Counting Tokens in OpenAI API Requests Using tiktoken
Introduction to Tokens
Tokens are the fundamental units of text that language models like GPT-3.5 and GPT-4 process. Understanding tokens is key to optimizing the use of OpenAI’s models, as every API request is bound by a token limit. Tokens can represent as little as a single character or as much as a word or punctuation mark. For example:
- The word “hello” is a single token.
- The phrase “How are you?” breaks down into four tokens: “How”, “are”, “you”, “?”
Managing token limits ensures your API calls are within bounds and helps in cost optimization since OpenAI charges based on the number of tokens processed.
The Importance of Counting Tokens
Counting tokens is crucial for:
- Avoiding Errors: Each API call has a maximum token limit (e.g., 4,096 tokens for GPT-3.5-turbo), and exceeding this limit can cause errors.
- Cost Management: OpenAI charges per token, so understanding and controlling token usage can help in managing costs effectively.
Introduction to the tiktoken Library
The tiktoken library is designed to tokenize text according to the specific tokenization rules of OpenAI’s models. It helps you:
- Encode Text: Convert text into tokens.
- Decode Tokens: Convert tokens back into text.
- Manage Tokens: Efficiently handle tokenization to stay within limits.
Installation and Setup
Before you start using tiktoken, you need to install the library. This can be done using pip. Once installed, you can import it into your Python script.
Choosing the Right Encoder
Each OpenAI model has its own tokenization rules. Tiktoken provides specific encoders for each model, such as gpt-3.5-turbo. You must select the appropriate encoder based on the model you’re working with.
Encoding Text into Tokens
Encoding is the process of converting text into tokens. This allows you to count the tokens and make decisions based on the token count. You can encode individual text strings or combine multiple strings to count tokens for both prompts and completions.
Counting Tokens for Prompt and Completion
In many cases, you’ll need to count tokens for both the input prompt and the expected output completion. This ensures that the combined token count stays within the API’s limit.
Handling Edge Cases
When dealing with large texts, you might need to truncate or split the text to ensure that it fits within the token limit. Tiktoken allows you to handle these edge cases by providing flexibility in managing tokenized text.
Decoding Tokens
Decoding is the reverse process of encoding, where tokens are converted back into human-readable text. This is particularly useful for verification or display purposes.
Complete Code Example
!pip install tiktoken
import tiktoken
encoder = tiktoken.encoding_for_model("gpt-3.5-turbo")
text = "Hello, how are you doing today?"
tokens = encoder.encode(text)
print(f"Token count: {len(tokens)}")
print(f"Tokens: {tokens}")
prompt = "Translate the following English text to French: 'OpenAI is creating amazing tools for developers.'"
completion = "OpenAI crée des outils incroyables pour les développeurs."
prompt_tokens = encoder.encode(prompt)
completion_tokens = encoder.encode(completion)
total_tokens = len(prompt_tokens) + len(completion_tokens)
print(f"Total token count: {total_tokens}")
max_tokens = 100 # Example token limit
long_text = "A very long text that might exceed the token limit..."
tokens = encoder.encode(long_text)
if len(tokens) > max_tokens:
tokens = tokens[:max_tokens]
print(f"Truncated tokens: {tokens}")
decoded_text = encoder.decode(tokens)
print(f"Decoded text: {decoded_text}")Conclusion
Counting tokens using the tiktoken library is a straightforward yet crucial task when working with OpenAI's models. It helps you avoid errors, manage costs, and optimize the performance of your applications. By following the steps outlined in this guide, you can efficiently manage token usage in your OpenAI API requests.
