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@langchain/groq
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| # @langchain/groq | ||
| ## 1.2.0 | ||
| ### Minor Changes | ||
| - [#10603](https://github.com/langchain-ai/langchainjs/pull/10603) [`66effb0`](https://github.com/langchain-ai/langchainjs/commit/66effb028baf5c923501801c989c7377efb8b77a) Thanks [@dependabot](https://github.com/apps/dependabot)! - chore(deps): bump groq-sdk from 0.37.0 to 1.1.2 | ||
| ## 1.1.5 | ||
@@ -4,0 +10,0 @@ |
@@ -9,12 +9,8 @@ //#region \0rolldown/runtime.js | ||
| var __copyProps = (to, from, except, desc) => { | ||
| if (from && typeof from === "object" || typeof from === "function") { | ||
| for (var keys = __getOwnPropNames(from), i = 0, n = keys.length, key; i < n; i++) { | ||
| key = keys[i]; | ||
| if (!__hasOwnProp.call(to, key) && key !== except) { | ||
| __defProp(to, key, { | ||
| get: ((k) => from[k]).bind(null, key), | ||
| enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable | ||
| }); | ||
| } | ||
| } | ||
| if (from && typeof from === "object" || typeof from === "function") for (var keys = __getOwnPropNames(from), i = 0, n = keys.length, key; i < n; i++) { | ||
| key = keys[i]; | ||
| if (!__hasOwnProp.call(to, key) && key !== except) __defProp(to, key, { | ||
| get: ((k) => from[k]).bind(null, key), | ||
| enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable | ||
| }); | ||
| } | ||
@@ -27,5 +23,3 @@ return to; | ||
| }) : target, mod)); | ||
| //#endregion | ||
| exports.__toESM = __toESM; | ||
| exports.__toESM = __toESM; |
@@ -1,4 +0,4 @@ | ||
| const require_runtime = require('./_virtual/_rolldown/runtime.cjs'); | ||
| const require_profiles = require('./profiles.cjs'); | ||
| const require_groq_schema = require('./utils/groq-schema.cjs'); | ||
| const require_runtime = require("./_virtual/_rolldown/runtime.cjs"); | ||
| const require_profiles = require("./profiles.cjs"); | ||
| const require_groq_schema = require("./utils/groq-schema.cjs"); | ||
| let _langchain_core_utils_json_schema = require("@langchain/core/utils/json_schema"); | ||
@@ -16,3 +16,2 @@ let _langchain_core_language_models_chat_models = require("@langchain/core/language_models/chat_models"); | ||
| let _langchain_core_language_models_structured_output = require("@langchain/core/language_models/structured_output"); | ||
| //#region src/chat_models.ts | ||
@@ -603,3 +602,3 @@ /** | ||
| super(params); | ||
| this._addVersion("@langchain/groq", "1.1.5"); | ||
| this._addVersion("@langchain/groq", "1.2.0"); | ||
| const apiKey = params.apiKey || (0, _langchain_core_utils_env.getEnvironmentVariable)("GROQ_API_KEY"); | ||
@@ -705,3 +704,2 @@ if (!apiKey) throw new Error(`Groq API key not found. Please set the GROQ_API_KEY environment variable or provide the key into "apiKey"`); | ||
| let role; | ||
| let lastMessageId; | ||
| let responseMetadata; | ||
@@ -714,3 +712,3 @@ for await (const data of response) { | ||
| if (choice.delta?.role) role = choice.delta.role; | ||
| const chunk = _convertDeltaToMessageChunk(choice.delta, role, data, lastMessageId); | ||
| const chunk = _convertDeltaToMessageChunk(choice.delta, role, data, void 0); | ||
| const newTokenIndices = { | ||
@@ -922,6 +920,6 @@ prompt: options.promptIndex ?? 0, | ||
| } | ||
| //#endregion | ||
| exports.ChatGroq = ChatGroq; | ||
| exports.messageToGroqRole = messageToGroqRole; | ||
| //# sourceMappingURL=chat_models.cjs.map |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"file":"chat_models.cjs","names":["ChatMessage","convertLangChainToolCallToOpenAI","AIMessage","HumanMessageChunk","AIMessageChunk","SystemMessageChunk","FunctionMessageChunk","ToolMessageChunk","ChatMessageChunk","BaseChatModel","Groq","ChatGenerationChunk","PROFILES","getGroqStructuredOutputMethod","groqStrictifySchema"],"sources":["../src/chat_models.ts"],"sourcesContent":["import { toJsonSchema } from \"@langchain/core/utils/json_schema\";\nimport { NewTokenIndices } from \"@langchain/core/callbacks/base\";\nimport { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport {\n BaseChatModel,\n BaseChatModelCallOptions,\n BindToolsInput,\n LangSmithParams,\n type BaseChatModelParams,\n} from \"@langchain/core/language_models/chat_models\";\nimport * as ChatCompletionsAPI from \"groq-sdk/resources/chat/completions\";\nimport * as CompletionsAPI from \"groq-sdk/resources/completions\";\nimport {\n AIMessage,\n AIMessageChunk,\n BaseMessage,\n ChatMessage,\n ChatMessageChunk,\n HumanMessageChunk,\n SystemMessageChunk,\n ToolMessage,\n OpenAIToolCall,\n isAIMessage,\n BaseMessageChunk,\n UsageMetadata,\n FunctionMessageChunk,\n ToolMessageChunk,\n} from \"@langchain/core/messages\";\nimport {\n ChatGeneration,\n ChatGenerationChunk,\n ChatResult,\n} from \"@langchain/core/outputs\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\nimport {\n InteropZodType,\n isInteropZodSchema,\n} from \"@langchain/core/utils/types\";\nimport Groq from \"groq-sdk\";\nimport type {\n ChatCompletion,\n ChatCompletionCreateParams,\n ChatCompletionCreateParamsNonStreaming,\n ChatCompletionCreateParamsStreaming,\n ChatCompletionTool,\n} from \"groq-sdk/resources/chat/completions\";\nimport type { RequestOptions } from \"groq-sdk/core\";\nimport { Runnable } from \"@langchain/core/runnables\";\nimport {\n BaseLanguageModelInput,\n FunctionDefinition,\n StructuredOutputMethodOptions,\n} from \"@langchain/core/language_models/base\";\nimport { ModelProfile } from \"@langchain/core/language_models/profile\";\nimport { BaseLLMOutputParser } from \"@langchain/core/output_parsers\";\nimport {\n parseToolCall,\n makeInvalidToolCall,\n convertLangChainToolCallToOpenAI,\n} from \"@langchain/core/output_parsers/openai_tools\";\nimport { convertToOpenAITool } from \"@langchain/core/utils/function_calling\";\nimport { ToolCallChunk } from \"@langchain/core/messages/tool\";\nimport { getSchemaDescription } from \"@langchain/core/utils/types\";\n\nimport PROFILES from \"./profiles.js\";\nimport {\n getGroqStructuredOutputMethod,\n groqStrictifySchema,\n} from \"./utils/groq-schema.js\";\nimport {\n isSerializableSchema,\n SerializableSchema,\n} from \"@langchain/core/utils/standard_schema\";\nimport {\n assembleStructuredOutputPipeline,\n createContentParser,\n createFunctionCallingParser,\n} from \"@langchain/core/language_models/structured_output\";\n\ntype ChatGroqToolType = BindToolsInput | ChatCompletionTool;\n\n/**\n * Const list of fields that we'll pick from the `ChatCompletionCreateParams` interface\n * to use as the options allowed to be passed to invocation methods.\n *\n * @internal\n */\nconst CREATE_PARAMS_BASE_CALL_KEYS = [\n // \"messages\", // passed as input arg to invocation methods\n // \"model\", // don't allow override on invoke\n \"frequency_penalty\",\n \"function_call\",\n \"functions\",\n \"logit_bias\", // not supported, but left in for forward compatibility\n \"logprobs\", // not supported, but left in for forward compatibility\n \"max_completion_tokens\",\n \"max_tokens\",\n \"n\", // not supported, but left in for forward compatibility\n \"parallel_tool_calls\",\n \"presence_penalty\",\n \"reasoning_effort\",\n \"reasoning_format\",\n \"response_format\",\n \"seed\",\n // TODO: also pass as constructor arg\n \"service_tier\",\n \"stop\",\n // \"stream\", // determined by invocation method\n // other models only specify temperature as a constructor arg, but I don't see the harm in\n // allowing overrides on invocation\n \"temperature\",\n \"tool_choice\",\n // \"tools\", // need to allow users to specify langchain style tools, so we use a different type\n \"top_logprobs\",\n \"top_p\",\n // \"user\", // don't allow override on invoke\n] as const;\n\nconst ADDED_CALL_KEYS = [\n \"headers\",\n \"promptIndex\",\n \"stream_options\",\n \"tools\",\n] as const;\n\nexport type ChatGroqCallOptions = Pick<\n ChatCompletionsAPI.ChatCompletionCreateParamsBase,\n (typeof CREATE_PARAMS_BASE_CALL_KEYS)[number]\n> &\n BaseChatModelCallOptions & {\n /**\n * Additional headers to pass to the API.\n */\n headers?: Record<string, string | null | undefined>;\n /**\n * The index of the prompt in the list of prompts.\n */\n promptIndex?: number;\n /**\n * Additional options to pass to streamed completions.\n * If provided takes precedence over \"streamUsage\" set at initialization time.\n */\n stream_options?: {\n /**\n * Whether or not to include token usage in the stream.\n * If set to `true`, this will include an additional\n * chunk at the end of the stream with the token usage.\n *\n * Defaults to `true` when streaming, `false` otherwise.\n */\n include_usage: boolean;\n };\n\n tools?: ChatGroqToolType[];\n\n // IMPORTANT: If you add a new key here you MUST add it to the `ADDED_CALL_KEYS`\n // list above. Keep this comment at the bottom so people see it when they go to\n // make additions.\n };\n\nconst ALL_CALL_KEYS: readonly (keyof ChatGroqCallOptions)[] = [\n ...CREATE_PARAMS_BASE_CALL_KEYS,\n ...ADDED_CALL_KEYS,\n] as const;\n\n/**\n * Timing details about the request, useful for collecting performance metrics.\n */\ninterface TimingMetadata {\n /**\n * Time spent generating tokens\n */\n completion_time?: number;\n\n /**\n * Time spent processing input tokens\n */\n prompt_time?: number;\n\n /**\n * Time the requests was spent queued\n */\n queue_time?: number;\n\n /**\n * completion time and prompt time combined\n */\n total_time?: number;\n}\nexport interface ChatGroqInput extends BaseChatModelParams {\n /**\n * The temperature to use for sampling.\n * @default 0.7\n */\n temperature?: number;\n\n /**\n * The maximum number of tokens that the model can process in a single response.\n * This limits ensures computational efficiency and resource management.\n */\n maxTokens?: number;\n\n /** Total probability mass of tokens to consider at each step */\n topP?: number;\n\n /** Penalizes repeated tokens according to frequency */\n frequencyPenalty?: number;\n\n /** Penalizes repeated tokens */\n presencePenalty?: number;\n\n /** Number of completions to generate for each prompt */\n n?: number;\n\n /** Dictionary used to adjust the probability of specific tokens being generated */\n logitBias?: Record<string, number>;\n\n /** Unique string identifier representing your end-user, which can help OpenAI to monitor and detect abuse. */\n user?: string;\n\n /**\n * Whether or not to include token usage data in streamed chunks.\n * @default true\n */\n streamUsage?: boolean;\n\n /**\n * Whether to return log probabilities of the output tokens or not.\n * If true, returns the log probabilities of each output token returned in the content of message.\n */\n logprobs?: boolean;\n\n /**\n * An integer between 0 and 5 specifying the number of most likely tokens to return at each token position,\n * each with an associated log probability. logprobs must be set to true if this parameter is used.\n */\n topLogprobs?: number;\n\n /**\n * The Groq API key to use for requests.\n * @default process.env.GROQ_API_KEY\n */\n apiKey?: string;\n /**\n * The name of the model to use.\n */\n model: ChatCompletionCreateParams[\"model\"];\n /**\n * Up to 4 sequences where the API will stop generating further tokens. The\n * returned text will not contain the stop sequence.\n * Alias for `stopSequences`\n */\n stop?: string | null | Array<string>;\n /**\n * Up to 4 sequences where the API will stop generating further tokens. The\n * returned text will not contain the stop sequence.\n */\n stopSequences?: Array<string>;\n /**\n * Whether or not to stream responses.\n */\n streaming?: boolean;\n /**\n * Override the default base URL for the API\n */\n baseUrl?: string;\n /**\n * The maximum amount of time (in milliseconds) the client will wait for a response\n */\n timeout?: number;\n /**\n * HTTP agent used to manage connections\n */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n httpAgent?: any;\n /**\n * Custom fetch function implementation\n */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n fetch?: (...args: any) => any;\n\n /**\n * Default headers included with every request\n */\n defaultHeaders?: Record<string, string>;\n\n /**\n * Default query parameters included with every request\n */\n defaultQuery?: Record<string, string>;\n\n /**\n * The level of reasoning effort the model will use.\n * Supported by `openai/gpt-oss-20b`, `openai/gpt-oss-120b`, and `qwen/qwen3-32b`.\n * @see https://console.groq.com/docs/reasoning#options-for-reasoning-effort\n */\n reasoningEffort?: \"none\" | \"default\" | \"low\" | \"medium\" | \"high\" | null;\n}\n\ntype GroqRoleEnum = \"system\" | \"assistant\" | \"user\" | \"function\";\n\ninterface TokenUsage {\n completionTokens?: number;\n promptTokens?: number;\n totalTokens?: number;\n}\n\n/**\n * Extract the custom role from a message.\n * @param message - The message to extract the custom role from.\n * @returns The custom role of the message.\n */\nfunction extractGenericMessageCustomRole(message: ChatMessage): GroqRoleEnum {\n if (\n message.role !== \"system\" &&\n message.role !== \"assistant\" &&\n message.role !== \"user\" &&\n message.role !== \"function\"\n ) {\n throw new Error(\n `Unsupported message role: ${message.role}. Expected \"system\", \"assistant\", \"user\", or \"function\"`\n );\n }\n return message.role as GroqRoleEnum;\n}\n\n/**\n * Extract the role from a message.\n * @param message - The message to extract the role from.\n * @returns The role of the message.\n */\nexport function messageToGroqRole(message: BaseMessage): GroqRoleEnum {\n switch (message.type) {\n case \"system\":\n return \"system\";\n case \"ai\":\n return \"assistant\";\n case \"human\":\n return \"user\";\n case \"function\":\n return \"function\";\n case \"tool\":\n // Not yet supported as a type\n return \"tool\" as GroqRoleEnum;\n case \"generic\": {\n if (!ChatMessage.isInstance(message))\n throw new Error(\"Invalid generic chat message\");\n return extractGenericMessageCustomRole(message);\n }\n default:\n throw new Error(`Unknown message type: ${message.type}`);\n }\n}\n\nfunction convertMessagesToGroqParams(\n messages: BaseMessage[]\n): Array<ChatCompletionsAPI.ChatCompletionMessage> {\n return messages.map((message): ChatCompletionsAPI.ChatCompletionMessage => {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const completionParam: Record<string, any> = {\n role: messageToGroqRole(message),\n content: message.content,\n name: message.name,\n function_call: message.additional_kwargs.function_call,\n tool_calls: message.additional_kwargs.tool_calls,\n tool_call_id: (message as ToolMessage).tool_call_id,\n };\n if (isAIMessage(message) && !!message.tool_calls?.length) {\n completionParam.tool_calls = message.tool_calls.map(\n convertLangChainToolCallToOpenAI\n );\n } else {\n if (message.additional_kwargs.tool_calls != null) {\n completionParam.tool_calls = message.additional_kwargs.tool_calls;\n }\n if ((message as ToolMessage).tool_call_id != null) {\n completionParam.tool_call_id = (message as ToolMessage).tool_call_id;\n }\n }\n return completionParam as ChatCompletionsAPI.ChatCompletionMessage;\n });\n}\n\nfunction groqResponseToChatMessage(\n message: ChatCompletionsAPI.ChatCompletionMessage,\n usageMetadata?: UsageMetadata,\n responseMetadata?: Record<string, unknown>\n): BaseMessage {\n const rawToolCalls: OpenAIToolCall[] | undefined = message.tool_calls as\n | OpenAIToolCall[]\n | undefined;\n // Add model_provider for block translator lookup\n const enrichedMetadata = { ...responseMetadata, model_provider: \"groq\" };\n switch (message.role) {\n case \"assistant\": {\n const toolCalls = [];\n const invalidToolCalls = [];\n for (const rawToolCall of rawToolCalls ?? []) {\n try {\n toolCalls.push(parseToolCall(rawToolCall, { returnId: true }));\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (e: any) {\n invalidToolCalls.push(makeInvalidToolCall(rawToolCall, e.message));\n }\n }\n return new AIMessage({\n content: message.content || \"\",\n additional_kwargs: { tool_calls: rawToolCalls },\n tool_calls: toolCalls,\n invalid_tool_calls: invalidToolCalls,\n usage_metadata: usageMetadata,\n response_metadata: enrichedMetadata,\n });\n }\n default:\n return new ChatMessage(message.content || \"\", message.role ?? \"unknown\");\n }\n}\n\nfunction _convertDeltaToMessageChunk(\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n delta: Record<string, any>,\n defaultRole: GroqRoleEnum | undefined,\n rawResponse: ChatCompletionsAPI.ChatCompletionChunk,\n lastMessageId: string | undefined\n): BaseMessageChunk {\n const role = delta.role ?? defaultRole;\n const content = delta.content ?? \"\";\n let additional_kwargs: Record<string, unknown>;\n if (delta.function_call) {\n additional_kwargs = {\n function_call: delta.function_call,\n };\n } else if (delta.tool_calls) {\n additional_kwargs = {\n tool_calls: delta.tool_calls,\n };\n } else {\n additional_kwargs = {};\n }\n if (delta.audio) {\n additional_kwargs.audio = {\n ...delta.audio,\n index: rawResponse.choices[0].index,\n };\n }\n\n let usage: UsageMetadata | undefined;\n let groqMessageId: string | undefined = lastMessageId;\n let timing: TimingMetadata | undefined;\n\n const xGroq = rawResponse.x_groq;\n if (xGroq?.usage) {\n usage = {\n input_tokens: xGroq.usage.prompt_tokens,\n output_tokens: xGroq.usage.completion_tokens,\n total_tokens: xGroq.usage.total_tokens,\n };\n timing = {\n completion_time: xGroq.usage.completion_time,\n prompt_time: xGroq.usage.prompt_time,\n queue_time: xGroq.usage.queue_time,\n total_time: xGroq.usage.total_time,\n };\n }\n\n if (xGroq?.id) {\n groqMessageId = xGroq.id;\n }\n\n const response_metadata = { usage, timing, model_provider: \"groq\" };\n if (role === \"user\") {\n return new HumanMessageChunk({ content, response_metadata });\n } else if (role === \"assistant\") {\n const toolCallChunks: ToolCallChunk[] = [];\n if (Array.isArray(delta.tool_calls)) {\n for (const rawToolCall of delta.tool_calls) {\n toolCallChunks.push({\n name: rawToolCall.function?.name,\n args: rawToolCall.function?.arguments,\n id: rawToolCall.id,\n index: rawToolCall.index,\n type: \"tool_call_chunk\",\n });\n }\n }\n return new AIMessageChunk({\n content,\n tool_call_chunks: toolCallChunks,\n additional_kwargs,\n id: groqMessageId,\n response_metadata,\n });\n } else if (role === \"system\") {\n return new SystemMessageChunk({ content, response_metadata });\n } else if (role === \"developer\") {\n return new SystemMessageChunk({\n content,\n response_metadata,\n additional_kwargs: {\n __openai_role__: \"developer\",\n },\n });\n } else if (role === \"function\") {\n return new FunctionMessageChunk({\n content,\n additional_kwargs,\n name: delta.name,\n response_metadata,\n });\n } else if (role === \"tool\") {\n return new ToolMessageChunk({\n content,\n additional_kwargs,\n tool_call_id: delta.tool_call_id,\n response_metadata,\n });\n } else {\n return new ChatMessageChunk({ content, role, response_metadata });\n }\n}\n\n/*\nfunction _oldConvertDeltaToMessageChunk(\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n delta: Record<string, any>,\n rawResponse: ChatCompletionsAPI.ChatCompletionChunk,\n index: number,\n defaultRole: GroqRoleEnum | undefined,\n xGroq?: ChatCompletionsAPI.ChatCompletionChunk.XGroq\n): {\n message: BaseMessageChunk;\n toolCallData?: {\n id: string;\n name: string;\n index: number;\n type: \"tool_call_chunk\";\n }[];\n} {\n const { role } = delta;\n const content = delta.content ?? \"\";\n let additional_kwargs;\n if (delta.function_call) {\n additional_kwargs = {\n function_call: delta.function_call,\n };\n } else if (delta.tool_calls) {\n additional_kwargs = {\n tool_calls: delta.tool_calls,\n };\n } else {\n additional_kwargs = {};\n }\n\n let usageMetadata: UsageMetadata | undefined;\n let groqMessageId: string | undefined;\n if (xGroq?.usage) {\n usageMetadata = {\n input_tokens: xGroq.usage.prompt_tokens,\n output_tokens: xGroq.usage.completion_tokens,\n total_tokens: xGroq.usage.total_tokens,\n };\n groqMessageId = xGroq.id;\n }\n\n if (role === \"user\") {\n return {\n message: new HumanMessageChunk({ content }),\n };\n } else if (role === \"assistant\") {\n const toolCallChunks = _convertDeltaToolCallToToolCallChunk(\n delta.tool_calls,\n index\n );\n return {\n message: new AIMessageChunk({\n content,\n additional_kwargs,\n tool_call_chunks: toolCallChunks\n ? toolCallChunks.map((tc) => ({\n type: tc.type,\n args: tc.args,\n index: tc.index,\n }))\n : undefined,\n usage_metadata: usageMetadata,\n id: groqMessageId,\n }),\n toolCallData: toolCallChunks\n ? toolCallChunks.map((tc) => ({\n id: tc.id ?? \"\",\n name: tc.name ?? \"\",\n index: tc.index ?? index,\n type: \"tool_call_chunk\",\n }))\n : undefined,\n };\n } else if (role === \"system\") {\n return {\n message: new SystemMessageChunk({ content }),\n };\n } else {\n return {\n message: new ChatMessageChunk({ content, role }),\n };\n }\n}\n*/\n\n/**\n * Groq chat model integration.\n *\n * The Groq API is compatible to the OpenAI API with some limitations. View the\n * full API ref at:\n * @link {https://docs.api.groq.com/md/openai.oas.html}\n *\n * Setup:\n * Install `@langchain/groq` and set an environment variable named `GROQ_API_KEY`.\n *\n * ```bash\n * npm install @langchain/groq\n * export GROQ_API_KEY=\"your-api-key\"\n * ```\n *\n * ## [Constructor args](https://api.js.langchain.com/classes/langchain_groq.ChatGroq.html#constructor)\n *\n * ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_groq.ChatGroqCallOptions.html)\n *\n * Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.\n * They can also be passed via `.withConfig`, or the second arg in `.bindTools`, like shown in the examples below:\n *\n * ```typescript\n * // When calling `.withConfig`, call options should be passed via the first argument\n * const llmWithArgsBound = llm.withConfig({\n * stop: [\"\\n\"],\n * });\n *\n * // When calling `.bindTools`, call options should be passed via the second argument\n * const llmWithTools = llm.bindTools(\n * [...],\n * {\n * tool_choice: \"auto\",\n * }\n * );\n * ```\n *\n * ## Examples\n *\n * <details open>\n * <summary><strong>Instantiate</strong></summary>\n *\n * ```typescript\n * import { ChatGroq } from '@langchain/groq';\n *\n * const llm = new ChatGroq({\n * model: \"llama-3.3-70b-versatile\",\n * temperature: 0,\n * // other params...\n * });\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Invoking</strong></summary>\n *\n * ```typescript\n * const input = `Translate \"I love programming\" into French.`;\n *\n * // Models also accept a list of chat messages or a formatted prompt\n * const result = await llm.invoke(input);\n * console.log(result);\n * ```\n *\n * ```txt\n * AIMessage {\n * \"content\": \"The French translation of \\\"I love programming\\\" is \\\"J'aime programmer\\\". In this sentence, \\\"J'aime\\\" is the first person singular conjugation of the French verb \\\"aimer\\\" which means \\\"to love\\\", and \\\"programmer\\\" is the French infinitive for \\\"to program\\\". I hope this helps! Let me know if you have any other questions.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"tokenUsage\": {\n * \"completionTokens\": 82,\n * \"promptTokens\": 20,\n * \"totalTokens\": 102\n * },\n * \"finish_reason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Streaming Chunks</strong></summary>\n *\n * ```typescript\n * for await (const chunk of await llm.stream(input)) {\n * console.log(chunk);\n * }\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"The\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" French\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" translation\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" of\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" \\\"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"I\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" love\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ...\n * AIMessageChunk {\n * \"content\": \".\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Aggregate Streamed Chunks</strong></summary>\n *\n * ```typescript\n * import { AIMessageChunk } from '@langchain/core/messages';\n * import { concat } from '@langchain/core/utils/stream';\n *\n * const stream = await llm.stream(input);\n * let full: AIMessageChunk | undefined;\n * for await (const chunk of stream) {\n * full = !full ? chunk : concat(full, chunk);\n * }\n * console.log(full);\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"The French translation of \\\"I love programming\\\" is \\\"J'aime programmer\\\". In this sentence, \\\"J'aime\\\" is the first person singular conjugation of the French verb \\\"aimer\\\" which means \\\"to love\\\", and \\\"programmer\\\" is the French infinitive for \\\"to program\\\". I hope this helps! Let me know if you have any other questions.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Bind tools</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const llmForToolCalling = new ChatGroq({\n * model: \"llama3-groq-70b-8192-tool-use-preview\",\n * temperature: 0,\n * // other params...\n * });\n *\n * const GetWeather = {\n * name: \"GetWeather\",\n * description: \"Get the current weather in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const GetPopulation = {\n * name: \"GetPopulation\",\n * description: \"Get the current population in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const llmWithTools = llmForToolCalling.bindTools([GetWeather, GetPopulation]);\n * const aiMsg = await llmWithTools.invoke(\n * \"Which city is hotter today and which is bigger: LA or NY?\"\n * );\n * console.log(aiMsg.tool_calls);\n * ```\n *\n * ```txt\n * [\n * {\n * name: 'GetWeather',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'call_cd34'\n * },\n * {\n * name: 'GetWeather',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_68rf'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'call_f81z'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_8byt'\n * }\n * ]\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Structured Output</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const Joke = z.object({\n * setup: z.string().describe(\"The setup of the joke\"),\n * punchline: z.string().describe(\"The punchline to the joke\"),\n * rating: z.number().optional().describe(\"How funny the joke is, from 1 to 10\")\n * }).describe('Joke to tell user.');\n *\n * const structuredLlm = llmForToolCalling.withStructuredOutput(Joke, { name: \"Joke\" });\n * const jokeResult = await structuredLlm.invoke(\"Tell me a joke about cats\");\n * console.log(jokeResult);\n * ```\n *\n * ```txt\n * {\n * setup: \"Why don't cats play poker in the wild?\",\n * punchline: 'Because there are too many cheetahs.'\n * }\n * ```\n * </details>\n *\n * <br />\n */\nexport class ChatGroq extends BaseChatModel<\n ChatGroqCallOptions,\n AIMessageChunk\n> {\n lc_namespace = [\"langchain\", \"chat_models\", \"groq\"];\n\n client: Groq;\n\n model: string;\n\n temperature = 0.7;\n\n stop?: string[];\n\n stopSequences?: string[];\n\n maxTokens?: number;\n\n streaming = false;\n\n apiKey?: string;\n\n streamUsage: boolean = true;\n\n topP: number | null | undefined;\n\n frequencyPenalty: number | null | undefined;\n\n presencePenalty: number | null | undefined;\n\n logprobs: boolean | null | undefined;\n\n n: number | null | undefined;\n\n logitBias: Record<string, number> | null | undefined;\n\n user: string | null | undefined;\n\n reasoningEffort: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"reasoning_effort\"];\n\n reasoningFormat: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"reasoning_format\"];\n\n serviceTier: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"service_tier\"];\n\n topLogprobs: number | null | undefined;\n\n lc_serializable = true;\n\n get lc_serialized_keys(): string[] {\n return [\n \"client\",\n \"model\",\n \"temperature\",\n \"stop\",\n \"stopSequences\",\n \"maxTokens\",\n \"streaming\",\n \"apiKey\",\n \"streamUsage\",\n \"topP\",\n \"frequencyPenalty\",\n \"presencePenalty\",\n \"logprobs\",\n \"n\",\n \"logitBias\",\n \"user\",\n \"reasoningEffort\",\n \"reasoningFormat\",\n \"serviceTier\",\n \"topLogprobs\",\n ];\n }\n\n static lc_name() {\n return \"ChatGroq\";\n }\n\n _llmType() {\n return \"groq\";\n }\n\n get lc_secrets(): { [key: string]: string } | undefined {\n return {\n apiKey: \"GROQ_API_KEY\",\n };\n }\n\n get callKeys() {\n return [...super.callKeys, ...ALL_CALL_KEYS];\n }\n\n constructor(\n model: ChatCompletionCreateParams[\"model\"],\n fields?: Omit<ChatGroqInput, \"model\">\n );\n constructor(fields: ChatGroqInput);\n constructor(\n modelOrFields?: string | ChatGroqInput,\n fields?: Omit<ChatGroqInput, \"model\">\n ) {\n const params =\n typeof modelOrFields === \"string\"\n ? { ...(fields ?? {}), model: modelOrFields }\n : (modelOrFields ?? {});\n super(params);\n this._addVersion(\"@langchain/groq\", __PKG_VERSION__);\n\n const apiKey = params.apiKey || getEnvironmentVariable(\"GROQ_API_KEY\");\n if (!apiKey) {\n throw new Error(\n `Groq API key not found. Please set the GROQ_API_KEY environment variable or provide the key into \"apiKey\"`\n );\n }\n const defaultHeaders = {\n \"User-Agent\": \"langchainjs\",\n ...(params.defaultHeaders ?? {}),\n };\n\n this.client = new Groq({\n apiKey,\n dangerouslyAllowBrowser: true,\n baseURL: params.baseUrl,\n timeout: params.timeout,\n httpAgent: params.httpAgent,\n fetch: params.fetch,\n maxRetries: 0,\n defaultHeaders,\n defaultQuery: params.defaultQuery,\n });\n this.apiKey = apiKey;\n this.temperature = params.temperature ?? this.temperature;\n this.model = params.model;\n this.streaming = params.streaming ?? this.streaming;\n this.stop =\n params.stopSequences ??\n (typeof params.stop === \"string\" ? [params.stop] : params.stop) ??\n [];\n this.stopSequences = this.stop;\n this.maxTokens = params.maxTokens;\n this.topP = params.topP;\n this.frequencyPenalty = params.frequencyPenalty;\n this.presencePenalty = params.presencePenalty;\n this.logprobs = params.logprobs;\n this.n = params.n;\n this.logitBias = params.logitBias;\n this.user = params.user;\n this.reasoningEffort = params.reasoningEffort;\n }\n\n getLsParams(options: this[\"ParsedCallOptions\"]): LangSmithParams {\n const params = this.invocationParams(options);\n return {\n ls_provider: \"groq\",\n ls_model_name: this.model,\n ls_model_type: \"chat\",\n ls_temperature: params.temperature ?? this.temperature,\n ls_max_tokens: params.max_tokens ?? this.maxTokens,\n ls_stop: options.stop,\n };\n }\n\n async completionWithRetry(\n request: ChatCompletionCreateParamsStreaming,\n options?: RequestOptions\n ): Promise<AsyncIterable<ChatCompletionsAPI.ChatCompletionChunk>>;\n\n async completionWithRetry(\n request: ChatCompletionCreateParamsNonStreaming,\n options?: RequestOptions\n ): Promise<ChatCompletion>;\n\n async completionWithRetry(\n request: ChatCompletionCreateParams,\n options?: RequestOptions\n ): Promise<\n AsyncIterable<ChatCompletionsAPI.ChatCompletionChunk> | ChatCompletion\n > {\n return this.caller.call(async () =>\n this.client.chat.completions.create(request, options)\n );\n }\n\n invocationParams(\n options: this[\"ParsedCallOptions\"],\n extra?: { streaming?: boolean }\n ): Omit<ChatCompletionCreateParams, \"messages\"> {\n const params = super.invocationParams(options);\n\n let streamOptionsConfig = {};\n\n if (options?.stream_options !== undefined) {\n streamOptionsConfig = { stream_options: options.stream_options };\n } else if ((this.streamUsage && this.streaming) || extra?.streaming) {\n streamOptionsConfig = { stream_options: { include_usage: true } };\n }\n\n const toReturn: Omit<ChatCompletionCreateParams, \"messages\"> = {\n model: this.model,\n frequency_penalty: this.frequencyPenalty,\n function_call: options?.function_call,\n functions: options?.functions,\n logit_bias: this.logitBias,\n logprobs: this.logprobs,\n // max_completion_tokens\n // max_tokens\n n: this.n,\n parallel_tool_calls: options?.parallel_tool_calls,\n presence_penalty: this.presencePenalty,\n reasoning_effort: options?.reasoning_effort ?? this.reasoningEffort,\n reasoning_format: this.reasoningFormat,\n response_format: options?.response_format,\n seed: options?.seed,\n service_tier: this.serviceTier,\n stop: options?.stop ?? this.stopSequences,\n temperature: options?.temperature ?? this.temperature,\n tool_choice: _formatToGroqToolChoice(options?.tool_choice),\n tools: options?.tools?.length\n ? options.tools.map((tool) => convertToOpenAITool(tool))\n : undefined,\n top_logprobs: this.topLogprobs,\n top_p: this.topP,\n user: this.user,\n // if include_usage is set or streamUsage then stream must be set to true.\n stream: this.streaming,\n ...params,\n ...streamOptionsConfig,\n };\n\n toReturn.max_completion_tokens =\n options?.max_completion_tokens ?? options?.max_tokens ?? this.maxTokens;\n if (toReturn.max_completion_tokens === -1) {\n delete toReturn.max_completion_tokens;\n }\n\n return toReturn;\n }\n\n override bindTools(\n tools: ChatGroqToolType[],\n kwargs?: Partial<ChatGroqCallOptions>\n ): Runnable<BaseLanguageModelInput, AIMessageChunk, ChatGroqCallOptions> {\n return this.withConfig({\n tools: tools.map((tool) => convertToOpenAITool(tool)),\n ...kwargs,\n });\n }\n\n override async *_streamResponseChunks(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): AsyncGenerator<ChatGenerationChunk> {\n const params = this.invocationParams(options, { streaming: true });\n const messagesMapped = convertMessagesToGroqParams(messages);\n const response = await this.completionWithRetry(\n {\n ...params,\n messages: messagesMapped,\n stream: true,\n },\n {\n signal: options?.signal,\n headers: options?.headers,\n }\n );\n let role: GroqRoleEnum | undefined;\n let lastMessageId: string | undefined;\n\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n let responseMetadata: Record<string, any> | undefined;\n\n for await (const data of response) {\n if (options.signal?.aborted) {\n return;\n }\n responseMetadata = data;\n const choice = data?.choices[0];\n if (!choice) {\n continue;\n }\n // The `role` field is populated in the first delta of the response\n // but is not present in subsequent deltas. Extract it when available.\n if (choice.delta?.role) {\n role = choice.delta.role as GroqRoleEnum;\n }\n\n const chunk = _convertDeltaToMessageChunk(\n choice.delta,\n role,\n data,\n lastMessageId\n );\n const newTokenIndices = {\n prompt: options.promptIndex ?? 0,\n completion: choice.index ?? 0,\n };\n if (typeof chunk.content !== \"string\") {\n console.log(\n \"[WARNING]: Received non-string content from OpenAI. This is currently not supported.\"\n );\n continue;\n }\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const generationInfo: Record<string, any> = { ...newTokenIndices };\n if (choice.finish_reason != null) {\n generationInfo.finish_reason = choice.finish_reason;\n // Only include system fingerprint in the last chunk for now\n // to avoid concatenation issues\n generationInfo.system_fingerprint = data.system_fingerprint;\n generationInfo.model_name = data.model;\n }\n const generationChunk = new ChatGenerationChunk({\n message: chunk,\n text: chunk.content,\n generationInfo,\n });\n yield generationChunk;\n await runManager?.handleLLMNewToken(\n generationChunk.text ?? \"\",\n newTokenIndices,\n undefined,\n undefined,\n undefined,\n { chunk: generationChunk }\n );\n }\n\n if (responseMetadata) {\n if (\"choices\" in responseMetadata) {\n delete responseMetadata.choices;\n }\n yield new ChatGenerationChunk({\n message: new AIMessageChunk({\n content: \"\",\n response_metadata: responseMetadata,\n }),\n text: \"\",\n });\n }\n\n if (options.signal?.aborted) {\n throw new Error(\"AbortError\");\n }\n }\n\n override async _generate(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n options.signal?.throwIfAborted();\n if (this.streaming) {\n const tokenUsage: TokenUsage = {};\n const stream = this._streamResponseChunks(messages, options, runManager);\n const finalChunks: Record<number, ChatGenerationChunk> = {};\n for await (const chunk of stream) {\n const index =\n (chunk.generationInfo as NewTokenIndices)?.completion ?? 0;\n if (finalChunks[index] === undefined) {\n finalChunks[index] = chunk;\n } else {\n finalChunks[index] = finalChunks[index].concat(chunk);\n }\n }\n const generations = Object.entries(finalChunks)\n .sort(([aKey], [bKey]) => parseInt(aKey, 10) - parseInt(bKey, 10))\n .map(([_, value]) => value);\n\n return { generations, llmOutput: { estimatedTokenUsage: tokenUsage } };\n } else {\n return this._generateNonStreaming(messages, options, runManager);\n }\n }\n\n async _generateNonStreaming(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n _runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n const tokenUsage: TokenUsage = {};\n const params = this.invocationParams(options);\n const messagesMapped = convertMessagesToGroqParams(messages);\n\n const data = await this.completionWithRetry(\n {\n ...params,\n stream: false,\n messages: messagesMapped,\n },\n {\n signal: options?.signal,\n headers: options?.headers,\n }\n );\n\n if (\"usage\" in data && data.usage) {\n const {\n completion_tokens: completionTokens,\n prompt_tokens: promptTokens,\n total_tokens: totalTokens,\n } = data.usage as CompletionsAPI.CompletionUsage;\n\n if (completionTokens) {\n tokenUsage.completionTokens =\n (tokenUsage.completionTokens ?? 0) + completionTokens;\n }\n\n if (promptTokens) {\n tokenUsage.promptTokens = (tokenUsage.promptTokens ?? 0) + promptTokens;\n }\n\n if (totalTokens) {\n tokenUsage.totalTokens = (tokenUsage.totalTokens ?? 0) + totalTokens;\n }\n }\n\n const generations: ChatGeneration[] = [];\n\n if (\"choices\" in data && data.choices) {\n for (const part of (data as ChatCompletion).choices) {\n const text = part.message?.content ?? \"\";\n let usageMetadata: UsageMetadata | undefined;\n if (tokenUsage.totalTokens !== undefined) {\n usageMetadata = {\n input_tokens: tokenUsage.promptTokens ?? 0,\n output_tokens: tokenUsage.completionTokens ?? 0,\n total_tokens: tokenUsage.totalTokens,\n };\n }\n // extract all fields from the response object except\n // choices to be included as response metadata\n const { choices: _choices, ...metadata } = data;\n\n const generation: ChatGeneration = {\n text,\n message: groqResponseToChatMessage(\n part.message ?? { role: \"assistant\" },\n usageMetadata,\n metadata\n ),\n };\n generation.generationInfo = {\n ...(part.finish_reason ? { finish_reason: part.finish_reason } : {}),\n ...(part.logprobs ? { logprobs: part.logprobs } : {}),\n };\n generations.push(generation);\n }\n }\n\n return {\n generations,\n llmOutput: { tokenUsage },\n };\n }\n\n /**\n * Return profiling information for the model.\n *\n * Provides information about the model's capabilities and constraints,\n * including token limits, multimodal support, and advanced features like\n * tool calling and structured output.\n *\n * @returns {ModelProfile} An object describing the model's capabilities and constraints\n *\n * @example\n * ```typescript\n * const model = new ChatGroq({ model: \"llama-3.1-8b-instant\" });\n * const profile = model.profile;\n * console.log(profile.maxInputTokens); // 128000\n * console.log(profile.imageInputs); // true\n * ```\n */\n get profile(): ModelProfile {\n return PROFILES[this.model] ?? {};\n }\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<false>\n ): Runnable<BaseLanguageModelInput, RunOutput>;\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<true>\n ): Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<boolean>\n ):\n | Runnable<BaseLanguageModelInput, RunOutput>\n | Runnable<\n BaseLanguageModelInput,\n { raw: BaseMessage; parsed: RunOutput }\n > {\n const schema = outputSchema;\n const name = config?.name;\n const includeRaw = config?.includeRaw;\n\n // Determine the structured output method based on model capabilities\n const method = getGroqStructuredOutputMethod(this.model, config?.method);\n\n let functionName = name ?? \"extract\";\n let outputParser: BaseLLMOutputParser<RunOutput>;\n let llm: Runnable<BaseLanguageModelInput>;\n\n if (method === \"jsonSchema\") {\n // Native JSON Schema mode for gpt-oss models\n // Uses Groq's strict JSON Schema response format\n const schemaName = name ?? \"structured_output\";\n const description = getSchemaDescription(schema);\n const jsonSchema =\n isInteropZodSchema(schema) || isSerializableSchema(schema)\n ? toJsonSchema(schema)\n : schema;\n\n // Transform schema for Groq strict mode requirements\n const strictSchema = groqStrictifySchema(jsonSchema);\n\n llm = this.withConfig({\n response_format: {\n type: \"json_schema\",\n json_schema: {\n name: schemaName,\n description,\n strict: true,\n schema: strictSchema,\n },\n },\n ls_structured_output_format: {\n kwargs: { method: \"jsonSchema\" },\n schema: {\n title: schemaName,\n description,\n ...jsonSchema,\n },\n },\n });\n\n outputParser = createContentParser(schema);\n } else if (method === \"jsonMode\") {\n outputParser = createContentParser(schema);\n\n const outputSchema =\n isInteropZodSchema(schema) || isSerializableSchema(schema)\n ? toJsonSchema(schema)\n : schema;\n\n llm = this.withConfig({\n response_format: { type: \"json_object\" },\n ls_structured_output_format: {\n kwargs: { method: \"jsonMode\" },\n schema: outputSchema,\n },\n });\n } else {\n let toolFunction: FunctionDefinition;\n if (isInteropZodSchema(schema) || isSerializableSchema(schema)) {\n const asJsonSchema = toJsonSchema(schema);\n toolFunction = {\n name: functionName,\n description: asJsonSchema.description,\n parameters: asJsonSchema,\n };\n } else if (\n typeof schema.name === \"string\" &&\n typeof schema.parameters === \"object\" &&\n schema.parameters != null\n ) {\n toolFunction = schema as FunctionDefinition;\n functionName = schema.name;\n } else {\n functionName = schema.title ?? functionName;\n toolFunction = {\n name: functionName,\n description: schema.description ?? \"\",\n parameters: schema,\n };\n }\n\n llm = this.bindTools([\n { type: \"function\" as const, function: toolFunction },\n ]).withConfig({\n tool_choice: {\n type: \"function\" as const,\n function: { name: functionName },\n },\n ls_structured_output_format: {\n kwargs: { method: \"functionCalling\" },\n schema: toolFunction.parameters,\n },\n });\n\n outputParser = createFunctionCallingParser(schema, functionName);\n }\n\n 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| ||
| {"version":3,"file":"chat_models.cjs","names":["ChatMessage","convertLangChainToolCallToOpenAI","AIMessage","HumanMessageChunk","AIMessageChunk","SystemMessageChunk","FunctionMessageChunk","ToolMessageChunk","ChatMessageChunk","BaseChatModel","Groq","ChatGenerationChunk","PROFILES","getGroqStructuredOutputMethod","groqStrictifySchema"],"sources":["../src/chat_models.ts"],"sourcesContent":["import { toJsonSchema } from \"@langchain/core/utils/json_schema\";\nimport { NewTokenIndices } from \"@langchain/core/callbacks/base\";\nimport { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport {\n BaseChatModel,\n BaseChatModelCallOptions,\n BindToolsInput,\n LangSmithParams,\n type BaseChatModelParams,\n} from \"@langchain/core/language_models/chat_models\";\nimport * as ChatCompletionsAPI from \"groq-sdk/resources/chat/completions\";\nimport * as CompletionsAPI from \"groq-sdk/resources/completions\";\nimport {\n AIMessage,\n AIMessageChunk,\n BaseMessage,\n ChatMessage,\n ChatMessageChunk,\n HumanMessageChunk,\n SystemMessageChunk,\n ToolMessage,\n OpenAIToolCall,\n isAIMessage,\n BaseMessageChunk,\n UsageMetadata,\n FunctionMessageChunk,\n ToolMessageChunk,\n} from \"@langchain/core/messages\";\nimport {\n ChatGeneration,\n ChatGenerationChunk,\n ChatResult,\n} from \"@langchain/core/outputs\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\nimport {\n InteropZodType,\n isInteropZodSchema,\n} from \"@langchain/core/utils/types\";\nimport Groq from \"groq-sdk\";\nimport type {\n ChatCompletion,\n ChatCompletionCreateParams,\n ChatCompletionCreateParamsNonStreaming,\n ChatCompletionCreateParamsStreaming,\n ChatCompletionTool,\n} from \"groq-sdk/resources/chat/completions\";\nimport type { RequestOptions } from \"groq-sdk/core\";\nimport { Runnable } from \"@langchain/core/runnables\";\nimport {\n BaseLanguageModelInput,\n FunctionDefinition,\n StructuredOutputMethodOptions,\n} from \"@langchain/core/language_models/base\";\nimport { ModelProfile } from \"@langchain/core/language_models/profile\";\nimport { BaseLLMOutputParser } from \"@langchain/core/output_parsers\";\nimport {\n parseToolCall,\n makeInvalidToolCall,\n convertLangChainToolCallToOpenAI,\n} from \"@langchain/core/output_parsers/openai_tools\";\nimport { convertToOpenAITool } from \"@langchain/core/utils/function_calling\";\nimport { ToolCallChunk } from \"@langchain/core/messages/tool\";\nimport { getSchemaDescription } from \"@langchain/core/utils/types\";\n\nimport PROFILES from \"./profiles.js\";\nimport {\n getGroqStructuredOutputMethod,\n groqStrictifySchema,\n} from \"./utils/groq-schema.js\";\nimport {\n isSerializableSchema,\n SerializableSchema,\n} from \"@langchain/core/utils/standard_schema\";\nimport {\n assembleStructuredOutputPipeline,\n createContentParser,\n createFunctionCallingParser,\n} from \"@langchain/core/language_models/structured_output\";\n\ntype ChatGroqToolType = BindToolsInput | ChatCompletionTool;\n\n/**\n * Const list of fields that we'll pick from the `ChatCompletionCreateParams` interface\n * to use as the options allowed to be passed to invocation methods.\n *\n * @internal\n */\nconst CREATE_PARAMS_BASE_CALL_KEYS = [\n // \"messages\", // passed as input arg to invocation methods\n // \"model\", // don't allow override on invoke\n \"frequency_penalty\",\n \"function_call\",\n \"functions\",\n \"logit_bias\", // not supported, but left in for forward compatibility\n \"logprobs\", // not supported, but left in for forward compatibility\n \"max_completion_tokens\",\n \"max_tokens\",\n \"n\", // not supported, but left in for forward compatibility\n \"parallel_tool_calls\",\n \"presence_penalty\",\n \"reasoning_effort\",\n \"reasoning_format\",\n \"response_format\",\n \"seed\",\n // TODO: also pass as constructor arg\n \"service_tier\",\n \"stop\",\n // \"stream\", // determined by invocation method\n // other models only specify temperature as a constructor arg, but I don't see the harm in\n // allowing overrides on invocation\n \"temperature\",\n \"tool_choice\",\n // \"tools\", // need to allow users to specify langchain style tools, so we use a different type\n \"top_logprobs\",\n \"top_p\",\n // \"user\", // don't allow override on invoke\n] as const;\n\nconst ADDED_CALL_KEYS = [\n \"headers\",\n \"promptIndex\",\n \"stream_options\",\n \"tools\",\n] as const;\n\nexport type ChatGroqCallOptions = Pick<\n ChatCompletionsAPI.ChatCompletionCreateParamsBase,\n (typeof CREATE_PARAMS_BASE_CALL_KEYS)[number]\n> &\n BaseChatModelCallOptions & {\n /**\n * Additional headers to pass to the API.\n */\n headers?: Record<string, string | null | undefined>;\n /**\n * The index of the prompt in the list of prompts.\n */\n promptIndex?: number;\n /**\n * Additional options to pass to streamed completions.\n * If provided takes precedence over \"streamUsage\" set at initialization time.\n */\n stream_options?: {\n /**\n * Whether or not to include token usage in the stream.\n * If set to `true`, this will include an additional\n * chunk at the end of the stream with the token usage.\n *\n * Defaults to `true` when streaming, `false` otherwise.\n */\n include_usage: boolean;\n };\n\n tools?: ChatGroqToolType[];\n\n // IMPORTANT: If you add a new key here you MUST add it to the `ADDED_CALL_KEYS`\n // list above. Keep this comment at the bottom so people see it when they go to\n // make additions.\n };\n\nconst ALL_CALL_KEYS: readonly (keyof ChatGroqCallOptions)[] = [\n ...CREATE_PARAMS_BASE_CALL_KEYS,\n ...ADDED_CALL_KEYS,\n] as const;\n\n/**\n * Timing details about the request, useful for collecting performance metrics.\n */\ninterface TimingMetadata {\n /**\n * Time spent generating tokens\n */\n completion_time?: number;\n\n /**\n * Time spent processing input tokens\n */\n prompt_time?: number;\n\n /**\n * Time the requests was spent queued\n */\n queue_time?: number;\n\n /**\n * completion time and prompt time combined\n */\n total_time?: number;\n}\nexport interface ChatGroqInput extends BaseChatModelParams {\n /**\n * The temperature to use for sampling.\n * @default 0.7\n */\n temperature?: number;\n\n /**\n * The maximum number of tokens that the model can process in a single response.\n * This limits ensures computational efficiency and resource management.\n */\n maxTokens?: number;\n\n /** Total probability mass of tokens to consider at each step */\n topP?: number;\n\n /** Penalizes repeated tokens according to frequency */\n frequencyPenalty?: number;\n\n /** Penalizes repeated tokens */\n presencePenalty?: number;\n\n /** Number of completions to generate for each prompt */\n n?: number;\n\n /** Dictionary used to adjust the probability of specific tokens being generated */\n logitBias?: Record<string, number>;\n\n /** Unique string identifier representing your end-user, which can help OpenAI to monitor and detect abuse. */\n user?: string;\n\n /**\n * Whether or not to include token usage data in streamed chunks.\n * @default true\n */\n streamUsage?: boolean;\n\n /**\n * Whether to return log probabilities of the output tokens or not.\n * If true, returns the log probabilities of each output token returned in the content of message.\n */\n logprobs?: boolean;\n\n /**\n * An integer between 0 and 5 specifying the number of most likely tokens to return at each token position,\n * each with an associated log probability. logprobs must be set to true if this parameter is used.\n */\n topLogprobs?: number;\n\n /**\n * The Groq API key to use for requests.\n * @default process.env.GROQ_API_KEY\n */\n apiKey?: string;\n /**\n * The name of the model to use.\n */\n model: ChatCompletionCreateParams[\"model\"];\n /**\n * Up to 4 sequences where the API will stop generating further tokens. The\n * returned text will not contain the stop sequence.\n * Alias for `stopSequences`\n */\n stop?: string | null | Array<string>;\n /**\n * Up to 4 sequences where the API will stop generating further tokens. The\n * returned text will not contain the stop sequence.\n */\n stopSequences?: Array<string>;\n /**\n * Whether or not to stream responses.\n */\n streaming?: boolean;\n /**\n * Override the default base URL for the API\n */\n baseUrl?: string;\n /**\n * The maximum amount of time (in milliseconds) the client will wait for a response\n */\n timeout?: number;\n /**\n * HTTP agent used to manage connections\n */\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n httpAgent?: any;\n /**\n * Custom fetch function implementation\n */\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n fetch?: (...args: any) => any;\n\n /**\n * Default headers included with every request\n */\n defaultHeaders?: Record<string, string>;\n\n /**\n * Default query parameters included with every request\n */\n defaultQuery?: Record<string, string>;\n\n /**\n * The level of reasoning effort the model will use.\n * Supported by `openai/gpt-oss-20b`, `openai/gpt-oss-120b`, and `qwen/qwen3-32b`.\n * @see https://console.groq.com/docs/reasoning#options-for-reasoning-effort\n */\n reasoningEffort?: \"none\" | \"default\" | \"low\" | \"medium\" | \"high\" | null;\n}\n\ntype GroqRoleEnum = \"system\" | \"assistant\" | \"user\" | \"function\";\n\ninterface TokenUsage {\n completionTokens?: number;\n promptTokens?: number;\n totalTokens?: number;\n}\n\n/**\n * Extract the custom role from a message.\n * @param message - The message to extract the custom role from.\n * @returns The custom role of the message.\n */\nfunction extractGenericMessageCustomRole(message: ChatMessage): GroqRoleEnum {\n if (\n message.role !== \"system\" &&\n message.role !== \"assistant\" &&\n message.role !== \"user\" &&\n message.role !== \"function\"\n ) {\n throw new Error(\n `Unsupported message role: ${message.role}. Expected \"system\", \"assistant\", \"user\", or \"function\"`\n );\n }\n return message.role as GroqRoleEnum;\n}\n\n/**\n * Extract the role from a message.\n * @param message - The message to extract the role from.\n * @returns The role of the message.\n */\nexport function messageToGroqRole(message: BaseMessage): GroqRoleEnum {\n switch (message.type) {\n case \"system\":\n return \"system\";\n case \"ai\":\n return \"assistant\";\n case \"human\":\n return \"user\";\n case \"function\":\n return \"function\";\n case \"tool\":\n // Not yet supported as a type\n return \"tool\" as GroqRoleEnum;\n case \"generic\": {\n if (!ChatMessage.isInstance(message))\n throw new Error(\"Invalid generic chat message\");\n return extractGenericMessageCustomRole(message);\n }\n default:\n throw new Error(`Unknown message type: ${message.type}`);\n }\n}\n\nfunction convertMessagesToGroqParams(\n messages: BaseMessage[]\n): Array<ChatCompletionsAPI.ChatCompletionMessage> {\n return messages.map((message): ChatCompletionsAPI.ChatCompletionMessage => {\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n const completionParam: Record<string, any> = {\n role: messageToGroqRole(message),\n content: message.content,\n name: message.name,\n function_call: message.additional_kwargs.function_call,\n tool_calls: message.additional_kwargs.tool_calls,\n tool_call_id: (message as ToolMessage).tool_call_id,\n };\n if (isAIMessage(message) && !!message.tool_calls?.length) {\n completionParam.tool_calls = message.tool_calls.map(\n convertLangChainToolCallToOpenAI\n );\n } else {\n if (message.additional_kwargs.tool_calls != null) {\n completionParam.tool_calls = message.additional_kwargs.tool_calls;\n }\n if ((message as ToolMessage).tool_call_id != null) {\n completionParam.tool_call_id = (message as ToolMessage).tool_call_id;\n }\n }\n return completionParam as ChatCompletionsAPI.ChatCompletionMessage;\n });\n}\n\nfunction groqResponseToChatMessage(\n message: ChatCompletionsAPI.ChatCompletionMessage,\n usageMetadata?: UsageMetadata,\n responseMetadata?: Record<string, unknown>\n): BaseMessage {\n const rawToolCalls: OpenAIToolCall[] | undefined = message.tool_calls as\n | OpenAIToolCall[]\n | undefined;\n // Add model_provider for block translator lookup\n const enrichedMetadata = { ...responseMetadata, model_provider: \"groq\" };\n switch (message.role) {\n case \"assistant\": {\n const toolCalls = [];\n const invalidToolCalls = [];\n for (const rawToolCall of rawToolCalls ?? []) {\n try {\n toolCalls.push(parseToolCall(rawToolCall, { returnId: true }));\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (e: any) {\n invalidToolCalls.push(makeInvalidToolCall(rawToolCall, e.message));\n }\n }\n return new AIMessage({\n content: message.content || \"\",\n additional_kwargs: { tool_calls: rawToolCalls },\n tool_calls: toolCalls,\n invalid_tool_calls: invalidToolCalls,\n usage_metadata: usageMetadata,\n response_metadata: enrichedMetadata,\n });\n }\n default:\n return new ChatMessage(message.content || \"\", message.role ?? \"unknown\");\n }\n}\n\nfunction _convertDeltaToMessageChunk(\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n delta: Record<string, any>,\n defaultRole: GroqRoleEnum | undefined,\n rawResponse: ChatCompletionsAPI.ChatCompletionChunk,\n lastMessageId: string | undefined\n): BaseMessageChunk {\n const role = delta.role ?? defaultRole;\n const content = delta.content ?? \"\";\n let additional_kwargs: Record<string, unknown>;\n if (delta.function_call) {\n additional_kwargs = {\n function_call: delta.function_call,\n };\n } else if (delta.tool_calls) {\n additional_kwargs = {\n tool_calls: delta.tool_calls,\n };\n } else {\n additional_kwargs = {};\n }\n if (delta.audio) {\n additional_kwargs.audio = {\n ...delta.audio,\n index: rawResponse.choices[0].index,\n };\n }\n\n let usage: UsageMetadata | undefined;\n let groqMessageId: string | undefined = lastMessageId;\n let timing: TimingMetadata | undefined;\n\n const xGroq = rawResponse.x_groq;\n if (xGroq?.usage) {\n usage = {\n input_tokens: xGroq.usage.prompt_tokens,\n output_tokens: xGroq.usage.completion_tokens,\n total_tokens: xGroq.usage.total_tokens,\n };\n timing = {\n completion_time: xGroq.usage.completion_time,\n prompt_time: xGroq.usage.prompt_time,\n queue_time: xGroq.usage.queue_time,\n total_time: xGroq.usage.total_time,\n };\n }\n\n if (xGroq?.id) {\n groqMessageId = xGroq.id;\n }\n\n const response_metadata = { usage, timing, model_provider: \"groq\" };\n if (role === \"user\") {\n return new HumanMessageChunk({ content, response_metadata });\n } else if (role === \"assistant\") {\n const toolCallChunks: ToolCallChunk[] = [];\n if (Array.isArray(delta.tool_calls)) {\n for (const rawToolCall of delta.tool_calls) {\n toolCallChunks.push({\n name: rawToolCall.function?.name,\n args: rawToolCall.function?.arguments,\n id: rawToolCall.id,\n index: rawToolCall.index,\n type: \"tool_call_chunk\",\n });\n }\n }\n return new AIMessageChunk({\n content,\n tool_call_chunks: toolCallChunks,\n additional_kwargs,\n id: groqMessageId,\n response_metadata,\n });\n } else if (role === \"system\") {\n return new SystemMessageChunk({ content, response_metadata });\n } else if (role === \"developer\") {\n return new SystemMessageChunk({\n content,\n response_metadata,\n additional_kwargs: {\n __openai_role__: \"developer\",\n },\n });\n } else if (role === \"function\") {\n return new FunctionMessageChunk({\n content,\n additional_kwargs,\n name: delta.name,\n response_metadata,\n });\n } else if (role === \"tool\") {\n return new ToolMessageChunk({\n content,\n additional_kwargs,\n tool_call_id: delta.tool_call_id,\n response_metadata,\n });\n } else {\n return new ChatMessageChunk({ content, role, response_metadata });\n }\n}\n\n/*\nfunction _oldConvertDeltaToMessageChunk(\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n delta: Record<string, any>,\n rawResponse: ChatCompletionsAPI.ChatCompletionChunk,\n index: number,\n defaultRole: GroqRoleEnum | undefined,\n xGroq?: ChatCompletionsAPI.ChatCompletionChunk.XGroq\n): {\n message: BaseMessageChunk;\n toolCallData?: {\n id: string;\n name: string;\n index: number;\n type: \"tool_call_chunk\";\n }[];\n} {\n const { role } = delta;\n const content = delta.content ?? \"\";\n let additional_kwargs;\n if (delta.function_call) {\n additional_kwargs = {\n function_call: delta.function_call,\n };\n } else if (delta.tool_calls) {\n additional_kwargs = {\n tool_calls: delta.tool_calls,\n };\n } else {\n additional_kwargs = {};\n }\n\n let usageMetadata: UsageMetadata | undefined;\n let groqMessageId: string | undefined;\n if (xGroq?.usage) {\n usageMetadata = {\n input_tokens: xGroq.usage.prompt_tokens,\n output_tokens: xGroq.usage.completion_tokens,\n total_tokens: xGroq.usage.total_tokens,\n };\n groqMessageId = xGroq.id;\n }\n\n if (role === \"user\") {\n return {\n message: new HumanMessageChunk({ content }),\n };\n } else if (role === \"assistant\") {\n const toolCallChunks = _convertDeltaToolCallToToolCallChunk(\n delta.tool_calls,\n index\n );\n return {\n message: new AIMessageChunk({\n content,\n additional_kwargs,\n tool_call_chunks: toolCallChunks\n ? toolCallChunks.map((tc) => ({\n type: tc.type,\n args: tc.args,\n index: tc.index,\n }))\n : undefined,\n usage_metadata: usageMetadata,\n id: groqMessageId,\n }),\n toolCallData: toolCallChunks\n ? toolCallChunks.map((tc) => ({\n id: tc.id ?? \"\",\n name: tc.name ?? \"\",\n index: tc.index ?? index,\n type: \"tool_call_chunk\",\n }))\n : undefined,\n };\n } else if (role === \"system\") {\n return {\n message: new SystemMessageChunk({ content }),\n };\n } else {\n return {\n message: new ChatMessageChunk({ content, role }),\n };\n }\n}\n*/\n\n/**\n * Groq chat model integration.\n *\n * The Groq API is compatible to the OpenAI API with some limitations. View the\n * full API ref at:\n * @link {https://docs.api.groq.com/md/openai.oas.html}\n *\n * Setup:\n * Install `@langchain/groq` and set an environment variable named `GROQ_API_KEY`.\n *\n * ```bash\n * npm install @langchain/groq\n * export GROQ_API_KEY=\"your-api-key\"\n * ```\n *\n * ## [Constructor args](https://api.js.langchain.com/classes/langchain_groq.ChatGroq.html#constructor)\n *\n * ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_groq.ChatGroqCallOptions.html)\n *\n * Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.\n * They can also be passed via `.withConfig`, or the second arg in `.bindTools`, like shown in the examples below:\n *\n * ```typescript\n * // When calling `.withConfig`, call options should be passed via the first argument\n * const llmWithArgsBound = llm.withConfig({\n * stop: [\"\\n\"],\n * });\n *\n * // When calling `.bindTools`, call options should be passed via the second argument\n * const llmWithTools = llm.bindTools(\n * [...],\n * {\n * tool_choice: \"auto\",\n * }\n * );\n * ```\n *\n * ## Examples\n *\n * <details open>\n * <summary><strong>Instantiate</strong></summary>\n *\n * ```typescript\n * import { ChatGroq } from '@langchain/groq';\n *\n * const llm = new ChatGroq({\n * model: \"llama-3.3-70b-versatile\",\n * temperature: 0,\n * // other params...\n * });\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Invoking</strong></summary>\n *\n * ```typescript\n * const input = `Translate \"I love programming\" into French.`;\n *\n * // Models also accept a list of chat messages or a formatted prompt\n * const result = await llm.invoke(input);\n * console.log(result);\n * ```\n *\n * ```txt\n * AIMessage {\n * \"content\": \"The French translation of \\\"I love programming\\\" is \\\"J'aime programmer\\\". In this sentence, \\\"J'aime\\\" is the first person singular conjugation of the French verb \\\"aimer\\\" which means \\\"to love\\\", and \\\"programmer\\\" is the French infinitive for \\\"to program\\\". I hope this helps! Let me know if you have any other questions.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"tokenUsage\": {\n * \"completionTokens\": 82,\n * \"promptTokens\": 20,\n * \"totalTokens\": 102\n * },\n * \"finish_reason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Streaming Chunks</strong></summary>\n *\n * ```typescript\n * for await (const chunk of await llm.stream(input)) {\n * console.log(chunk);\n * }\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"The\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" French\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" translation\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" of\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" \\\"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"I\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" love\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ...\n * AIMessageChunk {\n * \"content\": \".\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Aggregate Streamed Chunks</strong></summary>\n *\n * ```typescript\n * import { AIMessageChunk } from '@langchain/core/messages';\n * import { concat } from '@langchain/core/utils/stream';\n *\n * const stream = await llm.stream(input);\n * let full: AIMessageChunk | undefined;\n * for await (const chunk of stream) {\n * full = !full ? chunk : concat(full, chunk);\n * }\n * console.log(full);\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"The French translation of \\\"I love programming\\\" is \\\"J'aime programmer\\\". In this sentence, \\\"J'aime\\\" is the first person singular conjugation of the French verb \\\"aimer\\\" which means \\\"to love\\\", and \\\"programmer\\\" is the French infinitive for \\\"to program\\\". I hope this helps! Let me know if you have any other questions.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Bind tools</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const llmForToolCalling = new ChatGroq({\n * model: \"llama3-groq-70b-8192-tool-use-preview\",\n * temperature: 0,\n * // other params...\n * });\n *\n * const GetWeather = {\n * name: \"GetWeather\",\n * description: \"Get the current weather in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const GetPopulation = {\n * name: \"GetPopulation\",\n * description: \"Get the current population in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const llmWithTools = llmForToolCalling.bindTools([GetWeather, GetPopulation]);\n * const aiMsg = await llmWithTools.invoke(\n * \"Which city is hotter today and which is bigger: LA or NY?\"\n * );\n * console.log(aiMsg.tool_calls);\n * ```\n *\n * ```txt\n * [\n * {\n * name: 'GetWeather',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'call_cd34'\n * },\n * {\n * name: 'GetWeather',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_68rf'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'call_f81z'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_8byt'\n * }\n * ]\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Structured Output</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const Joke = z.object({\n * setup: z.string().describe(\"The setup of the joke\"),\n * punchline: z.string().describe(\"The punchline to the joke\"),\n * rating: z.number().optional().describe(\"How funny the joke is, from 1 to 10\")\n * }).describe('Joke to tell user.');\n *\n * const structuredLlm = llmForToolCalling.withStructuredOutput(Joke, { name: \"Joke\" });\n * const jokeResult = await structuredLlm.invoke(\"Tell me a joke about cats\");\n * console.log(jokeResult);\n * ```\n *\n * ```txt\n * {\n * setup: \"Why don't cats play poker in the wild?\",\n * punchline: 'Because there are too many cheetahs.'\n * }\n * ```\n * </details>\n *\n * <br />\n */\nexport class ChatGroq extends BaseChatModel<\n ChatGroqCallOptions,\n AIMessageChunk\n> {\n lc_namespace = [\"langchain\", \"chat_models\", \"groq\"];\n\n client: Groq;\n\n model: string;\n\n temperature = 0.7;\n\n stop?: string[];\n\n stopSequences?: string[];\n\n maxTokens?: number;\n\n streaming = false;\n\n apiKey?: string;\n\n streamUsage: boolean = true;\n\n topP: number | null | undefined;\n\n frequencyPenalty: number | null | undefined;\n\n presencePenalty: number | null | undefined;\n\n logprobs: boolean | null | undefined;\n\n n: number | null | undefined;\n\n logitBias: Record<string, number> | null | undefined;\n\n user: string | null | undefined;\n\n reasoningEffort: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"reasoning_effort\"];\n\n reasoningFormat: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"reasoning_format\"];\n\n serviceTier: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"service_tier\"];\n\n topLogprobs: number | null | undefined;\n\n lc_serializable = true;\n\n get lc_serialized_keys(): string[] {\n return [\n \"client\",\n \"model\",\n \"temperature\",\n \"stop\",\n \"stopSequences\",\n \"maxTokens\",\n \"streaming\",\n \"apiKey\",\n \"streamUsage\",\n \"topP\",\n \"frequencyPenalty\",\n \"presencePenalty\",\n \"logprobs\",\n \"n\",\n \"logitBias\",\n \"user\",\n \"reasoningEffort\",\n \"reasoningFormat\",\n \"serviceTier\",\n \"topLogprobs\",\n ];\n }\n\n static lc_name() {\n return \"ChatGroq\";\n }\n\n _llmType() {\n return \"groq\";\n }\n\n get lc_secrets(): { [key: string]: string } | undefined {\n return {\n apiKey: \"GROQ_API_KEY\",\n };\n }\n\n get callKeys() {\n return [...super.callKeys, ...ALL_CALL_KEYS];\n }\n\n constructor(\n model: ChatCompletionCreateParams[\"model\"],\n fields?: Omit<ChatGroqInput, \"model\">\n );\n constructor(fields: ChatGroqInput);\n constructor(\n modelOrFields?: string | ChatGroqInput,\n fields?: Omit<ChatGroqInput, \"model\">\n ) {\n const params =\n typeof modelOrFields === \"string\"\n ? { ...(fields ?? {}), model: modelOrFields }\n : (modelOrFields ?? {});\n super(params);\n this._addVersion(\"@langchain/groq\", __PKG_VERSION__);\n\n const apiKey = params.apiKey || getEnvironmentVariable(\"GROQ_API_KEY\");\n if (!apiKey) {\n throw new Error(\n `Groq API key not found. Please set the GROQ_API_KEY environment variable or provide the key into \"apiKey\"`\n );\n }\n const defaultHeaders = {\n \"User-Agent\": \"langchainjs\",\n ...(params.defaultHeaders ?? {}),\n };\n\n this.client = new Groq({\n apiKey,\n dangerouslyAllowBrowser: true,\n baseURL: params.baseUrl,\n timeout: params.timeout,\n httpAgent: params.httpAgent,\n fetch: params.fetch,\n maxRetries: 0,\n defaultHeaders,\n defaultQuery: params.defaultQuery,\n });\n this.apiKey = apiKey;\n this.temperature = params.temperature ?? this.temperature;\n this.model = params.model;\n this.streaming = params.streaming ?? this.streaming;\n this.stop =\n params.stopSequences ??\n (typeof params.stop === \"string\" ? [params.stop] : params.stop) ??\n [];\n this.stopSequences = this.stop;\n this.maxTokens = params.maxTokens;\n this.topP = params.topP;\n this.frequencyPenalty = params.frequencyPenalty;\n this.presencePenalty = params.presencePenalty;\n this.logprobs = params.logprobs;\n this.n = params.n;\n this.logitBias = params.logitBias;\n this.user = params.user;\n this.reasoningEffort = params.reasoningEffort;\n }\n\n getLsParams(options: this[\"ParsedCallOptions\"]): LangSmithParams {\n const params = this.invocationParams(options);\n return {\n ls_provider: \"groq\",\n ls_model_name: this.model,\n ls_model_type: \"chat\",\n ls_temperature: params.temperature ?? this.temperature,\n ls_max_tokens: params.max_tokens ?? this.maxTokens,\n ls_stop: options.stop,\n };\n }\n\n async completionWithRetry(\n request: ChatCompletionCreateParamsStreaming,\n options?: RequestOptions\n ): Promise<AsyncIterable<ChatCompletionsAPI.ChatCompletionChunk>>;\n\n async completionWithRetry(\n request: ChatCompletionCreateParamsNonStreaming,\n options?: RequestOptions\n ): Promise<ChatCompletion>;\n\n async completionWithRetry(\n request: ChatCompletionCreateParams,\n options?: RequestOptions\n ): Promise<\n AsyncIterable<ChatCompletionsAPI.ChatCompletionChunk> | ChatCompletion\n > {\n return this.caller.call(async () =>\n this.client.chat.completions.create(request, options)\n );\n }\n\n invocationParams(\n options: this[\"ParsedCallOptions\"],\n extra?: { streaming?: boolean }\n ): Omit<ChatCompletionCreateParams, \"messages\"> {\n const params = super.invocationParams(options);\n\n let streamOptionsConfig = {};\n\n if (options?.stream_options !== undefined) {\n streamOptionsConfig = { stream_options: options.stream_options };\n } else if ((this.streamUsage && this.streaming) || extra?.streaming) {\n streamOptionsConfig = { stream_options: { include_usage: true } };\n }\n\n const toReturn: Omit<ChatCompletionCreateParams, \"messages\"> = {\n model: this.model,\n frequency_penalty: this.frequencyPenalty,\n function_call: options?.function_call,\n functions: options?.functions,\n logit_bias: this.logitBias,\n logprobs: this.logprobs,\n // max_completion_tokens\n // max_tokens\n n: this.n,\n parallel_tool_calls: options?.parallel_tool_calls,\n presence_penalty: this.presencePenalty,\n reasoning_effort: options?.reasoning_effort ?? this.reasoningEffort,\n reasoning_format: this.reasoningFormat,\n response_format: options?.response_format,\n seed: options?.seed,\n service_tier: this.serviceTier,\n stop: options?.stop ?? this.stopSequences,\n temperature: options?.temperature ?? this.temperature,\n tool_choice: _formatToGroqToolChoice(options?.tool_choice),\n tools: options?.tools?.length\n ? options.tools.map((tool) => convertToOpenAITool(tool))\n : undefined,\n top_logprobs: this.topLogprobs,\n top_p: this.topP,\n user: this.user,\n // if include_usage is set or streamUsage then stream must be set to true.\n stream: this.streaming,\n ...params,\n ...streamOptionsConfig,\n };\n\n toReturn.max_completion_tokens =\n options?.max_completion_tokens ?? options?.max_tokens ?? this.maxTokens;\n if (toReturn.max_completion_tokens === -1) {\n delete toReturn.max_completion_tokens;\n }\n\n return toReturn;\n }\n\n override bindTools(\n tools: ChatGroqToolType[],\n kwargs?: Partial<ChatGroqCallOptions>\n ): Runnable<BaseLanguageModelInput, AIMessageChunk, ChatGroqCallOptions> {\n return this.withConfig({\n tools: tools.map((tool) => convertToOpenAITool(tool)),\n ...kwargs,\n });\n }\n\n override async *_streamResponseChunks(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): AsyncGenerator<ChatGenerationChunk> {\n const params = this.invocationParams(options, { streaming: true });\n const messagesMapped = convertMessagesToGroqParams(messages);\n const response = await this.completionWithRetry(\n {\n ...params,\n messages: messagesMapped,\n stream: true,\n },\n {\n signal: options?.signal,\n headers: options?.headers,\n }\n );\n let role: GroqRoleEnum | undefined;\n\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n let responseMetadata: Record<string, any> | undefined;\n\n for await (const data of response) {\n if (options.signal?.aborted) {\n return;\n }\n responseMetadata = data;\n const choice = data?.choices[0];\n if (!choice) {\n continue;\n }\n // The `role` field is populated in the first delta of the response\n // but is not present in subsequent deltas. Extract it when available.\n if (choice.delta?.role) {\n role = choice.delta.role as GroqRoleEnum;\n }\n\n const chunk = _convertDeltaToMessageChunk(\n choice.delta,\n role,\n data,\n undefined\n );\n const newTokenIndices = {\n prompt: options.promptIndex ?? 0,\n completion: choice.index ?? 0,\n };\n if (typeof chunk.content !== \"string\") {\n console.log(\n \"[WARNING]: Received non-string content from OpenAI. This is currently not supported.\"\n );\n continue;\n }\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n const generationInfo: Record<string, any> = { ...newTokenIndices };\n if (choice.finish_reason != null) {\n generationInfo.finish_reason = choice.finish_reason;\n // Only include system fingerprint in the last chunk for now\n // to avoid concatenation issues\n generationInfo.system_fingerprint = data.system_fingerprint;\n generationInfo.model_name = data.model;\n }\n const generationChunk = new ChatGenerationChunk({\n message: chunk,\n text: chunk.content,\n generationInfo,\n });\n yield generationChunk;\n await runManager?.handleLLMNewToken(\n generationChunk.text ?? \"\",\n newTokenIndices,\n undefined,\n undefined,\n undefined,\n { chunk: generationChunk }\n );\n }\n\n if (responseMetadata) {\n if (\"choices\" in responseMetadata) {\n delete responseMetadata.choices;\n }\n yield new ChatGenerationChunk({\n message: new AIMessageChunk({\n content: \"\",\n response_metadata: responseMetadata,\n }),\n text: \"\",\n });\n }\n\n if (options.signal?.aborted) {\n throw new Error(\"AbortError\");\n }\n }\n\n override async _generate(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n options.signal?.throwIfAborted();\n if (this.streaming) {\n const tokenUsage: TokenUsage = {};\n const stream = this._streamResponseChunks(messages, options, runManager);\n const finalChunks: Record<number, ChatGenerationChunk> = {};\n for await (const chunk of stream) {\n const index =\n (chunk.generationInfo as NewTokenIndices)?.completion ?? 0;\n if (finalChunks[index] === undefined) {\n finalChunks[index] = chunk;\n } else {\n finalChunks[index] = finalChunks[index].concat(chunk);\n }\n }\n const generations = Object.entries(finalChunks)\n .sort(([aKey], [bKey]) => parseInt(aKey, 10) - parseInt(bKey, 10))\n .map(([_, value]) => value);\n\n return { generations, llmOutput: { estimatedTokenUsage: tokenUsage } };\n } else {\n return this._generateNonStreaming(messages, options, runManager);\n }\n }\n\n async _generateNonStreaming(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n _runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n const tokenUsage: TokenUsage = {};\n const params = this.invocationParams(options);\n const messagesMapped = convertMessagesToGroqParams(messages);\n\n const data = await this.completionWithRetry(\n {\n ...params,\n stream: false,\n messages: messagesMapped,\n },\n {\n signal: options?.signal,\n headers: options?.headers,\n }\n );\n\n if (\"usage\" in data && data.usage) {\n const {\n completion_tokens: completionTokens,\n prompt_tokens: promptTokens,\n total_tokens: totalTokens,\n } = data.usage as CompletionsAPI.CompletionUsage;\n\n if (completionTokens) {\n tokenUsage.completionTokens =\n (tokenUsage.completionTokens ?? 0) + completionTokens;\n }\n\n if (promptTokens) {\n tokenUsage.promptTokens = (tokenUsage.promptTokens ?? 0) + promptTokens;\n }\n\n if (totalTokens) {\n tokenUsage.totalTokens = (tokenUsage.totalTokens ?? 0) + totalTokens;\n }\n }\n\n const generations: ChatGeneration[] = [];\n\n if (\"choices\" in data && data.choices) {\n for (const part of (data as ChatCompletion).choices) {\n const text = part.message?.content ?? \"\";\n let usageMetadata: UsageMetadata | undefined;\n if (tokenUsage.totalTokens !== undefined) {\n usageMetadata = {\n input_tokens: tokenUsage.promptTokens ?? 0,\n output_tokens: tokenUsage.completionTokens ?? 0,\n total_tokens: tokenUsage.totalTokens,\n };\n }\n // extract all fields from the response object except\n // choices to be included as response metadata\n const { choices: _choices, ...metadata } = data;\n\n const generation: ChatGeneration = {\n text,\n message: groqResponseToChatMessage(\n part.message ?? { role: \"assistant\" },\n usageMetadata,\n metadata\n ),\n };\n generation.generationInfo = {\n ...(part.finish_reason ? { finish_reason: part.finish_reason } : {}),\n ...(part.logprobs ? { logprobs: part.logprobs } : {}),\n };\n generations.push(generation);\n }\n }\n\n return {\n generations,\n llmOutput: { tokenUsage },\n };\n }\n\n /**\n * Return profiling information for the model.\n *\n * Provides information about the model's capabilities and constraints,\n * including token limits, multimodal support, and advanced features like\n * tool calling and structured output.\n *\n * @returns {ModelProfile} An object describing the model's capabilities and constraints\n *\n * @example\n * ```typescript\n * const model = new ChatGroq({ model: \"llama-3.1-8b-instant\" });\n * const profile = model.profile;\n * console.log(profile.maxInputTokens); // 128000\n * console.log(profile.imageInputs); // true\n * ```\n */\n get profile(): ModelProfile {\n return PROFILES[this.model] ?? {};\n }\n\n withStructuredOutput<\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<false>\n ): Runnable<BaseLanguageModelInput, RunOutput>;\n\n withStructuredOutput<\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<true>\n ): Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;\n\n withStructuredOutput<\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<boolean>\n ):\n | Runnable<BaseLanguageModelInput, RunOutput>\n | Runnable<\n BaseLanguageModelInput,\n { raw: BaseMessage; parsed: RunOutput }\n > {\n const schema = outputSchema;\n const name = config?.name;\n const includeRaw = config?.includeRaw;\n\n // Determine the structured output method based on model capabilities\n const method = getGroqStructuredOutputMethod(this.model, config?.method);\n\n let functionName = name ?? \"extract\";\n let outputParser: BaseLLMOutputParser<RunOutput>;\n let llm: Runnable<BaseLanguageModelInput>;\n\n if (method === \"jsonSchema\") {\n // Native JSON Schema mode for gpt-oss models\n // Uses Groq's strict JSON Schema response format\n const schemaName = name ?? \"structured_output\";\n const description = getSchemaDescription(schema);\n const jsonSchema =\n isInteropZodSchema(schema) || isSerializableSchema(schema)\n ? toJsonSchema(schema)\n : schema;\n\n // Transform schema for Groq strict mode requirements\n const strictSchema = groqStrictifySchema(jsonSchema);\n\n llm = this.withConfig({\n response_format: {\n type: \"json_schema\",\n json_schema: {\n name: schemaName,\n description,\n strict: true,\n schema: strictSchema,\n },\n },\n ls_structured_output_format: {\n kwargs: { method: \"jsonSchema\" },\n schema: {\n title: schemaName,\n description,\n ...jsonSchema,\n },\n },\n });\n\n outputParser = createContentParser(schema);\n } else if (method === \"jsonMode\") {\n outputParser = createContentParser(schema);\n\n const outputSchema =\n isInteropZodSchema(schema) || isSerializableSchema(schema)\n ? toJsonSchema(schema)\n : schema;\n\n llm = this.withConfig({\n response_format: { type: \"json_object\" },\n ls_structured_output_format: {\n kwargs: { method: \"jsonMode\" },\n schema: outputSchema,\n },\n });\n } else {\n let toolFunction: FunctionDefinition;\n if (isInteropZodSchema(schema) || isSerializableSchema(schema)) {\n const asJsonSchema = toJsonSchema(schema);\n toolFunction = {\n name: functionName,\n description: asJsonSchema.description,\n parameters: asJsonSchema,\n };\n } else if (\n typeof schema.name === \"string\" &&\n typeof schema.parameters === \"object\" &&\n schema.parameters != null\n ) {\n toolFunction = schema as FunctionDefinition;\n functionName = schema.name;\n } else {\n functionName = schema.title ?? functionName;\n toolFunction = {\n name: functionName,\n description: schema.description ?? \"\",\n parameters: schema,\n };\n }\n\n llm = this.bindTools([\n { type: \"function\" as const, function: toolFunction },\n ]).withConfig({\n tool_choice: {\n type: \"function\" as const,\n function: { name: functionName },\n },\n ls_structured_output_format: {\n kwargs: { method: \"functionCalling\" },\n schema: toolFunction.parameters,\n },\n });\n\n outputParser = createFunctionCallingParser(schema, functionName);\n }\n\n return assembleStructuredOutputPipeline(\n llm,\n outputParser,\n includeRaw,\n \"ChatGroqStructuredOutput\"\n );\n }\n}\n\nfunction _formatToGroqToolChoice(\n toolChoice?: string | ChatCompletionsAPI.ChatCompletionNamedToolChoice\n):\n | ChatCompletionsAPI.ChatCompletionToolChoiceOption\n | Record<string, unknown>\n | null\n | undefined {\n if (!toolChoice) {\n return undefined;\n } else if (toolChoice === \"any\" || toolChoice === \"required\") {\n return \"required\";\n } else if (toolChoice === \"auto\") {\n return \"auto\";\n } else if (toolChoice === \"none\") {\n return \"none\";\n } else if (typeof toolChoice === \"string\") {\n return {\n type: \"function\",\n function: {\n name: toolChoice,\n },\n };\n } else {\n return toolChoice;\n 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|
@@ -1,1 +0,1 @@ | ||
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@@ -1,1 +0,1 @@ | ||
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@@ -14,3 +14,2 @@ import PROFILES from "./profiles.js"; | ||
| import { assembleStructuredOutputPipeline, createContentParser, createFunctionCallingParser } from "@langchain/core/language_models/structured_output"; | ||
| //#region src/chat_models.ts | ||
@@ -601,3 +600,3 @@ /** | ||
| super(params); | ||
| this._addVersion("@langchain/groq", "1.1.5"); | ||
| this._addVersion("@langchain/groq", "1.2.0"); | ||
| const apiKey = params.apiKey || getEnvironmentVariable("GROQ_API_KEY"); | ||
@@ -703,3 +702,2 @@ if (!apiKey) throw new Error(`Groq API key not found. Please set the GROQ_API_KEY environment variable or provide the key into "apiKey"`); | ||
| let role; | ||
| let lastMessageId; | ||
| let responseMetadata; | ||
@@ -712,3 +710,3 @@ for await (const data of response) { | ||
| if (choice.delta?.role) role = choice.delta.role; | ||
| const chunk = _convertDeltaToMessageChunk(choice.delta, role, data, lastMessageId); | ||
| const chunk = _convertDeltaToMessageChunk(choice.delta, role, data, void 0); | ||
| const newTokenIndices = { | ||
@@ -920,5 +918,5 @@ prompt: options.promptIndex ?? 0, | ||
| } | ||
| //#endregion | ||
| export { ChatGroq, messageToGroqRole }; | ||
| //# sourceMappingURL=chat_models.js.map |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"file":"chat_models.js","names":[],"sources":["../src/chat_models.ts"],"sourcesContent":["import { toJsonSchema } from \"@langchain/core/utils/json_schema\";\nimport { NewTokenIndices } from \"@langchain/core/callbacks/base\";\nimport { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport {\n BaseChatModel,\n BaseChatModelCallOptions,\n BindToolsInput,\n LangSmithParams,\n type BaseChatModelParams,\n} from \"@langchain/core/language_models/chat_models\";\nimport * as ChatCompletionsAPI from \"groq-sdk/resources/chat/completions\";\nimport * as CompletionsAPI from \"groq-sdk/resources/completions\";\nimport {\n AIMessage,\n AIMessageChunk,\n BaseMessage,\n ChatMessage,\n ChatMessageChunk,\n HumanMessageChunk,\n SystemMessageChunk,\n ToolMessage,\n OpenAIToolCall,\n isAIMessage,\n BaseMessageChunk,\n UsageMetadata,\n FunctionMessageChunk,\n ToolMessageChunk,\n} from \"@langchain/core/messages\";\nimport {\n ChatGeneration,\n ChatGenerationChunk,\n ChatResult,\n} from \"@langchain/core/outputs\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\nimport {\n InteropZodType,\n isInteropZodSchema,\n} from \"@langchain/core/utils/types\";\nimport Groq from \"groq-sdk\";\nimport type {\n ChatCompletion,\n ChatCompletionCreateParams,\n ChatCompletionCreateParamsNonStreaming,\n ChatCompletionCreateParamsStreaming,\n ChatCompletionTool,\n} from \"groq-sdk/resources/chat/completions\";\nimport type { RequestOptions } from \"groq-sdk/core\";\nimport { Runnable } from \"@langchain/core/runnables\";\nimport {\n BaseLanguageModelInput,\n FunctionDefinition,\n StructuredOutputMethodOptions,\n} from \"@langchain/core/language_models/base\";\nimport { ModelProfile } from \"@langchain/core/language_models/profile\";\nimport { BaseLLMOutputParser } from \"@langchain/core/output_parsers\";\nimport {\n parseToolCall,\n makeInvalidToolCall,\n convertLangChainToolCallToOpenAI,\n} from \"@langchain/core/output_parsers/openai_tools\";\nimport { convertToOpenAITool } from \"@langchain/core/utils/function_calling\";\nimport { ToolCallChunk } from \"@langchain/core/messages/tool\";\nimport { getSchemaDescription } from \"@langchain/core/utils/types\";\n\nimport PROFILES from \"./profiles.js\";\nimport {\n getGroqStructuredOutputMethod,\n groqStrictifySchema,\n} from \"./utils/groq-schema.js\";\nimport {\n isSerializableSchema,\n SerializableSchema,\n} from \"@langchain/core/utils/standard_schema\";\nimport {\n assembleStructuredOutputPipeline,\n createContentParser,\n createFunctionCallingParser,\n} from \"@langchain/core/language_models/structured_output\";\n\ntype ChatGroqToolType = BindToolsInput | ChatCompletionTool;\n\n/**\n * Const list of fields that we'll pick from the `ChatCompletionCreateParams` interface\n * to use as the options allowed to be passed to invocation methods.\n *\n * @internal\n */\nconst CREATE_PARAMS_BASE_CALL_KEYS = [\n // \"messages\", // passed as input arg to invocation methods\n // \"model\", // don't allow override on invoke\n \"frequency_penalty\",\n \"function_call\",\n \"functions\",\n \"logit_bias\", // not supported, but left in for forward compatibility\n \"logprobs\", // not supported, but left in for forward compatibility\n \"max_completion_tokens\",\n \"max_tokens\",\n \"n\", // not supported, but left in for forward compatibility\n \"parallel_tool_calls\",\n \"presence_penalty\",\n \"reasoning_effort\",\n \"reasoning_format\",\n \"response_format\",\n \"seed\",\n // TODO: also pass as constructor arg\n \"service_tier\",\n \"stop\",\n // \"stream\", // determined by invocation method\n // other models only specify temperature as a constructor arg, but I don't see the harm in\n // allowing overrides on invocation\n \"temperature\",\n \"tool_choice\",\n // \"tools\", // need to allow users to specify langchain style tools, so we use a different type\n \"top_logprobs\",\n \"top_p\",\n // \"user\", // don't allow override on invoke\n] as const;\n\nconst ADDED_CALL_KEYS = [\n \"headers\",\n \"promptIndex\",\n \"stream_options\",\n \"tools\",\n] as const;\n\nexport type ChatGroqCallOptions = Pick<\n ChatCompletionsAPI.ChatCompletionCreateParamsBase,\n (typeof CREATE_PARAMS_BASE_CALL_KEYS)[number]\n> &\n BaseChatModelCallOptions & {\n /**\n * Additional headers to pass to the API.\n */\n headers?: Record<string, string | null | undefined>;\n /**\n * The index of the prompt in the list of prompts.\n */\n promptIndex?: number;\n /**\n * Additional options to pass to streamed completions.\n * If provided takes precedence over \"streamUsage\" set at initialization time.\n */\n stream_options?: {\n /**\n * Whether or not to include token usage in the stream.\n * If set to `true`, this will include an additional\n * chunk at the end of the stream with the token usage.\n *\n * Defaults to `true` when streaming, `false` otherwise.\n */\n include_usage: boolean;\n };\n\n tools?: ChatGroqToolType[];\n\n // IMPORTANT: If you add a new key here you MUST add it to the `ADDED_CALL_KEYS`\n // list above. Keep this comment at the bottom so people see it when they go to\n // make additions.\n };\n\nconst ALL_CALL_KEYS: readonly (keyof ChatGroqCallOptions)[] = [\n ...CREATE_PARAMS_BASE_CALL_KEYS,\n ...ADDED_CALL_KEYS,\n] as const;\n\n/**\n * Timing details about the request, useful for collecting performance metrics.\n */\ninterface TimingMetadata {\n /**\n * Time spent generating tokens\n */\n completion_time?: number;\n\n /**\n * Time spent processing input tokens\n */\n prompt_time?: number;\n\n /**\n * Time the requests was spent queued\n */\n queue_time?: number;\n\n /**\n * completion time and prompt time combined\n */\n total_time?: number;\n}\nexport interface ChatGroqInput extends BaseChatModelParams {\n /**\n * The temperature to use for sampling.\n * @default 0.7\n */\n temperature?: number;\n\n /**\n * The maximum number of tokens that the model can process in a single response.\n * This limits ensures computational efficiency and resource management.\n */\n maxTokens?: number;\n\n /** Total probability mass of tokens to consider at each step */\n topP?: number;\n\n /** Penalizes repeated tokens according to frequency */\n frequencyPenalty?: number;\n\n /** Penalizes repeated tokens */\n presencePenalty?: number;\n\n /** Number of completions to generate for each prompt */\n n?: number;\n\n /** Dictionary used to adjust the probability of specific tokens being generated */\n logitBias?: Record<string, number>;\n\n /** Unique string identifier representing your end-user, which can help OpenAI to monitor and detect abuse. */\n user?: string;\n\n /**\n * Whether or not to include token usage data in streamed chunks.\n * @default true\n */\n streamUsage?: boolean;\n\n /**\n * Whether to return log probabilities of the output tokens or not.\n * If true, returns the log probabilities of each output token returned in the content of message.\n */\n logprobs?: boolean;\n\n /**\n * An integer between 0 and 5 specifying the number of most likely tokens to return at each token position,\n * each with an associated log probability. logprobs must be set to true if this parameter is used.\n */\n topLogprobs?: number;\n\n /**\n * The Groq API key to use for requests.\n * @default process.env.GROQ_API_KEY\n */\n apiKey?: string;\n /**\n * The name of the model to use.\n */\n model: ChatCompletionCreateParams[\"model\"];\n /**\n * Up to 4 sequences where the API will stop generating further tokens. The\n * returned text will not contain the stop sequence.\n * Alias for `stopSequences`\n */\n stop?: string | null | Array<string>;\n /**\n * Up to 4 sequences where the API will stop generating further tokens. The\n * returned text will not contain the stop sequence.\n */\n stopSequences?: Array<string>;\n /**\n * Whether or not to stream responses.\n */\n streaming?: boolean;\n /**\n * Override the default base URL for the API\n */\n baseUrl?: string;\n /**\n * The maximum amount of time (in milliseconds) the client will wait for a response\n */\n timeout?: number;\n /**\n * HTTP agent used to manage connections\n */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n httpAgent?: any;\n /**\n * Custom fetch function implementation\n */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n fetch?: (...args: any) => any;\n\n /**\n * Default headers included with every request\n */\n defaultHeaders?: Record<string, string>;\n\n /**\n * Default query parameters included with every request\n */\n defaultQuery?: Record<string, string>;\n\n /**\n * The level of reasoning effort the model will use.\n * Supported by `openai/gpt-oss-20b`, `openai/gpt-oss-120b`, and `qwen/qwen3-32b`.\n * @see https://console.groq.com/docs/reasoning#options-for-reasoning-effort\n */\n reasoningEffort?: \"none\" | \"default\" | \"low\" | \"medium\" | \"high\" | null;\n}\n\ntype GroqRoleEnum = \"system\" | \"assistant\" | \"user\" | \"function\";\n\ninterface TokenUsage {\n completionTokens?: number;\n promptTokens?: number;\n totalTokens?: number;\n}\n\n/**\n * Extract the custom role from a message.\n * @param message - The message to extract the custom role from.\n * @returns The custom role of the message.\n */\nfunction extractGenericMessageCustomRole(message: ChatMessage): GroqRoleEnum {\n if (\n message.role !== \"system\" &&\n message.role !== \"assistant\" &&\n message.role !== \"user\" &&\n message.role !== \"function\"\n ) {\n throw new Error(\n `Unsupported message role: ${message.role}. Expected \"system\", \"assistant\", \"user\", or \"function\"`\n );\n }\n return message.role as GroqRoleEnum;\n}\n\n/**\n * Extract the role from a message.\n * @param message - The message to extract the role from.\n * @returns The role of the message.\n */\nexport function messageToGroqRole(message: BaseMessage): GroqRoleEnum {\n switch (message.type) {\n case \"system\":\n return \"system\";\n case \"ai\":\n return \"assistant\";\n case \"human\":\n return \"user\";\n case \"function\":\n return \"function\";\n case \"tool\":\n // Not yet supported as a type\n return \"tool\" as GroqRoleEnum;\n case \"generic\": {\n if (!ChatMessage.isInstance(message))\n throw new Error(\"Invalid generic chat message\");\n return extractGenericMessageCustomRole(message);\n }\n default:\n throw new Error(`Unknown message type: ${message.type}`);\n }\n}\n\nfunction convertMessagesToGroqParams(\n messages: BaseMessage[]\n): Array<ChatCompletionsAPI.ChatCompletionMessage> {\n return messages.map((message): ChatCompletionsAPI.ChatCompletionMessage => {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const completionParam: Record<string, any> = {\n role: messageToGroqRole(message),\n content: message.content,\n name: message.name,\n function_call: message.additional_kwargs.function_call,\n tool_calls: message.additional_kwargs.tool_calls,\n tool_call_id: (message as ToolMessage).tool_call_id,\n };\n if (isAIMessage(message) && !!message.tool_calls?.length) {\n completionParam.tool_calls = message.tool_calls.map(\n convertLangChainToolCallToOpenAI\n );\n } else {\n if (message.additional_kwargs.tool_calls != null) {\n completionParam.tool_calls = message.additional_kwargs.tool_calls;\n }\n if ((message as ToolMessage).tool_call_id != null) {\n completionParam.tool_call_id = (message as ToolMessage).tool_call_id;\n }\n }\n return completionParam as ChatCompletionsAPI.ChatCompletionMessage;\n });\n}\n\nfunction groqResponseToChatMessage(\n message: ChatCompletionsAPI.ChatCompletionMessage,\n usageMetadata?: UsageMetadata,\n responseMetadata?: Record<string, unknown>\n): BaseMessage {\n const rawToolCalls: OpenAIToolCall[] | undefined = message.tool_calls as\n | OpenAIToolCall[]\n | undefined;\n // Add model_provider for block translator lookup\n const enrichedMetadata = { ...responseMetadata, model_provider: \"groq\" };\n switch (message.role) {\n case \"assistant\": {\n const toolCalls = [];\n const invalidToolCalls = [];\n for (const rawToolCall of rawToolCalls ?? []) {\n try {\n toolCalls.push(parseToolCall(rawToolCall, { returnId: true }));\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (e: any) {\n invalidToolCalls.push(makeInvalidToolCall(rawToolCall, e.message));\n }\n }\n return new AIMessage({\n content: message.content || \"\",\n additional_kwargs: { tool_calls: rawToolCalls },\n tool_calls: toolCalls,\n invalid_tool_calls: invalidToolCalls,\n usage_metadata: usageMetadata,\n response_metadata: enrichedMetadata,\n });\n }\n default:\n return new ChatMessage(message.content || \"\", message.role ?? \"unknown\");\n }\n}\n\nfunction _convertDeltaToMessageChunk(\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n delta: Record<string, any>,\n defaultRole: GroqRoleEnum | undefined,\n rawResponse: ChatCompletionsAPI.ChatCompletionChunk,\n lastMessageId: string | undefined\n): BaseMessageChunk {\n const role = delta.role ?? defaultRole;\n const content = delta.content ?? \"\";\n let additional_kwargs: Record<string, unknown>;\n if (delta.function_call) {\n additional_kwargs = {\n function_call: delta.function_call,\n };\n } else if (delta.tool_calls) {\n additional_kwargs = {\n tool_calls: delta.tool_calls,\n };\n } else {\n additional_kwargs = {};\n }\n if (delta.audio) {\n additional_kwargs.audio = {\n ...delta.audio,\n index: rawResponse.choices[0].index,\n };\n }\n\n let usage: UsageMetadata | undefined;\n let groqMessageId: string | undefined = lastMessageId;\n let timing: TimingMetadata | undefined;\n\n const xGroq = rawResponse.x_groq;\n if (xGroq?.usage) {\n usage = {\n input_tokens: xGroq.usage.prompt_tokens,\n output_tokens: xGroq.usage.completion_tokens,\n total_tokens: xGroq.usage.total_tokens,\n };\n timing = {\n completion_time: xGroq.usage.completion_time,\n prompt_time: xGroq.usage.prompt_time,\n queue_time: xGroq.usage.queue_time,\n total_time: xGroq.usage.total_time,\n };\n }\n\n if (xGroq?.id) {\n groqMessageId = xGroq.id;\n }\n\n const response_metadata = { usage, timing, model_provider: \"groq\" };\n if (role === \"user\") {\n return new HumanMessageChunk({ content, response_metadata });\n } else if (role === \"assistant\") {\n const toolCallChunks: ToolCallChunk[] = [];\n if (Array.isArray(delta.tool_calls)) {\n for (const rawToolCall of delta.tool_calls) {\n toolCallChunks.push({\n name: rawToolCall.function?.name,\n args: rawToolCall.function?.arguments,\n id: rawToolCall.id,\n index: rawToolCall.index,\n type: \"tool_call_chunk\",\n });\n }\n }\n return new AIMessageChunk({\n content,\n tool_call_chunks: toolCallChunks,\n additional_kwargs,\n id: groqMessageId,\n response_metadata,\n });\n } else if (role === \"system\") {\n return new SystemMessageChunk({ content, response_metadata });\n } else if (role === \"developer\") {\n return new SystemMessageChunk({\n content,\n response_metadata,\n additional_kwargs: {\n __openai_role__: \"developer\",\n },\n });\n } else if (role === \"function\") {\n return new FunctionMessageChunk({\n content,\n additional_kwargs,\n name: delta.name,\n response_metadata,\n });\n } else if (role === \"tool\") {\n return new ToolMessageChunk({\n content,\n additional_kwargs,\n tool_call_id: delta.tool_call_id,\n response_metadata,\n });\n } else {\n return new ChatMessageChunk({ content, role, response_metadata });\n }\n}\n\n/*\nfunction _oldConvertDeltaToMessageChunk(\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n delta: Record<string, any>,\n rawResponse: ChatCompletionsAPI.ChatCompletionChunk,\n index: number,\n defaultRole: GroqRoleEnum | undefined,\n xGroq?: ChatCompletionsAPI.ChatCompletionChunk.XGroq\n): {\n message: BaseMessageChunk;\n toolCallData?: {\n id: string;\n name: string;\n index: number;\n type: \"tool_call_chunk\";\n }[];\n} {\n const { role } = delta;\n const content = delta.content ?? \"\";\n let additional_kwargs;\n if (delta.function_call) {\n additional_kwargs = {\n function_call: delta.function_call,\n };\n } else if (delta.tool_calls) {\n additional_kwargs = {\n tool_calls: delta.tool_calls,\n };\n } else {\n additional_kwargs = {};\n }\n\n let usageMetadata: UsageMetadata | undefined;\n let groqMessageId: string | undefined;\n if (xGroq?.usage) {\n usageMetadata = {\n input_tokens: xGroq.usage.prompt_tokens,\n output_tokens: xGroq.usage.completion_tokens,\n total_tokens: xGroq.usage.total_tokens,\n };\n groqMessageId = xGroq.id;\n }\n\n if (role === \"user\") {\n return {\n message: new HumanMessageChunk({ content }),\n };\n } else if (role === \"assistant\") {\n const toolCallChunks = _convertDeltaToolCallToToolCallChunk(\n delta.tool_calls,\n index\n );\n return {\n message: new AIMessageChunk({\n content,\n additional_kwargs,\n tool_call_chunks: toolCallChunks\n ? toolCallChunks.map((tc) => ({\n type: tc.type,\n args: tc.args,\n index: tc.index,\n }))\n : undefined,\n usage_metadata: usageMetadata,\n id: groqMessageId,\n }),\n toolCallData: toolCallChunks\n ? toolCallChunks.map((tc) => ({\n id: tc.id ?? \"\",\n name: tc.name ?? \"\",\n index: tc.index ?? index,\n type: \"tool_call_chunk\",\n }))\n : undefined,\n };\n } else if (role === \"system\") {\n return {\n message: new SystemMessageChunk({ content }),\n };\n } else {\n return {\n message: new ChatMessageChunk({ content, role }),\n };\n }\n}\n*/\n\n/**\n * Groq chat model integration.\n *\n * The Groq API is compatible to the OpenAI API with some limitations. View the\n * full API ref at:\n * @link {https://docs.api.groq.com/md/openai.oas.html}\n *\n * Setup:\n * Install `@langchain/groq` and set an environment variable named `GROQ_API_KEY`.\n *\n * ```bash\n * npm install @langchain/groq\n * export GROQ_API_KEY=\"your-api-key\"\n * ```\n *\n * ## [Constructor args](https://api.js.langchain.com/classes/langchain_groq.ChatGroq.html#constructor)\n *\n * ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_groq.ChatGroqCallOptions.html)\n *\n * Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.\n * They can also be passed via `.withConfig`, or the second arg in `.bindTools`, like shown in the examples below:\n *\n * ```typescript\n * // When calling `.withConfig`, call options should be passed via the first argument\n * const llmWithArgsBound = llm.withConfig({\n * stop: [\"\\n\"],\n * });\n *\n * // When calling `.bindTools`, call options should be passed via the second argument\n * const llmWithTools = llm.bindTools(\n * [...],\n * {\n * tool_choice: \"auto\",\n * }\n * );\n * ```\n *\n * ## Examples\n *\n * <details open>\n * <summary><strong>Instantiate</strong></summary>\n *\n * ```typescript\n * import { ChatGroq } from '@langchain/groq';\n *\n * const llm = new ChatGroq({\n * model: \"llama-3.3-70b-versatile\",\n * temperature: 0,\n * // other params...\n * });\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Invoking</strong></summary>\n *\n * ```typescript\n * const input = `Translate \"I love programming\" into French.`;\n *\n * // Models also accept a list of chat messages or a formatted prompt\n * const result = await llm.invoke(input);\n * console.log(result);\n * ```\n *\n * ```txt\n * AIMessage {\n * \"content\": \"The French translation of \\\"I love programming\\\" is \\\"J'aime programmer\\\". In this sentence, \\\"J'aime\\\" is the first person singular conjugation of the French verb \\\"aimer\\\" which means \\\"to love\\\", and \\\"programmer\\\" is the French infinitive for \\\"to program\\\". I hope this helps! Let me know if you have any other questions.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"tokenUsage\": {\n * \"completionTokens\": 82,\n * \"promptTokens\": 20,\n * \"totalTokens\": 102\n * },\n * \"finish_reason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Streaming Chunks</strong></summary>\n *\n * ```typescript\n * for await (const chunk of await llm.stream(input)) {\n * console.log(chunk);\n * }\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"The\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" French\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" translation\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" of\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" \\\"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"I\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" love\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ...\n * AIMessageChunk {\n * \"content\": \".\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Aggregate Streamed Chunks</strong></summary>\n *\n * ```typescript\n * import { AIMessageChunk } from '@langchain/core/messages';\n * import { concat } from '@langchain/core/utils/stream';\n *\n * const stream = await llm.stream(input);\n * let full: AIMessageChunk | undefined;\n * for await (const chunk of stream) {\n * full = !full ? chunk : concat(full, chunk);\n * }\n * console.log(full);\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"The French translation of \\\"I love programming\\\" is \\\"J'aime programmer\\\". In this sentence, \\\"J'aime\\\" is the first person singular conjugation of the French verb \\\"aimer\\\" which means \\\"to love\\\", and \\\"programmer\\\" is the French infinitive for \\\"to program\\\". I hope this helps! Let me know if you have any other questions.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Bind tools</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const llmForToolCalling = new ChatGroq({\n * model: \"llama3-groq-70b-8192-tool-use-preview\",\n * temperature: 0,\n * // other params...\n * });\n *\n * const GetWeather = {\n * name: \"GetWeather\",\n * description: \"Get the current weather in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const GetPopulation = {\n * name: \"GetPopulation\",\n * description: \"Get the current population in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const llmWithTools = llmForToolCalling.bindTools([GetWeather, GetPopulation]);\n * const aiMsg = await llmWithTools.invoke(\n * \"Which city is hotter today and which is bigger: LA or NY?\"\n * );\n * console.log(aiMsg.tool_calls);\n * ```\n *\n * ```txt\n * [\n * {\n * name: 'GetWeather',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'call_cd34'\n * },\n * {\n * name: 'GetWeather',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_68rf'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'call_f81z'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_8byt'\n * }\n * ]\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Structured Output</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const Joke = z.object({\n * setup: z.string().describe(\"The setup of the joke\"),\n * punchline: z.string().describe(\"The punchline to the joke\"),\n * rating: z.number().optional().describe(\"How funny the joke is, from 1 to 10\")\n * }).describe('Joke to tell user.');\n *\n * const structuredLlm = llmForToolCalling.withStructuredOutput(Joke, { name: \"Joke\" });\n * const jokeResult = await structuredLlm.invoke(\"Tell me a joke about cats\");\n * console.log(jokeResult);\n * ```\n *\n * ```txt\n * {\n * setup: \"Why don't cats play poker in the wild?\",\n * punchline: 'Because there are too many cheetahs.'\n * }\n * ```\n * </details>\n *\n * <br />\n */\nexport class ChatGroq extends BaseChatModel<\n ChatGroqCallOptions,\n AIMessageChunk\n> {\n lc_namespace = [\"langchain\", \"chat_models\", \"groq\"];\n\n client: Groq;\n\n model: string;\n\n temperature = 0.7;\n\n stop?: string[];\n\n stopSequences?: string[];\n\n maxTokens?: number;\n\n streaming = false;\n\n apiKey?: string;\n\n streamUsage: boolean = true;\n\n topP: number | null | undefined;\n\n frequencyPenalty: number | null | undefined;\n\n presencePenalty: number | null | undefined;\n\n logprobs: boolean | null | undefined;\n\n n: number | null | undefined;\n\n logitBias: Record<string, number> | null | undefined;\n\n user: string | null | undefined;\n\n reasoningEffort: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"reasoning_effort\"];\n\n reasoningFormat: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"reasoning_format\"];\n\n serviceTier: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"service_tier\"];\n\n topLogprobs: number | null | undefined;\n\n lc_serializable = true;\n\n get lc_serialized_keys(): string[] {\n return [\n \"client\",\n \"model\",\n \"temperature\",\n \"stop\",\n \"stopSequences\",\n \"maxTokens\",\n \"streaming\",\n \"apiKey\",\n \"streamUsage\",\n \"topP\",\n \"frequencyPenalty\",\n \"presencePenalty\",\n \"logprobs\",\n \"n\",\n \"logitBias\",\n \"user\",\n \"reasoningEffort\",\n \"reasoningFormat\",\n \"serviceTier\",\n \"topLogprobs\",\n ];\n }\n\n static lc_name() {\n return \"ChatGroq\";\n }\n\n _llmType() {\n return \"groq\";\n }\n\n get lc_secrets(): { [key: string]: string } | undefined {\n return {\n apiKey: \"GROQ_API_KEY\",\n };\n }\n\n get callKeys() {\n return [...super.callKeys, ...ALL_CALL_KEYS];\n }\n\n constructor(\n model: ChatCompletionCreateParams[\"model\"],\n fields?: Omit<ChatGroqInput, \"model\">\n );\n constructor(fields: ChatGroqInput);\n constructor(\n modelOrFields?: string | ChatGroqInput,\n fields?: Omit<ChatGroqInput, \"model\">\n ) {\n const params =\n typeof modelOrFields === \"string\"\n ? { ...(fields ?? {}), model: modelOrFields }\n : (modelOrFields ?? {});\n super(params);\n this._addVersion(\"@langchain/groq\", __PKG_VERSION__);\n\n const apiKey = params.apiKey || getEnvironmentVariable(\"GROQ_API_KEY\");\n if (!apiKey) {\n throw new Error(\n `Groq API key not found. Please set the GROQ_API_KEY environment variable or provide the key into \"apiKey\"`\n );\n }\n const defaultHeaders = {\n \"User-Agent\": \"langchainjs\",\n ...(params.defaultHeaders ?? {}),\n };\n\n this.client = new Groq({\n apiKey,\n dangerouslyAllowBrowser: true,\n baseURL: params.baseUrl,\n timeout: params.timeout,\n httpAgent: params.httpAgent,\n fetch: params.fetch,\n maxRetries: 0,\n defaultHeaders,\n defaultQuery: params.defaultQuery,\n });\n this.apiKey = apiKey;\n this.temperature = params.temperature ?? this.temperature;\n this.model = params.model;\n this.streaming = params.streaming ?? this.streaming;\n this.stop =\n params.stopSequences ??\n (typeof params.stop === \"string\" ? [params.stop] : params.stop) ??\n [];\n this.stopSequences = this.stop;\n this.maxTokens = params.maxTokens;\n this.topP = params.topP;\n this.frequencyPenalty = params.frequencyPenalty;\n this.presencePenalty = params.presencePenalty;\n this.logprobs = params.logprobs;\n this.n = params.n;\n this.logitBias = params.logitBias;\n this.user = params.user;\n this.reasoningEffort = params.reasoningEffort;\n }\n\n getLsParams(options: this[\"ParsedCallOptions\"]): LangSmithParams {\n const params = this.invocationParams(options);\n return {\n ls_provider: \"groq\",\n ls_model_name: this.model,\n ls_model_type: \"chat\",\n ls_temperature: params.temperature ?? this.temperature,\n ls_max_tokens: params.max_tokens ?? this.maxTokens,\n ls_stop: options.stop,\n };\n }\n\n async completionWithRetry(\n request: ChatCompletionCreateParamsStreaming,\n options?: RequestOptions\n ): Promise<AsyncIterable<ChatCompletionsAPI.ChatCompletionChunk>>;\n\n async completionWithRetry(\n request: ChatCompletionCreateParamsNonStreaming,\n options?: RequestOptions\n ): Promise<ChatCompletion>;\n\n async completionWithRetry(\n request: ChatCompletionCreateParams,\n options?: RequestOptions\n ): Promise<\n AsyncIterable<ChatCompletionsAPI.ChatCompletionChunk> | ChatCompletion\n > {\n return this.caller.call(async () =>\n this.client.chat.completions.create(request, options)\n );\n }\n\n invocationParams(\n options: this[\"ParsedCallOptions\"],\n extra?: { streaming?: boolean }\n ): Omit<ChatCompletionCreateParams, \"messages\"> {\n const params = super.invocationParams(options);\n\n let streamOptionsConfig = {};\n\n if (options?.stream_options !== undefined) {\n streamOptionsConfig = { stream_options: options.stream_options };\n } else if ((this.streamUsage && this.streaming) || extra?.streaming) {\n streamOptionsConfig = { stream_options: { include_usage: true } };\n }\n\n const toReturn: Omit<ChatCompletionCreateParams, \"messages\"> = {\n model: this.model,\n frequency_penalty: this.frequencyPenalty,\n function_call: options?.function_call,\n functions: options?.functions,\n logit_bias: this.logitBias,\n logprobs: this.logprobs,\n // max_completion_tokens\n // max_tokens\n n: this.n,\n parallel_tool_calls: options?.parallel_tool_calls,\n presence_penalty: this.presencePenalty,\n reasoning_effort: options?.reasoning_effort ?? this.reasoningEffort,\n reasoning_format: this.reasoningFormat,\n response_format: options?.response_format,\n seed: options?.seed,\n service_tier: this.serviceTier,\n stop: options?.stop ?? this.stopSequences,\n temperature: options?.temperature ?? this.temperature,\n tool_choice: _formatToGroqToolChoice(options?.tool_choice),\n tools: options?.tools?.length\n ? options.tools.map((tool) => convertToOpenAITool(tool))\n : undefined,\n top_logprobs: this.topLogprobs,\n top_p: this.topP,\n user: this.user,\n // if include_usage is set or streamUsage then stream must be set to true.\n stream: this.streaming,\n ...params,\n ...streamOptionsConfig,\n };\n\n toReturn.max_completion_tokens =\n options?.max_completion_tokens ?? options?.max_tokens ?? this.maxTokens;\n if (toReturn.max_completion_tokens === -1) {\n delete toReturn.max_completion_tokens;\n }\n\n return toReturn;\n }\n\n override bindTools(\n tools: ChatGroqToolType[],\n kwargs?: Partial<ChatGroqCallOptions>\n ): Runnable<BaseLanguageModelInput, AIMessageChunk, ChatGroqCallOptions> {\n return this.withConfig({\n tools: tools.map((tool) => convertToOpenAITool(tool)),\n ...kwargs,\n });\n }\n\n override async *_streamResponseChunks(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): AsyncGenerator<ChatGenerationChunk> {\n const params = this.invocationParams(options, { streaming: true });\n const messagesMapped = convertMessagesToGroqParams(messages);\n const response = await this.completionWithRetry(\n {\n ...params,\n messages: messagesMapped,\n stream: true,\n },\n {\n signal: options?.signal,\n headers: options?.headers,\n }\n );\n let role: GroqRoleEnum | undefined;\n let lastMessageId: string | undefined;\n\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n let responseMetadata: Record<string, any> | undefined;\n\n for await (const data of response) {\n if (options.signal?.aborted) {\n return;\n }\n responseMetadata = data;\n const choice = data?.choices[0];\n if (!choice) {\n continue;\n }\n // The `role` field is populated in the first delta of the response\n // but is not present in subsequent deltas. Extract it when available.\n if (choice.delta?.role) {\n role = choice.delta.role as GroqRoleEnum;\n }\n\n const chunk = _convertDeltaToMessageChunk(\n choice.delta,\n role,\n data,\n lastMessageId\n );\n const newTokenIndices = {\n prompt: options.promptIndex ?? 0,\n completion: choice.index ?? 0,\n };\n if (typeof chunk.content !== \"string\") {\n console.log(\n \"[WARNING]: Received non-string content from OpenAI. This is currently not supported.\"\n );\n continue;\n }\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const generationInfo: Record<string, any> = { ...newTokenIndices };\n if (choice.finish_reason != null) {\n generationInfo.finish_reason = choice.finish_reason;\n // Only include system fingerprint in the last chunk for now\n // to avoid concatenation issues\n generationInfo.system_fingerprint = data.system_fingerprint;\n generationInfo.model_name = data.model;\n }\n const generationChunk = new ChatGenerationChunk({\n message: chunk,\n text: chunk.content,\n generationInfo,\n });\n yield generationChunk;\n await runManager?.handleLLMNewToken(\n generationChunk.text ?? \"\",\n newTokenIndices,\n undefined,\n undefined,\n undefined,\n { chunk: generationChunk }\n );\n }\n\n if (responseMetadata) {\n if (\"choices\" in responseMetadata) {\n delete responseMetadata.choices;\n }\n yield new ChatGenerationChunk({\n message: new AIMessageChunk({\n content: \"\",\n response_metadata: responseMetadata,\n }),\n text: \"\",\n });\n }\n\n if (options.signal?.aborted) {\n throw new Error(\"AbortError\");\n }\n }\n\n override async _generate(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n options.signal?.throwIfAborted();\n if (this.streaming) {\n const tokenUsage: TokenUsage = {};\n const stream = this._streamResponseChunks(messages, options, runManager);\n const finalChunks: Record<number, ChatGenerationChunk> = {};\n for await (const chunk of stream) {\n const index =\n (chunk.generationInfo as NewTokenIndices)?.completion ?? 0;\n if (finalChunks[index] === undefined) {\n finalChunks[index] = chunk;\n } else {\n finalChunks[index] = finalChunks[index].concat(chunk);\n }\n }\n const generations = Object.entries(finalChunks)\n .sort(([aKey], [bKey]) => parseInt(aKey, 10) - parseInt(bKey, 10))\n .map(([_, value]) => value);\n\n return { generations, llmOutput: { estimatedTokenUsage: tokenUsage } };\n } else {\n return this._generateNonStreaming(messages, options, runManager);\n }\n }\n\n async _generateNonStreaming(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n _runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n const tokenUsage: TokenUsage = {};\n const params = this.invocationParams(options);\n const messagesMapped = convertMessagesToGroqParams(messages);\n\n const data = await this.completionWithRetry(\n {\n ...params,\n stream: false,\n messages: messagesMapped,\n },\n {\n signal: options?.signal,\n headers: options?.headers,\n }\n );\n\n if (\"usage\" in data && data.usage) {\n const {\n completion_tokens: completionTokens,\n prompt_tokens: promptTokens,\n total_tokens: totalTokens,\n } = data.usage as CompletionsAPI.CompletionUsage;\n\n if (completionTokens) {\n tokenUsage.completionTokens =\n (tokenUsage.completionTokens ?? 0) + completionTokens;\n }\n\n if (promptTokens) {\n tokenUsage.promptTokens = (tokenUsage.promptTokens ?? 0) + promptTokens;\n }\n\n if (totalTokens) {\n tokenUsage.totalTokens = (tokenUsage.totalTokens ?? 0) + totalTokens;\n }\n }\n\n const generations: ChatGeneration[] = [];\n\n if (\"choices\" in data && data.choices) {\n for (const part of (data as ChatCompletion).choices) {\n const text = part.message?.content ?? \"\";\n let usageMetadata: UsageMetadata | undefined;\n if (tokenUsage.totalTokens !== undefined) {\n usageMetadata = {\n input_tokens: tokenUsage.promptTokens ?? 0,\n output_tokens: tokenUsage.completionTokens ?? 0,\n total_tokens: tokenUsage.totalTokens,\n };\n }\n // extract all fields from the response object except\n // choices to be included as response metadata\n const { choices: _choices, ...metadata } = data;\n\n const generation: ChatGeneration = {\n text,\n message: groqResponseToChatMessage(\n part.message ?? { role: \"assistant\" },\n usageMetadata,\n metadata\n ),\n };\n generation.generationInfo = {\n ...(part.finish_reason ? { finish_reason: part.finish_reason } : {}),\n ...(part.logprobs ? { logprobs: part.logprobs } : {}),\n };\n generations.push(generation);\n }\n }\n\n return {\n generations,\n llmOutput: { tokenUsage },\n };\n }\n\n /**\n * Return profiling information for the model.\n *\n * Provides information about the model's capabilities and constraints,\n * including token limits, multimodal support, and advanced features like\n * tool calling and structured output.\n *\n * @returns {ModelProfile} An object describing the model's capabilities and constraints\n *\n * @example\n * ```typescript\n * const model = new ChatGroq({ model: \"llama-3.1-8b-instant\" });\n * const profile = model.profile;\n * console.log(profile.maxInputTokens); // 128000\n * console.log(profile.imageInputs); // true\n * ```\n */\n get profile(): ModelProfile {\n return PROFILES[this.model] ?? {};\n }\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<false>\n ): Runnable<BaseLanguageModelInput, RunOutput>;\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<true>\n ): Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<boolean>\n ):\n | Runnable<BaseLanguageModelInput, RunOutput>\n | Runnable<\n BaseLanguageModelInput,\n { raw: BaseMessage; parsed: RunOutput }\n > {\n const schema = outputSchema;\n const name = config?.name;\n const includeRaw = config?.includeRaw;\n\n // Determine the structured output method based on model capabilities\n const method = getGroqStructuredOutputMethod(this.model, config?.method);\n\n let functionName = name ?? \"extract\";\n let outputParser: BaseLLMOutputParser<RunOutput>;\n let llm: Runnable<BaseLanguageModelInput>;\n\n if (method === \"jsonSchema\") {\n // Native JSON Schema mode for gpt-oss models\n // Uses Groq's strict JSON Schema response format\n const schemaName = name ?? \"structured_output\";\n const description = getSchemaDescription(schema);\n const jsonSchema =\n isInteropZodSchema(schema) || isSerializableSchema(schema)\n ? toJsonSchema(schema)\n : schema;\n\n // Transform schema for Groq strict mode requirements\n const strictSchema = groqStrictifySchema(jsonSchema);\n\n llm = this.withConfig({\n response_format: {\n type: \"json_schema\",\n json_schema: {\n name: schemaName,\n description,\n strict: true,\n schema: strictSchema,\n },\n },\n ls_structured_output_format: {\n kwargs: { method: \"jsonSchema\" },\n schema: {\n title: schemaName,\n description,\n ...jsonSchema,\n },\n },\n });\n\n outputParser = createContentParser(schema);\n } else if (method === \"jsonMode\") {\n outputParser = createContentParser(schema);\n\n const outputSchema =\n isInteropZodSchema(schema) || isSerializableSchema(schema)\n ? toJsonSchema(schema)\n : schema;\n\n llm = this.withConfig({\n response_format: { type: \"json_object\" },\n ls_structured_output_format: {\n kwargs: { method: \"jsonMode\" },\n schema: outputSchema,\n },\n });\n } else {\n let toolFunction: FunctionDefinition;\n if (isInteropZodSchema(schema) || isSerializableSchema(schema)) {\n const asJsonSchema = toJsonSchema(schema);\n toolFunction = {\n name: functionName,\n description: asJsonSchema.description,\n parameters: asJsonSchema,\n };\n } else if (\n typeof schema.name === \"string\" &&\n typeof schema.parameters === \"object\" &&\n schema.parameters != null\n ) {\n toolFunction = schema as FunctionDefinition;\n functionName = schema.name;\n } else {\n functionName = schema.title ?? functionName;\n toolFunction = {\n name: functionName,\n description: schema.description ?? \"\",\n parameters: schema,\n };\n }\n\n llm = this.bindTools([\n { type: \"function\" as const, function: toolFunction },\n ]).withConfig({\n tool_choice: {\n type: \"function\" as const,\n function: { name: functionName },\n },\n ls_structured_output_format: {\n kwargs: { method: \"functionCalling\" },\n schema: toolFunction.parameters,\n },\n });\n\n outputParser = createFunctionCallingParser(schema, functionName);\n }\n\n return assembleStructuredOutputPipeline(\n llm,\n outputParser,\n includeRaw,\n \"ChatGroqStructuredOutput\"\n );\n }\n}\n\nfunction _formatToGroqToolChoice(\n toolChoice?: string | ChatCompletionsAPI.ChatCompletionNamedToolChoice\n):\n | ChatCompletionsAPI.ChatCompletionToolChoiceOption\n | Record<string, unknown>\n | null\n | undefined {\n if (!toolChoice) {\n return undefined;\n } else if (toolChoice === \"any\" || toolChoice === \"required\") {\n return \"required\";\n } else if (toolChoice === \"auto\") {\n return \"auto\";\n } else if (toolChoice === \"none\") {\n return \"none\";\n } else if (typeof toolChoice === \"string\") {\n return {\n type: \"function\",\n function: {\n name: toolChoice,\n },\n };\n } else {\n return toolChoice;\n 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| ||
| {"version":3,"file":"chat_models.js","names":[],"sources":["../src/chat_models.ts"],"sourcesContent":["import { toJsonSchema } from \"@langchain/core/utils/json_schema\";\nimport { NewTokenIndices } from \"@langchain/core/callbacks/base\";\nimport { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport {\n BaseChatModel,\n BaseChatModelCallOptions,\n BindToolsInput,\n LangSmithParams,\n type BaseChatModelParams,\n} from \"@langchain/core/language_models/chat_models\";\nimport * as ChatCompletionsAPI from \"groq-sdk/resources/chat/completions\";\nimport * as CompletionsAPI from \"groq-sdk/resources/completions\";\nimport {\n AIMessage,\n AIMessageChunk,\n BaseMessage,\n ChatMessage,\n ChatMessageChunk,\n HumanMessageChunk,\n SystemMessageChunk,\n ToolMessage,\n OpenAIToolCall,\n isAIMessage,\n BaseMessageChunk,\n UsageMetadata,\n FunctionMessageChunk,\n ToolMessageChunk,\n} from \"@langchain/core/messages\";\nimport {\n ChatGeneration,\n ChatGenerationChunk,\n ChatResult,\n} from \"@langchain/core/outputs\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\nimport {\n InteropZodType,\n isInteropZodSchema,\n} from \"@langchain/core/utils/types\";\nimport Groq from \"groq-sdk\";\nimport type {\n ChatCompletion,\n ChatCompletionCreateParams,\n ChatCompletionCreateParamsNonStreaming,\n ChatCompletionCreateParamsStreaming,\n ChatCompletionTool,\n} from \"groq-sdk/resources/chat/completions\";\nimport type { RequestOptions } from \"groq-sdk/core\";\nimport { Runnable } from \"@langchain/core/runnables\";\nimport {\n BaseLanguageModelInput,\n FunctionDefinition,\n StructuredOutputMethodOptions,\n} from \"@langchain/core/language_models/base\";\nimport { ModelProfile } from \"@langchain/core/language_models/profile\";\nimport { BaseLLMOutputParser } from \"@langchain/core/output_parsers\";\nimport {\n parseToolCall,\n makeInvalidToolCall,\n convertLangChainToolCallToOpenAI,\n} from \"@langchain/core/output_parsers/openai_tools\";\nimport { convertToOpenAITool } from \"@langchain/core/utils/function_calling\";\nimport { ToolCallChunk } from \"@langchain/core/messages/tool\";\nimport { getSchemaDescription } from \"@langchain/core/utils/types\";\n\nimport PROFILES from \"./profiles.js\";\nimport {\n getGroqStructuredOutputMethod,\n groqStrictifySchema,\n} from \"./utils/groq-schema.js\";\nimport {\n isSerializableSchema,\n SerializableSchema,\n} from \"@langchain/core/utils/standard_schema\";\nimport {\n assembleStructuredOutputPipeline,\n createContentParser,\n createFunctionCallingParser,\n} from \"@langchain/core/language_models/structured_output\";\n\ntype ChatGroqToolType = BindToolsInput | ChatCompletionTool;\n\n/**\n * Const list of fields that we'll pick from the `ChatCompletionCreateParams` interface\n * to use as the options allowed to be passed to invocation methods.\n *\n * @internal\n */\nconst CREATE_PARAMS_BASE_CALL_KEYS = [\n // \"messages\", // passed as input arg to invocation methods\n // \"model\", // don't allow override on invoke\n \"frequency_penalty\",\n \"function_call\",\n \"functions\",\n \"logit_bias\", // not supported, but left in for forward compatibility\n \"logprobs\", // not supported, but left in for forward compatibility\n \"max_completion_tokens\",\n \"max_tokens\",\n \"n\", // not supported, but left in for forward compatibility\n \"parallel_tool_calls\",\n \"presence_penalty\",\n \"reasoning_effort\",\n \"reasoning_format\",\n \"response_format\",\n \"seed\",\n // TODO: also pass as constructor arg\n \"service_tier\",\n \"stop\",\n // \"stream\", // determined by invocation method\n // other models only specify temperature as a constructor arg, but I don't see the harm in\n // allowing overrides on invocation\n \"temperature\",\n \"tool_choice\",\n // \"tools\", // need to allow users to specify langchain style tools, so we use a different type\n \"top_logprobs\",\n \"top_p\",\n // \"user\", // don't allow override on invoke\n] as const;\n\nconst ADDED_CALL_KEYS = [\n \"headers\",\n \"promptIndex\",\n \"stream_options\",\n \"tools\",\n] as const;\n\nexport type ChatGroqCallOptions = Pick<\n ChatCompletionsAPI.ChatCompletionCreateParamsBase,\n (typeof CREATE_PARAMS_BASE_CALL_KEYS)[number]\n> &\n BaseChatModelCallOptions & {\n /**\n * Additional headers to pass to the API.\n */\n headers?: Record<string, string | null | undefined>;\n /**\n * The index of the prompt in the list of prompts.\n */\n promptIndex?: number;\n /**\n * Additional options to pass to streamed completions.\n * If provided takes precedence over \"streamUsage\" set at initialization time.\n */\n stream_options?: {\n /**\n * Whether or not to include token usage in the stream.\n * If set to `true`, this will include an additional\n * chunk at the end of the stream with the token usage.\n *\n * Defaults to `true` when streaming, `false` otherwise.\n */\n include_usage: boolean;\n };\n\n tools?: ChatGroqToolType[];\n\n // IMPORTANT: If you add a new key here you MUST add it to the `ADDED_CALL_KEYS`\n // list above. Keep this comment at the bottom so people see it when they go to\n // make additions.\n };\n\nconst ALL_CALL_KEYS: readonly (keyof ChatGroqCallOptions)[] = [\n ...CREATE_PARAMS_BASE_CALL_KEYS,\n ...ADDED_CALL_KEYS,\n] as const;\n\n/**\n * Timing details about the request, useful for collecting performance metrics.\n */\ninterface TimingMetadata {\n /**\n * Time spent generating tokens\n */\n completion_time?: number;\n\n /**\n * Time spent processing input tokens\n */\n prompt_time?: number;\n\n /**\n * Time the requests was spent queued\n */\n queue_time?: number;\n\n /**\n * completion time and prompt time combined\n */\n total_time?: number;\n}\nexport interface ChatGroqInput extends BaseChatModelParams {\n /**\n * The temperature to use for sampling.\n * @default 0.7\n */\n temperature?: number;\n\n /**\n * The maximum number of tokens that the model can process in a single response.\n * This limits ensures computational efficiency and resource management.\n */\n maxTokens?: number;\n\n /** Total probability mass of tokens to consider at each step */\n topP?: number;\n\n /** Penalizes repeated tokens according to frequency */\n frequencyPenalty?: number;\n\n /** Penalizes repeated tokens */\n presencePenalty?: number;\n\n /** Number of completions to generate for each prompt */\n n?: number;\n\n /** Dictionary used to adjust the probability of specific tokens being generated */\n logitBias?: Record<string, number>;\n\n /** Unique string identifier representing your end-user, which can help OpenAI to monitor and detect abuse. */\n user?: string;\n\n /**\n * Whether or not to include token usage data in streamed chunks.\n * @default true\n */\n streamUsage?: boolean;\n\n /**\n * Whether to return log probabilities of the output tokens or not.\n * If true, returns the log probabilities of each output token returned in the content of message.\n */\n logprobs?: boolean;\n\n /**\n * An integer between 0 and 5 specifying the number of most likely tokens to return at each token position,\n * each with an associated log probability. logprobs must be set to true if this parameter is used.\n */\n topLogprobs?: number;\n\n /**\n * The Groq API key to use for requests.\n * @default process.env.GROQ_API_KEY\n */\n apiKey?: string;\n /**\n * The name of the model to use.\n */\n model: ChatCompletionCreateParams[\"model\"];\n /**\n * Up to 4 sequences where the API will stop generating further tokens. The\n * returned text will not contain the stop sequence.\n * Alias for `stopSequences`\n */\n stop?: string | null | Array<string>;\n /**\n * Up to 4 sequences where the API will stop generating further tokens. The\n * returned text will not contain the stop sequence.\n */\n stopSequences?: Array<string>;\n /**\n * Whether or not to stream responses.\n */\n streaming?: boolean;\n /**\n * Override the default base URL for the API\n */\n baseUrl?: string;\n /**\n * The maximum amount of time (in milliseconds) the client will wait for a response\n */\n timeout?: number;\n /**\n * HTTP agent used to manage connections\n */\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n httpAgent?: any;\n /**\n * Custom fetch function implementation\n */\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n fetch?: (...args: any) => any;\n\n /**\n * Default headers included with every request\n */\n defaultHeaders?: Record<string, string>;\n\n /**\n * Default query parameters included with every request\n */\n defaultQuery?: Record<string, string>;\n\n /**\n * The level of reasoning effort the model will use.\n * Supported by `openai/gpt-oss-20b`, `openai/gpt-oss-120b`, and `qwen/qwen3-32b`.\n * @see https://console.groq.com/docs/reasoning#options-for-reasoning-effort\n */\n reasoningEffort?: \"none\" | \"default\" | \"low\" | \"medium\" | \"high\" | null;\n}\n\ntype GroqRoleEnum = \"system\" | \"assistant\" | \"user\" | \"function\";\n\ninterface TokenUsage {\n completionTokens?: number;\n promptTokens?: number;\n totalTokens?: number;\n}\n\n/**\n * Extract the custom role from a message.\n * @param message - The message to extract the custom role from.\n * @returns The custom role of the message.\n */\nfunction extractGenericMessageCustomRole(message: ChatMessage): GroqRoleEnum {\n if (\n message.role !== \"system\" &&\n message.role !== \"assistant\" &&\n message.role !== \"user\" &&\n message.role !== \"function\"\n ) {\n throw new Error(\n `Unsupported message role: ${message.role}. Expected \"system\", \"assistant\", \"user\", or \"function\"`\n );\n }\n return message.role as GroqRoleEnum;\n}\n\n/**\n * Extract the role from a message.\n * @param message - The message to extract the role from.\n * @returns The role of the message.\n */\nexport function messageToGroqRole(message: BaseMessage): GroqRoleEnum {\n switch (message.type) {\n case \"system\":\n return \"system\";\n case \"ai\":\n return \"assistant\";\n case \"human\":\n return \"user\";\n case \"function\":\n return \"function\";\n case \"tool\":\n // Not yet supported as a type\n return \"tool\" as GroqRoleEnum;\n case \"generic\": {\n if (!ChatMessage.isInstance(message))\n throw new Error(\"Invalid generic chat message\");\n return extractGenericMessageCustomRole(message);\n }\n default:\n throw new Error(`Unknown message type: ${message.type}`);\n }\n}\n\nfunction convertMessagesToGroqParams(\n messages: BaseMessage[]\n): Array<ChatCompletionsAPI.ChatCompletionMessage> {\n return messages.map((message): ChatCompletionsAPI.ChatCompletionMessage => {\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n const completionParam: Record<string, any> = {\n role: messageToGroqRole(message),\n content: message.content,\n name: message.name,\n function_call: message.additional_kwargs.function_call,\n tool_calls: message.additional_kwargs.tool_calls,\n tool_call_id: (message as ToolMessage).tool_call_id,\n };\n if (isAIMessage(message) && !!message.tool_calls?.length) {\n completionParam.tool_calls = message.tool_calls.map(\n convertLangChainToolCallToOpenAI\n );\n } else {\n if (message.additional_kwargs.tool_calls != null) {\n completionParam.tool_calls = message.additional_kwargs.tool_calls;\n }\n if ((message as ToolMessage).tool_call_id != null) {\n completionParam.tool_call_id = (message as ToolMessage).tool_call_id;\n }\n }\n return completionParam as ChatCompletionsAPI.ChatCompletionMessage;\n });\n}\n\nfunction groqResponseToChatMessage(\n message: ChatCompletionsAPI.ChatCompletionMessage,\n usageMetadata?: UsageMetadata,\n responseMetadata?: Record<string, unknown>\n): BaseMessage {\n const rawToolCalls: OpenAIToolCall[] | undefined = message.tool_calls as\n | OpenAIToolCall[]\n | undefined;\n // Add model_provider for block translator lookup\n const enrichedMetadata = { ...responseMetadata, model_provider: \"groq\" };\n switch (message.role) {\n case \"assistant\": {\n const toolCalls = [];\n const invalidToolCalls = [];\n for (const rawToolCall of rawToolCalls ?? []) {\n try {\n toolCalls.push(parseToolCall(rawToolCall, { returnId: true }));\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (e: any) {\n invalidToolCalls.push(makeInvalidToolCall(rawToolCall, e.message));\n }\n }\n return new AIMessage({\n content: message.content || \"\",\n additional_kwargs: { tool_calls: rawToolCalls },\n tool_calls: toolCalls,\n invalid_tool_calls: invalidToolCalls,\n usage_metadata: usageMetadata,\n response_metadata: enrichedMetadata,\n });\n }\n default:\n return new ChatMessage(message.content || \"\", message.role ?? \"unknown\");\n }\n}\n\nfunction _convertDeltaToMessageChunk(\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n delta: Record<string, any>,\n defaultRole: GroqRoleEnum | undefined,\n rawResponse: ChatCompletionsAPI.ChatCompletionChunk,\n lastMessageId: string | undefined\n): BaseMessageChunk {\n const role = delta.role ?? defaultRole;\n const content = delta.content ?? \"\";\n let additional_kwargs: Record<string, unknown>;\n if (delta.function_call) {\n additional_kwargs = {\n function_call: delta.function_call,\n };\n } else if (delta.tool_calls) {\n additional_kwargs = {\n tool_calls: delta.tool_calls,\n };\n } else {\n additional_kwargs = {};\n }\n if (delta.audio) {\n additional_kwargs.audio = {\n ...delta.audio,\n index: rawResponse.choices[0].index,\n };\n }\n\n let usage: UsageMetadata | undefined;\n let groqMessageId: string | undefined = lastMessageId;\n let timing: TimingMetadata | undefined;\n\n const xGroq = rawResponse.x_groq;\n if (xGroq?.usage) {\n usage = {\n input_tokens: xGroq.usage.prompt_tokens,\n output_tokens: xGroq.usage.completion_tokens,\n total_tokens: xGroq.usage.total_tokens,\n };\n timing = {\n completion_time: xGroq.usage.completion_time,\n prompt_time: xGroq.usage.prompt_time,\n queue_time: xGroq.usage.queue_time,\n total_time: xGroq.usage.total_time,\n };\n }\n\n if (xGroq?.id) {\n groqMessageId = xGroq.id;\n }\n\n const response_metadata = { usage, timing, model_provider: \"groq\" };\n if (role === \"user\") {\n return new HumanMessageChunk({ content, response_metadata });\n } else if (role === \"assistant\") {\n const toolCallChunks: ToolCallChunk[] = [];\n if (Array.isArray(delta.tool_calls)) {\n for (const rawToolCall of delta.tool_calls) {\n toolCallChunks.push({\n name: rawToolCall.function?.name,\n args: rawToolCall.function?.arguments,\n id: rawToolCall.id,\n index: rawToolCall.index,\n type: \"tool_call_chunk\",\n });\n }\n }\n return new AIMessageChunk({\n content,\n tool_call_chunks: toolCallChunks,\n additional_kwargs,\n id: groqMessageId,\n response_metadata,\n });\n } else if (role === \"system\") {\n return new SystemMessageChunk({ content, response_metadata });\n } else if (role === \"developer\") {\n return new SystemMessageChunk({\n content,\n response_metadata,\n additional_kwargs: {\n __openai_role__: \"developer\",\n },\n });\n } else if (role === \"function\") {\n return new FunctionMessageChunk({\n content,\n additional_kwargs,\n name: delta.name,\n response_metadata,\n });\n } else if (role === \"tool\") {\n return new ToolMessageChunk({\n content,\n additional_kwargs,\n tool_call_id: delta.tool_call_id,\n response_metadata,\n });\n } else {\n return new ChatMessageChunk({ content, role, response_metadata });\n }\n}\n\n/*\nfunction _oldConvertDeltaToMessageChunk(\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n delta: Record<string, any>,\n rawResponse: ChatCompletionsAPI.ChatCompletionChunk,\n index: number,\n defaultRole: GroqRoleEnum | undefined,\n xGroq?: ChatCompletionsAPI.ChatCompletionChunk.XGroq\n): {\n message: BaseMessageChunk;\n toolCallData?: {\n id: string;\n name: string;\n index: number;\n type: \"tool_call_chunk\";\n }[];\n} {\n const { role } = delta;\n const content = delta.content ?? \"\";\n let additional_kwargs;\n if (delta.function_call) {\n additional_kwargs = {\n function_call: delta.function_call,\n };\n } else if (delta.tool_calls) {\n additional_kwargs = {\n tool_calls: delta.tool_calls,\n };\n } else {\n additional_kwargs = {};\n }\n\n let usageMetadata: UsageMetadata | undefined;\n let groqMessageId: string | undefined;\n if (xGroq?.usage) {\n usageMetadata = {\n input_tokens: xGroq.usage.prompt_tokens,\n output_tokens: xGroq.usage.completion_tokens,\n total_tokens: xGroq.usage.total_tokens,\n };\n groqMessageId = xGroq.id;\n }\n\n if (role === \"user\") {\n return {\n message: new HumanMessageChunk({ content }),\n };\n } else if (role === \"assistant\") {\n const toolCallChunks = _convertDeltaToolCallToToolCallChunk(\n delta.tool_calls,\n index\n );\n return {\n message: new AIMessageChunk({\n content,\n additional_kwargs,\n tool_call_chunks: toolCallChunks\n ? toolCallChunks.map((tc) => ({\n type: tc.type,\n args: tc.args,\n index: tc.index,\n }))\n : undefined,\n usage_metadata: usageMetadata,\n id: groqMessageId,\n }),\n toolCallData: toolCallChunks\n ? toolCallChunks.map((tc) => ({\n id: tc.id ?? \"\",\n name: tc.name ?? \"\",\n index: tc.index ?? index,\n type: \"tool_call_chunk\",\n }))\n : undefined,\n };\n } else if (role === \"system\") {\n return {\n message: new SystemMessageChunk({ content }),\n };\n } else {\n return {\n message: new ChatMessageChunk({ content, role }),\n };\n }\n}\n*/\n\n/**\n * Groq chat model integration.\n *\n * The Groq API is compatible to the OpenAI API with some limitations. View the\n * full API ref at:\n * @link {https://docs.api.groq.com/md/openai.oas.html}\n *\n * Setup:\n * Install `@langchain/groq` and set an environment variable named `GROQ_API_KEY`.\n *\n * ```bash\n * npm install @langchain/groq\n * export GROQ_API_KEY=\"your-api-key\"\n * ```\n *\n * ## [Constructor args](https://api.js.langchain.com/classes/langchain_groq.ChatGroq.html#constructor)\n *\n * ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_groq.ChatGroqCallOptions.html)\n *\n * Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.\n * They can also be passed via `.withConfig`, or the second arg in `.bindTools`, like shown in the examples below:\n *\n * ```typescript\n * // When calling `.withConfig`, call options should be passed via the first argument\n * const llmWithArgsBound = llm.withConfig({\n * stop: [\"\\n\"],\n * });\n *\n * // When calling `.bindTools`, call options should be passed via the second argument\n * const llmWithTools = llm.bindTools(\n * [...],\n * {\n * tool_choice: \"auto\",\n * }\n * );\n * ```\n *\n * ## Examples\n *\n * <details open>\n * <summary><strong>Instantiate</strong></summary>\n *\n * ```typescript\n * import { ChatGroq } from '@langchain/groq';\n *\n * const llm = new ChatGroq({\n * model: \"llama-3.3-70b-versatile\",\n * temperature: 0,\n * // other params...\n * });\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Invoking</strong></summary>\n *\n * ```typescript\n * const input = `Translate \"I love programming\" into French.`;\n *\n * // Models also accept a list of chat messages or a formatted prompt\n * const result = await llm.invoke(input);\n * console.log(result);\n * ```\n *\n * ```txt\n * AIMessage {\n * \"content\": \"The French translation of \\\"I love programming\\\" is \\\"J'aime programmer\\\". In this sentence, \\\"J'aime\\\" is the first person singular conjugation of the French verb \\\"aimer\\\" which means \\\"to love\\\", and \\\"programmer\\\" is the French infinitive for \\\"to program\\\". I hope this helps! Let me know if you have any other questions.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"tokenUsage\": {\n * \"completionTokens\": 82,\n * \"promptTokens\": 20,\n * \"totalTokens\": 102\n * },\n * \"finish_reason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Streaming Chunks</strong></summary>\n *\n * ```typescript\n * for await (const chunk of await llm.stream(input)) {\n * console.log(chunk);\n * }\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"The\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" French\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" translation\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" of\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" \\\"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"I\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" love\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ...\n * AIMessageChunk {\n * \"content\": \".\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": null\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Aggregate Streamed Chunks</strong></summary>\n *\n * ```typescript\n * import { AIMessageChunk } from '@langchain/core/messages';\n * import { concat } from '@langchain/core/utils/stream';\n *\n * const stream = await llm.stream(input);\n * let full: AIMessageChunk | undefined;\n * for await (const chunk of stream) {\n * full = !full ? chunk : concat(full, chunk);\n * }\n * console.log(full);\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"The French translation of \\\"I love programming\\\" is \\\"J'aime programmer\\\". In this sentence, \\\"J'aime\\\" is the first person singular conjugation of the French verb \\\"aimer\\\" which means \\\"to love\\\", and \\\"programmer\\\" is the French infinitive for \\\"to program\\\". I hope this helps! Let me know if you have any other questions.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"finishReason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Bind tools</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const llmForToolCalling = new ChatGroq({\n * model: \"llama3-groq-70b-8192-tool-use-preview\",\n * temperature: 0,\n * // other params...\n * });\n *\n * const GetWeather = {\n * name: \"GetWeather\",\n * description: \"Get the current weather in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const GetPopulation = {\n * name: \"GetPopulation\",\n * description: \"Get the current population in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const llmWithTools = llmForToolCalling.bindTools([GetWeather, GetPopulation]);\n * const aiMsg = await llmWithTools.invoke(\n * \"Which city is hotter today and which is bigger: LA or NY?\"\n * );\n * console.log(aiMsg.tool_calls);\n * ```\n *\n * ```txt\n * [\n * {\n * name: 'GetWeather',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'call_cd34'\n * },\n * {\n * name: 'GetWeather',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_68rf'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'call_f81z'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_8byt'\n * }\n * ]\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Structured Output</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const Joke = z.object({\n * setup: z.string().describe(\"The setup of the joke\"),\n * punchline: z.string().describe(\"The punchline to the joke\"),\n * rating: z.number().optional().describe(\"How funny the joke is, from 1 to 10\")\n * }).describe('Joke to tell user.');\n *\n * const structuredLlm = llmForToolCalling.withStructuredOutput(Joke, { name: \"Joke\" });\n * const jokeResult = await structuredLlm.invoke(\"Tell me a joke about cats\");\n * console.log(jokeResult);\n * ```\n *\n * ```txt\n * {\n * setup: \"Why don't cats play poker in the wild?\",\n * punchline: 'Because there are too many cheetahs.'\n * }\n * ```\n * </details>\n *\n * <br />\n */\nexport class ChatGroq extends BaseChatModel<\n ChatGroqCallOptions,\n AIMessageChunk\n> {\n lc_namespace = [\"langchain\", \"chat_models\", \"groq\"];\n\n client: Groq;\n\n model: string;\n\n temperature = 0.7;\n\n stop?: string[];\n\n stopSequences?: string[];\n\n maxTokens?: number;\n\n streaming = false;\n\n apiKey?: string;\n\n streamUsage: boolean = true;\n\n topP: number | null | undefined;\n\n frequencyPenalty: number | null | undefined;\n\n presencePenalty: number | null | undefined;\n\n logprobs: boolean | null | undefined;\n\n n: number | null | undefined;\n\n logitBias: Record<string, number> | null | undefined;\n\n user: string | null | undefined;\n\n reasoningEffort: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"reasoning_effort\"];\n\n reasoningFormat: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"reasoning_format\"];\n\n serviceTier: ChatCompletionsAPI.ChatCompletionCreateParamsBase[\"service_tier\"];\n\n topLogprobs: number | null | undefined;\n\n lc_serializable = true;\n\n get lc_serialized_keys(): string[] {\n return [\n \"client\",\n \"model\",\n \"temperature\",\n \"stop\",\n \"stopSequences\",\n \"maxTokens\",\n \"streaming\",\n \"apiKey\",\n \"streamUsage\",\n \"topP\",\n \"frequencyPenalty\",\n \"presencePenalty\",\n \"logprobs\",\n \"n\",\n \"logitBias\",\n \"user\",\n \"reasoningEffort\",\n \"reasoningFormat\",\n \"serviceTier\",\n \"topLogprobs\",\n ];\n }\n\n static lc_name() {\n return \"ChatGroq\";\n }\n\n _llmType() {\n return \"groq\";\n }\n\n get lc_secrets(): { [key: string]: string } | undefined {\n return {\n apiKey: \"GROQ_API_KEY\",\n };\n }\n\n get callKeys() {\n return [...super.callKeys, ...ALL_CALL_KEYS];\n }\n\n constructor(\n model: ChatCompletionCreateParams[\"model\"],\n fields?: Omit<ChatGroqInput, \"model\">\n );\n constructor(fields: ChatGroqInput);\n constructor(\n modelOrFields?: string | ChatGroqInput,\n fields?: Omit<ChatGroqInput, \"model\">\n ) {\n const params =\n typeof modelOrFields === \"string\"\n ? { ...(fields ?? {}), model: modelOrFields }\n : (modelOrFields ?? {});\n super(params);\n this._addVersion(\"@langchain/groq\", __PKG_VERSION__);\n\n const apiKey = params.apiKey || getEnvironmentVariable(\"GROQ_API_KEY\");\n if (!apiKey) {\n throw new Error(\n `Groq API key not found. Please set the GROQ_API_KEY environment variable or provide the key into \"apiKey\"`\n );\n }\n const defaultHeaders = {\n \"User-Agent\": \"langchainjs\",\n ...(params.defaultHeaders ?? {}),\n };\n\n this.client = new Groq({\n apiKey,\n dangerouslyAllowBrowser: true,\n baseURL: params.baseUrl,\n timeout: params.timeout,\n httpAgent: params.httpAgent,\n fetch: params.fetch,\n maxRetries: 0,\n defaultHeaders,\n defaultQuery: params.defaultQuery,\n });\n this.apiKey = apiKey;\n this.temperature = params.temperature ?? this.temperature;\n this.model = params.model;\n this.streaming = params.streaming ?? this.streaming;\n this.stop =\n params.stopSequences ??\n (typeof params.stop === \"string\" ? [params.stop] : params.stop) ??\n [];\n this.stopSequences = this.stop;\n this.maxTokens = params.maxTokens;\n this.topP = params.topP;\n this.frequencyPenalty = params.frequencyPenalty;\n this.presencePenalty = params.presencePenalty;\n this.logprobs = params.logprobs;\n this.n = params.n;\n this.logitBias = params.logitBias;\n this.user = params.user;\n this.reasoningEffort = params.reasoningEffort;\n }\n\n getLsParams(options: this[\"ParsedCallOptions\"]): LangSmithParams {\n const params = this.invocationParams(options);\n return {\n ls_provider: \"groq\",\n ls_model_name: this.model,\n ls_model_type: \"chat\",\n ls_temperature: params.temperature ?? this.temperature,\n ls_max_tokens: params.max_tokens ?? this.maxTokens,\n ls_stop: options.stop,\n };\n }\n\n async completionWithRetry(\n request: ChatCompletionCreateParamsStreaming,\n options?: RequestOptions\n ): Promise<AsyncIterable<ChatCompletionsAPI.ChatCompletionChunk>>;\n\n async completionWithRetry(\n request: ChatCompletionCreateParamsNonStreaming,\n options?: RequestOptions\n ): Promise<ChatCompletion>;\n\n async completionWithRetry(\n request: ChatCompletionCreateParams,\n options?: RequestOptions\n ): Promise<\n AsyncIterable<ChatCompletionsAPI.ChatCompletionChunk> | ChatCompletion\n > {\n return this.caller.call(async () =>\n this.client.chat.completions.create(request, options)\n );\n }\n\n invocationParams(\n options: this[\"ParsedCallOptions\"],\n extra?: { streaming?: boolean }\n ): Omit<ChatCompletionCreateParams, \"messages\"> {\n const params = super.invocationParams(options);\n\n let streamOptionsConfig = {};\n\n if (options?.stream_options !== undefined) {\n streamOptionsConfig = { stream_options: options.stream_options };\n } else if ((this.streamUsage && this.streaming) || extra?.streaming) {\n streamOptionsConfig = { stream_options: { include_usage: true } };\n }\n\n const toReturn: Omit<ChatCompletionCreateParams, \"messages\"> = {\n model: this.model,\n frequency_penalty: this.frequencyPenalty,\n function_call: options?.function_call,\n functions: options?.functions,\n logit_bias: this.logitBias,\n logprobs: this.logprobs,\n // max_completion_tokens\n // max_tokens\n n: this.n,\n parallel_tool_calls: options?.parallel_tool_calls,\n presence_penalty: this.presencePenalty,\n reasoning_effort: options?.reasoning_effort ?? this.reasoningEffort,\n reasoning_format: this.reasoningFormat,\n response_format: options?.response_format,\n seed: options?.seed,\n service_tier: this.serviceTier,\n stop: options?.stop ?? this.stopSequences,\n temperature: options?.temperature ?? this.temperature,\n tool_choice: _formatToGroqToolChoice(options?.tool_choice),\n tools: options?.tools?.length\n ? options.tools.map((tool) => convertToOpenAITool(tool))\n : undefined,\n top_logprobs: this.topLogprobs,\n top_p: this.topP,\n user: this.user,\n // if include_usage is set or streamUsage then stream must be set to true.\n stream: this.streaming,\n ...params,\n ...streamOptionsConfig,\n };\n\n toReturn.max_completion_tokens =\n options?.max_completion_tokens ?? options?.max_tokens ?? this.maxTokens;\n if (toReturn.max_completion_tokens === -1) {\n delete toReturn.max_completion_tokens;\n }\n\n return toReturn;\n }\n\n override bindTools(\n tools: ChatGroqToolType[],\n kwargs?: Partial<ChatGroqCallOptions>\n ): Runnable<BaseLanguageModelInput, AIMessageChunk, ChatGroqCallOptions> {\n return this.withConfig({\n tools: tools.map((tool) => convertToOpenAITool(tool)),\n ...kwargs,\n });\n }\n\n override async *_streamResponseChunks(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): AsyncGenerator<ChatGenerationChunk> {\n const params = this.invocationParams(options, { streaming: true });\n const messagesMapped = convertMessagesToGroqParams(messages);\n const response = await this.completionWithRetry(\n {\n ...params,\n messages: messagesMapped,\n stream: true,\n },\n {\n signal: options?.signal,\n headers: options?.headers,\n }\n );\n let role: GroqRoleEnum | undefined;\n\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n let responseMetadata: Record<string, any> | undefined;\n\n for await (const data of response) {\n if (options.signal?.aborted) {\n return;\n }\n responseMetadata = data;\n const choice = data?.choices[0];\n if (!choice) {\n continue;\n }\n // The `role` field is populated in the first delta of the response\n // but is not present in subsequent deltas. Extract it when available.\n if (choice.delta?.role) {\n role = choice.delta.role as GroqRoleEnum;\n }\n\n const chunk = _convertDeltaToMessageChunk(\n choice.delta,\n role,\n data,\n undefined\n );\n const newTokenIndices = {\n prompt: options.promptIndex ?? 0,\n completion: choice.index ?? 0,\n };\n if (typeof chunk.content !== \"string\") {\n console.log(\n \"[WARNING]: Received non-string content from OpenAI. This is currently not supported.\"\n );\n continue;\n }\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n const generationInfo: Record<string, any> = { ...newTokenIndices };\n if (choice.finish_reason != null) {\n generationInfo.finish_reason = choice.finish_reason;\n // Only include system fingerprint in the last chunk for now\n // to avoid concatenation issues\n generationInfo.system_fingerprint = data.system_fingerprint;\n generationInfo.model_name = data.model;\n }\n const generationChunk = new ChatGenerationChunk({\n message: chunk,\n text: chunk.content,\n generationInfo,\n });\n yield generationChunk;\n await runManager?.handleLLMNewToken(\n generationChunk.text ?? \"\",\n newTokenIndices,\n undefined,\n undefined,\n undefined,\n { chunk: generationChunk }\n );\n }\n\n if (responseMetadata) {\n if (\"choices\" in responseMetadata) {\n delete responseMetadata.choices;\n }\n yield new ChatGenerationChunk({\n message: new AIMessageChunk({\n content: \"\",\n response_metadata: responseMetadata,\n }),\n text: \"\",\n });\n }\n\n if (options.signal?.aborted) {\n throw new Error(\"AbortError\");\n }\n }\n\n override async _generate(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n options.signal?.throwIfAborted();\n if (this.streaming) {\n const tokenUsage: TokenUsage = {};\n const stream = this._streamResponseChunks(messages, options, runManager);\n const finalChunks: Record<number, ChatGenerationChunk> = {};\n for await (const chunk of stream) {\n const index =\n (chunk.generationInfo as NewTokenIndices)?.completion ?? 0;\n if (finalChunks[index] === undefined) {\n finalChunks[index] = chunk;\n } else {\n finalChunks[index] = finalChunks[index].concat(chunk);\n }\n }\n const generations = Object.entries(finalChunks)\n .sort(([aKey], [bKey]) => parseInt(aKey, 10) - parseInt(bKey, 10))\n .map(([_, value]) => value);\n\n return { generations, llmOutput: { estimatedTokenUsage: tokenUsage } };\n } else {\n return this._generateNonStreaming(messages, options, runManager);\n }\n }\n\n async _generateNonStreaming(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n _runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n const tokenUsage: TokenUsage = {};\n const params = this.invocationParams(options);\n const messagesMapped = convertMessagesToGroqParams(messages);\n\n const data = await this.completionWithRetry(\n {\n ...params,\n stream: false,\n messages: messagesMapped,\n },\n {\n signal: options?.signal,\n headers: options?.headers,\n }\n );\n\n if (\"usage\" in data && data.usage) {\n const {\n completion_tokens: completionTokens,\n prompt_tokens: promptTokens,\n total_tokens: totalTokens,\n } = data.usage as CompletionsAPI.CompletionUsage;\n\n if (completionTokens) {\n tokenUsage.completionTokens =\n (tokenUsage.completionTokens ?? 0) + completionTokens;\n }\n\n if (promptTokens) {\n tokenUsage.promptTokens = (tokenUsage.promptTokens ?? 0) + promptTokens;\n }\n\n if (totalTokens) {\n tokenUsage.totalTokens = (tokenUsage.totalTokens ?? 0) + totalTokens;\n }\n }\n\n const generations: ChatGeneration[] = [];\n\n if (\"choices\" in data && data.choices) {\n for (const part of (data as ChatCompletion).choices) {\n const text = part.message?.content ?? \"\";\n let usageMetadata: UsageMetadata | undefined;\n if (tokenUsage.totalTokens !== undefined) {\n usageMetadata = {\n input_tokens: tokenUsage.promptTokens ?? 0,\n output_tokens: tokenUsage.completionTokens ?? 0,\n total_tokens: tokenUsage.totalTokens,\n };\n }\n // extract all fields from the response object except\n // choices to be included as response metadata\n const { choices: _choices, ...metadata } = data;\n\n const generation: ChatGeneration = {\n text,\n message: groqResponseToChatMessage(\n part.message ?? { role: \"assistant\" },\n usageMetadata,\n metadata\n ),\n };\n generation.generationInfo = {\n ...(part.finish_reason ? { finish_reason: part.finish_reason } : {}),\n ...(part.logprobs ? { logprobs: part.logprobs } : {}),\n };\n generations.push(generation);\n }\n }\n\n return {\n generations,\n llmOutput: { tokenUsage },\n };\n }\n\n /**\n * Return profiling information for the model.\n *\n * Provides information about the model's capabilities and constraints,\n * including token limits, multimodal support, and advanced features like\n * tool calling and structured output.\n *\n * @returns {ModelProfile} An object describing the model's capabilities and constraints\n *\n * @example\n * ```typescript\n * const model = new ChatGroq({ model: \"llama-3.1-8b-instant\" });\n * const profile = model.profile;\n * console.log(profile.maxInputTokens); // 128000\n * console.log(profile.imageInputs); // true\n * ```\n */\n get profile(): ModelProfile {\n return PROFILES[this.model] ?? {};\n }\n\n withStructuredOutput<\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<false>\n ): Runnable<BaseLanguageModelInput, RunOutput>;\n\n withStructuredOutput<\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<true>\n ): Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;\n\n withStructuredOutput<\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n | SerializableSchema<RunOutput>\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<boolean>\n ):\n | Runnable<BaseLanguageModelInput, RunOutput>\n | Runnable<\n BaseLanguageModelInput,\n { raw: BaseMessage; parsed: RunOutput }\n > {\n const schema = outputSchema;\n const name = config?.name;\n const includeRaw = config?.includeRaw;\n\n // Determine the structured output method based on model capabilities\n const method = getGroqStructuredOutputMethod(this.model, config?.method);\n\n let functionName = name ?? \"extract\";\n let outputParser: BaseLLMOutputParser<RunOutput>;\n let llm: Runnable<BaseLanguageModelInput>;\n\n if (method === \"jsonSchema\") {\n // Native JSON Schema mode for gpt-oss models\n // Uses Groq's strict JSON Schema response format\n const schemaName = name ?? \"structured_output\";\n const description = getSchemaDescription(schema);\n const jsonSchema =\n isInteropZodSchema(schema) || isSerializableSchema(schema)\n ? toJsonSchema(schema)\n : schema;\n\n // Transform schema for Groq strict mode requirements\n const strictSchema = groqStrictifySchema(jsonSchema);\n\n llm = this.withConfig({\n response_format: {\n type: \"json_schema\",\n json_schema: {\n name: schemaName,\n description,\n strict: true,\n schema: strictSchema,\n },\n },\n ls_structured_output_format: {\n kwargs: { method: \"jsonSchema\" },\n schema: {\n title: schemaName,\n description,\n ...jsonSchema,\n },\n },\n });\n\n outputParser = createContentParser(schema);\n } else if (method === \"jsonMode\") {\n outputParser = createContentParser(schema);\n\n const outputSchema =\n isInteropZodSchema(schema) || isSerializableSchema(schema)\n ? toJsonSchema(schema)\n : schema;\n\n llm = this.withConfig({\n response_format: { type: \"json_object\" },\n ls_structured_output_format: {\n kwargs: { method: \"jsonMode\" },\n schema: outputSchema,\n },\n });\n } else {\n let toolFunction: FunctionDefinition;\n if (isInteropZodSchema(schema) || isSerializableSchema(schema)) {\n const asJsonSchema = toJsonSchema(schema);\n toolFunction = {\n name: functionName,\n description: asJsonSchema.description,\n parameters: asJsonSchema,\n };\n } else if (\n typeof schema.name === \"string\" &&\n typeof schema.parameters === \"object\" &&\n schema.parameters != null\n ) {\n toolFunction = schema as FunctionDefinition;\n functionName = schema.name;\n } else {\n functionName = schema.title ?? functionName;\n toolFunction = {\n name: functionName,\n description: schema.description ?? \"\",\n parameters: schema,\n };\n }\n\n llm = this.bindTools([\n { type: \"function\" as const, function: toolFunction },\n ]).withConfig({\n tool_choice: {\n type: \"function\" as const,\n function: { name: functionName },\n },\n ls_structured_output_format: {\n kwargs: { method: \"functionCalling\" },\n schema: toolFunction.parameters,\n },\n });\n\n outputParser = createFunctionCallingParser(schema, functionName);\n }\n\n return assembleStructuredOutputPipeline(\n llm,\n outputParser,\n includeRaw,\n \"ChatGroqStructuredOutput\"\n );\n }\n}\n\nfunction _formatToGroqToolChoice(\n toolChoice?: string | ChatCompletionsAPI.ChatCompletionNamedToolChoice\n):\n | ChatCompletionsAPI.ChatCompletionToolChoiceOption\n | Record<string, unknown>\n | null\n | undefined {\n if (!toolChoice) {\n return undefined;\n } else if (toolChoice === \"any\" || toolChoice === \"required\") {\n return \"required\";\n } else if (toolChoice === \"auto\") {\n return \"auto\";\n } else if (toolChoice === \"none\") {\n return \"none\";\n } else if (typeof toolChoice === \"string\") {\n return {\n type: \"function\",\n function: {\n name: toolChoice,\n },\n };\n } else {\n return toolChoice;\n 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|
+3
-4
@@ -1,5 +0,4 @@ | ||
| Object.defineProperty(exports, Symbol.toStringTag, { value: 'Module' }); | ||
| const require_chat_models = require('./chat_models.cjs'); | ||
| Object.defineProperty(exports, Symbol.toStringTag, { value: "Module" }); | ||
| const require_chat_models = require("./chat_models.cjs"); | ||
| exports.ChatGroq = require_chat_models.ChatGroq; | ||
| exports.messageToGroqRole = require_chat_models.messageToGroqRole; | ||
| exports.messageToGroqRole = require_chat_models.messageToGroqRole; |
+1
-2
| import { ChatGroq, messageToGroqRole } from "./chat_models.js"; | ||
| export { ChatGroq, messageToGroqRole }; | ||
| export { ChatGroq, messageToGroqRole }; |
@@ -1,2 +0,1 @@ | ||
| //#region src/profiles.ts | ||
@@ -243,5 +242,5 @@ const PROFILES = { | ||
| }; | ||
| //#endregion | ||
| exports.default = PROFILES; | ||
| //# sourceMappingURL=profiles.cjs.map |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"file":"profiles.cjs","names":[],"sources":["../src/profiles.ts"],"sourcesContent":["/**\n * This file was automatically generated by an automated script. Do not edit manually.\n */\nimport type { ModelProfile } from \"@langchain/core/language_models/profile\";\nconst PROFILES: Record<string, ModelProfile> = {\n \"llama3-70b-8192\": {\n maxInputTokens: 8192,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"qwen-qwq-32b\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 16384,\n reasoningOutput: true,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"llama-3.1-8b-instant\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 131072,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"llama-guard-3-8b\": {\n maxInputTokens: 8192,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: false,\n structuredOutput: false,\n },\n \"deepseek-r1-distill-llama-70b\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: true,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"llama3-8b-8192\": {\n maxInputTokens: 8192,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"mistral-saba-24b\": {\n maxInputTokens: 32768,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 32768,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"llama-3.3-70b-versatile\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 32768,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"gemma2-9b-it\": {\n maxInputTokens: 8192,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"moonshotai/kimi-k2-instruct\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 16384,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"moonshotai/kimi-k2-instruct-0905\": {\n maxInputTokens: 262144,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 16384,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: true,\n },\n \"qwen/qwen3-32b\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 16384,\n reasoningOutput: true,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"meta-llama/llama-4-scout-17b-16e-instruct\": {\n maxInputTokens: 131072,\n imageInputs: true,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: true,\n },\n \"meta-llama/llama-guard-4-12b\": {\n maxInputTokens: 131072,\n imageInputs: true,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 1024,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: false,\n structuredOutput: false,\n },\n \"meta-llama/llama-4-maverick-17b-128e-instruct\": {\n maxInputTokens: 131072,\n imageInputs: true,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: true,\n },\n 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| ||
| {"version":3,"file":"profiles.cjs","names":[],"sources":["../src/profiles.ts"],"sourcesContent":["/**\n * This file was automatically generated by an automated script. Do not edit manually.\n */\nimport type { ModelProfile } from \"@langchain/core/language_models/profile\";\nconst PROFILES: Record<string, ModelProfile> = {\n \"llama3-70b-8192\": {\n maxInputTokens: 8192,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"qwen-qwq-32b\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 16384,\n reasoningOutput: true,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"llama-3.1-8b-instant\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 131072,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"llama-guard-3-8b\": {\n maxInputTokens: 8192,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: false,\n structuredOutput: false,\n },\n \"deepseek-r1-distill-llama-70b\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: true,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"llama3-8b-8192\": {\n maxInputTokens: 8192,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"mistral-saba-24b\": {\n maxInputTokens: 32768,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 32768,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"llama-3.3-70b-versatile\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 32768,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"gemma2-9b-it\": {\n maxInputTokens: 8192,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"moonshotai/kimi-k2-instruct\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 16384,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"moonshotai/kimi-k2-instruct-0905\": {\n maxInputTokens: 262144,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 16384,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: true,\n },\n \"qwen/qwen3-32b\": {\n maxInputTokens: 131072,\n imageInputs: false,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 16384,\n reasoningOutput: true,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: false,\n },\n \"meta-llama/llama-4-scout-17b-16e-instruct\": {\n maxInputTokens: 131072,\n imageInputs: true,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: true,\n },\n \"meta-llama/llama-guard-4-12b\": {\n maxInputTokens: 131072,\n imageInputs: true,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 1024,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: false,\n structuredOutput: false,\n },\n \"meta-llama/llama-4-maverick-17b-128e-instruct\": {\n maxInputTokens: 131072,\n imageInputs: true,\n audioInputs: false,\n pdfInputs: false,\n videoInputs: false,\n maxOutputTokens: 8192,\n reasoningOutput: false,\n imageOutputs: false,\n audioOutputs: false,\n videoOutputs: false,\n toolCalling: true,\n structuredOutput: true,\n },\n 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|
+1
-1
@@ -242,5 +242,5 @@ //#region src/profiles.ts | ||
| }; | ||
| //#endregion | ||
| export { PROFILES as default }; | ||
| //# sourceMappingURL=profiles.js.map |
@@ -1,2 +0,1 @@ | ||
| //#region src/utils/groq-schema.ts | ||
@@ -137,6 +136,6 @@ /** | ||
| } | ||
| //#endregion | ||
| exports.getGroqStructuredOutputMethod = getGroqStructuredOutputMethod; | ||
| exports.groqStrictifySchema = groqStrictifySchema; | ||
| //# sourceMappingURL=groq-schema.cjs.map |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"file":"groq-schema.cjs","names":[],"sources":["../../src/utils/groq-schema.ts"],"sourcesContent":["/**\n * Groq JSON Schema Strict Mode Transformation\n *\n * Groq's strict JSON Schema mode has specific requirements:\n * 1. additionalProperties: false on all objects\n * 2. ALL properties must be in the `required` array\n * 3. Optional properties must be nullable (type union with \"null\")\n * 4. Root schema cannot have anyOf/oneOf/enum/not\n *\n * @see https://console.groq.com/docs/structured-outputs\n */\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\ntype JsonSchema = Record<string, any>;\n\n/**\n * Make a schema nullable using type array syntax (NOT anyOf).\n * Groq strict mode forbids anyOf at top level, so we use type: [T, \"null\"].\n * Handles: simple types, enums, anyOf, arrays, and objects.\n * Unknown schema structures (e.g., `$ref`) are returned unchanged.\n */\nfunction makeNullable(schema: JsonSchema): JsonSchema {\n if (!schema || typeof schema !== \"object\") return schema;\n\n // Already nullable\n if (schema.type === \"null\") return schema;\n if (Array.isArray(schema.type) && schema.type.includes(\"null\")) return schema;\n\n // Enum: add null to enum values and type\n if (Array.isArray(schema.enum)) {\n const nextEnum = schema.enum.includes(null)\n ? schema.enum\n : [...schema.enum, null];\n const baseType = Array.isArray(schema.type)\n ? // eslint-disable-next-line @typescript-eslint/no-explicit-any\n schema.type.filter((t: any) => t !== \"null\")\n : typeof schema.type === \"string\"\n ? [schema.type]\n : [\"string\"]; // Default to string for enums\n const nextType = Array.from(new Set([...baseType, \"null\"]));\n return { ...schema, type: nextType, enum: nextEnum };\n }\n\n // anyOf: flatten to type array if possible, otherwise add null variant\n if (Array.isArray(schema.anyOf)) {\n const hasNull = schema.anyOf.some(\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n (s: any) =>\n s?.type === \"null\" ||\n (Array.isArray(s?.type) && s.type.includes(\"null\"))\n );\n if (hasNull) return schema;\n\n // Try to extract types from anyOf and use type array instead\n const types = schema.anyOf\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n .map((s: any) => s?.type)\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n .filter((t: any) => t && typeof t === \"string\");\n\n if (types.length === schema.anyOf.length) {\n // All variants are simple types - can convert to type array\n const { anyOf: _, ...rest } = schema;\n return { ...rest, type: [...types, \"null\"] };\n }\n\n // Complex anyOf - add null variant (but this should be avoided at root)\n return { ...schema, anyOf: [...schema.anyOf, { type: \"null\" }] };\n }\n\n // Simple type: convert to array with null\n if (typeof schema.type === \"string\") {\n return { ...schema, type: [schema.type, \"null\"] };\n }\n\n // Type array: add null\n if (Array.isArray(schema.type)) {\n return { ...schema, type: Array.from(new Set([...schema.type, \"null\"])) };\n }\n\n // Has properties but no type - it's an object\n if (schema.properties) {\n return { ...schema, type: [\"object\", \"null\"] };\n }\n\n // Unknown schema structure (e.g., $ref) - return unchanged\n return schema;\n}\n\n/**\n * Transform a JSON Schema to be Groq strict-mode compatible.\n *\n * Recursively:\n * 1. Sets additionalProperties: false on all objects\n * 2. Moves all properties to required array\n * 3. Makes previously optional properties nullable\n * 4. Throws if root schema has oneOf/not; extracts object variant from anyOf (Groq forbids these at top level)\n *\n * @param schema - The schema to transform\n * @param isRoot - Whether this is the root schema (default: true on first call)\n */\nexport function groqStrictifySchema(\n schema: JsonSchema,\n isRoot = true\n): JsonSchema {\n if (!schema || typeof schema !== \"object\") return schema;\n if (Array.isArray(schema))\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n return schema.map((s) => groqStrictifySchema(s, false)) as any;\n\n // Deep clone to avoid mutating original\n const result = { ...schema };\n\n // Recurse into compound schemas (NOT root after this)\n for (const k of [\"anyOf\", \"oneOf\", \"allOf\"]) {\n if (Array.isArray(result[k])) {\n result[k] = result[k].map((s: JsonSchema) =>\n groqStrictifySchema(s, false)\n );\n }\n }\n\n // Recurse into array items\n if (result.items) {\n result.items = groqStrictifySchema(result.items, false);\n }\n\n // Recurse into $defs (definitions)\n if (result.$defs) {\n result.$defs = { ...result.$defs };\n for (const key of Object.keys(result.$defs)) {\n result.$defs[key] = groqStrictifySchema(result.$defs[key], false);\n }\n }\n\n // Check if this is an object schema\n const isObject =\n result.type === \"object\" ||\n (Array.isArray(result.type) && result.type.includes(\"object\")) ||\n result.properties;\n\n if (isObject && result.properties && typeof result.properties === \"object\") {\n // Set additionalProperties: false for strict mode\n result.additionalProperties = false;\n\n const propKeys = Object.keys(result.properties);\n const prevRequired = new Set<string>(\n Array.isArray(result.required) ? result.required : []\n );\n\n // Clone and transform properties\n result.properties = { ...result.properties };\n\n for (const key of propKeys) {\n // Recursively strictify nested schemas\n result.properties[key] = groqStrictifySchema(\n result.properties[key],\n false\n );\n\n // If property was optional (not in required), make it nullable\n if (!prevRequired.has(key)) {\n result.properties[key] = makeNullable(result.properties[key]);\n }\n }\n\n // All properties must be required in strict mode\n result.required = propKeys;\n }\n\n // ROOT PROTECTION: Groq forbids anyOf/oneOf/enum/not at top level\n if (isRoot) {\n // If root has anyOf, extract the object variant\n if (Array.isArray(result.anyOf)) {\n const objectVariant = result.anyOf.find(\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n (s: any) => s?.type === \"object\" || s?.properties\n );\n if (objectVariant) {\n // Replace root with the object variant\n const cleaned = groqStrictifySchema(objectVariant, true);\n return { ...cleaned, type: \"object\" };\n }\n // No object variant found - schema can't be used with Groq strict mode\n throw new Error(\n \"Groq strict mode requires the root schema to be an object type, \" +\n \"but the schema has anyOf/oneOf with no object variant.\"\n );\n }\n\n // Throw for oneOf/not at root (Groq forbids these)\n if (result.oneOf) {\n throw new Error(\n \"Groq strict mode does not support oneOf at the root schema level.\"\n );\n }\n if (result.not) {\n throw new Error(\n \"Groq strict mode does not support 'not' at the root schema level.\"\n );\n }\n\n if (result.properties) {\n result.type = \"object\";\n }\n }\n\n return result;\n}\n\n/**\n * Check if a model supports native JSON Schema structured output.\n * Uses prefix matching so new gpt-oss models are automatically supported.\n */\nfunction supportsJsonSchema(model: string): boolean {\n return model.startsWith(\"openai/gpt-oss\");\n}\n\n/**\n * Supported structured output methods for Groq.\n */\nexport const SUPPORTED_STRUCTURED_OUTPUT_METHODS = [\n \"jsonSchema\",\n \"functionCalling\",\n \"jsonMode\",\n] as const;\n\nexport type GroqStructuredOutputMethod =\n (typeof SUPPORTED_STRUCTURED_OUTPUT_METHODS)[number];\n\n/**\n * Get the structured output method for a given Groq model.\n *\n * - Returns `jsonSchema` if the model supports native JSON Schema and no method is specified\n * - Throws if an invalid method is provided\n * - Throws if `jsonSchema` is requested for a model that doesn't support it\n *\n * @param model - The model name\n * @param method - Optional method override\n * @returns The structured output method to use\n */\nexport function getGroqStructuredOutputMethod(\n model: string,\n method?: string\n): GroqStructuredOutputMethod {\n // Validate method if provided\n if (\n method !== undefined &&\n !SUPPORTED_STRUCTURED_OUTPUT_METHODS.includes(\n method as GroqStructuredOutputMethod\n )\n ) {\n throw new Error(\n `Invalid structured output method: ${method}. 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| {"version":3,"file":"groq-schema.cjs","names":[],"sources":["../../src/utils/groq-schema.ts"],"sourcesContent":["/**\n * Groq JSON Schema Strict Mode Transformation\n *\n * Groq's strict JSON Schema mode has specific requirements:\n * 1. additionalProperties: false on all objects\n * 2. ALL properties must be in the `required` array\n * 3. Optional properties must be nullable (type union with \"null\")\n * 4. Root schema cannot have anyOf/oneOf/enum/not\n *\n * @see https://console.groq.com/docs/structured-outputs\n */\n\n// oxlint-disable-next-line @typescript-eslint/no-explicit-any\ntype JsonSchema = Record<string, any>;\n\n/**\n * Make a schema nullable using type array syntax (NOT anyOf).\n * Groq strict mode forbids anyOf at top level, so we use type: [T, \"null\"].\n * Handles: simple types, enums, anyOf, arrays, and objects.\n * Unknown schema structures (e.g., `$ref`) are returned unchanged.\n */\nfunction makeNullable(schema: JsonSchema): JsonSchema {\n if (!schema || typeof schema !== \"object\") return schema;\n\n // Already nullable\n if (schema.type === \"null\") return schema;\n if (Array.isArray(schema.type) && schema.type.includes(\"null\")) return schema;\n\n // Enum: add null to enum values and type\n if (Array.isArray(schema.enum)) {\n const nextEnum = schema.enum.includes(null)\n ? schema.enum\n : [...schema.enum, null];\n const baseType = Array.isArray(schema.type)\n ? // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n schema.type.filter((t: any) => t !== \"null\")\n : typeof schema.type === \"string\"\n ? [schema.type]\n : [\"string\"]; // Default to string for enums\n const nextType = Array.from(new Set([...baseType, \"null\"]));\n return { ...schema, type: nextType, enum: nextEnum };\n }\n\n // anyOf: flatten to type array if possible, otherwise add null variant\n if (Array.isArray(schema.anyOf)) {\n const hasNull = schema.anyOf.some(\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n (s: any) =>\n s?.type === \"null\" ||\n (Array.isArray(s?.type) && s.type.includes(\"null\"))\n );\n if (hasNull) return schema;\n\n // Try to extract types from anyOf and use type array instead\n const types = schema.anyOf\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n .map((s: any) => s?.type)\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n .filter((t: any) => t && typeof t === \"string\");\n\n if (types.length === schema.anyOf.length) {\n // All variants are simple types - can convert to type array\n const { anyOf: _, ...rest } = schema;\n return { ...rest, type: [...types, \"null\"] };\n }\n\n // Complex anyOf - add null variant (but this should be avoided at root)\n return { ...schema, anyOf: [...schema.anyOf, { type: \"null\" }] };\n }\n\n // Simple type: convert to array with null\n if (typeof schema.type === \"string\") {\n return { ...schema, type: [schema.type, \"null\"] };\n }\n\n // Type array: add null\n if (Array.isArray(schema.type)) {\n return { ...schema, type: Array.from(new Set([...schema.type, \"null\"])) };\n }\n\n // Has properties but no type - it's an object\n if (schema.properties) {\n return { ...schema, type: [\"object\", \"null\"] };\n }\n\n // Unknown schema structure (e.g., $ref) - return unchanged\n return schema;\n}\n\n/**\n * Transform a JSON Schema to be Groq strict-mode compatible.\n *\n * Recursively:\n * 1. Sets additionalProperties: false on all objects\n * 2. Moves all properties to required array\n * 3. Makes previously optional properties nullable\n * 4. Throws if root schema has oneOf/not; extracts object variant from anyOf (Groq forbids these at top level)\n *\n * @param schema - The schema to transform\n * @param isRoot - Whether this is the root schema (default: true on first call)\n */\nexport function groqStrictifySchema(\n schema: JsonSchema,\n isRoot = true\n): JsonSchema {\n if (!schema || typeof schema !== \"object\") return schema;\n if (Array.isArray(schema))\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n return schema.map((s) => groqStrictifySchema(s, false)) as any;\n\n // Deep clone to avoid mutating original\n const result = { ...schema };\n\n // Recurse into compound schemas (NOT root after this)\n for (const k of [\"anyOf\", \"oneOf\", \"allOf\"]) {\n if (Array.isArray(result[k])) {\n result[k] = result[k].map((s: JsonSchema) =>\n groqStrictifySchema(s, false)\n );\n }\n }\n\n // Recurse into array items\n if (result.items) {\n result.items = groqStrictifySchema(result.items, false);\n }\n\n // Recurse into $defs (definitions)\n if (result.$defs) {\n result.$defs = { ...result.$defs };\n for (const key of Object.keys(result.$defs)) {\n result.$defs[key] = groqStrictifySchema(result.$defs[key], false);\n }\n }\n\n // Check if this is an object schema\n const isObject =\n result.type === \"object\" ||\n (Array.isArray(result.type) && result.type.includes(\"object\")) ||\n result.properties;\n\n if (isObject && result.properties && typeof result.properties === \"object\") {\n // Set additionalProperties: false for strict mode\n result.additionalProperties = false;\n\n const propKeys = Object.keys(result.properties);\n const prevRequired = new Set<string>(\n Array.isArray(result.required) ? result.required : []\n );\n\n // Clone and transform properties\n result.properties = { ...result.properties };\n\n for (const key of propKeys) {\n // Recursively strictify nested schemas\n result.properties[key] = groqStrictifySchema(\n result.properties[key],\n false\n );\n\n // If property was optional (not in required), make it nullable\n if (!prevRequired.has(key)) {\n result.properties[key] = makeNullable(result.properties[key]);\n }\n }\n\n // All properties must be required in strict mode\n result.required = propKeys;\n }\n\n // ROOT PROTECTION: Groq forbids anyOf/oneOf/enum/not at top level\n if (isRoot) {\n // If root has anyOf, extract the object variant\n if (Array.isArray(result.anyOf)) {\n const objectVariant = result.anyOf.find(\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n (s: any) => s?.type === \"object\" || s?.properties\n );\n if (objectVariant) {\n // Replace root with the object variant\n const cleaned = groqStrictifySchema(objectVariant, true);\n return { ...cleaned, type: \"object\" };\n }\n // No object variant found - schema can't be used with Groq strict mode\n throw new Error(\n \"Groq strict mode requires the root schema to be an object type, \" +\n \"but the schema has anyOf/oneOf with no object variant.\"\n );\n }\n\n // Throw for oneOf/not at root (Groq forbids these)\n if (result.oneOf) {\n throw new Error(\n \"Groq strict mode does not support oneOf at the root schema level.\"\n );\n }\n if (result.not) {\n throw new Error(\n \"Groq strict mode does not support 'not' at the root schema level.\"\n );\n }\n\n if (result.properties) {\n result.type = \"object\";\n }\n }\n\n return result;\n}\n\n/**\n * Check if a model supports native JSON Schema structured output.\n * Uses prefix matching so new gpt-oss models are automatically supported.\n */\nfunction supportsJsonSchema(model: string): boolean {\n return model.startsWith(\"openai/gpt-oss\");\n}\n\n/**\n * Supported structured output methods for Groq.\n */\nexport const SUPPORTED_STRUCTURED_OUTPUT_METHODS = [\n \"jsonSchema\",\n \"functionCalling\",\n \"jsonMode\",\n] as const;\n\nexport type GroqStructuredOutputMethod =\n (typeof SUPPORTED_STRUCTURED_OUTPUT_METHODS)[number];\n\n/**\n * Get the structured output method for a given Groq model.\n *\n * - Returns `jsonSchema` if the model supports native JSON Schema and no method is specified\n * - Throws if an invalid method is provided\n * - Throws if `jsonSchema` is requested for a model that doesn't support it\n *\n * @param model - The model name\n * @param method - Optional method override\n * @returns The structured output method to use\n */\nexport function getGroqStructuredOutputMethod(\n model: string,\n method?: string\n): GroqStructuredOutputMethod {\n // Validate method if provided\n if (\n method !== undefined &&\n !SUPPORTED_STRUCTURED_OUTPUT_METHODS.includes(\n method as GroqStructuredOutputMethod\n )\n ) {\n throw new Error(\n `Invalid structured output method: ${method}. Supported methods are: ${SUPPORTED_STRUCTURED_OUTPUT_METHODS.join(\", \")}`\n );\n }\n\n const modelSupportsJsonSchema = supportsJsonSchema(model);\n\n // If model supports JSON Schema and no method specified, use it by default\n if (modelSupportsJsonSchema && !method) {\n return \"jsonSchema\";\n }\n\n // If jsonSchema requested but not supported, throw\n if (!modelSupportsJsonSchema && method === \"jsonSchema\") {\n throw new Error(\n `Native JSON Schema structured output is not supported for model \"${model}\". ` +\n `Only models with the \"openai/gpt-oss\" prefix are supported. ` +\n `Use \"functionCalling\" or \"jsonMode\" instead.`\n );\n }\n\n // Return the specified method or default to functionCalling\n return (method as GroqStructuredOutputMethod) ?? \"functionCalling\";\n}\n"],"mappings":";;;;;;;AAqBA,SAAS,aAAa,QAAgC;AACpD,KAAI,CAAC,UAAU,OAAO,WAAW,SAAU,QAAO;AAGlD,KAAI,OAAO,SAAS,OAAQ,QAAO;AACnC,KAAI,MAAM,QAAQ,OAAO,KAAK,IAAI,OAAO,KAAK,SAAS,OAAO,CAAE,QAAO;AAGvE,KAAI,MAAM,QAAQ,OAAO,KAAK,EAAE;EAC9B,MAAM,WAAW,OAAO,KAAK,SAAS,KAAK,GACvC,OAAO,OACP,CAAC,GAAG,OAAO,MAAM,KAAK;EAC1B,MAAM,WAAW,MAAM,QAAQ,OAAO,KAAK,GAEvC,OAAO,KAAK,QAAQ,MAAW,MAAM,OAAO,GAC5C,OAAO,OAAO,SAAS,WACrB,CAAC,OAAO,KAAK,GACb,CAAC,SAAS;EAChB,MAAM,WAAW,MAAM,KAAK,IAAI,IAAI,CAAC,GAAG,UAAU,OAAO,CAAC,CAAC;AAC3D,SAAO;GAAE,GAAG;GAAQ,MAAM;GAAU,MAAM;GAAU;;AAItD,KAAI,MAAM,QAAQ,OAAO,MAAM,EAAE;AAO/B,MANgB,OAAO,MAAM,MAE1B,MACC,GAAG,SAAS,UACX,MAAM,QAAQ,GAAG,KAAK,IAAI,EAAE,KAAK,SAAS,OAAO,CACrD,CACY,QAAO;EAGpB,MAAM,QAAQ,OAAO,MAElB,KAAK,MAAW,GAAG,KAAK,CAExB,QAAQ,MAAW,KAAK,OAAO,MAAM,SAAS;AAEjD,MAAI,MAAM,WAAW,OAAO,MAAM,QAAQ;GAExC,MAAM,EAAE,OAAO,GAAG,GAAG,SAAS;AAC9B,UAAO;IAAE,GAAG;IAAM,MAAM,CAAC,GAAG,OAAO,OAAO;IAAE;;AAI9C,SAAO;GAAE,GAAG;GAAQ,OAAO,CAAC,GAAG,OAAO,OAAO,EAAE,MAAM,QAAQ,CAAC;GAAE;;AAIlE,KAAI,OAAO,OAAO,SAAS,SACzB,QAAO;EAAE,GAAG;EAAQ,MAAM,CAAC,OAAO,MAAM,OAAO;EAAE;AAInD,KAAI,MAAM,QAAQ,OAAO,KAAK,CAC5B,QAAO;EAAE,GAAG;EAAQ,MAAM,MAAM,KAAK,IAAI,IAAI,CAAC,GAAG,OAAO,MAAM,OAAO,CAAC,CAAC;EAAE;AAI3E,KAAI,OAAO,WACT,QAAO;EAAE,GAAG;EAAQ,MAAM,CAAC,UAAU,OAAO;EAAE;AAIhD,QAAO;;;;;;;;;;;;;;AAeT,SAAgB,oBACd,QACA,SAAS,MACG;AACZ,KAAI,CAAC,UAAU,OAAO,WAAW,SAAU,QAAO;AAClD,KAAI,MAAM,QAAQ,OAAO,CAEvB,QAAO,OAAO,KAAK,MAAM,oBAAoB,GAAG,MAAM,CAAC;CAGzD,MAAM,SAAS,EAAE,GAAG,QAAQ;AAG5B,MAAK,MAAM,KAAK;EAAC;EAAS;EAAS;EAAQ,CACzC,KAAI,MAAM,QAAQ,OAAO,GAAG,CAC1B,QAAO,KAAK,OAAO,GAAG,KAAK,MACzB,oBAAoB,GAAG,MAAM,CAC9B;AAKL,KAAI,OAAO,MACT,QAAO,QAAQ,oBAAoB,OAAO,OAAO,MAAM;AAIzD,KAAI,OAAO,OAAO;AAChB,SAAO,QAAQ,EAAE,GAAG,OAAO,OAAO;AAClC,OAAK,MAAM,OAAO,OAAO,KAAK,OAAO,MAAM,CACzC,QAAO,MAAM,OAAO,oBAAoB,OAAO,MAAM,MAAM,MAAM;;AAUrE,MAJE,OAAO,SAAS,YACf,MAAM,QAAQ,OAAO,KAAK,IAAI,OAAO,KAAK,SAAS,SAAS,IAC7D,OAAO,eAEO,OAAO,cAAc,OAAO,OAAO,eAAe,UAAU;AAE1E,SAAO,uBAAuB;EAE9B,MAAM,WAAW,OAAO,KAAK,OAAO,WAAW;EAC/C,MAAM,eAAe,IAAI,IACvB,MAAM,QAAQ,OAAO,SAAS,GAAG,OAAO,WAAW,EAAE,CACtD;AAGD,SAAO,aAAa,EAAE,GAAG,OAAO,YAAY;AAE5C,OAAK,MAAM,OAAO,UAAU;AAE1B,UAAO,WAAW,OAAO,oBACvB,OAAO,WAAW,MAClB,MACD;AAGD,OAAI,CAAC,aAAa,IAAI,IAAI,CACxB,QAAO,WAAW,OAAO,aAAa,OAAO,WAAW,KAAK;;AAKjE,SAAO,WAAW;;AAIpB,KAAI,QAAQ;AAEV,MAAI,MAAM,QAAQ,OAAO,MAAM,EAAE;GAC/B,MAAM,gBAAgB,OAAO,MAAM,MAEhC,MAAW,GAAG,SAAS,YAAY,GAAG,WACxC;AACD,OAAI,cAGF,QAAO;IAAE,GADO,oBAAoB,eAAe,KAAK;IACnC,MAAM;IAAU;AAGvC,SAAM,IAAI,MACR,yHAED;;AAIH,MAAI,OAAO,MACT,OAAM,IAAI,MACR,oEACD;AAEH,MAAI,OAAO,IACT,OAAM,IAAI,MACR,oEACD;AAGH,MAAI,OAAO,WACT,QAAO,OAAO;;AAIlB,QAAO;;;;;;AAOT,SAAS,mBAAmB,OAAwB;AAClD,QAAO,MAAM,WAAW,iBAAiB;;;;;AAM3C,MAAa,sCAAsC;CACjD;CACA;CACA;CACD;;;;;;;;;;;;AAgBD,SAAgB,8BACd,OACA,QAC4B;AAE5B,KACE,WAAW,KAAA,KACX,CAAC,oCAAoC,SACnC,OACD,CAED,OAAM,IAAI,MACR,qCAAqC,OAAO,2BAA2B,oCAAoC,KAAK,KAAK,GACtH;CAGH,MAAM,0BAA0B,mBAAmB,MAAM;AAGzD,KAAI,2BAA2B,CAAC,OAC9B,QAAO;AAIT,KAAI,CAAC,2BAA2B,WAAW,aACzC,OAAM,IAAI,MACR,oEAAoE,MAAM,6GAG3E;AAIH,QAAQ,UAAyC"} |
@@ -136,5 +136,5 @@ //#region src/utils/groq-schema.ts | ||
| } | ||
| //#endregion | ||
| export { getGroqStructuredOutputMethod, groqStrictifySchema }; | ||
| //# sourceMappingURL=groq-schema.js.map |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"file":"groq-schema.js","names":[],"sources":["../../src/utils/groq-schema.ts"],"sourcesContent":["/**\n * Groq JSON Schema Strict Mode Transformation\n *\n * Groq's strict JSON Schema mode has specific requirements:\n * 1. additionalProperties: false on all objects\n * 2. ALL properties must be in the `required` array\n * 3. Optional properties must be nullable (type union with \"null\")\n * 4. Root schema cannot have anyOf/oneOf/enum/not\n *\n * @see https://console.groq.com/docs/structured-outputs\n */\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\ntype JsonSchema = Record<string, any>;\n\n/**\n * Make a schema nullable using type array syntax (NOT anyOf).\n * Groq strict mode forbids anyOf at top level, so we use type: [T, \"null\"].\n * Handles: simple types, enums, anyOf, arrays, and objects.\n * Unknown schema structures (e.g., `$ref`) are returned unchanged.\n */\nfunction makeNullable(schema: JsonSchema): JsonSchema {\n if (!schema || typeof schema !== \"object\") return schema;\n\n // Already nullable\n if (schema.type === \"null\") return schema;\n if (Array.isArray(schema.type) && schema.type.includes(\"null\")) return schema;\n\n // Enum: add null to enum values and type\n if (Array.isArray(schema.enum)) {\n const nextEnum = schema.enum.includes(null)\n ? schema.enum\n : [...schema.enum, null];\n const baseType = Array.isArray(schema.type)\n ? // eslint-disable-next-line @typescript-eslint/no-explicit-any\n schema.type.filter((t: any) => t !== \"null\")\n : typeof schema.type === \"string\"\n ? [schema.type]\n : [\"string\"]; // Default to string for enums\n const nextType = Array.from(new Set([...baseType, \"null\"]));\n return { ...schema, type: nextType, enum: nextEnum };\n }\n\n // anyOf: flatten to type array if possible, otherwise add null variant\n if (Array.isArray(schema.anyOf)) {\n const hasNull = schema.anyOf.some(\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n (s: any) =>\n s?.type === \"null\" ||\n (Array.isArray(s?.type) && s.type.includes(\"null\"))\n );\n if (hasNull) return schema;\n\n // Try to extract types from anyOf and use type array instead\n const types = schema.anyOf\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n .map((s: any) => s?.type)\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n .filter((t: any) => t && typeof t === \"string\");\n\n if (types.length === schema.anyOf.length) {\n // All variants are simple types - can convert to type array\n const { anyOf: _, ...rest } = schema;\n return { ...rest, type: [...types, \"null\"] };\n }\n\n // Complex anyOf - add null variant (but this should be avoided at root)\n return { ...schema, anyOf: [...schema.anyOf, { type: \"null\" }] };\n }\n\n // Simple type: convert to array with null\n if (typeof schema.type === \"string\") {\n return { ...schema, type: [schema.type, \"null\"] };\n }\n\n // Type array: add null\n if (Array.isArray(schema.type)) {\n return { ...schema, type: Array.from(new Set([...schema.type, \"null\"])) };\n }\n\n // Has properties but no type - it's an object\n if (schema.properties) {\n return { ...schema, type: [\"object\", \"null\"] };\n }\n\n // Unknown schema structure (e.g., $ref) - return unchanged\n return schema;\n}\n\n/**\n * Transform a JSON Schema to be Groq strict-mode compatible.\n *\n * Recursively:\n * 1. Sets additionalProperties: false on all objects\n * 2. Moves all properties to required array\n * 3. Makes previously optional properties nullable\n * 4. Throws if root schema has oneOf/not; extracts object variant from anyOf (Groq forbids these at top level)\n *\n * @param schema - The schema to transform\n * @param isRoot - Whether this is the root schema (default: true on first call)\n */\nexport function groqStrictifySchema(\n schema: JsonSchema,\n isRoot = true\n): JsonSchema {\n if (!schema || typeof schema !== \"object\") return schema;\n if (Array.isArray(schema))\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n return schema.map((s) => groqStrictifySchema(s, false)) as any;\n\n // Deep clone to avoid mutating original\n const result = { ...schema };\n\n // Recurse into compound schemas (NOT root after this)\n for (const k of [\"anyOf\", \"oneOf\", \"allOf\"]) {\n if (Array.isArray(result[k])) {\n result[k] = result[k].map((s: JsonSchema) =>\n groqStrictifySchema(s, false)\n );\n }\n }\n\n // Recurse into array items\n if (result.items) {\n result.items = groqStrictifySchema(result.items, false);\n }\n\n // Recurse into $defs (definitions)\n if (result.$defs) {\n result.$defs = { ...result.$defs };\n for (const key of Object.keys(result.$defs)) {\n result.$defs[key] = groqStrictifySchema(result.$defs[key], false);\n }\n }\n\n // Check if this is an object schema\n const isObject =\n result.type === \"object\" ||\n (Array.isArray(result.type) && result.type.includes(\"object\")) ||\n result.properties;\n\n if (isObject && result.properties && typeof result.properties === \"object\") {\n // Set additionalProperties: false for strict mode\n result.additionalProperties = false;\n\n const propKeys = Object.keys(result.properties);\n const prevRequired = new Set<string>(\n Array.isArray(result.required) ? result.required : []\n );\n\n // Clone and transform properties\n result.properties = { ...result.properties };\n\n for (const key of propKeys) {\n // Recursively strictify nested schemas\n result.properties[key] = groqStrictifySchema(\n result.properties[key],\n false\n );\n\n // If property was optional (not in required), make it nullable\n if (!prevRequired.has(key)) {\n result.properties[key] = makeNullable(result.properties[key]);\n }\n }\n\n // All properties must be required in strict mode\n result.required = propKeys;\n }\n\n // ROOT PROTECTION: Groq forbids anyOf/oneOf/enum/not at top level\n if (isRoot) {\n // If root has anyOf, extract the object variant\n if (Array.isArray(result.anyOf)) {\n const objectVariant = result.anyOf.find(\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n (s: any) => s?.type === \"object\" || s?.properties\n );\n if (objectVariant) {\n // Replace root with the object variant\n const cleaned = groqStrictifySchema(objectVariant, true);\n return { ...cleaned, type: \"object\" };\n }\n // No object variant found - schema can't be used with Groq strict mode\n throw new Error(\n \"Groq strict mode requires the root schema to be an object type, \" +\n \"but the schema has anyOf/oneOf with no object variant.\"\n );\n }\n\n // Throw for oneOf/not at root (Groq forbids these)\n if (result.oneOf) {\n throw new Error(\n \"Groq strict mode does not support oneOf at the root schema level.\"\n );\n }\n if (result.not) {\n throw new Error(\n \"Groq strict mode does not support 'not' at the root schema level.\"\n );\n }\n\n if (result.properties) {\n result.type = \"object\";\n }\n }\n\n return result;\n}\n\n/**\n * Check if a model supports native JSON Schema structured output.\n * Uses prefix matching so new gpt-oss models are automatically supported.\n */\nfunction supportsJsonSchema(model: string): boolean {\n return model.startsWith(\"openai/gpt-oss\");\n}\n\n/**\n * Supported structured output methods for Groq.\n */\nexport const SUPPORTED_STRUCTURED_OUTPUT_METHODS = [\n \"jsonSchema\",\n \"functionCalling\",\n \"jsonMode\",\n] as const;\n\nexport type GroqStructuredOutputMethod =\n (typeof SUPPORTED_STRUCTURED_OUTPUT_METHODS)[number];\n\n/**\n * Get the structured output method for a given Groq model.\n *\n * - Returns `jsonSchema` if the model supports native JSON Schema and no method is specified\n * - Throws if an invalid method is provided\n * - Throws if `jsonSchema` is requested for a model that doesn't support it\n *\n * @param model - The model name\n * @param method - Optional method override\n * @returns The structured output method to use\n */\nexport function getGroqStructuredOutputMethod(\n model: string,\n method?: string\n): GroqStructuredOutputMethod {\n // Validate method if provided\n if (\n method !== undefined &&\n !SUPPORTED_STRUCTURED_OUTPUT_METHODS.includes(\n method as GroqStructuredOutputMethod\n )\n ) {\n throw new Error(\n `Invalid structured output method: ${method}. 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| {"version":3,"file":"groq-schema.js","names":[],"sources":["../../src/utils/groq-schema.ts"],"sourcesContent":["/**\n * Groq JSON Schema Strict Mode Transformation\n *\n * Groq's strict JSON Schema mode has specific requirements:\n * 1. additionalProperties: false on all objects\n * 2. ALL properties must be in the `required` array\n * 3. Optional properties must be nullable (type union with \"null\")\n * 4. Root schema cannot have anyOf/oneOf/enum/not\n *\n * @see https://console.groq.com/docs/structured-outputs\n */\n\n// oxlint-disable-next-line @typescript-eslint/no-explicit-any\ntype JsonSchema = Record<string, any>;\n\n/**\n * Make a schema nullable using type array syntax (NOT anyOf).\n * Groq strict mode forbids anyOf at top level, so we use type: [T, \"null\"].\n * Handles: simple types, enums, anyOf, arrays, and objects.\n * Unknown schema structures (e.g., `$ref`) are returned unchanged.\n */\nfunction makeNullable(schema: JsonSchema): JsonSchema {\n if (!schema || typeof schema !== \"object\") return schema;\n\n // Already nullable\n if (schema.type === \"null\") return schema;\n if (Array.isArray(schema.type) && schema.type.includes(\"null\")) return schema;\n\n // Enum: add null to enum values and type\n if (Array.isArray(schema.enum)) {\n const nextEnum = schema.enum.includes(null)\n ? schema.enum\n : [...schema.enum, null];\n const baseType = Array.isArray(schema.type)\n ? // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n schema.type.filter((t: any) => t !== \"null\")\n : typeof schema.type === \"string\"\n ? [schema.type]\n : [\"string\"]; // Default to string for enums\n const nextType = Array.from(new Set([...baseType, \"null\"]));\n return { ...schema, type: nextType, enum: nextEnum };\n }\n\n // anyOf: flatten to type array if possible, otherwise add null variant\n if (Array.isArray(schema.anyOf)) {\n const hasNull = schema.anyOf.some(\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n (s: any) =>\n s?.type === \"null\" ||\n (Array.isArray(s?.type) && s.type.includes(\"null\"))\n );\n if (hasNull) return schema;\n\n // Try to extract types from anyOf and use type array instead\n const types = schema.anyOf\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n .map((s: any) => s?.type)\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n .filter((t: any) => t && typeof t === \"string\");\n\n if (types.length === schema.anyOf.length) {\n // All variants are simple types - can convert to type array\n const { anyOf: _, ...rest } = schema;\n return { ...rest, type: [...types, \"null\"] };\n }\n\n // Complex anyOf - add null variant (but this should be avoided at root)\n return { ...schema, anyOf: [...schema.anyOf, { type: \"null\" }] };\n }\n\n // Simple type: convert to array with null\n if (typeof schema.type === \"string\") {\n return { ...schema, type: [schema.type, \"null\"] };\n }\n\n // Type array: add null\n if (Array.isArray(schema.type)) {\n return { ...schema, type: Array.from(new Set([...schema.type, \"null\"])) };\n }\n\n // Has properties but no type - it's an object\n if (schema.properties) {\n return { ...schema, type: [\"object\", \"null\"] };\n }\n\n // Unknown schema structure (e.g., $ref) - return unchanged\n return schema;\n}\n\n/**\n * Transform a JSON Schema to be Groq strict-mode compatible.\n *\n * Recursively:\n * 1. Sets additionalProperties: false on all objects\n * 2. Moves all properties to required array\n * 3. Makes previously optional properties nullable\n * 4. Throws if root schema has oneOf/not; extracts object variant from anyOf (Groq forbids these at top level)\n *\n * @param schema - The schema to transform\n * @param isRoot - Whether this is the root schema (default: true on first call)\n */\nexport function groqStrictifySchema(\n schema: JsonSchema,\n isRoot = true\n): JsonSchema {\n if (!schema || typeof schema !== \"object\") return schema;\n if (Array.isArray(schema))\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n return schema.map((s) => groqStrictifySchema(s, false)) as any;\n\n // Deep clone to avoid mutating original\n const result = { ...schema };\n\n // Recurse into compound schemas (NOT root after this)\n for (const k of [\"anyOf\", \"oneOf\", \"allOf\"]) {\n if (Array.isArray(result[k])) {\n result[k] = result[k].map((s: JsonSchema) =>\n groqStrictifySchema(s, false)\n );\n }\n }\n\n // Recurse into array items\n if (result.items) {\n result.items = groqStrictifySchema(result.items, false);\n }\n\n // Recurse into $defs (definitions)\n if (result.$defs) {\n result.$defs = { ...result.$defs };\n for (const key of Object.keys(result.$defs)) {\n result.$defs[key] = groqStrictifySchema(result.$defs[key], false);\n }\n }\n\n // Check if this is an object schema\n const isObject =\n result.type === \"object\" ||\n (Array.isArray(result.type) && result.type.includes(\"object\")) ||\n result.properties;\n\n if (isObject && result.properties && typeof result.properties === \"object\") {\n // Set additionalProperties: false for strict mode\n result.additionalProperties = false;\n\n const propKeys = Object.keys(result.properties);\n const prevRequired = new Set<string>(\n Array.isArray(result.required) ? result.required : []\n );\n\n // Clone and transform properties\n result.properties = { ...result.properties };\n\n for (const key of propKeys) {\n // Recursively strictify nested schemas\n result.properties[key] = groqStrictifySchema(\n result.properties[key],\n false\n );\n\n // If property was optional (not in required), make it nullable\n if (!prevRequired.has(key)) {\n result.properties[key] = makeNullable(result.properties[key]);\n }\n }\n\n // All properties must be required in strict mode\n result.required = propKeys;\n }\n\n // ROOT PROTECTION: Groq forbids anyOf/oneOf/enum/not at top level\n if (isRoot) {\n // If root has anyOf, extract the object variant\n if (Array.isArray(result.anyOf)) {\n const objectVariant = result.anyOf.find(\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n (s: any) => s?.type === \"object\" || s?.properties\n );\n if (objectVariant) {\n // Replace root with the object variant\n const cleaned = groqStrictifySchema(objectVariant, true);\n return { ...cleaned, type: \"object\" };\n }\n // No object variant found - schema can't be used with Groq strict mode\n throw new Error(\n \"Groq strict mode requires the root schema to be an object type, \" +\n \"but the schema has anyOf/oneOf with no object variant.\"\n );\n }\n\n // Throw for oneOf/not at root (Groq forbids these)\n if (result.oneOf) {\n throw new Error(\n \"Groq strict mode does not support oneOf at the root schema level.\"\n );\n }\n if (result.not) {\n throw new Error(\n \"Groq strict mode does not support 'not' at the root schema level.\"\n );\n }\n\n if (result.properties) {\n result.type = \"object\";\n }\n }\n\n return result;\n}\n\n/**\n * Check if a model supports native JSON Schema structured output.\n * Uses prefix matching so new gpt-oss models are automatically supported.\n */\nfunction supportsJsonSchema(model: string): boolean {\n return model.startsWith(\"openai/gpt-oss\");\n}\n\n/**\n * Supported structured output methods for Groq.\n */\nexport const SUPPORTED_STRUCTURED_OUTPUT_METHODS = [\n \"jsonSchema\",\n \"functionCalling\",\n \"jsonMode\",\n] as const;\n\nexport type GroqStructuredOutputMethod =\n (typeof SUPPORTED_STRUCTURED_OUTPUT_METHODS)[number];\n\n/**\n * Get the structured output method for a given Groq model.\n *\n * - Returns `jsonSchema` if the model supports native JSON Schema and no method is specified\n * - Throws if an invalid method is provided\n * - Throws if `jsonSchema` is requested for a model that doesn't support it\n *\n * @param model - The model name\n * @param method - Optional method override\n * @returns The structured output method to use\n */\nexport function getGroqStructuredOutputMethod(\n model: string,\n method?: string\n): GroqStructuredOutputMethod {\n // Validate method if provided\n if (\n method !== undefined &&\n !SUPPORTED_STRUCTURED_OUTPUT_METHODS.includes(\n method as GroqStructuredOutputMethod\n )\n ) {\n throw new Error(\n `Invalid structured output method: ${method}. Supported methods are: ${SUPPORTED_STRUCTURED_OUTPUT_METHODS.join(\", \")}`\n );\n }\n\n const modelSupportsJsonSchema = supportsJsonSchema(model);\n\n // If model supports JSON Schema and no method specified, use it by default\n if (modelSupportsJsonSchema && !method) {\n return \"jsonSchema\";\n }\n\n // If jsonSchema requested but not supported, throw\n if (!modelSupportsJsonSchema && method === \"jsonSchema\") {\n throw new Error(\n `Native JSON Schema structured output is not supported for model \"${model}\". ` +\n `Only models with the \"openai/gpt-oss\" prefix are supported. ` +\n `Use \"functionCalling\" or \"jsonMode\" instead.`\n );\n }\n\n // Return the specified method or default to functionCalling\n return (method as GroqStructuredOutputMethod) ?? \"functionCalling\";\n}\n"],"mappings":";;;;;;;AAqBA,SAAS,aAAa,QAAgC;AACpD,KAAI,CAAC,UAAU,OAAO,WAAW,SAAU,QAAO;AAGlD,KAAI,OAAO,SAAS,OAAQ,QAAO;AACnC,KAAI,MAAM,QAAQ,OAAO,KAAK,IAAI,OAAO,KAAK,SAAS,OAAO,CAAE,QAAO;AAGvE,KAAI,MAAM,QAAQ,OAAO,KAAK,EAAE;EAC9B,MAAM,WAAW,OAAO,KAAK,SAAS,KAAK,GACvC,OAAO,OACP,CAAC,GAAG,OAAO,MAAM,KAAK;EAC1B,MAAM,WAAW,MAAM,QAAQ,OAAO,KAAK,GAEvC,OAAO,KAAK,QAAQ,MAAW,MAAM,OAAO,GAC5C,OAAO,OAAO,SAAS,WACrB,CAAC,OAAO,KAAK,GACb,CAAC,SAAS;EAChB,MAAM,WAAW,MAAM,KAAK,IAAI,IAAI,CAAC,GAAG,UAAU,OAAO,CAAC,CAAC;AAC3D,SAAO;GAAE,GAAG;GAAQ,MAAM;GAAU,MAAM;GAAU;;AAItD,KAAI,MAAM,QAAQ,OAAO,MAAM,EAAE;AAO/B,MANgB,OAAO,MAAM,MAE1B,MACC,GAAG,SAAS,UACX,MAAM,QAAQ,GAAG,KAAK,IAAI,EAAE,KAAK,SAAS,OAAO,CACrD,CACY,QAAO;EAGpB,MAAM,QAAQ,OAAO,MAElB,KAAK,MAAW,GAAG,KAAK,CAExB,QAAQ,MAAW,KAAK,OAAO,MAAM,SAAS;AAEjD,MAAI,MAAM,WAAW,OAAO,MAAM,QAAQ;GAExC,MAAM,EAAE,OAAO,GAAG,GAAG,SAAS;AAC9B,UAAO;IAAE,GAAG;IAAM,MAAM,CAAC,GAAG,OAAO,OAAO;IAAE;;AAI9C,SAAO;GAAE,GAAG;GAAQ,OAAO,CAAC,GAAG,OAAO,OAAO,EAAE,MAAM,QAAQ,CAAC;GAAE;;AAIlE,KAAI,OAAO,OAAO,SAAS,SACzB,QAAO;EAAE,GAAG;EAAQ,MAAM,CAAC,OAAO,MAAM,OAAO;EAAE;AAInD,KAAI,MAAM,QAAQ,OAAO,KAAK,CAC5B,QAAO;EAAE,GAAG;EAAQ,MAAM,MAAM,KAAK,IAAI,IAAI,CAAC,GAAG,OAAO,MAAM,OAAO,CAAC,CAAC;EAAE;AAI3E,KAAI,OAAO,WACT,QAAO;EAAE,GAAG;EAAQ,MAAM,CAAC,UAAU,OAAO;EAAE;AAIhD,QAAO;;;;;;;;;;;;;;AAeT,SAAgB,oBACd,QACA,SAAS,MACG;AACZ,KAAI,CAAC,UAAU,OAAO,WAAW,SAAU,QAAO;AAClD,KAAI,MAAM,QAAQ,OAAO,CAEvB,QAAO,OAAO,KAAK,MAAM,oBAAoB,GAAG,MAAM,CAAC;CAGzD,MAAM,SAAS,EAAE,GAAG,QAAQ;AAG5B,MAAK,MAAM,KAAK;EAAC;EAAS;EAAS;EAAQ,CACzC,KAAI,MAAM,QAAQ,OAAO,GAAG,CAC1B,QAAO,KAAK,OAAO,GAAG,KAAK,MACzB,oBAAoB,GAAG,MAAM,CAC9B;AAKL,KAAI,OAAO,MACT,QAAO,QAAQ,oBAAoB,OAAO,OAAO,MAAM;AAIzD,KAAI,OAAO,OAAO;AAChB,SAAO,QAAQ,EAAE,GAAG,OAAO,OAAO;AAClC,OAAK,MAAM,OAAO,OAAO,KAAK,OAAO,MAAM,CACzC,QAAO,MAAM,OAAO,oBAAoB,OAAO,MAAM,MAAM,MAAM;;AAUrE,MAJE,OAAO,SAAS,YACf,MAAM,QAAQ,OAAO,KAAK,IAAI,OAAO,KAAK,SAAS,SAAS,IAC7D,OAAO,eAEO,OAAO,cAAc,OAAO,OAAO,eAAe,UAAU;AAE1E,SAAO,uBAAuB;EAE9B,MAAM,WAAW,OAAO,KAAK,OAAO,WAAW;EAC/C,MAAM,eAAe,IAAI,IACvB,MAAM,QAAQ,OAAO,SAAS,GAAG,OAAO,WAAW,EAAE,CACtD;AAGD,SAAO,aAAa,EAAE,GAAG,OAAO,YAAY;AAE5C,OAAK,MAAM,OAAO,UAAU;AAE1B,UAAO,WAAW,OAAO,oBACvB,OAAO,WAAW,MAClB,MACD;AAGD,OAAI,CAAC,aAAa,IAAI,IAAI,CACxB,QAAO,WAAW,OAAO,aAAa,OAAO,WAAW,KAAK;;AAKjE,SAAO,WAAW;;AAIpB,KAAI,QAAQ;AAEV,MAAI,MAAM,QAAQ,OAAO,MAAM,EAAE;GAC/B,MAAM,gBAAgB,OAAO,MAAM,MAEhC,MAAW,GAAG,SAAS,YAAY,GAAG,WACxC;AACD,OAAI,cAGF,QAAO;IAAE,GADO,oBAAoB,eAAe,KAAK;IACnC,MAAM;IAAU;AAGvC,SAAM,IAAI,MACR,yHAED;;AAIH,MAAI,OAAO,MACT,OAAM,IAAI,MACR,oEACD;AAEH,MAAI,OAAO,IACT,OAAM,IAAI,MACR,oEACD;AAGH,MAAI,OAAO,WACT,QAAO,OAAO;;AAIlB,QAAO;;;;;;AAOT,SAAS,mBAAmB,OAAwB;AAClD,QAAO,MAAM,WAAW,iBAAiB;;;;;AAM3C,MAAa,sCAAsC;CACjD;CACA;CACA;CACD;;;;;;;;;;;;AAgBD,SAAgB,8BACd,OACA,QAC4B;AAE5B,KACE,WAAW,KAAA,KACX,CAAC,oCAAoC,SACnC,OACD,CAED,OAAM,IAAI,MACR,qCAAqC,OAAO,2BAA2B,oCAAoC,KAAK,KAAK,GACtH;CAGH,MAAM,0BAA0B,mBAAmB,MAAM;AAGzD,KAAI,2BAA2B,CAAC,OAC9B,QAAO;AAIT,KAAI,CAAC,2BAA2B,WAAW,aACzC,OAAM,IAAI,MACR,oEAAoE,MAAM,6GAG3E;AAIH,QAAQ,UAAyC"} |
+6
-15
| { | ||
| "name": "@langchain/groq", | ||
| "version": "1.1.5", | ||
| "version": "1.2.0", | ||
| "description": "Groq integration for LangChain.js", | ||
@@ -17,3 +17,3 @@ "author": "LangChain", | ||
| "dependencies": { | ||
| "groq-sdk": "^0.37.0" | ||
| "groq-sdk": "^1.1.2" | ||
| }, | ||
@@ -27,12 +27,9 @@ "peerDependencies": { | ||
| "@vitest/coverage-v8": "^3.2.4", | ||
| "dotenv": "^16.3.1", | ||
| "dotenv": "^17.4.0", | ||
| "dpdm": "^3.14.0", | ||
| "eslint": "^9.34.0", | ||
| "prettier": "^3.5.0", | ||
| "typescript": "~5.8.3", | ||
| "vitest": "^3.2.4", | ||
| "vitest": "^4.1.2", | ||
| "zod": "^4.0.14", | ||
| "@langchain/core": "^1.1.32", | ||
| "@langchain/eslint": "0.1.1", | ||
| "@langchain/openai": "^1.2.13", | ||
| "@langchain/core": "^1.1.39", | ||
| "@langchain/openai": "^1.4.2", | ||
| "@langchain/standard-tests": "0.0.23", | ||
@@ -70,6 +67,2 @@ "@langchain/tsconfig": "0.0.1" | ||
| "build:compile": "tsdown", | ||
| "lint:eslint": "eslint --cache src/", | ||
| "lint:dpdm": "dpdm --skip-dynamic-imports circular --exit-code circular:1 --no-warning --no-tree src/*.ts src/**/*.ts", | ||
| "lint": "pnpm lint:eslint && pnpm lint:dpdm", | ||
| "lint:fix": "pnpm lint:eslint --fix && pnpm lint:dpdm", | ||
| "clean": "rm -rf .turbo dist/", | ||
@@ -82,4 +75,2 @@ "test": "vitest run", | ||
| "test:standard": "pnpm test:standard:unit && pnpm test:standard:int", | ||
| "format": "prettier --write \"src\"", | ||
| "format:check": "prettier --check \"src\"", | ||
| "typegen": "pnpm run typegen:profiles", | ||
@@ -86,0 +77,0 @@ "typegen:profiles": "pnpm --filter @langchain/model-profiles make --config profiles.toml" |
No alert changes
Improved metrics
- Total package byte prevSize
291544
0.06%- Dev dependency count
12
-20%Worsened metrics
- Lines of code
3173
-0.09%Dependency changes
+ Added
groq-sdk@1.4.1(transitive)- Removed
@types/node@18.19.130(transitive)- Removed
@types/node-fetch@2.6.13(transitive)- Removed
abort-controller@3.0.0(transitive)- Removed
agentkeepalive@4.6.0(transitive)- Removed
async-function@1.0.0(transitive)- Removed
async-generator-function@1.0.0(transitive)- Removed
asynckit@0.4.0(transitive)- Removed
call-bind-apply-helpers@1.0.2(transitive)- Removed
combined-stream@1.0.8(transitive)- Removed
delayed-stream@1.0.0(transitive)- Removed
dunder-proto@1.0.1(transitive)- Removed
es-define-property@1.0.1(transitive)- Removed
es-errors@1.3.0(transitive)- Removed
es-object-atoms@1.1.2(transitive)- Removed
es-set-tostringtag@2.1.0(transitive)- Removed
event-target-shim@5.0.1(transitive)- Removed
form-data@4.0.6(transitive)- Removed
form-data-encoder@1.7.2(transitive)- Removed
formdata-node@4.4.1(transitive)- Removed
function-bind@1.1.2(transitive)- Removed
generator-function@2.0.1(transitive)- Removed
get-intrinsic@1.3.1(transitive)- Removed
get-proto@1.0.1(transitive)- Removed
gopd@1.2.0(transitive)- Removed
groq-sdk@0.37.0(transitive)- Removed
has-symbols@1.1.0(transitive)- Removed
has-tostringtag@1.0.2(transitive)- Removed
hasown@2.0.4(transitive)- Removed
humanize-ms@1.2.1(transitive)- Removed
math-intrinsics@1.1.0(transitive)- Removed
mime-db@1.52.0(transitive)- Removed
mime-types@2.1.35(transitive)- Removed
ms@2.1.3(transitive)- Removed
node-domexception@1.0.0(transitive)- Removed
node-fetch@2.7.0(transitive)- Removed
tr46@0.0.3(transitive)- Removed
undici-types@5.26.5(transitive)- Removed
web-streams-polyfill@4.0.0-beta.3(transitive)- Removed
webidl-conversions@3.0.1(transitive)- Removed
whatwg-url@5.0.0(transitive)Updated
groq-sdk@^1.1.2