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Tiktoken

tiktoken is an OpenAI-developed library that efficiently encodes text using model-specific byte-pair-encoding tokenizers and supports token counting.

How 2 Manning books explain it

Essential GraphRAG

tiktoken is a tokenization library developed by OpenAI for encoding text and counting language-model tokens.

How it is used
Uses token counts to detect oversized sections before splitting documents for retrieval and LLM generation.
How deep it goes
Defines and demonstrates model-specific encoding and counting, then stops short of explaining tokenizer internals.
Examples
Counts tokens across extracted sections, including sections with 154, 4,186, and 2,703 tokens.
What you can do after
Apply it via tiktoken to measure text length and decide when retrieval sections require further splitting.

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Generative AI in Action

tiktoken is an OpenAI library for encoding text into the token sequences used by OpenAI language models.

How it is used
Supports prompt-cost and context planning by counting the tokens processed or generated by language models.
How deep it goes
Explains BPE, direct and model-derived encoding selection, counting, and reversible decoding without descending into algorithmic internals.
Examples
Uses Python helpers, the prompt “I have a white dog named Champ,” token ID lists, and OpenAI encoding tables.
What you can do after
Apply it via Python to count, encode, and decode text using an encoding selected for an OpenAI model.

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Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.