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lib.rs
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lib.rs
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//! [![Build Status]][actions] [![Latest Version]][crates.io]
//!
//! [Build Status]: https://github.com/schneiderfelipe/chat-splitter/actions/workflows/rust.yml/badge.svg
//! [actions]: https://github.com/schneiderfelipe/chat-splitter/actions/workflows/rust.yml
//! [Latest Version]: https://img.shields.io/crates/v/chat_splitter.svg
//! [crates.io]: https://crates.io/crates/chat_splitter
//!
//! Never exceed [OpenAI](https://openai.com/)'s [chat models](https://platform.openai.com/docs/api-reference/chat)' [maximum number of tokens](https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them) when using the [`async_openai`](https://github.com/64bit/async-openai) Rust crate.
//!
//! `chat-splitter` splits chats into 'outdated' and 'recent' messages.
//! You can split by
//! both
//! maximum message count and
//! maximum chat completion token count.
//! We use [`tiktoken_rs`](https://github.com/zurawiki/tiktoken-rs) for counting tokens.
//!
//! # Usage
//!
//! Here's a basic example:
//!
//! ```ignore
//! // Get all your previously stored chat messages...
//! let mut stored_messages = /* get_stored_messages()? */;
//!
//! // ...and split into 'outdated' and 'recent',
//! // where 'recent' always fits the context size.
//! let (outdated_messages, recent_messages) =
//! ChatSplitter::default().split(&stored_messages);
//! ```
//!
//! For a more detailed example,
//! see [`examples/chat.rs`](https://github.com/schneiderfelipe/chat-splitter/blob/main/examples/chat.rs).
//!
//! # Contributing
//!
//! Contributions to `chat-splitter` are welcome!
//! If you find a bug or have a feature request,
//! please [submit an issue](https://github.com/schneiderfelipe/chat-splitter/issues).
//! If you'd like to contribute code,
//! please feel free to [submit a pull request](https://github.com/schneiderfelipe/chat-splitter/pulls).
use std::cmp::Ordering;
use indxvec::Search;
use tiktoken_rs::get_chat_completion_max_tokens;
use tiktoken_rs::model::get_context_size;
/// Chat splitter for [OpenAI](https://openai.com/)'s [chat models](https://platform.openai.com/docs/api-reference/chat) when using [`async_openai`].
///
/// For more detailed information,
/// see the [crate documentation](`crate`).
#[derive(Clone, Debug)]
pub struct ChatSplitter {
/// The model to use for tokenization, e.g., `gpt-3.5-turbo`.
///
/// It is passed to [`tiktoken_rs`] to select the correct tokenizer.
model: String,
/// The maximum number of tokens to leave for chat completion.
///
/// This is the same as in the [official API](https://platform.openai.com/docs/api-reference/chat#completions/create-prompt) and given to [`async_openai`].
/// The total length of input tokens and generated tokens is limited by the
/// model's context size.
/// Splits will have at least that many tokens
/// available for chat completion,
/// never less.
max_tokens: u16,
/// The maximum number of messages to have in the chat.
///
/// Splits will have at most that many messages,
/// never more.
max_messages: usize,
}
/// Hard limit that seems to be imposed by the `OpenAI` API.
const MAX_MESSAGES_LIMIT: usize = 2_048;
/// Recommended minimum for maximum chat completion tokens.
const RECOMMENDED_MIN_MAX_TOKENS: u16 = 256;
impl Default for ChatSplitter {
#[inline]
fn default() -> Self {
Self::new("gpt-3.5-turbo")
}
}
impl ChatSplitter {
/// Create a new [`ChatSplitter`] for the given model.
///
/// # Panics
///
/// If for some reason [`tiktoken_rs`] gives a context size twice as large
/// as what would fit in a [`u16`].
/// If this happens,
/// it should be considered a bug,
/// but this behaviour might change in the future,
/// as models with larger context sizes are released.
#[inline]
pub fn new(model: impl Into<String>) -> Self {
let model = model.into();
let max_tokens = u16::try_from(get_context_size(&model) / 2).unwrap();
let max_messages = MAX_MESSAGES_LIMIT / 2;
Self {
model,
max_tokens,
max_messages,
}
}
/// Set the maximum number of messages to have in the chat.
///
/// Splits will have at most that many messages,
/// never more.
#[inline]
#[must_use]
pub fn max_messages(mut self, max_messages: impl Into<usize>) -> Self {
self.max_messages = max_messages.into();
if self.max_messages > MAX_MESSAGES_LIMIT {
log::warn!(
"max_messages = {} > {MAX_MESSAGES_LIMIT}",
self.max_messages
);
}
self
}
/// Set the maximum number of tokens to leave for chat completion.
///
/// This is the same as in the [official API](https://platform.openai.com/docs/api-reference/chat#completions/create-prompt) and given to [`async_openai`].
/// The total length of input tokens and generated tokens is limited by the
/// model's context size.
/// Splits will have at least that many tokens
/// available for chat completion,
/// never less.
#[inline]
#[must_use]
pub fn max_tokens(mut self, max_tokens: impl Into<u16>) -> Self {
self.max_tokens = max_tokens.into();
if self.max_tokens < RECOMMENDED_MIN_MAX_TOKENS {
log::warn!(
"max_tokens = {} < {RECOMMENDED_MIN_MAX_TOKENS}",
self.max_tokens
);
}
self
}
/// Set the model to use for tokenization, e.g., `gpt-3.5-turbo`.
///
/// It is passed to [`tiktoken_rs`] to select the correct tokenizer.
#[inline]
#[must_use]
pub fn model(mut self, model: impl Into<String>) -> Self {
self.model = model.into();
self
}
/// Get a split position by only considering `max_messages`.
#[inline]
fn position_by_max_messages<M>(&self, messages: &[M]) -> usize {
let upper_limit = self.max_messages.min(MAX_MESSAGES_LIMIT);
let n = messages.len();
let n = if n <= upper_limit { 0 } else { n - upper_limit };
debug_assert!(messages[n..].len() <= upper_limit);
n
}
/// Get a split position by only considering `max_tokens`.
///
/// # Panics
///
/// If tokenizer for the specified model is not found or is not a supported
/// chat model.
#[inline]
fn position_by_max_tokens<M>(&self, messages: &[M]) -> usize
where
M: IntoChatCompletionRequestMessage + Clone,
{
let max_tokens = self.max_tokens as usize;
let lower_limit = max_tokens.min(get_context_size(&self.model));
let messages: Vec<_> = messages
.iter()
.cloned()
.map(IntoChatCompletionRequestMessage::into_tiktoken_rs)
.collect();
let (n, _range) = (0..=messages.len()).binary_any(|n| {
debug_assert!(n < messages.len());
let tokens = get_chat_completion_max_tokens(&self.model, &messages[n..])
.expect("tokenizer should be available");
let cmp = tokens.cmp(&lower_limit);
debug_assert_ne!(cmp, Ordering::Equal);
cmp
});
debug_assert!(
get_chat_completion_max_tokens(&self.model, &messages[n..])
.expect("tokenizer should be available")
>= lower_limit
);
n
}
/// Get a split position by first considering the `max_messages` limit,
/// then
/// the `max_tokens` limit.
///
/// # Panics
///
/// If tokenizer for the specified model is not found or is not a supported
/// chat model.
#[inline]
fn position<M>(&self, messages: &[M]) -> usize
where
M: IntoChatCompletionRequestMessage + Clone,
{
let n = self.position_by_max_messages(messages);
n + self.position_by_max_tokens(&messages[n..])
}
/// Split the chat into two groups of messages,
/// the 'outdated' and the
/// 'recent' ones.
///
/// The 'recent' messages are guaranteed to satisfy the given limits,
/// while
/// the 'outdated' ones contain all the ones before 'recent'.
///
/// For a detailed usage example,
/// see [`examples/chat.rs`](https://github.com/schneiderfelipe/chat-splitter/blob/main/examples/chat.rs).
///
/// # Panics
///
/// If tokenizer for the specified model is not found or is not a supported
/// chat model.
#[inline]
pub fn split<'a, M>(&self, messages: &'a [M]) -> (&'a [M], &'a [M])
where
M: IntoChatCompletionRequestMessage + Clone,
{
messages.split_at(self.position(messages))
}
}
/// Extension trait for converting between different chat completion request
/// message types.
///
/// For a usage example,
/// see [`examples/chat.rs`](https://github.com/schneiderfelipe/chat-splitter/blob/736f4fceb57bc12adb2b70deb990030a266a95a5/examples/chat.rs#L44-L55).
pub trait IntoChatCompletionRequestMessage {
/// Convert to [`tiktoken_rs` chat completion request message
/// type](`tiktoken_rs::ChatCompletionRequestMessage`).
fn into_tiktoken_rs(self) -> tiktoken_rs::ChatCompletionRequestMessage;
/// Convert to [`async_openai` chat completion request message
/// type](`async_openai::types::ChatCompletionRequestMessage`).
fn into_async_openai(self) -> async_openai::types::ChatCompletionRequestMessage;
}
impl IntoChatCompletionRequestMessage for tiktoken_rs::ChatCompletionRequestMessage {
#[inline]
fn into_tiktoken_rs(self) -> tiktoken_rs::ChatCompletionRequestMessage {
self
}
#[inline]
fn into_async_openai(self) -> async_openai::types::ChatCompletionRequestMessage {
async_openai::types::ChatCompletionRequestMessage {
role: match self.role.as_ref() {
"user" => async_openai::types::Role::User,
"system" => async_openai::types::Role::System,
"assistant" => async_openai::types::Role::Assistant,
"function" => async_openai::types::Role::Function,
role => panic!("unknown role '{role}'"),
},
content: self.content,
function_call: self.function_call.map(|fc| {
async_openai::types::FunctionCall {
name: fc.name,
arguments: fc.arguments,
}
}),
name: self.name,
}
}
}
impl IntoChatCompletionRequestMessage for async_openai::types::ChatCompletionRequestMessage {
#[inline]
fn into_tiktoken_rs(self) -> tiktoken_rs::ChatCompletionRequestMessage {
tiktoken_rs::ChatCompletionRequestMessage {
role: self.role.to_string(),
content: self.content,
function_call: self.function_call.map(|fc| {
tiktoken_rs::FunctionCall {
name: fc.name,
arguments: fc.arguments,
}
}),
name: self.name,
}
}
#[inline]
fn into_async_openai(self) -> async_openai::types::ChatCompletionRequestMessage {
self
}
}
impl IntoChatCompletionRequestMessage for async_openai::types::ChatCompletionResponseMessage {
#[inline]
fn into_tiktoken_rs(self) -> tiktoken_rs::ChatCompletionRequestMessage {
tiktoken_rs::ChatCompletionRequestMessage {
role: self.role.to_string(),
content: self.content,
function_call: self.function_call.map(|fc| {
tiktoken_rs::FunctionCall {
name: fc.name,
arguments: fc.arguments,
}
}),
name: None,
}
}
#[inline]
fn into_async_openai(self) -> async_openai::types::ChatCompletionRequestMessage {
async_openai::types::ChatCompletionRequestMessage {
role: self.role,
content: self.content,
function_call: self.function_call,
name: None,
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn it_works() {
let messages: Vec<async_openai::types::ChatCompletionRequestMessage> = Vec::new();
assert_eq!(ChatSplitter::default().split(&messages).0, &[]);
assert_eq!(ChatSplitter::default().split(&messages).1, &[]);
}
}