{"version":3,"file":"index.cjs","names":["ChatOpenAI","AZURE_ALIASES","AZURE_SECRETS","AZURE_SERIALIZABLE_KEYS","getAzureChatOpenAIParams","AzureChatOpenAICompletions","AzureChatOpenAIResponses","_serializeAzureChat"],"sources":["../../../src/azure/chat_models/index.ts"],"sourcesContent":["import { StructuredOutputMethodOptions } from \"@langchain/core/language_models/base\";\nimport type { Serialized } from \"@langchain/core/load/serializable\";\nimport { LangSmithParams } from \"@langchain/core/language_models/chat_models\";\nimport { ChatOpenAI, ChatOpenAICallOptions } from \"../../chat_models/index.js\";\nimport { AzureOpenAIChatInput } from \"../../types.js\";\nimport {\n _constructAzureFields,\n _serializeAzureChat,\n AZURE_ALIASES,\n AZURE_SECRETS,\n AZURE_SERIALIZABLE_KEYS,\n AzureChatOpenAIFields,\n getAzureChatOpenAIParams,\n} from \"./common.js\";\nimport { AzureChatOpenAICompletions } from \"./completions.js\";\nimport { AzureChatOpenAIResponses } from \"./responses.js\";\n\n/**\n * Azure OpenAI chat model integration.\n *\n * Setup:\n * Install `@langchain/openai` and set the following environment variables:\n *\n * ```bash\n * npm install @langchain/openai\n * export AZURE_OPENAI_API_KEY=\"your-api-key\"\n * export AZURE_OPENAI_API_DEPLOYMENT_NAME=\"your-deployment-name\"\n * export AZURE_OPENAI_API_VERSION=\"your-version\"\n * export AZURE_OPENAI_BASE_PATH=\"your-base-path\"\n * ```\n *\n * ## [Constructor args](https://api.js.langchain.com/classes/langchain_openai.AzureChatOpenAI.html#constructor)\n *\n * ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_openai.ChatOpenAICallOptions.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 * tools: [...],\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 * \n * Instantiate
\n *\n * ```typescript\n * import { AzureChatOpenAI } from '@langchain/openai';\n *\n * const llm = new AzureChatOpenAI({\n * azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY, // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY\n * azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME\n * azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME\n * azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION\n * temperature: 0,\n * maxTokens: undefined,\n * timeout: undefined,\n * maxRetries: 2,\n * // apiKey: \"...\",\n * // baseUrl: \"...\",\n * // other params...\n * });\n * ```\n * \n *\n *
\n *\n * \n * Invoking
\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 * \"id\": \"chatcmpl-9u4Mpu44CbPjwYFkTbeoZgvzB00Tz\",\n * \"content\": \"J'adore la programmation.\",\n * \"response_metadata\": {\n * \"tokenUsage\": {\n * \"completionTokens\": 5,\n * \"promptTokens\": 28,\n * \"totalTokens\": 33\n * },\n * \"finish_reason\": \"stop\",\n * \"system_fingerprint\": \"fp_3aa7262c27\"\n * },\n * \"usage_metadata\": {\n * \"input_tokens\": 28,\n * \"output_tokens\": 5,\n * \"total_tokens\": 33\n * }\n * }\n * ```\n * \n *\n *
\n *\n * \n * Streaming Chunks
\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 * \"id\": \"chatcmpl-9u4NWB7yUeHCKdLr6jP3HpaOYHTqs\",\n * \"content\": \"\"\n * }\n * AIMessageChunk {\n * \"content\": \"J\"\n * }\n * AIMessageChunk {\n * \"content\": \"'adore\"\n * }\n * AIMessageChunk {\n * \"content\": \" la\"\n * }\n * AIMessageChunk {\n * \"content\": \" programmation\",,\n * }\n * AIMessageChunk {\n * \"content\": \".\",,\n * }\n * AIMessageChunk {\n * \"content\": \"\",\n * \"response_metadata\": {\n * \"finish_reason\": \"stop\",\n * \"system_fingerprint\": \"fp_c9aa9c0491\"\n * },\n * }\n * AIMessageChunk {\n * \"content\": \"\",\n * \"usage_metadata\": {\n * \"input_tokens\": 28,\n * \"output_tokens\": 5,\n * \"total_tokens\": 33\n * }\n * }\n * ```\n * \n *\n *
\n *\n * \n * Aggregate Streamed Chunks
\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 * \"id\": \"chatcmpl-9u4PnX6Fy7OmK46DASy0bH6cxn5Xu\",\n * \"content\": \"J'adore la programmation.\",\n * \"response_metadata\": {\n * \"prompt\": 0,\n * \"completion\": 0,\n * \"finish_reason\": \"stop\",\n * },\n * \"usage_metadata\": {\n * \"input_tokens\": 28,\n * \"output_tokens\": 5,\n * \"total_tokens\": 33\n * }\n * }\n * ```\n * \n *\n *
\n *\n * \n * Bind tools
\n *\n * ```typescript\n * import { z } from 'zod';\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 = llm.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_uPU4FiFzoKAtMxfmPnfQL6UK'\n * },\n * {\n * name: 'GetWeather',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_UNkEwuQsHrGYqgDQuH9nPAtX'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'call_kL3OXxaq9OjIKqRTpvjaCH14'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'call_s9KQB1UWj45LLGaEnjz0179q'\n * }\n * ]\n * ```\n * \n *\n *
\n *\n * \n * Structured Output
\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().nullable().describe(\"How funny the joke is, from 1 to 10\")\n * }).describe('Joke to tell user.');\n *\n * const structuredLlm = llm.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 was the cat sitting on the computer?',\n * punchline: 'Because it wanted to keep an eye on the mouse!',\n * rating: 7\n * }\n * ```\n * \n *\n *
\n *\n * \n * JSON Object Response Format
\n *\n * ```typescript\n * const jsonLlm = llm.withConfig({ response_format: { type: \"json_object\" } });\n * const jsonLlmAiMsg = await jsonLlm.invoke(\n * \"Return a JSON object with key 'randomInts' and a value of 10 random ints in [0-99]\"\n * );\n * console.log(jsonLlmAiMsg.content);\n * ```\n *\n * ```txt\n * {\n * \"randomInts\": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\n * }\n * ```\n * \n *\n *
\n *\n * \n * Multimodal
\n *\n * ```typescript\n * import { HumanMessage } from '@langchain/core/messages';\n *\n * const imageUrl = \"https://example.com/image.jpg\";\n * const imageData = await fetch(imageUrl).then(res => res.arrayBuffer());\n * const base64Image = Buffer.from(imageData).toString('base64');\n *\n * const message = new HumanMessage({\n * content: [\n * { type: \"text\", text: \"describe the weather in this image\" },\n * {\n * type: \"image_url\",\n * image_url: { url: `data:image/jpeg;base64,${base64Image}` },\n * },\n * ]\n * });\n *\n * const imageDescriptionAiMsg = await llm.invoke([message]);\n * console.log(imageDescriptionAiMsg.content);\n * ```\n *\n * ```txt\n * The weather in the image appears to be clear and sunny. The sky is mostly blue with a few scattered white clouds, indicating fair weather. The bright sunlight is casting shadows on the green, grassy hill, suggesting it is a pleasant day with good visibility. There are no signs of rain or stormy conditions.\n * ```\n * \n *\n *
\n *\n * \n * Usage Metadata
\n *\n * ```typescript\n * const aiMsgForMetadata = await llm.invoke(input);\n * console.log(aiMsgForMetadata.usage_metadata);\n * ```\n *\n * ```txt\n * { input_tokens: 28, output_tokens: 5, total_tokens: 33 }\n * ```\n * \n *\n *
\n *\n * \n * Logprobs
\n *\n * ```typescript\n * const logprobsLlm = new ChatOpenAI({ model: \"gpt-4o-mini\", logprobs: true });\n * const aiMsgForLogprobs = await logprobsLlm.invoke(input);\n * console.log(aiMsgForLogprobs.response_metadata.logprobs);\n * ```\n *\n * ```txt\n * {\n * content: [\n * {\n * token: 'J',\n * logprob: -0.000050616763,\n * bytes: [Array],\n * top_logprobs: []\n * },\n * {\n * token: \"'\",\n * logprob: -0.01868736,\n * bytes: [Array],\n * top_logprobs: []\n * },\n * {\n * token: 'ad',\n * logprob: -0.0000030545007,\n * bytes: [Array],\n * top_logprobs: []\n * },\n * { token: 'ore', logprob: 0, bytes: [Array], top_logprobs: [] },\n * {\n * token: ' la',\n * logprob: -0.515404,\n * bytes: [Array],\n * top_logprobs: []\n * },\n * {\n * token: ' programm',\n * logprob: -0.0000118755715,\n * bytes: [Array],\n * top_logprobs: []\n * },\n * { token: 'ation', logprob: 0, bytes: [Array], top_logprobs: [] },\n * {\n * token: '.',\n * logprob: -0.0000037697225,\n * bytes: [Array],\n * top_logprobs: []\n * }\n * ],\n * refusal: null\n * }\n * ```\n * \n *\n *
\n *\n * \n * Response Metadata
\n *\n * ```typescript\n * const aiMsgForResponseMetadata = await llm.invoke(input);\n * console.log(aiMsgForResponseMetadata.response_metadata);\n * ```\n *\n * ```txt\n * {\n * tokenUsage: { completionTokens: 5, promptTokens: 28, totalTokens: 33 },\n * finish_reason: 'stop',\n * system_fingerprint: 'fp_3aa7262c27'\n * }\n * ```\n * \n */\nexport class AzureChatOpenAI<\n CallOptions extends ChatOpenAICallOptions = ChatOpenAICallOptions,\n>\n extends ChatOpenAI\n implements Partial\n{\n azureOpenAIApiVersion?: string;\n\n azureOpenAIApiKey?: string;\n\n azureADTokenProvider?: () => Promise;\n\n azureOpenAIApiInstanceName?: string;\n\n azureOpenAIApiDeploymentName?: string;\n\n azureOpenAIBasePath?: string;\n\n azureOpenAIEndpoint?: string;\n\n _llmType(): string {\n return \"azure_openai\";\n }\n\n get lc_aliases(): Record {\n return {\n ...super.lc_aliases,\n ...AZURE_ALIASES,\n };\n }\n\n get lc_secrets(): { [key: string]: string } | undefined {\n return {\n ...super.lc_secrets,\n ...AZURE_SECRETS,\n };\n }\n\n get lc_serializable_keys(): string[] {\n return [...super.lc_serializable_keys, ...AZURE_SERIALIZABLE_KEYS];\n }\n\n getLsParams(options: this[\"ParsedCallOptions\"]): LangSmithParams {\n const params = super.getLsParams(options);\n params.ls_provider = \"azure\";\n return params;\n }\n\n constructor(\n deploymentName: string,\n fields?: Omit<\n AzureChatOpenAIFields,\n \"deploymentName\" | \"azureOpenAIApiDeploymentName\" | \"model\"\n >\n );\n constructor(fields?: AzureChatOpenAIFields);\n constructor(\n deploymentOrFields?: string | AzureChatOpenAIFields,\n fieldsArg?: Omit<\n AzureChatOpenAIFields,\n \"deploymentName\" | \"azureOpenAIApiDeploymentName\" | \"model\"\n >\n ) {\n const fields = getAzureChatOpenAIParams(deploymentOrFields, fieldsArg);\n super({\n ...fields,\n completions: new AzureChatOpenAICompletions(fields),\n responses: new AzureChatOpenAIResponses(fields),\n });\n _constructAzureFields.call(this, fields);\n }\n\n /** @internal */\n override _getStructuredOutputMethod(\n config: StructuredOutputMethodOptions\n ) {\n const ensuredConfig = { ...config };\n // Not all Azure gpt-4o deployments models support jsonSchema yet\n if (this.model.startsWith(\"gpt-4o\")) {\n if (ensuredConfig?.method === undefined) {\n return \"functionCalling\";\n }\n }\n return super._getStructuredOutputMethod(ensuredConfig);\n }\n\n override toJSON(): Serialized {\n return _serializeAzureChat.call(this, super.toJSON());\n }\n}\n"],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AA6aA,IAAa,kBAAb,cAGUA,cAAAA,WAEV;CACE;CAEA;CAEA;CAEA;CAEA;CAEA;CAEA;CAEA,WAAmB;AACjB,SAAO;;CAGT,IAAI,aAAqC;AACvC,SAAO;GACL,GAAG,MAAM;GACT,GAAGC,eAAAA;GACJ;;CAGH,IAAI,aAAoD;AACtD,SAAO;GACL,GAAG,MAAM;GACT,GAAGC,eAAAA;GACJ;;CAGH,IAAI,uBAAiC;AACnC,SAAO,CAAC,GAAG,MAAM,sBAAsB,GAAGC,eAAAA,wBAAwB;;CAGpE,YAAY,SAAqD;EAC/D,MAAM,SAAS,MAAM,YAAY,QAAQ;AACzC,SAAO,cAAc;AACrB,SAAO;;CAWT,YACE,oBACA,WAIA;EACA,MAAM,SAASC,eAAAA,yBAAyB,oBAAoB,UAAU;AACtE,QAAM;GACJ,GAAG;GACH,aAAa,IAAIC,oBAAAA,2BAA2B,OAAO;GACnD,WAAW,IAAIC,kBAAAA,yBAAyB,OAAO;GAChD,CAAC;AACF,iBAAA,sBAAsB,KAAK,MAAM,OAAO;;;CAI1C,2BACE,QACA;EACA,MAAM,gBAAgB,EAAE,GAAG,QAAQ;AAEnC,MAAI,KAAK,MAAM,WAAW,SAAS;OAC7B,eAAe,WAAW,KAAA,EAC5B,QAAO;;AAGX,SAAO,MAAM,2BAA2B,cAAc;;CAGxD,SAA8B;AAC5B,SAAOC,eAAAA,oBAAoB,KAAK,MAAM,MAAM,QAAQ,CAAC"}