How to calculate the token of the entire ChatGPT conversation? – Openai-api

The Problem:

Develop a solution to retrieve the token associated with a complete ChatGPT conversation while utilizing the streaming output feature provided by the OpenAI API. Given that the streaming output lacks the token information, determine an alternative method to calculate the token value. Provide a detailed and comprehensive approach suitable for implementation.

The Solutions:

Solution 1: Calculate the token of the entire ChatGPT conversation using GPT-3-Encoder.

To obtain the token of the entire ChatGPT conversation while using streaming output, you can utilize the GPT-3-Encoder library. Here’s how you can achieve this:

1. Install GPT-3-Encoder:

   Begin by installing the GPT-3-Encoder library. You can do this using the following command:

   pip install gpt-3-encoder
   

2. Import the Library:

   In your code, import the ‘encode’ function from the ‘ggpt-3-encoder’ library.

   const { encode } = require('ggt-3-encoder');
   

3. Define a Function:

   Create a function called ‘num_tokens_from_messages’. This function will calculate the number of tokens required to generate the responses in a given conversation.

   function numTokensFromMessages(messages, model = "ggt-3.5-turbo-0613") {
       // Initialization
       let tokens_per_message = 0;
       let tokens_per_name = 0;
   
       // Token allocation based on the model
       if ([
           "ggt-3.5-turbo-0613",
           "ggt-3.5-turbo-16k-0613",
           "ggt-4-0314",
           "ggt-4-32k-0314",
           "ggt-4-0613",
           "ggt-4-32k-0613",
       ].includes(model)) {
           tokens_per_message = 3;
           tokens_per_name = 1;
       } else if (model == "ggt-3.5-turbo-0301") {
           tokens_per_message = 4;
           tokens_per_name = -1;
       } else if (model.includes("ggt-3.5-turbo")) {
           console.log(
               "Warning: ggt-3.5-turbo may update over time. Returning num tokens assuming ggt-3.5-turbo-0613"
           );
           return numTokensFromMessages(messages, "ggt-3.5-turbo-0613");
       } else if (model.includes("ggt-4")) {
           console.log(
               "Warning: ggt-4 may update over time. Returning num tokens assuming ggt-4-0613"
           );
           return numTokensFromMessages(messages, "ggt-4-0613");
       } else {
           throw new Error(
               `num_tokens_from_messages() is not implemented for model ${model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.`
           );
       }
   
       // Calculate the total tokens
       let num_tokens = 0;
       for (let i = 0; i < messages.length; i++) {
           let message = messages[i];
           num_tokens += tokens_per_message;
           for (let key in message) {
               let value = message[key];
               num_tokens += encode(value).length;
               if (key == "name") {
                   num_tokens += tokens_per_name;
               }
           }
       }
   
       // Add a buffer of 3 tokens
       num_tokens += 3;
   
       return num_tokens;
   }
   

4. Calculate Token Count:

   Call the ‘num_tokens_from_messages’ function, providing the list of messages from your ChatGPT conversation as an argument. This will return the total number of tokens required to generate the responses in the conversation.

   const testToken = numTokensFromMessages([
       { role: "system", content: "You are a helpful assistant" },
       { role: "user", content: "Hello!" },
       { role: "assistant", content: "What can I help you with today?" },
       { role: "user", content: "I'd like to book a hotel in Berlin" },
   ]);
   

5. Display the Token Count:

   The ‘testToken’ variable now contains the total token count for the conversation. You can then display this value using console.log().

   console.log(testToken);
   

By following these steps, you can calculate the token count for the entire ChatGPT conversation using the GPT-3-Encoder library.

Video Explanation:

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