"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.ChatBedrock = exports.BedrockChat = exports.convertMessagesToPrompt = exports.convertMessagesToPromptAnthropic = void 0;
const signature_v4_1 = require("@smithy/signature-v4");
const protocol_http_1 = require("@smithy/protocol-http");
const eventstream_codec_1 = require("@smithy/eventstream-codec");
const util_utf8_1 = require("@smithy/util-utf8");
const sha256_js_1 = require("@aws-crypto/sha256-js");
const chat_models_1 = require("@langchain/core/language_models/chat_models");
const base_1 = require("@langchain/core/language_models/base");
const env_1 = require("@langchain/core/utils/env");
const messages_1 = require("@langchain/core/messages");
const outputs_1 = require("@langchain/core/outputs");
const function_calling_1 = require("@langchain/core/utils/function_calling");
const zod_to_json_schema_1 = require("zod-to-json-schema");
const index_js_1 = require("../../utils/bedrock/index.cjs");
const anthropic_js_1 = require("../../utils/bedrock/anthropic.cjs");
const AWS_REGIONS = [
    "us",
    "sa",
    "me",
    "il",
    "eu",
    "cn",
    "ca",
    "ap",
    "af",
    "us-gov",
];
const ALLOWED_MODEL_PROVIDERS = [
    "ai21",
    "anthropic",
    "amazon",
    "cohere",
    "meta",
    "mistral",
];
const PRELUDE_TOTAL_LENGTH_BYTES = 4;
function convertOneMessageToText(message, humanPrompt, aiPrompt) {
    if (message._getType() === "human") {
        return `${humanPrompt} ${message.content}`;
    }
    else if (message._getType() === "ai") {
        return `${aiPrompt} ${message.content}`;
    }
    else if (message._getType() === "system") {
        return `${humanPrompt} <admin>${message.content}</admin>`;
    }
    else if (message._getType() === "function") {
        return `${humanPrompt} ${message.content}`;
    }
    else if (messages_1.ChatMessage.isInstance(message)) {
        return `\n\n${message.role[0].toUpperCase() + message.role.slice(1)}: {message.content}`;
    }
    throw new Error(`Unknown role: ${message._getType()}`);
}
function convertMessagesToPromptAnthropic(messages, humanPrompt = "\n\nHuman:", aiPrompt = "\n\nAssistant:") {
    const messagesCopy = [...messages];
    if (messagesCopy.length === 0 ||
        messagesCopy[messagesCopy.length - 1]._getType() !== "ai") {
        messagesCopy.push(new messages_1.AIMessage({ content: "" }));
    }
    return messagesCopy
        .map((message) => convertOneMessageToText(message, humanPrompt, aiPrompt))
        .join("");
}
exports.convertMessagesToPromptAnthropic = convertMessagesToPromptAnthropic;
/**
 * Function that converts an array of messages into a single string prompt
 * that can be used as input for a chat model. It delegates the conversion
 * logic to the appropriate provider-specific function.
 * @param messages Array of messages to be converted.
 * @param options Options to be used during the conversion.
 * @returns A string prompt that can be used as input for a chat model.
 */
function convertMessagesToPrompt(messages, provider) {
    if (provider === "anthropic") {
        return convertMessagesToPromptAnthropic(messages);
    }
    throw new Error(`Provider ${provider} does not support chat.`);
}
exports.convertMessagesToPrompt = convertMessagesToPrompt;
function formatTools(tools) {
    if (!tools || !tools.length) {
        return [];
    }
    if (tools.every(function_calling_1.isLangChainTool)) {
        return tools.map((tc) => ({
            name: tc.name,
            description: tc.description,
            input_schema: (0, zod_to_json_schema_1.zodToJsonSchema)(tc.schema),
        }));
    }
    if (tools.every(base_1.isOpenAITool)) {
        return tools.map((tc) => ({
            name: tc.function.name,
            description: tc.function.description,
            input_schema: tc.function.parameters,
        }));
    }
    if (tools.every(anthropic_js_1.isAnthropicTool)) {
        return tools;
    }
    if (tools.some(function_calling_1.isStructuredTool) ||
        tools.some(base_1.isOpenAITool) ||
        tools.some(anthropic_js_1.isAnthropicTool)) {
        throw new Error("All tools passed to BedrockChat must be of the same type.");
    }
    throw new Error("Invalid tool format received.");
}
/**
 * AWS Bedrock chat model integration.
 *
 * Setup:
 * Install `@langchain/community` and set the following environment variables:
 *
 * ```bash
 * npm install @langchain/openai
 * export AWS_REGION="your-aws-region"
 * export AWS_SECRET_ACCESS_KEY="your-aws-secret-access-key"
 * export AWS_ACCESS_KEY_ID="your-aws-access-key-id"
 * ```
 *
 * ## [Constructor args](/classes/langchain_community_chat_models_bedrock.BedrockChat.html#constructor)
 *
 * ## [Runtime args](/interfaces/langchain_community_chat_models_bedrock_web.BedrockChatCallOptions.html)
 *
 * Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.
 * They can also be passed via `.bind`, or the second arg in `.bindTools`, like shown in the examples below:
 *
 * ```typescript
 * // When calling `.bind`, call options should be passed via the first argument
 * const llmWithArgsBound = llm.bind({
 *   stop: ["\n"],
 *   tools: [...],
 * });
 *
 * // When calling `.bindTools`, call options should be passed via the second argument
 * const llmWithTools = llm.bindTools(
 *   [...],
 *   {
 *     stop: ["stop on this token!"],
 *   }
 * );
 * ```
 *
 * ## Examples
 *
 * <details open>
 * <summary><strong>Instantiate</strong></summary>
 *
 * ```typescript
 * import { BedrockChat } from '@langchain/community/chat_models/bedrock/web';
 *
 * const llm = new BedrockChat({
 *   region: process.env.AWS_REGION,
 *   maxRetries: 0,
 *   model: "anthropic.claude-3-5-sonnet-20240620-v1:0",
 *   temperature: 0,
 *   maxTokens: undefined,
 *   // other params...
 * });
 *
 * // You can also pass credentials in explicitly:
 * const llmWithCredentials = new BedrockChat({
 *   region: process.env.BEDROCK_AWS_REGION,
 *   model: "anthropic.claude-3-5-sonnet-20240620-v1:0",
 *   credentials: {
 *     secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
 *     accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
 *   },
 * });
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Invoking</strong></summary>
 *
 * ```typescript
 * const input = `Translate "I love programming" into French.`;
 *
 * // Models also accept a list of chat messages or a formatted prompt
 * const result = await llm.invoke(input);
 * console.log(result);
 * ```
 *
 * ```txt
 * AIMessage {
 *   "content": "Here's the translation to French:\n\nJ'adore la programmation.",
 *   "additional_kwargs": {
 *     "id": "msg_bdrk_01HCZHa2mKbMZeTeHjLDd286"
 *   },
 *   "response_metadata": {
 *     "type": "message",
 *     "role": "assistant",
 *     "model": "claude-3-5-sonnet-20240620",
 *     "stop_reason": "end_turn",
 *     "stop_sequence": null,
 *     "usage": {
 *       "input_tokens": 25,
 *       "output_tokens": 19
 *     }
 *   },
 *   "tool_calls": [],
 *   "invalid_tool_calls": []
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Streaming Chunks</strong></summary>
 *
 * ```typescript
 * for await (const chunk of await llm.stream(input)) {
 *   console.log(chunk);
 * }
 * ```
 *
 * ```txt
 * AIMessageChunk {
 *   "content": "",
 *   "additional_kwargs": {
 *     "id": "msg_bdrk_01RhFuGR9uJ2bj5GbdAma4y6"
 *   },
 *   "response_metadata": {
 *     "type": "message",
 *     "role": "assistant",
 *     "model": "claude-3-5-sonnet-20240620",
 *     "stop_reason": null,
 *     "stop_sequence": null
 *   },
 * }
 * AIMessageChunk {
 *   "content": "J",
 * }
 * AIMessageChunk {
 *   "content": "'adore la",
 * }
 * AIMessageChunk {
 *   "content": " programmation.",
 * }
 * AIMessageChunk {
 *   "content": "",
 *   "additional_kwargs": {
 *     "stop_reason": "end_turn",
 *     "stop_sequence": null
 *   },
 * }
 * AIMessageChunk {
 *   "content": "",
 *   "response_metadata": {
 *     "amazon-bedrock-invocationMetrics": {
 *       "inputTokenCount": 25,
 *       "outputTokenCount": 11,
 *       "invocationLatency": 659,
 *       "firstByteLatency": 506
 *     }
 *   },
 *   "usage_metadata": {
 *     "input_tokens": 25,
 *     "output_tokens": 11,
 *     "total_tokens": 36
 *   }
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Aggregate Streamed Chunks</strong></summary>
 *
 * ```typescript
 * import { AIMessageChunk } from '@langchain/core/messages';
 * import { concat } from '@langchain/core/utils/stream';
 *
 * const stream = await llm.stream(input);
 * let full: AIMessageChunk | undefined;
 * for await (const chunk of stream) {
 *   full = !full ? chunk : concat(full, chunk);
 * }
 * console.log(full);
 * ```
 *
 * ```txt
 * AIMessageChunk {
 *   "content": "J'adore la programmation.",
 *   "additional_kwargs": {
 *     "id": "msg_bdrk_017b6PuBybA51P5LZ9K6gZHm",
 *     "stop_reason": "end_turn",
 *     "stop_sequence": null
 *   },
 *   "response_metadata": {
 *     "type": "message",
 *     "role": "assistant",
 *     "model": "claude-3-5-sonnet-20240620",
 *     "stop_reason": null,
 *     "stop_sequence": null,
 *     "amazon-bedrock-invocationMetrics": {
 *       "inputTokenCount": 25,
 *       "outputTokenCount": 11,
 *       "invocationLatency": 1181,
 *       "firstByteLatency": 1177
 *     }
 *   },
 *   "usage_metadata": {
 *     "input_tokens": 25,
 *     "output_tokens": 11,
 *     "total_tokens": 36
 *   }
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Bind tools</strong></summary>
 *
 * ```typescript
 * import { z } from 'zod';
 * import { AIMessage } from '@langchain/core/messages';
 *
 * const GetWeather = {
 *   name: "GetWeather",
 *   description: "Get the current weather in a given location",
 *   schema: z.object({
 *     location: z.string().describe("The city and state, e.g. San Francisco, CA")
 *   }),
 * }
 *
 * const GetPopulation = {
 *   name: "GetPopulation",
 *   description: "Get the current population in a given location",
 *   schema: z.object({
 *     location: z.string().describe("The city and state, e.g. San Francisco, CA")
 *   }),
 * }
 *
 * const llmWithTools = llm.bindTools([GetWeather, GetPopulation]);
 * const aiMsg: AIMessage = await llmWithTools.invoke(
 *   "Which city is hotter today and which is bigger: LA or NY?"
 * );
 * console.log(aiMsg.tool_calls);
 * ```
 *
 * ```txt
 * [
 *   {
 *     name: 'GetWeather',
 *     args: { location: 'Los Angeles, CA' },
 *     id: 'toolu_bdrk_01R2daqwHR931r4baVNzbe38',
 *     type: 'tool_call'
 *   },
 *   {
 *     name: 'GetWeather',
 *     args: { location: 'New York, NY' },
 *     id: 'toolu_bdrk_01WDadwNc7PGqVZvCN7Dr7eD',
 *     type: 'tool_call'
 *   },
 *   {
 *     name: 'GetPopulation',
 *     args: { location: 'Los Angeles, CA' },
 *     id: 'toolu_bdrk_014b8zLkpAgpxrPfewKinJFc',
 *     type: 'tool_call'
 *   },
 *   {
 *     name: 'GetPopulation',
 *     args: { location: 'New York, NY' },
 *     id: 'toolu_bdrk_01Tt8K2MUP15kNuMDFCLEFKN',
 *     type: 'tool_call'
 *   }
 * ]
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Structured Output</strong></summary>
 *
 * ```typescript
 * const Joke = z.object({
 *   setup: z.string().describe("The setup of the joke"),
 *   punchline: z.string().describe("The punchline to the joke"),
 *   rating: z.number().optional().describe("How funny the joke is, from 1 to 10")
 * }).describe('Joke to tell user.');
 *
 * const structuredLlm = llm.withStructuredOutput(Joke);
 * const jokeResult = await structuredLlm.invoke("Tell me a joke about cats");
 * console.log(jokeResult);
 * ```
 *
 * ```txt
 * {
 *   setup: "Why don't cats play poker in the jungle?",
 *   punchline: 'Too many cheetahs!'
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Response Metadata</strong></summary>
 *
 * ```typescript
 * const aiMsgForResponseMetadata = await llm.invoke(input);
 * console.log(aiMsgForResponseMetadata.response_metadata);
 * ```
 *
 * ```txt
 * "response_metadata": {
 *   "type": "message",
 *   "role": "assistant",
 *   "model": "claude-3-5-sonnet-20240620",
 *   "stop_reason": "end_turn",
 *   "stop_sequence": null,
 *   "usage": {
 *     "input_tokens": 25,
 *     "output_tokens": 19
 *   }
 * }
 * ```
 * </details>
 */
class BedrockChat extends chat_models_1.BaseChatModel {
    get lc_aliases() {
        return {
            model: "model_id",
            region: "region_name",
        };
    }
    get lc_secrets() {
        return {
            "credentials.accessKeyId": "AWS_ACCESS_KEY_ID",
            "credentials.secretAccessKey": "AWS_SECRET_ACCESS_KEY",
            "credentials.sessionToken": "AWS_SECRET_ACCESS_KEY",
            awsAccessKeyId: "AWS_ACCESS_KEY_ID",
            awsSecretAccessKey: "AWS_SECRET_ACCESS_KEY",
            awsSessionToken: "AWS_SESSION_TOKEN",
        };
    }
    get lc_attributes() {
        return { region: this.region };
    }
    _identifyingParams() {
        return {
            model: this.model,
        };
    }
    _llmType() {
        return "bedrock";
    }
    static lc_name() {
        return "BedrockChat";
    }
    constructor(fields) {
        const awsAccessKeyId = fields?.awsAccessKeyId ?? (0, env_1.getEnvironmentVariable)("AWS_ACCESS_KEY_ID");
        const awsSecretAccessKey = fields?.awsSecretAccessKey ??
            (0, env_1.getEnvironmentVariable)("AWS_SECRET_ACCESS_KEY");
        const awsSessionToken = fields?.awsSessionToken ?? (0, env_1.getEnvironmentVariable)("AWS_SESSION_TOKEN");
        let credentials = fields?.credentials;
        if (credentials === undefined) {
            if (awsAccessKeyId === undefined || awsSecretAccessKey === undefined) {
                throw new Error("Please set your AWS credentials in the 'credentials' field or set env vars AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, and optionally AWS_SESSION_TOKEN.");
            }
            credentials = {
                accessKeyId: awsAccessKeyId,
                secretAccessKey: awsSecretAccessKey,
                sessionToken: awsSessionToken,
            };
        }
        // eslint-disable-next-line no-param-reassign
        fields = { ...fields, awsAccessKeyId, awsSecretAccessKey, awsSessionToken };
        super(fields);
        Object.defineProperty(this, "model", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: "amazon.titan-tg1-large"
        });
        Object.defineProperty(this, "modelProvider", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "region", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "credentials", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "temperature", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: undefined
        });
        Object.defineProperty(this, "maxTokens", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: undefined
        });
        Object.defineProperty(this, "fetchFn", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "endpointHost", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        /** @deprecated Use as a call option using .bind() instead. */
        Object.defineProperty(this, "stopSequences", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "modelKwargs", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "codec", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: new eventstream_codec_1.EventStreamCodec(util_utf8_1.toUtf8, util_utf8_1.fromUtf8)
        });
        Object.defineProperty(this, "streaming", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: false
        });
        Object.defineProperty(this, "usesMessagesApi", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: false
        });
        Object.defineProperty(this, "lc_serializable", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: true
        });
        Object.defineProperty(this, "trace", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "guardrailIdentifier", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: ""
        });
        Object.defineProperty(this, "guardrailVersion", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: ""
        });
        Object.defineProperty(this, "guardrailConfig", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        this.model = fields?.model ?? this.model;
        this.modelProvider = getModelProvider(this.model);
        if (!ALLOWED_MODEL_PROVIDERS.includes(this.modelProvider)) {
            throw new Error(`Unknown model provider: '${this.modelProvider}', only these are supported: ${ALLOWED_MODEL_PROVIDERS}`);
        }
        const region = fields?.region ?? (0, env_1.getEnvironmentVariable)("AWS_DEFAULT_REGION");
        if (!region) {
            throw new Error("Please set the AWS_DEFAULT_REGION environment variable or pass it to the constructor as the region field.");
        }
        this.region = region;
        this.credentials = credentials;
        this.temperature = fields?.temperature ?? this.temperature;
        this.maxTokens = fields?.maxTokens ?? this.maxTokens;
        this.fetchFn = fields?.fetchFn ?? fetch.bind(globalThis);
        this.endpointHost = fields?.endpointHost ?? fields?.endpointUrl;
        this.stopSequences = fields?.stopSequences;
        this.modelKwargs = fields?.modelKwargs;
        this.streaming = fields?.streaming ?? this.streaming;
        this.usesMessagesApi = canUseMessagesApi(this.model);
        this.trace = fields?.trace ?? this.trace;
        this.guardrailVersion = fields?.guardrailVersion ?? this.guardrailVersion;
        this.guardrailIdentifier =
            fields?.guardrailIdentifier ?? this.guardrailIdentifier;
        this.guardrailConfig = fields?.guardrailConfig;
    }
    invocationParams(options) {
        if (options?.tool_choice) {
            throw new Error("'tool_choice' call option is not supported by BedrockChat.");
        }
        return {
            tools: options?.tools ? formatTools(options.tools) : undefined,
            temperature: this.temperature,
            max_tokens: this.maxTokens,
            stop: options?.stop ?? this.stopSequences,
            modelKwargs: this.modelKwargs,
            guardrailConfig: this.guardrailConfig,
        };
    }
    getLsParams(options) {
        const params = this.invocationParams(options);
        return {
            ls_provider: "bedrock",
            ls_model_name: this.model,
            ls_model_type: "chat",
            ls_temperature: params.temperature ?? undefined,
            ls_max_tokens: params.max_tokens ?? undefined,
            ls_stop: options.stop,
        };
    }
    async _generate(messages, options, runManager) {
        if (this.streaming) {
            const stream = this._streamResponseChunks(messages, options, runManager);
            let finalResult;
            for await (const chunk of stream) {
                if (finalResult === undefined) {
                    finalResult = chunk;
                }
                else {
                    finalResult = finalResult.concat(chunk);
                }
            }
            if (finalResult === undefined) {
                throw new Error("Could not parse final output from Bedrock streaming call.");
            }
            return {
                generations: [finalResult],
                llmOutput: finalResult.generationInfo,
            };
        }
        return this._generateNonStreaming(messages, options, runManager);
    }
    async _generateNonStreaming(messages, options, _runManager) {
        const service = "bedrock-runtime";
        const endpointHost = this.endpointHost ?? `${service}.${this.region}.amazonaws.com`;
        const provider = this.modelProvider;
        const response = await this._signedFetch(messages, options, {
            bedrockMethod: "invoke",
            endpointHost,
            provider,
        });
        const json = await response.json();
        if (!response.ok) {
            throw new Error(`Error ${response.status}: ${json.message ?? JSON.stringify(json)}`);
        }
        if (this.usesMessagesApi) {
            const outputGeneration = index_js_1.BedrockLLMInputOutputAdapter.prepareMessagesOutput(provider, json);
            if (outputGeneration === undefined) {
                throw new Error("Failed to parse output generation.");
            }
            return {
                generations: [outputGeneration],
                llmOutput: outputGeneration.generationInfo,
            };
        }
        else {
            const text = index_js_1.BedrockLLMInputOutputAdapter.prepareOutput(provider, json);
            return { generations: [{ text, message: new messages_1.AIMessage(text) }] };
        }
    }
    async _signedFetch(messages, options, fields) {
        const { bedrockMethod, endpointHost, provider } = fields;
        const { max_tokens, temperature, stop, modelKwargs, guardrailConfig, tools, } = this.invocationParams(options);
        const inputBody = this.usesMessagesApi
            ? index_js_1.BedrockLLMInputOutputAdapter.prepareMessagesInput(provider, messages, max_tokens, temperature, stop, modelKwargs, guardrailConfig, tools)
            : index_js_1.BedrockLLMInputOutputAdapter.prepareInput(provider, convertMessagesToPromptAnthropic(messages), max_tokens, temperature, stop, modelKwargs, fields.bedrockMethod, guardrailConfig);
        const url = new URL(`https://${endpointHost}/model/${this.model}/${bedrockMethod}`);
        const request = new protocol_http_1.HttpRequest({
            hostname: url.hostname,
            path: url.pathname,
            protocol: url.protocol,
            method: "POST",
            body: JSON.stringify(inputBody),
            query: Object.fromEntries(url.searchParams.entries()),
            headers: {
                // host is required by AWS Signature V4: https://docs.aws.amazon.com/general/latest/gr/sigv4-create-canonical-request.html
                host: url.host,
                accept: "application/json",
                "content-type": "application/json",
                ...(this.trace &&
                    this.guardrailIdentifier &&
                    this.guardrailVersion && {
                    "X-Amzn-Bedrock-Trace": this.trace,
                    "X-Amzn-Bedrock-GuardrailIdentifier": this.guardrailIdentifier,
                    "X-Amzn-Bedrock-GuardrailVersion": this.guardrailVersion,
                }),
            },
        });
        const signer = new signature_v4_1.SignatureV4({
            credentials: this.credentials,
            service: "bedrock",
            region: this.region,
            sha256: sha256_js_1.Sha256,
        });
        const signedRequest = await signer.sign(request);
        // Send request to AWS using the low-level fetch API
        const response = await this.caller.callWithOptions({ signal: options.signal }, async () => this.fetchFn(url, {
            headers: signedRequest.headers,
            body: signedRequest.body,
            method: signedRequest.method,
        }));
        return response;
    }
    async *_streamResponseChunks(messages, options, runManager) {
        const provider = this.modelProvider;
        const service = "bedrock-runtime";
        const endpointHost = this.endpointHost ?? `${service}.${this.region}.amazonaws.com`;
        const bedrockMethod = provider === "anthropic" ||
            provider === "cohere" ||
            provider === "meta" ||
            provider === "mistral"
            ? "invoke-with-response-stream"
            : "invoke";
        const response = await this._signedFetch(messages, options, {
            bedrockMethod,
            endpointHost,
            provider,
        });
        if (response.status < 200 || response.status >= 300) {
            throw Error(`Failed to access underlying url '${endpointHost}': got ${response.status} ${response.statusText}: ${await response.text()}`);
        }
        if (provider === "anthropic" ||
            provider === "cohere" ||
            provider === "meta" ||
            provider === "mistral") {
            const toolsInParams = (0, anthropic_js_1._toolsInParams)(options);
            const reader = response.body?.getReader();
            const decoder = new TextDecoder();
            for await (const chunk of this._readChunks(reader)) {
                const event = this.codec.decode(chunk);
                if ((event.headers[":event-type"] !== undefined &&
                    event.headers[":event-type"].value !== "chunk") ||
                    event.headers[":content-type"].value !== "application/json") {
                    throw Error(`Failed to get event chunk: got ${chunk}`);
                }
                const body = JSON.parse(decoder.decode(event.body));
                if (body.message) {
                    throw new Error(body.message);
                }
                if (body.bytes !== undefined) {
                    const chunkResult = JSON.parse(decoder.decode(Uint8Array.from(atob(body.bytes), (m) => m.codePointAt(0) ?? 0)));
                    if (this.usesMessagesApi) {
                        const chunk = index_js_1.BedrockLLMInputOutputAdapter.prepareMessagesOutput(provider, chunkResult, {
                            // Content should _ONLY_ be coerced if tools are not in params
                            // If they are, we need content to be of type MessageTypeComplex
                            // so the tools can be passed through.
                            coerceContentToString: !toolsInParams,
                        });
                        if (chunk === undefined) {
                            continue;
                        }
                        if (provider === "anthropic" &&
                            chunk.generationInfo?.usage !== undefined) {
                            // Avoid bad aggregation in chunks, rely on final Bedrock data
                            delete chunk.generationInfo.usage;
                        }
                        const finalMetrics = chunk.generationInfo?.["amazon-bedrock-invocationMetrics"];
                        if (finalMetrics != null &&
                            typeof finalMetrics === "object" &&
                            (0, messages_1.isAIMessage)(chunk.message)) {
                            chunk.message.usage_metadata = {
                                input_tokens: finalMetrics.inputTokenCount,
                                output_tokens: finalMetrics.outputTokenCount,
                                total_tokens: finalMetrics.inputTokenCount + finalMetrics.outputTokenCount,
                            };
                        }
                        if (isChatGenerationChunk(chunk)) {
                            yield chunk;
                        }
                        // eslint-disable-next-line no-void
                        void runManager?.handleLLMNewToken(chunk.text);
                    }
                    else {
                        const text = index_js_1.BedrockLLMInputOutputAdapter.prepareOutput(provider, chunkResult);
                        yield new outputs_1.ChatGenerationChunk({
                            text,
                            message: new messages_1.AIMessageChunk({ content: text }),
                        });
                        // eslint-disable-next-line no-void
                        void runManager?.handleLLMNewToken(text);
                    }
                }
            }
        }
        else {
            const json = await response.json();
            const text = index_js_1.BedrockLLMInputOutputAdapter.prepareOutput(provider, json);
            yield new outputs_1.ChatGenerationChunk({
                text,
                message: new messages_1.AIMessageChunk({ content: text }),
            });
            // eslint-disable-next-line no-void
            void runManager?.handleLLMNewToken(text);
        }
    }
    // eslint-disable-next-line @typescript-eslint/no-explicit-any
    _readChunks(reader) {
        function _concatChunks(a, b) {
            const newBuffer = new Uint8Array(a.length + b.length);
            newBuffer.set(a);
            newBuffer.set(b, a.length);
            return newBuffer;
        }
        function getMessageLength(buffer) {
            if (buffer.byteLength < PRELUDE_TOTAL_LENGTH_BYTES)
                return 0;
            const view = new DataView(buffer.buffer, buffer.byteOffset, buffer.byteLength);
            return view.getUint32(0, false);
        }
        return {
            async *[Symbol.asyncIterator]() {
                let readResult = await reader.read();
                let buffer = new Uint8Array(0);
                while (!readResult.done) {
                    const chunk = readResult.value;
                    buffer = _concatChunks(buffer, chunk);
                    let messageLength = getMessageLength(buffer);
                    while (buffer.byteLength >= PRELUDE_TOTAL_LENGTH_BYTES &&
                        buffer.byteLength >= messageLength) {
                        yield buffer.slice(0, messageLength);
                        buffer = buffer.slice(messageLength);
                        messageLength = getMessageLength(buffer);
                    }
                    readResult = await reader.read();
                }
            },
        };
    }
    _combineLLMOutput() {
        return {};
    }
    bindTools(tools, _kwargs) {
        const provider = this.modelProvider;
        if (provider !== "anthropic") {
            throw new Error("Currently, tool calling through Bedrock is only supported for Anthropic models.");
        }
        return this.bind({
            tools: formatTools(tools),
        });
    }
}
exports.BedrockChat = BedrockChat;
function isChatGenerationChunk(x) {
    return (x !== undefined && typeof x.concat === "function");
}
function canUseMessagesApi(model) {
    const modelProviderName = getModelProvider(model);
    if (modelProviderName === "anthropic" &&
        !model.includes("claude-v2") &&
        !model.includes("claude-instant-v1")) {
        return true;
    }
    if (modelProviderName === "cohere") {
        if (model.includes("command-r-v1")) {
            return true;
        }
        if (model.includes("command-r-plus-v1")) {
            return true;
        }
    }
    return false;
}
function isInferenceModel(modelId) {
    const parts = modelId.split(".");
    return AWS_REGIONS.some((region) => parts[0] === region);
}
function getModelProvider(modelId) {
    const parts = modelId.split(".");
    if (isInferenceModel(modelId)) {
        return parts[1];
    }
    else {
        return parts[0];
    }
}
/**
 * @deprecated Use `BedrockChat` instead.
 */
exports.ChatBedrock = BedrockChat;