@hashgraphonline/conversational-agent/dist/esm/index12.js

Hashgraph Online conversational AI agent implementing HCS-10 communication, HCS-2 registries, and content inscription on Hedera

import { encoding_for_model } from "tiktoken";
const _TokenCounter = class _TokenCounter {
  constructor(modelName = "gpt-4o") {
    this.modelName = String(modelName);
    try {
      this.encoding = encoding_for_model(modelName);
    } catch {
      this.encoding = encoding_for_model("gpt-4o");
      this.modelName = "gpt-4o";
    }
  }
  /**
   * Count tokens in raw text content
   * @param text - The text to count tokens for
   * @returns Number of tokens
   */
  countTokens(text) {
    if (!text || text.trim() === "") {
      return 0;
    }
    try {
      const tokens = this.encoding.encode(text);
      return tokens.length;
    } catch (error) {
      return Math.ceil(text.split(/\s+/).length * 1.3);
    }
  }
  /**
   * Count tokens for a single chat message including role overhead
   * @param message - The message to count tokens for
   * @returns Number of tokens including message formatting overhead
   */
  countMessageTokens(message) {
    const contentTokens = this.countTokens(String(message.content ?? ""));
    const roleTokens = this.countTokens(this.getMessageRole(message));
    return contentTokens + roleTokens + _TokenCounter.MESSAGE_OVERHEAD + _TokenCounter.ROLE_OVERHEAD;
  }
  /**
   * Count tokens for multiple messages
   * @param messages - Array of messages to count
   * @returns Total token count for all messages
   */
  countMessagesTokens(messages) {
    if (!messages || messages.length === 0) {
      return 0;
    }
    let total = 0;
    for (const message of messages) {
      total += this.countMessageTokens(message);
    }
    return total;
  }
  /**
   * Estimate tokens for system prompt
   * System prompts have slightly different overhead in chat completions
   * @param systemPrompt - The system prompt text
   * @returns Estimated token count
   */
  estimateSystemPromptTokens(systemPrompt) {
    if (!systemPrompt || systemPrompt.trim() === "") {
      return 0;
    }
    const contentTokens = this.countTokens(systemPrompt);
    const roleTokens = this.countTokens("system");
    return contentTokens + roleTokens + _TokenCounter.MESSAGE_OVERHEAD + _TokenCounter.ROLE_OVERHEAD;
  }
  /**
   * Get total context size estimate including system prompt and messages
   * @param systemPrompt - System prompt text
   * @param messages - Conversation messages
   * @returns Total estimated token count
   */
  estimateContextSize(systemPrompt, messages) {
    const systemTokens = this.estimateSystemPromptTokens(systemPrompt);
    const messageTokens = this.countMessagesTokens(messages);
    const completionOverhead = 10;
    return systemTokens + messageTokens + completionOverhead;
  }
  /**
   * Get the role string for a message
   * @param message - The message to get the role for
   * @returns Role string ('user', 'assistant', 'system', etc.)
   */
  getMessageRole(message) {
    const messageType = message._getType();
    switch (messageType) {
      case "human":
        return "user";
      case "ai":
        return "assistant";
      case "system":
        return "system";
      case "function":
        return "function";
      case "tool":
        return "tool";
      default:
        return "user";
    }
  }
  /**
   * Get the model name being used for token counting
   * @returns The tiktoken model name
   */
  getModelName() {
    return this.modelName;
  }
  /**
   * Clean up encoding resources
   */
  dispose() {
    try {
      this.encoding.free();
    } catch {
    }
  }
};
_TokenCounter.MESSAGE_OVERHEAD = 3;
_TokenCounter.ROLE_OVERHEAD = 1;
let TokenCounter = _TokenCounter;
export {
  TokenCounter
};
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