    }\n  }\n}\n","/**\n * Copilot Runtime adapter for OpenAI.\n *\n * ## Example\n *\n * ```ts\n * import { CopilotRuntime, OpenAIAdapter } from \"@copilotkit/runtime\";\n * import OpenAI from \"openai\";\n *\n * const copilotKit = new CopilotRuntime();\n *\n * const openai = new OpenAI({\n *   organization: \"<your-organization-id>\", // optional\n *   apiKey: \"<your-api-key>\",\n * });\n *\n * return new OpenAIAdapter({ openai });\n * ```\n *\n * ## Example with Azure OpenAI\n *\n * ```ts\n * import { CopilotRuntime, OpenAIAdapter } from \"@copilotkit/runtime\";\n * import OpenAI from \"openai\";\n *\n * // The name of your Azure OpenAI Instance.\n * // https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource\n * const instance = \"<your instance name>\";\n *\n * // Corresponds to your Model deployment within your OpenAI resource, e.g. my-gpt35-16k-deployment\n * // Navigate to the Azure OpenAI Studio to deploy a model.\n * const model = \"<your model>\";\n *\n * const apiKey = process.env[\"AZURE_OPENAI_API_KEY\"];\n * if (!apiKey) {\n *   throw new Error(\"The AZURE_OPENAI_API_KEY environment variable is missing or empty.\");\n * }\n *\n * const copilotKit = new CopilotRuntime();\n *\n * const openai = new OpenAI({\n *   apiKey,\n *   baseURL: `https://${instance}.openai.azure.com/openai/deployments/${model}`,\n *   defaultQuery: { \"api-version\": \"2024-04-01-preview\" },\n *   defaultHeaders: { \"api-key\": apiKey },\n * });\n *\n * return new OpenAIAdapter({ openai });\n * ```\n */\nimport OpenAI from \"openai\";\nimport {\n  CopilotServiceAdapter,\n  CopilotRuntimeChatCompletionRequest,\n  CopilotRuntimeChatCompletionResponse,\n} from \"../service-adapter\";\nimport {\n  convertActionInputToOpenAITool,\n  convertMessageToOpenAIMessage,\n  limitMessagesToTokenCount,\n} from \"./utils\";\nimport { randomUUID } from \"@copilotkit/shared\";\n\nconst DEFAULT_MODEL = \"gpt-4o\";\n\nexport interface OpenAIAdapterParams {\n  /**\n   * An optional OpenAI instance to use.  If not provided, a new instance will be\n   * created.\n   */\n  openai?: OpenAI;\n\n  /**\n   * The model to use.\n   */\n  model?: string;\n\n  /**\n   * Whether to disable parallel tool calls.\n   * You can disable parallel tool calls to force the model to execute tool calls sequentially.\n   * This is useful if you want to execute tool calls in a specific order so that the state changes\n   * introduced by one tool call are visible to the next tool call. (i.e. new actions or readables)\n   *\n   * @default false\n   */\n  disableParallelToolCalls?: boolean;\n\n  /**\n   * Whether to keep the role in system messages as \"System\".\n   * By default, it is converted to \"developer\", which is used by newer OpenAI models\n   *\n   * @default false\n   */\n  keepSystemRole?: boolean;\n}\n\nexport class OpenAIAdapter implements CopilotServiceAdapter {\n  private model: string = DEFAULT_MODEL;\n\n  private disableParallelToolCalls: boolean = false;\n  private _openai: OpenAI;\n  private keepSystemRole: boolean = false;\n\n  public get openai(): OpenAI {\n    return this._openai;\n  }\n\n  constructor(params?: OpenAIAdapterParams) {\n    this._openai = params?.openai || new OpenAI({});\n    if (params?.model) {\n      this.model = params.model;\n    }\n    this.disableParallelToolCalls = params?.disableParallelToolCalls || false;\n    this.keepSystemRole = params?.keepSystemRole ?? false;\n  }\n\n  async process(\n    request: CopilotRuntimeChatCompletionRequest,\n  ): Promise<CopilotRuntimeChatCompletionResponse> {\n    const {\n      threadId: threadIdFromRequest,\n      model = this.model,\n      messages,\n      actions,\n      eventSource,\n      forwardedParameters,\n    } = request;\n    const tools = actions.map(convertActionInputToOpenAITool);\n    const threadId = threadIdFromRequest ?? randomUUID();\n\n    // ALLOWLIST APPROACH: Only include tool_result messages that correspond to valid tool_calls\n    // Step 1: Extract valid tool_call IDs\n    const validToolUseIds = new Set<string>();\n\n    for (const message of messages) {\n      if (message.isActionExecutionMessage()) {\n        validToolUseIds.add(message.id);\n      }\n    }\n\n    // Step 2: Filter messages, keeping only those with valid tool_call IDs\n    const filteredMessages = messages.filter((message) => {\n      if (message.isResultMessage()) {\n        // Skip if there's no corresponding tool_call\n        if (!validToolUseIds.has(message.actionExecutionId)) {\n          return false;\n        }\n\n        // Remove this ID from valid IDs so we don't process duplicates\n        validToolUseIds.delete(message.actionExecutionId);\n        return true;\n      }\n\n      // Keep all non-tool-result messages\n      return true;\n    });\n\n    let openaiMessages = filteredMessages.map((m) =>\n      convertMessageToOpenAIMessage(m, { keepSystemRole: this.keepSystemRole }),\n    );\n    openaiMessages = limitMessagesToTokenCount(openaiMessages, tools, model);\n\n    let toolChoice: any = forwardedParameters?.toolChoice;\n    if (forwardedParameters?.toolChoice === \"function\") {\n      toolChoice = {\n        type: \"function\",\n        function: { name: forwardedParameters.toolChoiceFunctionName },\n      };\n    }\n\n    try {\n      const stream = this.openai.beta.chat.completions.stream({\n        model: model,\n        stream: true,\n        messages: openaiMessages,\n        ...(tools.length > 0 && { tools }),\n        ...(forwardedParameters?.maxTokens && { max_tokens: forwardedParameters.maxTokens }),\n        ...(forwardedParameters?.stop && { stop: forwardedParameters.stop }),\n        ...(toolChoice && { tool_choice: toolChoice }),\n        ...(this.disableParallelToolCalls && { parallel_tool_calls: false }),\n        ...(forwardedParameters?.temperature && { temperature: forwardedParameters.temperature }),\n      });\n\n      eventSource.stream(async (eventStream$) => {\n        let mode: \"function\" | \"message\" | null = null;\n        let currentMessageId: string;\n        let currentToolCallId: string;\n\n        try {\n          for await (const chunk of stream) {\n            if (chunk.choices.length === 0) {\n              continue;\n            }\n\n            const toolCall = chunk.choices[0].delta.tool_calls?.[0];\n            const content = chunk.choices[0].delta.content;\n\n            // When switching from message to function or vice versa,\n            // send the respective end event.\n            // If toolCall?.id is defined, it means a new tool call starts.\n            if (mode === \"message\" && toolCall?.id) {\n              mode = null;\n              eventStream$.sendTextMessageEnd({ messageId: currentMessageId });\n            } else if (mode === \"function\" && (toolCall === undefined || toolCall?.id)) {\n              mode = null;\n              eventStream$.sendActionExecutionEnd({ actionExecutionId: currentToolCallId });\n            }\n\n            // If we send a new message type, send the appropriate start event.\n            if (mode === null) {\n              if (toolCall?.id) {\n                mode = \"function\";\n                currentToolCallId = toolCall!.id;\n                eventStream$.sendActionExecutionStart({\n                  actionExecutionId: currentToolCallId,\n                  parentMessageId: chunk.id,\n                  actionName: toolCall!.function!.name,\n                });\n              } else if (content) {\n                mode = \"message\";\n                currentMessageId = chunk.id;\n                eventStream$.sendTextMessageStart({ messageId: currentMessageId });\n              }\n            }\n\n            // send the content events\n            if (mode === \"message\" && content) {\n              eventStream$.sendTextMessageContent({\n                messageId: currentMessageId,\n                content: content,\n              });\n            } else if (mode === \"function\" && toolCall?.function?.arguments) {\n              eventStream$.sendActionExecutionArgs({\n                actionExecutionId: currentToolCallId,\n                args: toolCall.function.arguments,\n              });\n            }\n          }\n\n          // send the end events\n          if (mode === \"message\") {\n            eventStream$.sendTextMessageEnd({ messageId: currentMessageId });\n          } else if (mode === \"function\") {\n            eventStream$.sendActionExecutionEnd({ actionExecutionId: currentToolCallId });\n          }\n        } catch (error) {\n          console.error(\"[OpenAI] Error processing stream:\", error);\n          throw error;\n        }\n\n        eventStream$.complete();\n      });\n    } catch (error) {\n      console.error(\"[OpenAI] Error during API call:\", error);\n      throw error;\n    }\n\n    return {\n      threadId,\n    };\n  }\n}\n","import { Message } from \"../../graphql/types/converted\";\nimport { ActionInput } from \"../../graphql/inputs/action.input\";\nimport {\n  ChatCompletionAssistantMessageParam,\n  ChatCompletionMessageParam,\n  ChatCompletionSystemMessageParam,\n  ChatCompletionTool,\n  ChatCompletionUserMessageParam,\n  ChatCompletionDeveloperMessageParam,\n} from \"openai/resources/chat\";\nimport { parseJson } from \"@copilotkit/shared\";\n\nexport function limitMessagesToTokenCount(\n  messages: any[],\n  tools: any[],\n  model: string,\n  maxTokens?: number,\n): any[] {\n  maxTokens ||= maxTokensForOpenAIModel(model);\n\n  const result: any[] = [];\n  const toolsNumTokens = countToolsTokens(model, tools);\n  if (toolsNumTokens > maxTokens) {\n    throw new Error(`Too many tokens in function definitions: ${toolsNumTokens} > ${maxTokens}`);\n  }\n  maxTokens -= toolsNumTokens;\n\n  for (const message of messages) {\n    if ([\"system\", \"developer\"].includes(message.role)) {\n      const numTokens = countMessageTokens(model, message);\n      maxTokens -= numTokens;\n\n      if (maxTokens < 0) {\n        throw new Error(\"Not enough tokens for system message.\");\n      }\n    }\n  }\n\n  let cutoff: boolean = false;\n\n  const reversedMessages = [...messages].reverse();\n  for (const message of reversedMessages) {\n    if ([\"system\", \"developer\"].includes(message.role)) {\n      result.unshift(message);\n      continue;\n    } else if (cutoff) {\n      continue;\n    }\n    let numTokens = countMessageTokens(model, message);\n    if (maxTokens < numTokens) {\n      cutoff = true;\n      continue;\n    }\n    result.unshift(message);\n    maxTokens -= numTokens;\n  }\n\n  return result;\n}\n\nexport function maxTokensForOpenAIModel(model: string): number {\n  return maxTokensByModel[model] || DEFAULT_MAX_TOKENS;\n}\n\nconst DEFAULT_MAX_TOKENS = 128000;\n\nconst maxTokensByModel: { [key: string]: number } = {\n  // o1\n  o1: 200000,\n  \"o1-2024-12-17\": 200000,\n  \"o1-mini\": 128000,\n  \"o1-mini-2024-09-12\": 128000,\n  \"o1-preview\": 128000,\n  \"o1-preview-2024-09-12\": 128000,\n  // o3-mini\n  \"o3-mini\": 200000,\n  \"o3-mini-2025-01-31\": 200000,\n  // GPT-4\n  \"gpt-4o\": 128000,\n  \"chatgpt-4o-latest\": 128000,\n  \"gpt-4o-2024-08-06\": 128000,\n  \"gpt-4o-2024-05-13\": 128000,\n  \"gpt-4o-mini\": 128000,\n  \"gpt-4o-mini-2024-07-18\": 128000,\n  \"gpt-4-turbo\": 128000,\n  \"gpt-4-turbo-2024-04-09\": 128000,\n  \"gpt-4-0125-preview\": 128000,\n  \"gpt-4-turbo-preview\": 128000,\n  \"gpt-4-1106-preview\": 128000,\n  \"gpt-4-vision-preview\": 128000,\n  \"gpt-4-1106-vision-preview\": 128000,\n  \"gpt-4-32k\": 32768,\n  \"gpt-4-32k-0613\": 32768,\n  \"gpt-4-32k-0314\": 32768,\n  \"gpt-4\": 8192,\n  \"gpt-4-0613\": 8192,\n  \"gpt-4-0314\": 8192,\n\n  // GPT-3.5\n  \"gpt-3.5-turbo-0125\": 16385,\n  \"gpt-3.5-turbo\": 16385,\n  \"gpt-3.5-turbo-1106\": 16385,\n  \"gpt-3.5-turbo-instruct\": 4096,\n  \"gpt-3.5-turbo-16k\": 16385,\n  \"gpt-3.5-turbo-0613\": 4096,\n  \"gpt-3.5-turbo-16k-0613\": 16385,\n  \"gpt-3.5-turbo-0301\": 4097,\n};\n\nfunction countToolsTokens(model: string, tools: any[]): number {\n  if (tools.length === 0) {\n    return 0;\n  }\n  const json = JSON.stringify(tools);\n  return countTokens(model, json);\n}\n\nfunction countMessageTokens(model: string, message: any): number {\n  return countTokens(model, message.content || \"\");\n}\n\nfunction countTokens(model: string, text: string): number {\n  return text.length / 3;\n}\n\nexport function convertActionInputToOpenAITool(action: ActionInput): ChatCompletionTool {\n  return {\n    type: \"function\",\n    function: {\n      name: action.name,\n      description: action.description,\n      parameters: parseJson(action.jsonSchema, {}),\n    },\n  };\n}\n\ntype UsedMessageParams =\n  | ChatCompletionUserMessageParam\n  | ChatCompletionAssistantMessageParam\n  | ChatCompletionDeveloperMessageParam\n  | ChatCompletionSystemMessageParam;\nexport function convertMessageToOpenAIMessage(\n  message: Message,\n  options?: { keepSystemRole: boolean },\n): ChatCompletionMessageParam {\n  const { keepSystemRole } = options || { keepSystemRole: false };\n  if (message.isTextMessage()) {\n    let role = message.role as UsedMessageParams[\"role\"];\n    if (message.role === \"system\" && !keepSystemRole) {\n      role = \"developer\";\n    }\n    return {\n      role,\n      content: message.content,\n    } satisfies UsedMessageParams;\n  } else if (message.isImageMessage()) {\n    return {\n      role: \"user\",\n      content: [\n        {\n          type: \"image_url\",\n          image_url: {\n            url: `data:image/${message.format};base64,${message.bytes}`,\n          },\n        },\n      ],\n    } satisfies UsedMessageParams;\n  } else if (message.isActionExecutionMessage()) {\n    return {\n      role: \"assistant\",\n      tool_calls: [\n        {\n          id: message.id,\n          type: \"function\",\n          function: {\n            name: message.name,\n            arguments: JSON.stringify(message.arguments),\n          },\n        },\n      ],\n    };\n  } else if (message.isResultMessage()) {\n    return {\n      role: \"tool\",\n      content: message.result,\n      tool_call_id: message.actionExecutionId,\n    };\n  }\n}\n\nexport function convertSystemMessageToAssistantAPI(message: ChatCompletionMessageParam) {\n  return {\n    ...message,\n    ...([\"system\", \"developer\"].includes(message.role) && {\n      role: \"assistant\",\n      content: \"THE FOLLOWING MESSAGE IS A SYSTEM MESSAGE: \" + message.content,\n    }),\n  };\n}\n","import {\n  ActionExecutionMessage,\n  Message,\n  ResultMessage,\n  TextMessage,\n} from \"../../graphql/types/converted\";\nimport {\n  AIMessage,\n  AIMessageChunk,\n  BaseMessage,\n  BaseMessageChunk,\n  HumanMessage,\n  SystemMessage,\n  ToolMessage,\n} from \"@langchain/core/messages\";\nimport { DynamicStructuredTool } from \"@langchain/core/tools\";\nimport { z } from \"zod\";\nimport { ActionInput } from \"../../graphql/inputs/action.input\";\nimport { LangChainReturnType } from \"./types\";\nimport { RuntimeEventSubject } from \"../events\";\nimport { randomId, convertJsonSchemaToZodSchema } from \"@copilotkit/shared\";\n\nexport function convertMessageToLangChainMessage(message: Message): BaseMessage {\n  if (message.isTextMessage()) {\n    if (message.role == \"user\") {\n      return new HumanMessage(message.content);\n    } else if (message.role == \"assistant\") {\n      return new AIMessage(message.content);\n    } else if (message.role === \"system\") {\n      return new SystemMessage(message.content);\n    }\n  } else if (message.isActionExecutionMessage()) {\n    return new AIMessage({\n      content: \"\",\n      tool_calls: [\n        {\n          id: message.id,\n          args: message.arguments,\n          name: message.name,\n        },\n      ],\n    });\n  } else if (message.isResultMessage()) {\n    return new ToolMessage({\n      content: message.result,\n      tool_call_id: message.actionExecutionId,\n    });\n  }\n}\n\nexport function convertActionInputToLangChainTool(actionInput: ActionInput): any {\n  return new DynamicStructuredTool({\n    name: actionInput.name,\n    description: actionInput.description,\n    schema: convertJsonSchemaToZodSchema(\n      JSON.parse(actionInput.jsonSchema),\n      true,\n    ) as z.ZodObject<any>,\n    func: async () => {\n      return \"\";\n    },\n  });\n}\n\ninterface StreamLangChainResponseParams {\n  result: LangChainReturnType;\n  eventStream$: RuntimeEventSubject;\n  actionExecution?: {\n    id: string;\n    name: string;\n  };\n}\n\nfunction getConstructorName(object: any): string {\n  if (object && typeof object === \"object\" && object.constructor && object.constructor.name) {\n    return object.constructor.name;\n  }\n  return \"\";\n}\n\nfunction isAIMessage(message: any): message is AIMessage {\n  return Object.prototype.toString.call(message) === \"[object AIMessage]\";\n}\n\nfunction isAIMessageChunk(message: any): message is AIMessageChunk {\n  return Object.prototype.toString.call(message) === \"[object AIMessageChunk]\";\n}\n\nfunction isBaseMessageChunk(message: any): message is BaseMessageChunk {\n  return Object.prototype.toString.call(message) === \"[object BaseMessageChunk]\";\n}\n\nfunction maybeSendActionExecutionResultIsMessage(\n  eventStream$: RuntimeEventSubject,\n  actionExecution?: { id: string; name: string },\n) {\n  // language models need a result after the function call\n  // we simply let them know that we are sending a message\n  if (actionExecution) {\n    eventStream$.sendActionExecutionResult({\n      actionExecutionId: actionExecution.id,\n      actionName: actionExecution.name,\n      result: \"Sending a message\",\n    });\n  }\n}\n\nexport async function streamLangChainResponse({\n  result,\n  eventStream$,\n  actionExecution,\n}: StreamLangChainResponseParams) {\n  // We support several types of return values from LangChain functions:\n\n  // 1. string\n\n  if (typeof result === \"string\") {\n    if (!actionExecution) {\n      // Just send one chunk with the string as the content.\n      eventStream$.sendTextMessage(randomId(), result);\n    } else {\n      // Send as a result\n      eventStream$.sendActionExecutionResult({\n        actionExecutionId: actionExecution.id,\n        actionName: actionExecution.name,\n        result: result,\n      });\n    }\n  }\n\n  // 2. AIMessage\n  // Send the content and function call of the AIMessage as the content of the chunk.\n  else if (isAIMessage(result)) {\n    maybeSendActionExecutionResultIsMessage(eventStream$, actionExecution);\n\n    if (result.content) {\n      eventStream$.sendTextMessage(randomId(), result.content as string);\n    }\n    for (const toolCall of result.tool_calls) {\n      eventStream$.sendActionExecution({\n        actionExecutionId: toolCall.id || randomId(),\n        actionName: toolCall.name,\n        args: JSON.stringify(toolCall.args),\n      });\n    }\n  }\n\n  // 3. BaseMessageChunk\n  // Send the content and function call of the AIMessage as the content of the chunk.\n  else if (isBaseMessageChunk(result)) {\n    maybeSendActionExecutionResultIsMessage(eventStream$, actionExecution);\n\n    if (result.lc_kwargs?.content) {\n      eventStream$.sendTextMessage(randomId(), result.content as string);\n    }\n    if (result.lc_kwargs?.tool_calls) {\n      for (const toolCall of result.lc_kwargs?.tool_calls) {\n        eventStream$.sendActionExecution({\n          actionExecutionId: toolCall.id || randomId(),\n          actionName: toolCall.name,\n          args: JSON.stringify(toolCall.args),\n        });\n      }\n    }\n  }\n\n  // 4. IterableReadableStream\n  // Stream the result of the LangChain function.\n  else if (result && \"getReader\" in result) {\n    maybeSendActionExecutionResultIsMessage(eventStream$, actionExecution);\n\n    let reader = result.getReader();\n\n    let mode: \"function\" | \"message\" | null = null;\n    let currentMessageId: string;\n\n    const toolCallDetails = {\n      name: null,\n      id: null,\n      index: null,\n      prevIndex: null,\n    };\n\n    while (true) {\n      try {\n        const { done, value } = await reader.read();\n\n        let toolCallName: string | undefined = undefined;\n        let toolCallId: string | undefined = undefined;\n        let toolCallArgs: string | undefined = undefined;\n        let hasToolCall: boolean = false;\n        let content = \"\";\n        if (value && value.content) {\n          content = Array.isArray(value.content)\n            ? (((value.content[0] as any)?.text ?? \"\") as string)\n            : value.content;\n        }\n\n        if (isAIMessageChunk(value)) {\n          let chunk = value.tool_call_chunks?.[0];\n          toolCallArgs = chunk?.args;\n          hasToolCall = chunk != undefined;\n          if (chunk?.name) toolCallDetails.name = chunk.name;\n          // track different index on the same tool cool\n          if (chunk?.index != null) {\n            toolCallDetails.index = chunk.index; // 1\n            if (toolCallDetails.prevIndex == null) toolCallDetails.prevIndex = chunk.index;\n          }\n          // Differentiate when calling the same tool but with different index\n          if (chunk?.id)\n            toolCallDetails.id = chunk.index != null ? `${chunk.id}-idx-${chunk.index}` : chunk.id;\n\n          // Assign to internal variables that the entire script here knows how to work with\n          toolCallName = toolCallDetails.name;\n          toolCallId = toolCallDetails.id;\n        } else if (isBaseMessageChunk(value)) {\n          let chunk = value.additional_kwargs?.tool_calls?.[0];\n          toolCallName = chunk?.function?.name;\n          toolCallId = chunk?.id;\n          toolCallArgs = chunk?.function?.arguments;\n          hasToolCall = chunk?.function != undefined;\n        }\n\n        // When switching from message to function or vice versa,\n        // send the respective end event.\n        // If toolCallName is defined, it means a new tool call starts.\n        if (mode === \"message\" && (toolCallId || done)) {\n          mode = null;\n          eventStream$.sendTextMessageEnd({ messageId: currentMessageId });\n        } else if (mode === \"function\" && (!hasToolCall || done)) {\n          mode = null;\n          eventStream$.sendActionExecutionEnd({ actionExecutionId: toolCallId });\n        }\n\n        if (done) {\n          break;\n        }\n\n        // If we send a new message type, send the appropriate start event.\n        if (mode === null) {\n          if (hasToolCall && toolCallId && toolCallName) {\n            mode = \"function\";\n            eventStream$.sendActionExecutionStart({\n              actionExecutionId: toolCallId,\n              actionName: toolCallName,\n              parentMessageId: value.lc_kwargs?.id,\n            });\n          } else if (content) {\n            mode = \"message\";\n            currentMessageId = value.lc_kwargs?.id || randomId();\n            eventStream$.sendTextMessageStart({ messageId: currentMessageId });\n          }\n        }\n\n        // send the content events\n        if (mode === \"message\" && content) {\n          eventStream$.sendTextMessageContent({\n            messageId: currentMessageId,\n            content,\n          });\n        } else if (mode === \"function\" && toolCallArgs) {\n          // For calls of the same tool with different index, we seal last tool call and register a new one\n          if (toolCallDetails.index !== toolCallDetails.prevIndex) {\n            eventStream$.sendActionExecutionEnd({ actionExecutionId: toolCallId });\n            eventStream$.sendActionExecutionStart({\n              actionExecutionId: toolCallId,\n              actionName: toolCallName,\n              parentMessageId: value.lc_kwargs?.id,\n            });\n            toolCallDetails.prevIndex = toolCallDetails.index;\n          }\n          eventStream$.sendActionExecutionArgs({\n            actionExecutionId: toolCallId,\n            args: toolCallArgs,\n          });\n        }\n      } catch (error) {\n        console.error(\"Error reading from stream\", error);\n        break;\n      }\n    }\n  } else if (actionExecution) {\n    eventStream$.sendActionExecutionResult({\n      actionExecutionId: actionExecution.id,\n      actionName: actionExecution.name,\n      result: encodeResult(result),\n    });\n  }\n\n  // unsupported type\n  else {\n    throw new Error(\"Invalid return type from LangChain function.\");\n  }\n\n  eventStream$.complete();\n}\n\nfunction encodeResult(result: any): string {\n  if (result === undefined) {\n    return \"\";\n  } else if (typeof result === \"string\") {\n    return result;\n  } else {\n    return JSON.stringify(result);\n  }\n}\n","/**\n * Copilot Runtime adapter for LangChain.\n *\n * ## Example\n *\n * ```ts\n * import { CopilotRuntime, LangChainAdapter } from \"@copilotkit/runtime\";\n * import { ChatOpenAI } from \"@langchain/openai\";\n *\n * const copilotKit = new CopilotRuntime();\n *\n * const model = new ChatOpenAI({\n *   model: \"gpt-4o\",\n *   apiKey: \"<your-api-key>\",\n * });\n *\n * return new LangChainAdapter({\n *   chainFn: async ({ messages, tools }) => {\n *     return model.bindTools(tools).stream(messages);\n *     // or optionally enable strict mode\n *     // return model.bindTools(tools, { strict: true }).stream(messages);\n *   }\n * });\n * ```\n *\n * The asynchronous handler function (`chainFn`) can return any of the following:\n *\n * - A simple `string` response\n * - A LangChain stream (`IterableReadableStream`)\n * - A LangChain `BaseMessageChunk` object\n * - A LangChain `AIMessage` object\n */\n\nimport { BaseMessage } from \"@langchain/core/messages\";\nimport { CopilotServiceAdapter } from \"../service-adapter\";\nimport {\n  CopilotRuntimeChatCompletionRequest,\n  CopilotRuntimeChatCompletionResponse,\n} from \"../service-adapter\";\nimport {\n  convertActionInputToLangChainTool,\n  convertMessageToLangChainMessage,\n  streamLangChainResponse,\n} from \"./utils\";\nimport { DynamicStructuredTool } from \"@langchain/core/tools\";\nimport { LangChainReturnType } from \"./types\";\nimport { randomUUID } from \"@copilotkit/shared\";\nimport { awaitAllCallbacks } from \"@langchain/core/callbacks/promises\";\n\ninterface ChainFnParameters {\n  model: string;\n  messages: BaseMessage[];\n  tools: DynamicStructuredTool[];\n  threadId?: string;\n  runId?: string;\n}\n\ninterface LangChainAdapterOptions {\n  /**\n   * A function that uses the LangChain API to generate a response.\n   */\n  chainFn: (parameters: ChainFnParameters) => Promise<LangChainReturnType>;\n}\n\nexport class LangChainAdapter implements CopilotServiceAdapter {\n  /**\n   * To use LangChain as a backend, provide a handler function to the adapter with your custom LangChain logic.\n   */\n  constructor(private options: LangChainAdapterOptions) {}\n\n  async process(\n    request: CopilotRuntimeChatCompletionRequest,\n  ): Promise<CopilotRuntimeChatCompletionResponse> {\n    try {\n      const {\n        eventSource,\n        model,\n        actions,\n        messages,\n        runId,\n        threadId: threadIdFromRequest,\n      } = request;\n      const threadId = threadIdFromRequest ?? randomUUID();\n      const result = await this.options.chainFn({\n        messages: messages.map(convertMessageToLangChainMessage),\n        tools: actions.map(convertActionInputToLangChainTool),\n        model,\n        threadId,\n        runId,\n      });\n\n      eventSource.stream(async (eventStream$) => {\n        await streamLangChainResponse({\n          result,\n          eventStream$,\n        });\n      });\n\n      return {\n        threadId,\n      };\n    } finally {\n      await awaitAllCallbacks();\n    }\n  }\n}\n","/**\n * Copilot Runtime adapter for Google Generative AI (e.g. Gemini).\n *\n * ## Example\n *\n * ```ts\n * import { CopilotRuntime, GoogleGenerativeAIAdapter } from \"@copilotkit/runtime\";\n * const { GoogleGenerativeAI } = require(\"@google/generative-ai\");\n *\n * const genAI = new GoogleGenerativeAI(process.env[\"GOOGLE_API_KEY\"]);\n *\n * const copilotKit = new CopilotRuntime();\n *\n * return new GoogleGenerativeAIAdapter({ model: \"gemini-1.5-pro\" });\n * ```\n */\nimport { ChatGoogle } from \"@langchain/google-gauth\";\nimport { LangChainAdapter } from \"../langchain/langchain-adapter\";\nimport { AIMessage } from \"@langchain/core/messages\";\n\ninterface GoogleGenerativeAIAdapterOptions {\n  /**\n   * A custom Google Generative AI model to use.\n   */\n  model?: string;\n}\n\nexport class GoogleGenerativeAIAdapter extends LangChainAdapter {\n  constructor(options?: GoogleGenerativeAIAdapterOptions) {\n    super({\n      chainFn: async ({ messages, tools, threadId }) => {\n        // Filter out empty assistant messages to prevent Gemini validation errors\n        // Gemini specifically rejects conversations containing AIMessages with empty content\n        const filteredMessages = messages.filter((message) => {\n          // Keep all non-AI messages (HumanMessage, SystemMessage, ToolMessage, etc.)\n          if (!(message instanceof AIMessage)) {\n            return true;\n          }\n\n          // For AIMessages, only keep those with non-empty content\n          // Also keep AIMessages with tool_calls even if content is empty\n          return (\n            (message.content && String(message.content).trim().length > 0) ||\n            (message.tool_calls && message.tool_calls.length > 0)\n          );\n        });\n\n        const model = new ChatGoogle({\n          modelName: options?.model ?? \"gemini-1.5-pro\",\n          apiVersion: \"v1beta\",\n        }).bindTools(tools);\n\n        return model.stream(filteredMessages, { metadata: { conversation_id: threadId } });\n      },\n    });\n  }\n}\n","/**\n * Copilot Runtime adapter for the OpenAI Assistant API.\n *\n * ## Example\n *\n * ```ts\n * import { CopilotRuntime, OpenAIAssistantAdapter } from \"@copilotkit/runtime\";\n * import OpenAI from \"openai\";\n *\n * const copilotKit = new CopilotRuntime();\n *\n * const openai = new OpenAI({\n *   organization: \"<your-organization-id>\",\n *   apiKey: \"<your-api-key>\",\n * });\n *\n * return new OpenAIAssistantAdapter({\n *   openai,\n *   assistantId: \"<your-assistant-id>\",\n *   codeInterpreterEnabled: true,\n *   fileSearchEnabled: true,\n * });\n * ```\n */\nimport OpenAI from \"openai\";\nimport {\n  CopilotServiceAdapter,\n  CopilotRuntimeChatCompletionRequest,\n  CopilotRuntimeChatCompletionResponse,\n} from \"../service-adapter\";\nimport { Message, ResultMessage, TextMessage } from \"../../graphql/types/converted\";\nimport {\n  convertActionInputToOpenAITool,\n  convertMessageToOpenAIMessage,\n  convertSystemMessageToAssistantAPI,\n} from \"./utils\";\nimport { RunSubmitToolOutputsStreamParams } from \"openai/resources/beta/threads/runs/runs\";\nimport { AssistantStream } from \"openai/lib/AssistantStream\";\nimport { RuntimeEventSource } from \"../events\";\nimport { ActionInput } from \"../../graphql/inputs/action.input\";\nimport { AssistantStreamEvent, AssistantTool } from \"openai/resources/beta/assistants\";\nimport { ForwardedParametersInput } from \"../../graphql/inputs/forwarded-parameters.input\";\n\nexport interface OpenAIAssistantAdapterParams {\n  /**\n   * The ID of the assistant to use.\n   */\n  assistantId: string;\n\n  /**\n   * An optional OpenAI instance to use. If not provided, a new instance will be created.\n   */\n  openai?: OpenAI;\n\n  /**\n   * Whether to enable code interpretation.\n   * @default true\n   */\n  codeInterpreterEnabled?: boolean;\n\n  /**\n   * Whether to enable file search.\n   * @default true\n   */\n  fileSearchEnabled?: boolean;\n\n  /**\n   * Whether to disable parallel tool calls.\n   * You can disable parallel tool calls to force the model to execute tool calls sequentially.\n   * This is useful if you want to execute tool calls in a specific order so that the state changes\n   * introduced by one tool call are visible to the next tool call. (i.e. new actions or readables)\n   *\n   * @default false\n   */\n  disableParallelToolCalls?: boolean;\n\n  /**\n   * Whether to keep the role in system messages as \"System\".\n   * By default, it is converted to \"developer\", which is used by newer OpenAI models\n   *\n   * @default false\n   */\n  keepSystemRole?: boolean;\n}\n\nexport class OpenAIAssistantAdapter implements CopilotServiceAdapter {\n  private openai: OpenAI;\n  private codeInterpreterEnabled: boolean;\n  private assistantId: string;\n  private fileSearchEnabled: boolean;\n  private disableParallelToolCalls: boolean;\n  private keepSystemRole: boolean = false;\n\n  constructor(params: OpenAIAssistantAdapterParams) {\n    this.openai = params.openai || new OpenAI({});\n    this.codeInterpreterEnabled = params.codeInterpreterEnabled === false || true;\n    this.fileSearchEnabled = params.fileSearchEnabled === false || true;\n    this.assistantId = params.assistantId;\n    this.disableParallelToolCalls = params?.disableParallelToolCalls || false;\n    this.keepSystemRole = params?.keepSystemRole ?? false;\n  }\n\n  async process(\n    request: CopilotRuntimeChatCompletionRequest,\n  ): Promise<CopilotRuntimeChatCompletionResponse> {\n    const { messages, actions, eventSource, runId, forwardedParameters } = request;\n\n    // if we don't have a threadId, create a new thread\n    let threadId = request.extensions?.openaiAssistantAPI?.threadId;\n\n    if (!threadId) {\n      threadId = (await this.openai.beta.threads.create()).id;\n    }\n\n    const lastMessage = messages.at(-1);\n\n    let nextRunId: string | undefined = undefined;\n\n    // submit function outputs\n    if (lastMessage.isResultMessage() && runId) {\n      nextRunId = await this.submitToolOutputs(threadId, runId, messages, eventSource);\n    }\n    // submit user message\n    else if (lastMessage.isTextMessage()) {\n      nextRunId = await this.submitUserMessage(\n        threadId,\n        messages,\n        actions,\n        eventSource,\n        forwardedParameters,\n      );\n    }\n    // unsupported message\n    else {\n      throw new Error(\"No actionable message found in the messages\");\n    }\n\n    return {\n      runId: nextRunId,\n      threadId,\n      extensions: {\n        ...request.extensions,\n        openaiAssistantAPI: {\n          threadId: threadId,\n          runId: nextRunId,\n        },\n      },\n    };\n  }\n\n  private async submitToolOutputs(\n    threadId: string,\n    runId: string,\n    messages: Message[],\n    eventSource: RuntimeEventSource,\n  ) {\n    let run = await this.openai.beta.threads.runs.retrieve(threadId, runId);\n\n    if (!run.required_action) {\n      throw new Error(\"No tool outputs required\");\n    }\n\n    // get the required tool call ids\n    const toolCallsIds = run.required_action.submit_tool_outputs.tool_calls.map(\n      (toolCall) => toolCall.id,\n    );\n\n    // search for these tool calls\n    const resultMessages = messages.filter(\n      (message) => message.isResultMessage() && toolCallsIds.includes(message.actionExecutionId),\n    ) as ResultMessage[];\n\n    if (toolCallsIds.length != resultMessages.length) {\n      throw new Error(\"Number of function results does not match the number of tool calls\");\n    }\n\n    // submit the tool outputs\n    const toolOutputs: RunSubmitToolOutputsStreamParams.ToolOutput[] = resultMessages.map(\n      (message) => {\n        return {\n          tool_call_id: message.actionExecutionId,\n          output: message.result,\n        };\n      },\n    );\n\n    const stream = this.openai.beta.threads.runs.submitToolOutputsStream(threadId, runId, {\n      tool_outputs: toolOutputs,\n      ...(this.disableParallelToolCalls && { parallel_tool_calls: false }),\n    });\n\n    await this.streamResponse(stream, eventSource);\n    return runId;\n  }\n\n  private async submitUserMessage(\n    threadId: string,\n    messages: Message[],\n    actions: ActionInput[],\n    eventSource: RuntimeEventSource,\n    forwardedParameters: ForwardedParametersInput,\n  ) {\n    messages = [...messages];\n\n    // get the instruction message\n    const instructionsMessage = messages.shift();\n    const instructions = instructionsMessage.isTextMessage() ? instructionsMessage.content : \"\";\n\n    // get the latest user message\n    const userMessage = messages\n      .map((m) => convertMessageToOpenAIMessage(m, { keepSystemRole: this.keepSystemRole }))\n      .map(convertSystemMessageToAssistantAPI)\n      .at(-1);\n\n    if (userMessage.role !== \"user\") {\n      throw new Error(\"No user message found\");\n    }\n\n    await this.openai.beta.threads.messages.create(threadId, {\n      role: \"user\",\n      content: userMessage.content,\n    });\n\n    const openaiTools = actions.map(convertActionInputToOpenAITool);\n\n    const tools = [\n      ...openaiTools,\n      ...(this.codeInterpreterEnabled ? [{ type: \"code_interpreter\" } as AssistantTool] : []),\n      ...(this.fileSearchEnabled ? [{ type: \"file_search\" } as AssistantTool] : []),\n    ];\n\n    let stream = this.openai.beta.threads.runs.stream(threadId, {\n      assistant_id: this.assistantId,\n      instructions,\n      tools: tools,\n      ...(forwardedParameters?.maxTokens && {\n        max_completion_tokens: forwardedParameters.maxTokens,\n      }),\n      ...(this.disableParallelToolCalls && { parallel_tool_calls: false }),\n    });\n\n    await this.streamResponse(stream, eventSource);\n\n    return getRunIdFromStream(stream);\n  }\n\n  private async streamResponse(stream: AssistantStream, eventSource: RuntimeEventSource) {\n    eventSource.stream(async (eventStream$) => {\n      let inFunctionCall = false;\n      let currentMessageId: string;\n      let currentToolCallId: string;\n\n      for await (const chunk of stream) {\n        switch (chunk.event) {\n          case \"thread.message.created\":\n            if (inFunctionCall) {\n              eventStream$.sendActionExecutionEnd({ actionExecutionId: currentToolCallId });\n            }\n            currentMessageId = chunk.data.id;\n            eventStream$.sendTextMessageStart({ messageId: currentMessageId });\n            break;\n          case \"thread.message.delta\":\n            if (chunk.data.delta.content?.[0].type === \"text\") {\n              eventStream$.sendTextMessageContent({\n                messageId: currentMessageId,\n                content: chunk.data.delta.content?.[0].text.value,\n              });\n            }\n            break;\n          case \"thread.message.completed\":\n            eventStream$.sendTextMessageEnd({ messageId: currentMessageId });\n            break;\n          case \"thread.run.step.delta\":\n            let toolCallId: string | undefined;\n            let toolCallName: string | undefined;\n            let toolCallArgs: string | undefined;\n            if (\n              chunk.data.delta.step_details.type === \"tool_calls\" &&\n              chunk.data.delta.step_details.tool_calls?.[0].type === \"function\"\n            ) {\n              toolCallId = chunk.data.delta.step_details.tool_calls?.[0].id;\n              toolCallName = chunk.data.delta.step_details.tool_calls?.[0].function.name;\n              toolCallArgs = chunk.data.delta.step_details.tool_calls?.[0].function.arguments;\n            }\n\n            if (toolCallName && toolCallId) {\n              if (inFunctionCall) {\n                eventStream$.sendActionExecutionEnd({ actionExecutionId: currentToolCallId });\n              }\n              inFunctionCall = true;\n              currentToolCallId = toolCallId;\n              eventStream$.sendActionExecutionStart({\n                actionExecutionId: currentToolCallId,\n                parentMessageId: chunk.data.id,\n                actionName: toolCallName,\n              });\n            } else if (toolCallArgs) {\n              eventStream$.sendActionExecutionArgs({\n                actionExecutionId: currentToolCallId,\n                args: toolCallArgs,\n              });\n            }\n            break;\n        }\n      }\n      if (inFunctionCall) {\n        eventStream$.sendActionExecutionEnd({ actionExecutionId: currentToolCallId });\n      }\n      eventStream$.complete();\n    });\n  }\n}\n\nfunction getRunIdFromStream(stream: AssistantStream): Promise<string> {\n  return new Promise<string>((resolve, reject) => {\n    let runIdGetter = (event: AssistantStreamEvent) => {\n      if (event.event === \"thread.run.created\") {\n        const runId = event.data.id;\n        stream.off(\"event\", runIdGetter);\n        resolve(runId);\n      }\n    };\n    stream.on(\"event\", runIdGetter);\n  });\n}\n","/**\n * CopilotKit Adapter for Unify\n *\n * <RequestExample>\n * ```jsx CopilotRuntime Example\n * const copilotKit = new CopilotRuntime();\n * return copilotKit.response(req, new UnifyAdapter());\n * ```\n * </RequestExample>\n *\n * You can easily set the model to use by passing it to the constructor.\n * ```jsx\n * const copilotKit = new CopilotRuntime();\n * return copilotKit.response(\n *   req,\n *   new UnifyAdapter({ model: \"llama-3-8b-chat@fireworks-ai\" }),\n * );\n * ```\n */\nimport {\n  CopilotRuntimeChatCompletionRequest,\n  CopilotRuntimeChatCompletionResponse,\n  CopilotServiceAdapter,\n} from \"../service-adapter\";\nimport OpenAI from \"openai\";\nimport { randomId, randomUUID } from \"@copilotkit/shared\";\nimport { convertActionInputToOpenAITool, convertMessageToOpenAIMessage } from \"../openai/utils\";\n\nexport interface UnifyAdapterParams {\n  apiKey?: string;\n  model: string;\n}\n\nexport class UnifyAdapter implements CopilotServiceAdapter {\n  private apiKey: string;\n  private model: string;\n  private start: boolean;\n\n  constructor(options?: UnifyAdapterParams) {\n    if (options?.apiKey) {\n      this.apiKey = options.apiKey;\n    } else {\n      this.apiKey = \"UNIFY_API_KEY\";\n    }\n    this.model = options?.model;\n    this.start = true;\n  }\n\n  async process(\n    request: CopilotRuntimeChatCompletionRequest,\n  ): Promise<CopilotRuntimeChatCompletionResponse> {\n    const tools = request.actions.map(convertActionInputToOpenAITool);\n    const openai = new OpenAI({\n      apiKey: this.apiKey,\n      baseURL: \"https://api.unify.ai/v0/\",\n    });\n    const forwardedParameters = request.forwardedParameters;\n\n    const messages = request.messages.map((m) => convertMessageToOpenAIMessage(m));\n\n    const stream = await openai.chat.completions.create({\n      model: this.model,\n      messages: messages,\n      stream: true,\n      ...(tools.length > 0 && { tools }),\n      ...(forwardedParameters?.temperature && { temperature: forwardedParameters.temperature }),\n    });\n\n    let model = null;\n    let currentMessageId: string;\n    let currentToolCallId: string;\n    request.eventSource.stream(async (eventStream$) => {\n      let mode: \"function\" | \"message\" | null = null;\n      for await (const chunk of stream) {\n        if (this.start) {\n          model = chunk.model;\n          currentMessageId = randomId();\n          eventStream$.sendTextMessageStart({ messageId: currentMessageId });\n          eventStream$.sendTextMessageContent({\n            messageId: currentMessageId,\n            content: `Model used: ${model}\\n`,\n          });\n          eventStream$.sendTextMessageEnd({ messageId: currentMessageId });\n          this.start = false;\n        }\n        const toolCall = chunk.choices[0].delta.tool_calls?.[0];\n        const content = chunk.choices[0].delta.content;\n\n        // When switching from message to function or vice versa,\n        // send the respective end event.\n        // If toolCall?.id is defined, it means a new tool call starts.\n        if (mode === \"message\" && toolCall?.id) {\n          mode = null;\n          eventStream$.sendTextMessageEnd({ messageId: currentMessageId });\n        } else if (mode === \"function\" && (toolCall === undefined || toolCall?.id)) {\n          mode = null;\n          eventStream$.sendActionExecutionEnd({ actionExecutionId: currentToolCallId });\n        }\n\n        // If we send a new message type, send the appropriate start event.\n        if (mode === null) {\n          if (toolCall?.id) {\n            mode = \"function\";\n            currentToolCallId = toolCall!.id;\n            eventStream$.sendActionExecutionStart({\n              actionExecutionId: currentToolCallId,\n              actionName: toolCall!.function!.name,\n            });\n          } else if (content) {\n            mode = \"message\";\n            currentMessageId = chunk.id;\n            eventStream$.sendTextMessageStart({ messageId: currentMessageId });\n          }\n        }\n\n        // send the content events\n        if (mode === \"message\" && content) {\n          eventStream$.sendTextMessageContent({\n            messageId: currentMessageId,\n            content: content,\n          });\n        } else if (mode === \"function\" && toolCall?.function?.arguments) {\n          eventStream$.sendActionExecutionArgs({\n            actionExecutionId: currentToolCallId,\n            args: toolCall.function.arguments,\n          });\n        }\n      }\n\n      // send the end events\n      if (mode === \"message\") {\n        eventStream$.sendTextMessageEnd({ messageId: currentMessageId });\n      } else if (mode === \"function\") {\n        eventStream$.sendActionExecutionEnd({ actionExecutionId: currentToolCallId });\n      }\n\n      eventStream$.complete();\n    });\n\n    return {\n      threadId: request.threadId || randomUUID(),\n    };\n  }\n}\n","/**\n * Copilot Runtime adapter for Groq.\n *\n * ## Example\n *\n * ```ts\n * import { CopilotRuntime, GroqAdapter } from \"@copilotkit/runtime\";\n * import { Groq } from \"groq-sdk\";\n *\n * const groq = new Groq({ apiKey: process.env[\"GROQ_API_KEY\"] });\n *\n * const copilotKit = new CopilotRuntime();\n *\n * return new GroqAdapter({ groq, model: \"<model-name>\" });\n * ```\n */\nimport { Groq } from \"groq-sdk\";\nimport type { ChatCompletionMessageParam } from \"groq-sdk/resources/chat\";\nimport {\n  CopilotServiceAdapter,\n  CopilotRuntimeChatCompletionRequest,\n  CopilotRuntimeChatCompletionResponse,\n} from \"../service-adapter\";\nimport {\n  convertActionInputToOpenAITool,\n  convertMessageToOpenAIMessage,\n  limitMessagesToTokenCount,\n} from \"../openai/utils\";\nimport { randomUUID } from