{"version":3,"file":"index.cjs","names":["BaseBedrockChat"],"sources":["../../../src/chat_models/bedrock/index.ts"],"sourcesContent":["import {\n defaultProvider,\n DefaultProviderInit,\n} from \"@aws-sdk/credential-provider-node\";\n\nimport type { BaseChatModelParams } from \"@langchain/core/language_models/chat_models\";\n\nimport { BaseBedrockInput } from \"../../utils/bedrock/index.js\";\nimport { BedrockChat as BaseBedrockChat } from \"./web.js\";\n\nexport interface BedrockChatFields\n extends\n Partial,\n BaseChatModelParams,\n Partial {}\n\n/**\n * AWS Bedrock chat model integration.\n *\n * Setup:\n * Install `@langchain/community` and set the following environment variables:\n *\n * ```bash\n * npm install @langchain/openai\n * export AWS_REGION=\"your-aws-region\"\n * export AWS_SECRET_ACCESS_KEY=\"your-aws-secret-access-key\"\n * export AWS_ACCESS_KEY_ID=\"your-aws-access-key-id\"\n * ```\n *\n * ## [Constructor args](/classes/langchain_community_chat_models_bedrock.BedrockChat.html#constructor)\n *\n * ## [Runtime args](/interfaces/langchain_community_chat_models_bedrock_web.BedrockChatCallOptions.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 * stop: [\"stop on this token!\"],\n * }\n * );\n * ```\n *\n * ## Examples\n *\n *
\n * Instantiate\n *\n * ```typescript\n * import { BedrockChat } from '@langchain/community/chat_models/bedrock';\n *\n * const llm = new BedrockChat({\n * region: process.env.BEDROCK_AWS_REGION,\n * maxRetries: 0,\n * model: \"anthropic.claude-sonnet-4-5-20250929-v1:0\",\n * temperature: 0,\n * maxTokens: undefined,\n * // other params...\n * });\n *\n * // You can also pass credentials in explicitly:\n * const llmWithCredentials = new BedrockChat({\n * region: process.env.BEDROCK_AWS_REGION,\n * model: \"anthropic.claude-sonnet-4-5-20250929-v1:0\",\n * credentials: {\n * secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,\n * accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,\n * },\n * });\n * ```\n *
\n *\n *
\n *\n *
\n * Invoking\n *\n * ```typescript\n * const messages = [\n * {\n * type: \"system\" as const,\n * content: \"You are a helpful translator. Translate the user sentence to French.\",\n * },\n * {\n * type: \"human\" as const,\n * content: \"I love programming.\",\n * },\n * ];\n * const result = await llm.invoke(messages);\n * console.log(result);\n * ```\n *\n * ```txt\n * AIMessage {\n * \"content\": \"Here's the translation to French:\\n\\nJ'adore la programmation.\",\n * \"additional_kwargs\": {\n * \"id\": \"msg_bdrk_01HCZHa2mKbMZeTeHjLDd286\"\n * },\n * \"response_metadata\": {\n * \"type\": \"message\",\n * \"role\": \"assistant\",\n * \"model\": \"claude-sonnet-4-5-20250929\",\n * \"stop_reason\": \"end_turn\",\n * \"stop_sequence\": null,\n * \"usage\": {\n * \"input_tokens\": 25,\n * \"output_tokens\": 19\n * }\n * },\n * \"tool_calls\": [],\n * \"invalid_tool_calls\": []\n * }\n * ```\n *
\n *\n *
\n *\n *
\n * Streaming Chunks\n *\n * ```typescript\n * for await (const chunk of await llm.stream(messages)) {\n * console.log(chunk);\n * }\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {\n * \"id\": \"msg_bdrk_01RhFuGR9uJ2bj5GbdAma4y6\"\n * },\n * \"response_metadata\": {\n * \"type\": \"message\",\n * \"role\": \"assistant\",\n * \"model\": \"claude-sonnet-4-5-20250929\",\n * \"stop_reason\": null,\n * \"stop_sequence\": null\n * },\n * }\n * AIMessageChunk {\n * \"content\": \"J\",\n * }\n * AIMessageChunk {\n * \"content\": \"'adore la\",\n * }\n * AIMessageChunk {\n * \"content\": \" programmation.\",\n * }\n * AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {\n * \"stop_reason\": \"end_turn\",\n * \"stop_sequence\": null\n * },\n * }\n * AIMessageChunk {\n * \"content\": \"\",\n * \"response_metadata\": {\n * \"amazon-bedrock-invocationMetrics\": {\n * \"inputTokenCount\": 25,\n * \"outputTokenCount\": 11,\n * \"invocationLatency\": 659,\n * \"firstByteLatency\": 506\n * }\n * },\n * \"usage_metadata\": {\n * \"input_tokens\": 25,\n * \"output_tokens\": 11,\n * \"total_tokens\": 36\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(messages);\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\": \"J'adore la programmation.\",\n * \"additional_kwargs\": {\n * \"id\": \"msg_bdrk_017b6PuBybA51P5LZ9K6gZHm\",\n * \"stop_reason\": \"end_turn\",\n * \"stop_sequence\": null\n * },\n * \"response_metadata\": {\n * \"type\": \"message\",\n * \"role\": \"assistant\",\n * \"model\": \"claude-sonnet-4-5-20250929\",\n * \"stop_reason\": null,\n * \"stop_sequence\": null,\n * \"amazon-bedrock-invocationMetrics\": {\n * \"inputTokenCount\": 25,\n * \"outputTokenCount\": 11,\n * \"invocationLatency\": 1181,\n * \"firstByteLatency\": 1177\n * }\n * },\n * \"usage_metadata\": {\n * \"input_tokens\": 25,\n * \"output_tokens\": 11,\n * \"total_tokens\": 36\n * }\n * }\n * ```\n *
\n *\n *
\n *\n *
\n * Bind tools\n *\n * ```typescript\n * import { z } from 'zod';\n * import { AIMessage } from '@langchain/core/messages';\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: AIMessage = 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 * id: 'toolu_bdrk_01R2daqwHR931r4baVNzbe38',\n * type: 'tool_call'\n * },\n * {\n * name: 'GetWeather',\n * args: { location: 'New York, NY' },\n * id: 'toolu_bdrk_01WDadwNc7PGqVZvCN7Dr7eD',\n * type: 'tool_call'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'Los Angeles, CA' },\n * id: 'toolu_bdrk_014b8zLkpAgpxrPfewKinJFc',\n * type: 'tool_call'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'New York, NY' },\n * id: 'toolu_bdrk_01Tt8K2MUP15kNuMDFCLEFKN',\n * type: 'tool_call'\n * }\n * ]\n * ```\n *
\n *\n *
\n *\n *
\n * Structured Output\n *\n * ```typescript\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 = llm.withStructuredOutput(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 jungle?\",\n * punchline: 'Too many cheetahs!'\n * }\n * ```\n *
\n *\n *
\n *\n *
\n * Response Metadata\n *\n * ```typescript\n * const aiMsgForResponseMetadata = await llm.invoke(messages);\n * console.log(aiMsgForResponseMetadata.response_metadata);\n * ```\n *\n * ```txt\n * \"response_metadata\": {\n * \"type\": \"message\",\n * \"role\": \"assistant\",\n * \"model\": \"claude-sonnet-4-5-20250929\",\n * \"stop_reason\": \"end_turn\",\n * \"stop_sequence\": null,\n * \"usage\": {\n * \"input_tokens\": 25,\n * \"output_tokens\": 19\n * }\n * }\n * ```\n *
\n */\nexport class BedrockChat extends BaseBedrockChat {\n static lc_name() {\n return \"BedrockChat\";\n }\n\n constructor(fields?: BedrockChatFields) {\n const {\n profile,\n filepath,\n configFilepath,\n ignoreCache,\n mfaCodeProvider,\n roleAssumer,\n roleArn,\n webIdentityTokenFile,\n roleAssumerWithWebIdentity,\n ...rest\n } = fields ?? {};\n super({\n ...rest,\n credentials:\n rest?.credentials ??\n defaultProvider({\n profile,\n filepath,\n configFilepath,\n ignoreCache,\n mfaCodeProvider,\n roleAssumer,\n roleArn,\n webIdentityTokenFile,\n roleAssumerWithWebIdentity,\n }),\n });\n }\n}\n\nexport {\n convertMessagesToPromptAnthropic,\n convertMessagesToPrompt,\n} from \"./web.js\";\n"],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAuVA,IAAa,cAAb,cAAiCA,gCAAAA,YAAgB;CAC/C,OAAO,UAAU;AACf,SAAO;;CAGT,YAAY,QAA4B;EACtC,MAAM,EACJ,SACA,UACA,gBACA,aACA,iBACA,aACA,SACA,sBACA,4BACA,GAAG,SACD,UAAU,EAAE;AAChB,QAAM;GACJ,GAAG;GACH,aACE,MAAM,gBAAA,GAAA,kCAAA,iBACU;IACd;IACA;IACA;IACA;IACA;IACA;IACA;IACA;IACA;IACD,CAAC;GACL,CAAC"}