@langchain/openai

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OpenAI integrations for LangChain.js

github.com/langchain-ai/langchainjs/tree/main/libs/providers/langchain-openai/

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{"version":3,"file":"index.d.ts","names":["StructuredOutputMethodOptions","Serialized","LangSmithParams","ChatOpenAI","ChatOpenAICallOptions","AzureOpenAIChatInput","AzureChatOpenAIFields","AzureChatOpenAI","CallOptions","Promise","Record","Partial"],"sources":["../../../src/azure/chat_models/index.d.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 { AzureChatOpenAIFields } from \"./common.js\";\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 * <details open>\n * <summary>Instantiate</summary>\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 * </details>\n *\n * \n *\n * <details>\n * <summary>Invoking</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 * \"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 * </details>\n *\n * \n *\n * <details>\n * <summary>Streaming Chunks</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 * \"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 * </details>\n *\n * \n *\n * <details>\n * <summary>Aggregate Streamed Chunks</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 * \"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 * </details>\n *\n * \n *\n * <details>\n * <summary>Bind tools</summary>\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 * </details>\n *\n * \n *\n * <details>\n * <summary>Structured Output</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().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 * </details>\n *\n * \n *\n * <details>\n * <summary>JSON Object Response Format</summary>\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 * </details>\n *\n * \n *\n * <details>\n * <summary>Multimodal</summary>\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 * </details>\n *\n * \n *\n * <details>\n * <summary>Usage Metadata</summary>\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 * </details>\n *\n * \n *\n * <details>\n * <summary>Logprobs</summary>\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 * </details>\n *\n * \n *\n * <details>\n * <summary>Response Metadata</summary>\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 * </details>\n */\nexport declare class AzureChatOpenAI<CallOptions extends ChatOpenAICallOptions = ChatOpenAICallOptions> extends ChatOpenAI<CallOptions> implements Partial<AzureOpenAIChatInput> {\n azureOpenAIApiVersion?: string;\n azureOpenAIApiKey?: string;\n azureADTokenProvider?: () => Promise<string>;\n azureOpenAIApiInstanceName?: string;\n azureOpenAIApiDeploymentName?: string;\n azureOpenAIBasePath?: string;\n azureOpenAIEndpoint?: string;\n _llmType(): string;\n get lc_aliases(): Record<string, string>;\n get lc_secrets(): {\n [key: string]: string;\n } | undefined;\n get lc_serializable_keys(): string[];\n getLsParams(options: this[\"ParsedCallOptions\"]): LangSmithParams;\n constructor(fields?: AzureChatOpenAIFields);\n /** @internal */\n _getStructuredOutputMethod(config: StructuredOutputMethodOptions<boolean>): string | undefined;\n toJSON(): Serialized;\n}\n//# sourceMappingURL=index.d.ts.map"],"mappings":";;;;;;;;;;;AAkaA;;;;;;;;;;;;;AAA0J;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;cAArIO,oCAAoCH,wBAAwBA,+BAA+BD,WAAWK,wBAAwBG,QAAQN;;;+BAG1HI;;;;;;oBAMXC;;;;;mDAK+BR;uBAC5BI;;qCAEcN;YACzBC"}