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from __future__ import annotations
from .core import Encoding
from .registry import get_encoding
# TODO: these will likely be replaced by an API endpoint
MODEL_PREFIX_TO_ENCODING: dict[str, str] = {
"o1-": "o200k_base",
# chat
"chatgpt-4o-": "o200k_base",
"gpt-4o-": "o200k_base", # e.g., gpt-4o-2024-05-13
"gpt-4-": "cl100k_base", # e.g., gpt-4-0314, etc., plus gpt-4-32k
"gpt-3.5-turbo-": "cl100k_base", # e.g, gpt-3.5-turbo-0301, -0401, etc.
"gpt-35-turbo-": "cl100k_base", # Azure deployment name
# fine-tuned
"ft:gpt-4": "cl100k_base",
"ft:gpt-3.5-turbo": "cl100k_base",
"ft:davinci-002": "cl100k_base",
"ft:babbage-002": "cl100k_base",
}
MODEL_TO_ENCODING: dict[str, str] = {
# chat
"gpt-4o": "o200k_base",
"gpt-4": "cl100k_base",
"gpt-3.5-turbo": "cl100k_base",
"gpt-3.5": "cl100k_base", # Common shorthand
"gpt-35-turbo": "cl100k_base", # Azure deployment name
# base
"davinci-002": "cl100k_base",
"babbage-002": "cl100k_base",
# embeddings
"text-embedding-ada-002": "cl100k_base",
"text-embedding-3-small": "cl100k_base",
"text-embedding-3-large": "cl100k_base",
# DEPRECATED MODELS
# text (DEPRECATED)
"text-davinci-003": "p50k_base",
"text-davinci-002": "p50k_base",
"text-davinci-001": "r50k_base",
"text-curie-001": "r50k_base",
"text-babbage-001": "r50k_base",
"text-ada-001": "r50k_base",
"davinci": "r50k_base",
"curie": "r50k_base",
"babbage": "r50k_base",
"ada": "r50k_base",
# code (DEPRECATED)
"code-davinci-002": "p50k_base",
"code-davinci-001": "p50k_base",
"code-cushman-002": "p50k_base",
"code-cushman-001": "p50k_base",
"davinci-codex": "p50k_base",
"cushman-codex": "p50k_base",
# edit (DEPRECATED)
"text-davinci-edit-001": "p50k_edit",
"code-davinci-edit-001": "p50k_edit",
# old embeddings (DEPRECATED)
"text-similarity-davinci-001": "r50k_base",
"text-similarity-curie-001": "r50k_base",
"text-similarity-babbage-001": "r50k_base",
"text-similarity-ada-001": "r50k_base",
"text-search-davinci-doc-001": "r50k_base",
"text-search-curie-doc-001": "r50k_base",
"text-search-babbage-doc-001": "r50k_base",
"text-search-ada-doc-001": "r50k_base",
"code-search-babbage-code-001": "r50k_base",
"code-search-ada-code-001": "r50k_base",
# open source
"gpt2": "gpt2",
"gpt-2": "gpt2", # Maintains consistency with gpt-4
}
def encoding_name_for_model(model_name: str) -> str:
"""Returns the name of the encoding used by a model.
Raises a KeyError if the model name is not recognised.
"""
encoding_name = None
if model_name in MODEL_TO_ENCODING:
encoding_name = MODEL_TO_ENCODING[model_name]
else:
# Check if the model matches a known prefix
# Prefix matching avoids needing library updates for every model version release
# Note that this can match on non-existent models (e.g., gpt-3.5-turbo-FAKE)
for model_prefix, model_encoding_name in MODEL_PREFIX_TO_ENCODING.items():
if model_name.startswith(model_prefix):
return model_encoding_name
if encoding_name is None:
raise KeyError(
f"Could not automatically map {model_name} to a tokeniser. "
"Please use `tiktoken.get_encoding` to explicitly get the tokeniser you expect."
) from None
return encoding_name
def encoding_for_model(model_name: str) -> Encoding:
"""Returns the encoding used by a model.
Raises a KeyError if the model name is not recognised.
"""
return get_encoding(encoding_name_for_model(model_name))
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