TikToken Tokenization Guide and Examples | Generated by AI
This code demonstrates how to use the TikToken library, which is OpenAI’s tokenizer for counting and processing tokens in text that will be sent to language models like GPT-3.5, GPT-4, etc.
What TikToken Does
TikToken converts text into tokens - the basic units that language models process. Each token roughly corresponds to a word or part of a word. Understanding tokenization is crucial because:
- API costs are based on token count
- Models have token limits for input/output
- Token count affects processing speed
Code Breakdown
1. Basic Encoding (basic_encoding())
enc = tiktoken.encoding_for_model("gpt-3.5-turbo")
tokens = enc.encode("Hello, how are you doing today?")
- Creates a tokenizer for GPT-3.5-turbo
- Converts text to a list of token IDs:
[9906, 11, 1268, 527, 499, 3815, 3432, 30] - Shows that “Hello, how are you doing today?” = 8 tokens
- Can decode tokens back to original text
2. Model Comparison (different_models())
Compares how different models tokenize the same text:
- GPT-4: 10 tokens for “The quick brown fox jumps over the lazy dog.”
- GPT-3.5-turbo: 10 tokens (same encoding)
- text-davinci-003: 10 tokens (same encoding)
Different models may use different tokenizers, so token counts can vary.
3. Batch Processing (batch_processing())
Shows how to efficiently process multiple texts:
- Processes 3 messages of different lengths
- Calculates total tokens across all messages (15 total)
- Useful for estimating costs when sending multiple messages
4. Special Tokens (special_tokens())
Handles special control tokens like <|endoftext|>:
- Shows available special tokens for the model
- Demonstrates encoding text that contains special tokens
- Uses
allowed_special="all"to process special tokens
5. Efficient Counting (count_tokens_efficiently())
Two methods to count tokens:
- Method 1: Store tokens then count (
len(enc.encode(text))) - Method 2: Direct counting (more memory-efficient for large texts)
- Calculates tokens-per-word ratio (1.29 in this example)
Practical Applications
- Cost Estimation: Calculate API costs before making requests
- Input Validation: Ensure text fits within model token limits
- Batch Optimization: Group messages efficiently for API calls
- Performance Monitoring: Track token usage in applications
Key Insights from the Output
- Simple phrases use fewer tokens than you might expect
- Token count doesn’t always equal word count
- Different models may tokenize differently
- Special tokens require careful handling
This library is essential for anyone building applications with OpenAI’s APIs, as it provides precise token counting that matches what the API will actually charge you for.