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lib/ai/pretend_tokenizer.ex
defmodule AI.PretendTokenizer do
@moduledoc """
OpenAI's tokenizer uses regexes that are not compatible with Erlang's regex
engine. There are a couple of modules available on hex, but all of them
require a working python installation, access to rustc, a number of external
dependencies, and some env flags set to allow it to compile.
Rather than impose that on end users, this module uses a deliberately
conservative token estimator. It guesstimates token counts with extra room
for token-dense inputs so callers can choose chunk sizes with a buffer for
inaccuracy.
"""
@type input :: String.t()
@type chunk_size :: non_neg_integer() | AI.Model.t()
@type reduction_factor :: float()
@type chunked_input :: [String.t()]
@chars_per_token 3
@spec chunk(input, chunk_size, reduction_factor) :: chunked_input
def chunk(input, %AI.Model{context: tokens}, reduction_factor) do
chunk(input, tokens, reduction_factor)
end
def chunk(input, chunk_size, reduction_factor) do
size = chunk_size(chunk_size, reduction_factor)
input
|> String.graphemes()
|> Enum.chunk_every(size)
|> Enum.map(&Enum.join/1)
end
def guesstimate_tokens(input) do
(String.length(input) / @chars_per_token)
|> ceil()
end
def over_max_for_openai_embeddings?(input) do
guesstimate_tokens(input) > 300_000
end
defp chunk_size(token_target, reduction_factor) do
target = trunc(token_target * @chars_per_token * reduction_factor)
case target do
0 -> 1
_ -> target
end
end
end