LibreChat/api/app/clients/AnthropicClient.js

const Anthropic = require('@anthropic-ai/sdk');
const { HttpsProxyAgent } = require('https-proxy-agent');
const {
  Constants,
  ErrorTypes,
  EModelEndpoint,
  parseTextParts,
  anthropicSettings,
  getResponseSender,
  validateVisionModel,
} = require('librechat-data-provider');
const { SplitStreamHandler: _Handler } = require('@librechat/agents');
const {
  Tokenizer,
  createFetch,
  matchModelName,
  getClaudeHeaders,
  getModelMaxTokens,
  configureReasoning,
  checkPromptCacheSupport,
  getModelMaxOutputTokens,
  createStreamEventHandlers,
} = require('@librechat/api');
const {
  truncateText,
  formatMessage,
  addCacheControl,
  titleFunctionPrompt,
  parseParamFromPrompt,
  createContextHandlers,
} = require('./prompts');
const { spendTokens, spendStructuredTokens } = require('~/models/spendTokens');
const { encodeAndFormat } = require('~/server/services/Files/images/encode');
const { sleep } = require('~/server/utils');
const BaseClient = require('./BaseClient');
const { logger } = require('~/config');

const HUMAN_PROMPT = '\n\nHuman:';
const AI_PROMPT = '\n\nAssistant:';

class SplitStreamHandler extends _Handler {
  getDeltaContent(chunk) {
    return (chunk?.delta?.text ?? chunk?.completion) || '';
  }
  getReasoningDelta(chunk) {
    return chunk?.delta?.thinking || '';
  }
}

/** Helper function to introduce a delay before retrying */
function delayBeforeRetry(attempts, baseDelay = 1000) {
  return new Promise((resolve) => setTimeout(resolve, baseDelay * attempts));
}

const tokenEventTypes = new Set(['message_start', 'message_delta']);
const { legacy } = anthropicSettings;

class AnthropicClient extends BaseClient {
  constructor(apiKey, options = {}) {
    super(apiKey, options);
    this.apiKey = apiKey || process.env.ANTHROPIC_API_KEY;
    this.userLabel = HUMAN_PROMPT;
    this.assistantLabel = AI_PROMPT;
    this.contextStrategy = options.contextStrategy
      ? options.contextStrategy.toLowerCase()
      : 'discard';
    this.setOptions(options);
    /** @type {string | undefined} */
    this.systemMessage;
    /** @type {AnthropicMessageStartEvent| undefined} */
    this.message_start;
    /** @type {AnthropicMessageDeltaEvent| undefined} */
    this.message_delta;
    /** Whether the model is part of the Claude 3 Family
     * @type {boolean} */
    this.isClaudeLatest;
    /** Whether to use Messages API or Completions API
     * @type {boolean} */
    this.useMessages;
    /** Whether or not the model supports Prompt Caching
     * @type {boolean} */
    this.supportsCacheControl;
    /** The key for the usage object's input tokens
     * @type {string} */
    this.inputTokensKey = 'input_tokens';
    /** The key for the usage object's output tokens
     * @type {string} */
    this.outputTokensKey = 'output_tokens';
    /** @type {SplitStreamHandler | undefined} */
    this.streamHandler;
  }

  setOptions(options) {
    if (this.options && !this.options.replaceOptions) {
      // nested options aren't spread properly, so we need to do this manually
      this.options.modelOptions = {
        ...this.options.modelOptions,
        ...options.modelOptions,
      };
      delete options.modelOptions;
      // now we can merge options
      this.options = {
        ...this.options,
        ...options,
      };
    } else {
      this.options = options;
    }

    this.modelOptions = Object.assign(
      {
        model: anthropicSettings.model.default,
      },
      this.modelOptions,
      this.options.modelOptions,
    );

    const modelMatch = matchModelName(this.modelOptions.model, EModelEndpoint.anthropic);
    this.isClaudeLatest =
      /claude-[3-9]/.test(modelMatch) || /claude-(?:sonnet|opus|haiku)-[4-9]/.test(modelMatch);
    const isLegacyOutput = !(
      /claude-3[-.]5-sonnet/.test(modelMatch) ||
      /claude-3[-.]7/.test(modelMatch) ||
      /claude-(?:sonnet|opus|haiku)-[4-9]/.test(modelMatch) ||
      /claude-[4-9]/.test(modelMatch)
    );
    this.supportsCacheControl = this.options.promptCache && checkPromptCacheSupport(modelMatch);

    if (
      isLegacyOutput &&
      this.modelOptions.maxOutputTokens &&
      this.modelOptions.maxOutputTokens > legacy.maxOutputTokens.default
    ) {
      this.modelOptions.maxOutputTokens = legacy.maxOutputTokens.default;
    }

    this.useMessages = this.isClaudeLatest || !!this.options.attachments;

    this.defaultVisionModel = this.options.visionModel ?? 'claude-3-sonnet-20240229';
    this.options.attachments?.then((attachments) => this.checkVisionRequest(attachments));

    this.maxContextTokens =
      this.options.maxContextTokens ??
      getModelMaxTokens(this.modelOptions.model, EModelEndpoint.anthropic) ??
      100000;
    this.maxResponseTokens =
      this.modelOptions.maxOutputTokens ??
      getModelMaxOutputTokens(
        this.modelOptions.model,
        this.options.endpointType ?? this.options.endpoint,
        this.options.endpointTokenConfig,
      ) ??
      anthropicSettings.maxOutputTokens.reset(this.modelOptions.model);
    this.maxPromptTokens =
      this.options.maxPromptTokens || this.maxContextTokens - this.maxResponseTokens;

    const reservedTokens = this.maxPromptTokens + this.maxResponseTokens;
    if (reservedTokens > this.maxContextTokens) {
      const info = `Total Possible Tokens + Max Output Tokens must be less than or equal to Max Context Tokens: ${this.maxPromptTokens} (total possible output) + ${this.maxResponseTokens} (max output) = ${reservedTokens}/${this.maxContextTokens} (max context)`;
      const errorMessage = `{ "type": "${ErrorTypes.INPUT_LENGTH}", "info": "${info}" }`;
      logger.warn(info);
      throw new Error(errorMessage);
    } else if (this.maxResponseTokens === this.maxContextTokens) {
      const info = `Max Output Tokens must be less than Max Context Tokens: ${this.maxResponseTokens} (max output) = ${this.maxContextTokens} (max context)`;
      const errorMessage = `{ "type": "${ErrorTypes.INPUT_LENGTH}", "info": "${info}" }`;
      logger.warn(info);
      throw new Error(errorMessage);
    }

    this.sender =
      this.options.sender ??
      getResponseSender({
        model: this.modelOptions.model,
        endpoint: EModelEndpoint.anthropic,
        modelLabel: this.options.modelLabel,
      });

    this.startToken = '||>';
    this.endToken = '';

    return this;
  }

  /**
   * Get the initialized Anthropic client.
   * @param {Partial<Anthropic.ClientOptions>} requestOptions - The options for the client.
   * @returns {Anthropic} The Anthropic client instance.
   */
  getClient(requestOptions) {
    /** @type {Anthropic.ClientOptions} */
    const options = {
      fetch: createFetch({
        directEndpoint: this.options.directEndpoint,
        reverseProxyUrl: this.options.reverseProxyUrl,
      }),
      apiKey: this.apiKey,
      fetchOptions: {},
    };

    if (this.options.proxy) {
      options.fetchOptions.agent = new HttpsProxyAgent(this.options.proxy);
    }

    if (this.options.reverseProxyUrl) {
      options.baseURL = this.options.reverseProxyUrl;
    }

    const headers = getClaudeHeaders(requestOptions?.model, this.supportsCacheControl);
    if (headers) {
      options.defaultHeaders = headers;
    }

    return new Anthropic(options);
  }

  /**
   * Get stream usage as returned by this client's API response.
   * @returns {AnthropicStreamUsage} The stream usage object.
   */
  getStreamUsage() {
    const inputUsage = this.message_start?.message?.usage ?? {};
    const outputUsage = this.message_delta?.usage ?? {};
    return Object.assign({}, inputUsage, outputUsage);
  }

  /**
   * Calculates the correct token count for the current user message based on the token count map and API usage.
   * Edge case: If the calculation results in a negative value, it returns the original estimate.
   * If revisiting a conversation with a chat history entirely composed of token estimates,
   * the cumulative token count going forward should become more accurate as the conversation progresses.
   * @param {Object} params - The parameters for the calculation.
   * @param {Record<string, number>} params.tokenCountMap - A map of message IDs to their token counts.
   * @param {string} params.currentMessageId - The ID of the current message to calculate.
   * @param {AnthropicStreamUsage} params.usage - The usage object returned by the API.
   * @returns {number} The correct token count for the current user message.
   */
  calculateCurrentTokenCount({ tokenCountMap, currentMessageId, usage }) {
    const originalEstimate = tokenCountMap[currentMessageId] || 0;

    if (!usage || typeof usage.input_tokens !== 'number') {
      return originalEstimate;
    }

    tokenCountMap[currentMessageId] = 0;
    const totalTokensFromMap = Object.values(tokenCountMap).reduce((sum, count) => {
      const numCount = Number(count);
      return sum + (isNaN(numCount) ? 0 : numCount);
    }, 0);
    const totalInputTokens =
      (usage.input_tokens ?? 0) +
      (usage.cache_creation_input_tokens ?? 0) +
      (usage.cache_read_input_tokens ?? 0);

    const currentMessageTokens = totalInputTokens - totalTokensFromMap;
    return currentMessageTokens > 0 ? currentMessageTokens : originalEstimate;
  }

  /**
   * Get Token Count for LibreChat Message
   * @param {TMessage} responseMessage
   * @returns {number}
   */
  getTokenCountForResponse(responseMessage) {
    return this.getTokenCountForMessage({
      role: 'assistant',
      content: responseMessage.text,
    });
  }

  /**
   *
   * Checks if the model is a vision model based on request attachments and sets the appropriate options:
   * - Sets `this.modelOptions.model` to `gpt-4-vision-preview` if the request is a vision request.
   * - Sets `this.isVisionModel` to `true` if vision request.
   * - Deletes `this.modelOptions.stop` if vision request.
   * @param {MongoFile[]} attachments
   */
  checkVisionRequest(attachments) {
    const availableModels = this.options.modelsConfig?.[EModelEndpoint.anthropic];
    this.isVisionModel = validateVisionModel({ model: this.modelOptions.model, availableModels });

    const visionModelAvailable = availableModels?.includes(this.defaultVisionModel);
    if (
      attachments &&
      attachments.some((file) => file?.type && file?.type?.includes('image')) &&
      visionModelAvailable &&
      !this.isVisionModel
    ) {
      this.modelOptions.model = this.defaultVisionModel;
      this.isVisionModel = true;
    }
  }

  /**
   * Calculate the token cost in tokens for an image based on its dimensions and detail level.
   *
   * For reference, see: https://docs.anthropic.com/claude/docs/vision#image-costs
   *
   * @param {Object} image - The image object.
   * @param {number} image.width - The width of the image.
   * @param {number} image.height - The height of the image.
   * @returns {number} The calculated token cost measured by tokens.
   *
   */
  calculateImageTokenCost({ width, height }) {
    return Math.ceil((width * height) / 750);
  }

  async addImageURLs(message, attachments) {
    const { files, image_urls } = await encodeAndFormat(
      this.options.req,
      attachments,
      EModelEndpoint.anthropic,
    );
    message.image_urls = image_urls.length ? image_urls : undefined;
    return files;
  }

  /**
   * @param {object} params
   * @param {number} params.promptTokens
   * @param {number} params.completionTokens
   * @param {AnthropicStreamUsage} [params.usage]
   * @param {string} [params.model]
   * @param {string} [params.context='message']
   * @returns {Promise<void>}
   */
  async recordTokenUsage({ promptTokens, completionTokens, usage, model, context = 'message' }) {
    if (usage != null && usage?.input_tokens != null) {
      const input = usage.input_tokens ?? 0;
      const write = usage.cache_creation_input_tokens ?? 0;
      const read = usage.cache_read_input_tokens ?? 0;

      await spendStructuredTokens(
        {
          context,
          user: this.user,
          conversationId: this.conversationId,
          model: model ?? this.modelOptions.model,
          endpointTokenConfig: this.options.endpointTokenConfig,
        },
        {
          promptTokens: { input, write, read },
          completionTokens,
        },
      );

      return;
    }

    await spendTokens(
      {
        context,
        user: this.user,
        conversationId: this.conversationId,
        model: model ?? this.modelOptions.model,
        endpointTokenConfig: this.options.endpointTokenConfig,
      },
      { promptTokens, completionTokens },
    );
  }

  async buildMessages(messages, parentMessageId) {
    const orderedMessages = this.constructor.getMessagesForConversation({
      messages,
      parentMessageId,
    });

    logger.debug('[AnthropicClient] orderedMessages', { orderedMessages, parentMessageId });

    if (this.options.attachments) {
      const attachments = await this.options.attachments;
      const images = attachments.filter((file) => file.type.includes('image'));

      if (images.length && !this.isVisionModel) {
        throw new Error('Images are only supported with the Claude 3 family of models');
      }

      const latestMessage = orderedMessages[orderedMessages.length - 1];

      if (this.message_file_map) {
        this.message_file_map[latestMessage.messageId] = attachments;
      } else {
        this.message_file_map = {
          [latestMessage.messageId]: attachments,
        };
      }

      const files = await this.addImageURLs(latestMessage, attachments);

      this.options.attachments = files;
    }

    if (this.message_file_map) {
      this.contextHandlers = createContextHandlers(
        this.options.req,
        orderedMessages[orderedMessages.length - 1].text,
      );
    }

    const formattedMessages = orderedMessages.map((message, i) => {
      const formattedMessage = this.useMessages
        ? formatMessage({
            message,
            endpoint: EModelEndpoint.anthropic,
          })
        : {
            author: message.isCreatedByUser ? this.userLabel : this.assistantLabel,
            content: message?.content ?? message.text,
          };

      const needsTokenCount = this.contextStrategy && !orderedMessages[i].tokenCount;
      /* If tokens were never counted, or, is a Vision request and the message has files, count again */
      if (needsTokenCount || (this.isVisionModel && (message.image_urls || message.files))) {
        orderedMessages[i].tokenCount = this.getTokenCountForMessage(formattedMessage);
      }

      /* If message has files, calculate image token cost */
      if (this.message_file_map && this.message_file_map[message.messageId]) {
        const attachments = this.message_file_map[message.messageId];
        for (const file of attachments) {
          if (file.embedded) {
            this.contextHandlers?.processFile(file);
            continue;
          }
          if (file.metadata?.fileIdentifier) {
            continue;
          }

          orderedMessages[i].tokenCount += this.calculateImageTokenCost({
            width: file.width,
            height: file.height,
          });
        }
      }

      formattedMessage.tokenCount = orderedMessages[i].tokenCount;
      return formattedMessage;
    });

    if (this.contextHandlers) {
      this.augmentedPrompt = await this.contextHandlers.createContext();
      this.options.promptPrefix = this.augmentedPrompt + (this.options.promptPrefix ?? '');
    }

    let { context: messagesInWindow, remainingContextTokens } =
      await this.getMessagesWithinTokenLimit({ messages: formattedMessages });

    const tokenCountMap = orderedMessages
      .slice(orderedMessages.length - messagesInWindow.length)
      .reduce((map, message, index) => {
        const { messageId } = message;
        if (!messageId) {
          return map;
        }

        map[messageId] = orderedMessages[index].tokenCount;
        return map;
      }, {});

    logger.debug('[AnthropicClient]', {
      messagesInWindow: messagesInWindow.length,
      remainingContextTokens,
    });

    let lastAuthor = '';
    let groupedMessages = [];

    for (let i = 0; i < messagesInWindow.length; i++) {
      const message = messagesInWindow[i];
      const author = message.role ?? message.author;
      // If last author is not same as current author, add to new group
      if (lastAuthor !== author) {
        const newMessage = {
          content: [message.content],
        };

        if (message.role) {
          newMessage.role = message.role;
        } else {
          newMessage.author = message.author;
        }

        groupedMessages.push(newMessage);
        lastAuthor = author;
        // If same author, append content to the last group
      } else {
        groupedMessages[groupedMessages.length - 1].content.push(message.content);
      }
    }

    groupedMessages = groupedMessages.map((msg, i) => {
      const isLast = i === groupedMessages.length - 1;
      if (msg.content.length === 1) {
        const content = msg.content[0];
        return {
          ...msg,
          // reason: final assistant content cannot end with trailing whitespace
          content:
            isLast && this.useMessages && msg.role === 'assistant' && typeof content === 'string'
              ? content?.trim()
              : content,
        };
      }

      if (!this.useMessages && msg.tokenCount) {
        delete msg.tokenCount;
      }

      return msg;
    });

    let identityPrefix = '';
    if (this.options.userLabel) {
      identityPrefix = `\nHuman's name: ${this.options.userLabel}`;
    }

    if (this.options.modelLabel) {
      identityPrefix = `${identityPrefix}\nYou are ${this.options.modelLabel}`;
    }

    let promptPrefix = (this.options.promptPrefix ?? '').trim();
    if (typeof this.options.artifactsPrompt === 'string' && this.options.artifactsPrompt) {
      promptPrefix = `${promptPrefix ?? ''}\n${this.options.artifactsPrompt}`.trim();
    }
    if (promptPrefix) {
      // If the prompt prefix doesn't end with the end token, add it.
      if (!promptPrefix.endsWith(`${this.endToken}`)) {
        promptPrefix = `${promptPrefix.trim()}${this.endToken}\n\n`;
      }
      promptPrefix = `\nContext:\n${promptPrefix}`;
    }

    if (identityPrefix) {
      promptPrefix = `${identityPrefix}${promptPrefix}`;
    }

    // Prompt AI to respond, empty if last message was from AI
    let isEdited = lastAuthor === this.assistantLabel;
    const promptSuffix = isEdited ? '' : `${promptPrefix}${this.assistantLabel}\n`;
    let currentTokenCount =
      isEdited || this.useMessages
        ? this.getTokenCount(promptPrefix)
        : this.getTokenCount(promptSuffix);

    let promptBody = '';
    const maxTokenCount = this.maxPromptTokens;

    const context = [];

    // Iterate backwards through the messages, adding them to the prompt until we reach the max token count.
    // Do this within a recursive async function so that it doesn't block the event loop for too long.
    // Also, remove the next message when the message that puts us over the token limit is created by the user.
    // Otherwise, remove only the exceeding message. This is due to Anthropic's strict payload rule to start with "Human:".
    const nextMessage = {
      remove: false,
      tokenCount: 0,
      messageString: '',
    };

    const buildPromptBody = async () => {
      if (currentTokenCount < maxTokenCount && groupedMessages.length > 0) {
        const message = groupedMessages.pop();
        const isCreatedByUser = message.author === this.userLabel;
        // Use promptPrefix if message is edited assistant'
        const messagePrefix =
          isCreatedByUser || !isEdited ? message.author : `${promptPrefix}${message.author}`;
        const messageString = `${messagePrefix}\n${message.content}${this.endToken}\n`;
        let newPromptBody = `${messageString}${promptBody}`;

        context.unshift(message);

        const tokenCountForMessage = this.getTokenCount(messageString);
        const newTokenCount = currentTokenCount + tokenCountForMessage;

        if (!isCreatedByUser) {
          nextMessage.messageString = messageString;
          nextMessage.tokenCount = tokenCountForMessage;
        }

        if (newTokenCount > maxTokenCount) {
          if (!promptBody) {
            // This is the first message, so we can't add it. Just throw an error.
            throw new Error(
              `Prompt is too long. Max token count is ${maxTokenCount}, but prompt is ${newTokenCount} tokens long.`,
            );
          }

          // Otherwise, ths message would put us over the token limit, so don't add it.
          // if created by user, remove next message, otherwise remove only this message
          if (isCreatedByUser) {
            nextMessage.remove = true;
          }

          return false;
        }
        promptBody = newPromptBody;
        currentTokenCount = newTokenCount;

        // Switch off isEdited after using it for the first time
        if (isEdited) {
          isEdited = false;
        }

        // wait for next tick to avoid blocking the event loop
        await new Promise((resolve) => setImmediate(resolve));
        return buildPromptBody();
      }
      return true;
    };

    const messagesPayload = [];
    const buildMessagesPayload = async () => {
      let canContinue = true;

      if (promptPrefix) {
        this.systemMessage = promptPrefix;
      }

      while (currentTokenCount < maxTokenCount && groupedMessages.length > 0 && canContinue) {
        const message = groupedMessages.pop();

        let tokenCountForMessage = message.tokenCount ?? this.getTokenCountForMessage(message);

        const newTokenCount = currentTokenCount + tokenCountForMessage;
        const exceededMaxCount = newTokenCount > maxTokenCount;

        if (exceededMaxCount && messagesPayload.length === 0) {
          throw new Error(
            `Prompt is too long. Max token count is ${maxTokenCount}, but prompt is ${newTokenCount} tokens long.`,
          );
        } else if (exceededMaxCount) {
          canContinue = false;
          break;
        }

        delete message.tokenCount;
        messagesPayload.unshift(message);
        currentTokenCount = newTokenCount;

        // Switch off isEdited after using it once
        if (isEdited && message.role === 'assistant') {
          isEdited = false;
        }

        // Wait for next tick to avoid blocking the event loop
        await new Promise((resolve) => setImmediate(resolve));
      }
    };

    const processTokens = () => {
      // Add 2 tokens for metadata after all messages have been counted.
      currentTokenCount += 2;

      // Use up to `this.maxContextTokens` tokens (prompt + response), but try to leave `this.maxTokens` tokens for the response.
      this.modelOptions.maxOutputTokens = Math.min(
        this.maxContextTokens - currentTokenCount,
        this.maxResponseTokens,
      );
    };

    if (
      /claude-[3-9]/.test(this.modelOptions.model) ||
      /claude-(?:sonnet|opus|haiku)-[4-9]/.test(this.modelOptions.model)
    ) {
      await buildMessagesPayload();
      processTokens();
      return {
        prompt: messagesPayload,
        context: messagesInWindow,
        promptTokens: currentTokenCount,
        tokenCountMap,
      };
    } else {
      await buildPromptBody();
      processTokens();
    }

    if (nextMessage.remove) {
      promptBody = promptBody.replace(nextMessage.messageString, '');
      currentTokenCount -= nextMessage.tokenCount;
      context.shift();
    }

    let prompt = `${promptBody}${promptSuffix}`;

    return { prompt, context, promptTokens: currentTokenCount, tokenCountMap };
  }

  getCompletion() {
    logger.debug("AnthropicClient doesn't use getCompletion (all handled in sendCompletion)");
  }

  /**
   * Creates a message or completion response using the Anthropic client.
   * @param {Anthropic} client - The Anthropic client instance.
   * @param {Anthropic.default.MessageCreateParams | Anthropic.default.CompletionCreateParams} options - The options for the message or completion.
   * @param {boolean} useMessages - Whether to use messages or completions. Defaults to `this.useMessages`.
   * @returns {Promise<Anthropic.default.Message | Anthropic.default.Completion>} The response from the Anthropic client.
   */
  async createResponse(client, options, useMessages) {
    return (useMessages ?? this.useMessages)
      ? await client.messages.create(options)
      : await client.completions.create(options);
  }

  getMessageMapMethod() {
    /**
     * @param {TMessage} msg
     */
    return (msg) => {
      if (msg.text != null && msg.text && msg.text.startsWith(':::thinking')) {
        msg.text = msg.text.replace(/:::thinking.*?:::/gs, '').trim();
      } else if (msg.content != null) {
        msg.text = parseTextParts(msg.content, true);
        delete msg.content;
      }

      return msg;
    };
  }

  /**
   * @param {string[]} [intermediateReply]
   * @returns {string}
   */
  getStreamText(intermediateReply) {
    if (!this.streamHandler) {
      return intermediateReply?.join('') ?? '';
    }

    const reasoningText = this.streamHandler.reasoningTokens.join('');

    const reasoningBlock = reasoningText.length > 0 ? `:::thinking\n${reasoningText}\n:::\n` : '';

    return `${reasoningBlock}${this.streamHandler.tokens.join('')}`;
  }

  async sendCompletion(payload, { onProgress, abortController }) {
    if (!abortController) {
      abortController = new AbortController();
    }

    const { signal } = abortController;

    const modelOptions = { ...this.modelOptions };
    if (typeof onProgress === 'function') {
      modelOptions.stream = true;
    }

    logger.debug('modelOptions', { modelOptions });
    const metadata = {
      user_id: this.user,
    };

    const {
      stream,
      model,
      temperature,
      maxOutputTokens,
      stop: stop_sequences,
      topP: top_p,
      topK: top_k,
    } = this.modelOptions;

    let requestOptions = {
      model,
      stream: stream || true,
      stop_sequences,
      temperature,
      metadata,
    };

    if (this.useMessages) {
      requestOptions.messages = payload;
      requestOptions.max_tokens =
        maxOutputTokens || anthropicSettings.maxOutputTokens.reset(requestOptions.model);
    } else {
      requestOptions.prompt = payload;
      requestOptions.max_tokens_to_sample = maxOutputTokens || legacy.maxOutputTokens.default;
    }

    requestOptions = configureReasoning(requestOptions, {
      thinking: this.options.thinking,
      thinkingBudget: this.options.thinkingBudget,
    });

    if (!/claude-3[-.]7/.test(model)) {
      requestOptions.top_p = top_p;
      requestOptions.top_k = top_k;
    } else if (requestOptions.thinking == null) {
      requestOptions.topP = top_p;
      requestOptions.topK = top_k;
    }

    if (this.systemMessage && this.supportsCacheControl === true) {
      requestOptions.system = [
        {
          type: 'text',
          text: this.systemMessage,
          cache_control: { type: 'ephemeral' },
        },
      ];
    } else if (this.systemMessage) {
      requestOptions.system = this.systemMessage;
    }

    if (this.supportsCacheControl === true && this.useMessages) {
      requestOptions.messages = addCacheControl(requestOptions.messages);
    }

    logger.debug('[AnthropicClient]', { ...requestOptions });
    const handlers = createStreamEventHandlers(this.options.res);
    this.streamHandler = new SplitStreamHandler({
      accumulate: true,
      runId: this.responseMessageId,
      handlers,
    });

    let intermediateReply = this.streamHandler.tokens;

    const maxRetries = 3;
    const streamRate = this.options.streamRate ?? Constants.DEFAULT_STREAM_RATE;
    async function processResponse() {
      let attempts = 0;

      while (attempts < maxRetries) {
        let response;
        try {
          const client = this.getClient(requestOptions);
          response = await this.createResponse(client, requestOptions);

          signal.addEventListener('abort', () => {
            logger.debug('[AnthropicClient] message aborted!');
            if (response.controller?.abort) {
              response.controller.abort();
            }
          });

          for await (const completion of response) {
            const type = completion?.type ?? '';
            if (tokenEventTypes.has(type)) {
              logger.debug(`[AnthropicClient] ${type}`, completion);
              this[type] = completion;
            }
            this.streamHandler.handle(completion);
            await sleep(streamRate);
          }

          break;
        } catch (error) {
          attempts += 1;
          logger.warn(
            `User: ${this.user} | Anthropic Request ${attempts} failed: ${error.message}`,
          );

          if (attempts < maxRetries) {
            await delayBeforeRetry(attempts, 350);
          } else if (this.streamHandler && this.streamHandler.reasoningTokens.length) {
            return this.getStreamText();
          } else if (intermediateReply.length > 0) {
            return this.getStreamText(intermediateReply);
          } else {
            throw new Error(`Operation failed after ${maxRetries} attempts: ${error.message}`);
          }
        } finally {
          signal.removeEventListener('abort', () => {
            logger.debug('[AnthropicClient] message aborted!');
            if (response.controller?.abort) {
              response.controller.abort();
            }
          });
        }
      }
    }

    await processResponse.bind(this)();
    return this.getStreamText(intermediateReply);
  }

  getSaveOptions() {
    return {
      maxContextTokens: this.options.maxContextTokens,
      artifacts: this.options.artifacts,
      promptPrefix: this.options.promptPrefix,
      modelLabel: this.options.modelLabel,
      promptCache: this.options.promptCache,
      thinking: this.options.thinking,
      thinkingBudget: this.options.thinkingBudget,
      resendFiles: this.options.resendFiles,
      iconURL: this.options.iconURL,
      greeting: this.options.greeting,
      spec: this.options.spec,
      ...this.modelOptions,
    };
  }

  getBuildMessagesOptions() {
    logger.debug("AnthropicClient doesn't use getBuildMessagesOptions");
  }

  getEncoding() {
    return 'cl100k_base';
  }

  /**
   * Returns the token count of a given text. It also checks and resets the tokenizers if necessary.
   * @param {string} text - The text to get the token count for.
   * @returns {number} The token count of the given text.
   */
  getTokenCount(text) {
    const encoding = this.getEncoding();
    return Tokenizer.getTokenCount(text, encoding);
  }

  /**
   * Generates a concise title for a conversation based on the user's input text and response.
   * Involves sending a chat completion request with specific instructions for title generation.
   *
   * This function capitlizes on [Anthropic's function calling training](https://docs.anthropic.com/claude/docs/functions-external-tools).
   *
   * @param {Object} params - The parameters for the conversation title generation.
   * @param {string} params.text - The user's input.
   * @param {string} [params.responseText=''] - The AI's immediate response to the user.
   *
   * @returns {Promise<string | 'New Chat'>} A promise that resolves to the generated conversation title.
   *                            In case of failure, it will return the default title, "New Chat".
   */
  async titleConvo({ text, responseText = '' }) {
    let title = 'New Chat';
    this.message_delta = undefined;
    this.message_start = undefined;
    const convo = `<initial_message>
  ${truncateText(text)}
  </initial_message>
  <response>
  ${JSON.stringify(truncateText(responseText))}
  </response>`;

    const { ANTHROPIC_TITLE_MODEL } = process.env ?? {};
    const model = this.options.titleModel ?? ANTHROPIC_TITLE_MODEL ?? 'claude-3-haiku-20240307';
    const system = titleFunctionPrompt;

    const titleChatCompletion = async () => {
      const content = `<conversation_context>
  ${convo}
  </conversation_context>
  
  Please generate a title for this conversation.`;

      const titleMessage = { role: 'user', content };
      const requestOptions = {
        model,
        temperature: 0.3,
        max_tokens: 1024,
        system,
        stop_sequences: ['\n\nHuman:', '\n\nAssistant', '</function_calls>'],
        messages: [titleMessage],
      };

      try {
        const response = await this.createResponse(
          this.getClient(requestOptions),
          requestOptions,
          true,
        );
        let promptTokens = response?.usage?.input_tokens;
        let completionTokens = response?.usage?.output_tokens;
        if (!promptTokens) {
          promptTokens = this.getTokenCountForMessage(titleMessage);
          promptTokens += this.getTokenCountForMessage({ role: 'system', content: system });
        }
        if (!completionTokens) {
          completionTokens = this.getTokenCountForMessage(response.content[0]);
        }
        await this.recordTokenUsage({
          model,
          promptTokens,
          completionTokens,
          context: 'title',
        });
        const text = response.content[0].text;
        title = parseParamFromPrompt(text, 'title');
      } catch (e) {
        logger.error('[AnthropicClient] There was an issue generating the title', e);
      }
    };

    await titleChatCompletion();
    logger.debug('[AnthropicClient] Convo Title: ' + title);
    return title;
  }
}

module.exports = AnthropicClient;