How Can I Debug an Excessive Token Count in a GPT-4 Chatbot?

Below is a summary of why you might see such a discrepancy and how to debug and resolve it:

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1. How Chat APIs Count Tokens

When you pass messages to a chat model (like GPT-4o) through LangChain, the API doesn’t just count the raw text content. It also adds tokens for:

• The role field (e.g., "system", "user", "assistant")
• Special formatting tokens for each message (e.g., headers, delimiters)
• Any extra metadata or formatting that’s applied behind the scenes

A simple tokenization of your string (as with the tiktoken encoder on your raw input) will not include these extra tokens. The API’s total token count (as reported by the callback) factors these in and hence is significantly higher.

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2. Potential Causes of Unexpectedly High Token Counts

• Message Formatting Overhead:
  Each message you pass is wrapped with additional formatting tokens. Even if your raw content token count is low, the formatting may add a dozen or more tokens per message.

• Stringification vs. Proper Structure:
  In your example, you are tokenizing a string representation of a list of message dictionaries. In practice, if you pass this string to the model, it will treat it like any other text. However, using LangChain’s chat wrapper, you should pass each message as a dictionary (or using the appropriate message class). LangChain then internally formats these messages according to the chat API’s protocol, which changes the token count.

• Additional Contextual Prompts:
  Some implementations inject extra system or context messages. Verify if your LangChain configuration or your application logic is automatically adding extra messages to your conversation.

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3. Debugging Steps

• Validate Message Structure:
  Instead of a stringified list, construct and pass messages as a list of dictionaries. For example:

  PYTHONCopied
  messages = [
      {"role": "system", "content": "Answer in the question's language, keeping personal data in its original language if provided in the context."},
      {"role": "system", "content": "Answer strictly based on the provided context. If a 'for more visit' link is present in the context, include it in your response. If multiple persons match the query, provide the requested information for all."},
      {"role": "system", "content": "تحديث بيانات الموظفين عن طريق رفع ملف مقيم ..."},  # truncated for brevity
      {"role": "system", "content": "user input:"},
      {"role": "user", "content": "Who is my annual salary?"}
  ]

  Passing structured messages helps ensure that the API uses the correct tokenization logic.

• Use a Token-Counting Function Tailored for Chat Messages:
  OpenAI’s documentation (and LangChain’s examples) include functions that account for the role, content, and formatting tokens. For instance, a function similar to the one below can be used:

  PYTHONCopied
  def tiktoken_counter(messages: List[dict]) -> int:
      num_tokens = 0
      tokens_per_message = 3  # base tokens per message for formatting
      tokens_per_role = 1     # extra tokens based on role field
      for msg in messages:
          num_tokens += tokens_per_message
          num_tokens += len(tiktoken.encoding_for_model("gpt-4").encode(msg["role"]))
          num_tokens += len(tiktoken.encoding_for_model("gpt-4").encode(msg["content"]))
      return num_tokens

  This gives you a closer approximation to what the API actually counts.

• Compare With Callback Reporting:
  The callback (get_openai_callback) from LangChain reports the actual token count that was used for the API call. Use that as your ground truth and adjust your input structure accordingly.

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4. Fixing the Issue

• Pass Structured Messages:
  Ensure you pass a list of properly structured message dictionaries (or use LangChain’s message classes) rather than a single string that represents that list.

• Review Model Configuration:
  Double-check that no extra context (e.g., injected system messages or user history) is unintentionally added to your input. Your configuration of the ChatOpenAI model should be coupled with the exact messages you mean to send.

• Use the Right Tokenization Technique:
  To pre-calculate tokens, use a helper function that mimics the chat formatting. Keep in mind that the numbers from a simple text tokenizer on your string might not reflect the count from the actual API call.

By inspecting your message structure and using a token counting method aligned with how chat messages are formatted, you should be able to debug and bring down the token usage closer to your expectations.

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Following these debugging and corrective measures should help you align your estimated token count with the actual usage reported by OpenAI’s API and LangChain’s callbacks.