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Provider-neutral context management

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This commit is contained in:
Aine
2026-06-28 22:31:06 +01:00
committed by Slavi Pantaleev
parent 0be08c0292
commit a445933c0b
13 changed files with 249 additions and 40 deletions
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7
CHANGELOG.md
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@@ -1,3 +1,10 @@
# (2026-06-28) Version 1.25.0
- (**Feature**) [♻️ Context management](./docs/configuration/text-generation.md#️-context-management) now works with every provider, not only [OpenAI](./docs/providers.md#openai). Token counting previously went through [tiktoken-rs](https://github.com/zurawiki/tiktoken-rs), which is accurate only for OpenAI models and silently mis-counted everything else (worst of all for non-English text). OpenAI agents keep using tiktoken-rs; every other provider, including the recommended [Venice](./docs/providers.md#venice), now uses a provider-neutral approximation that needs no per-model tokenizer (ASCII counted at about four characters per token, other scripts such as Cyrillic and CJK at about two), landing within roughly 10-20% of the real count. See the [context management docs](./docs/configuration/text-generation.md#️-context-management).
- (**Improvement**) Context management now trims a conversation on whole-turn boundaries for every provider, so an assistant reply is never kept without the user message it answered. This also adjusts how the OpenAI provider trims: a dangling assistant reply at the oldest edge of the kept history is now dropped along with its missing prompt, rather than left in place.
# (2026-06-26) Version 1.24.0
- (**Feature**) Add an opt-in 💭 **thinking notice** for text generation. When enabled, a slow response (for example, from a reasoning model that runs for minutes) posts a "thinking…" placeholder after a short delay, refreshes it periodically with varying flavor text, and then edits that same message into the final answer, so a long wait no longer looks like a stuck bot. The notice is **disabled by default** and configurable per-room or globally via `text-generation set-thinking-notice-enabled true`. Fast responses (under the delay threshold) never show a placeholder. See the [text-generation configuration docs](./docs/configuration/text-generation.md#-thinking-notice).

2
Cargo.lock generated
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@@ -315,7 +315,7 @@ dependencies = [
[[package]]
name = "baibot"
version = "1.24.0"
version = "1.25.0"
dependencies = [
"anthropic",
"anyhow",

2
Cargo.toml
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@@ -7,7 +7,7 @@ license = "AGPL-3.0-or-later"
readme = "README.md"
keywords = ["matrix", "chat", "bot", "AI", "LLM"]
include = ["/etc/assets/baibot-torso-768.png", "/src", "/README.md", "/CHANGELOG.md", "/LICENSE"]
version = "1.24.0"
version = "1.25.0"
edition = "2024"
[lib]

2
README.md
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@@ -30,7 +30,7 @@ It's influenced by [chaz](https://github.com/arcuru/chaz), but does **not** use
- 🔒 Supports [encryption](./docs/features.md#-encryption) for Matrix communication and Account-Data-stored configuration
- ♻️ Supports [context-management](./docs/configuration/text-generation.md#️-context-management) handling on some models (automatically adjusting the message history length, etc.)
- ♻️ Supports [context-management](./docs/configuration/text-generation.md#️-context-management) for every [provider](./docs/providers.md) (automatically trimming older messages on whole-turn boundaries once a conversation outgrows the context window)
- 🛠️ Allows **customizing much of the bot's [configuration](./docs/configuration/README.md)** at runtime (using commands sent via chat)

4
docs/configuration/text-generation.md
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@@ -50,9 +50,9 @@ Example: `!bai config room text-generation set-auto-usage only_for_voice` (this
### ♻️ Context Management
The bot also supports ♻️ **context management**, which automatically adjusts the message history length, etc.
The bot also supports ♻️ **context management**, which automatically trims the oldest messages once a conversation grows past the context window. It drops whole turns at a time, so a reply is never separated from the message it answered.
This feature relies on [tokenization](https://en.wikipedia.org/wiki/Large_language_model#Tokenization) performed by the [tiktoken-rs](https://github.com/zurawiki/tiktoken-rs) library which is [poorly well-maintained](https://github.com/zurawiki/tiktoken-rs/issues/50) and only works well for [OpenAI](../providers.md#openai) models.
Counting tokens precisely needs the model's own tokenizer. For [OpenAI](../providers.md#openai) models, the bot counts them with the [tiktoken-rs](https://github.com/zurawiki/tiktoken-rs) library. For every other provider, including the recommended [Venice](../providers.md#venice), the bot falls back to a provider-neutral **approximation** that needs no per-model tokenizer: it counts ASCII text at about four characters per token and other scripts (Cyrillic, CJK, and so on) at about two. Treat it as rough, within roughly 10-20% of the real count for typical text, which is plenty for keeping a long conversation inside the context window.
This setting is **disabled by default**, but can be enabled via `!bai config room text-generation set-context-management-enabled true` (this can also be set globally, see [🛠️ Room Settings](./README.md#room-settings)).

4
docs/providers.md
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@@ -24,7 +24,7 @@ The list of supported providers is below.
### How to choose a provider
If you're not sure which provider to start with, **we recommend [OpenAI](#openai)** as it's the most popular and has the **widest range of capabilities**: [💬 text-generation](./features.md#-text-generation) (incl. vision, incl. [🛠️ tools](./features.md#️-built-in-tools-openai-only)), [🖌️ image-generation](./features.md#️image-generation), [🦻 speech-to-text](./features.md#-speech-to-text), [🗣️ text-to-speech](./features.md#️-text-to-speech).
If you're not sure which provider to start with, **we recommend [Venice](#venice)**: it's the most capable provider baibot supports (covering [💬 text-generation](./features.md#-text-generation) with vision, file inputs, prompt caching, and native web search, plus [🖌️ image-generation](./features.md#️-image-creation) incl. editing, [🦻 speech-to-text](./features.md#-speech-to-text), and [🗣️ text-to-speech](./features.md#️-text-to-speech)) and the only one that runs inference with no logging and no training on your data. If you'd rather start with the most widely-used option, [OpenAI](#openai) is a solid, well-supported choice too.
You don't need to choose just one though. The bot supports [mixing & matching models](./features.md#-mixing--matching-models), so you can use multiple providers at the same time.
@@ -176,7 +176,7 @@ This provider is just as featureful as the [OpenAI](#openai) provider, but is mo
### Venice
[Venice AI](https://venice.ai/chat?ref=kpXDe6) _(ref link with $10 bonus for you)_ runs inference on Venice-controlled GPUs or zero-data-retention partner infrastructure and stores no prompts or responses, so your conversations don't linger anywhere. It serves both frontier proprietary models and the latest open-source ones.
[Venice AI](https://venice.ai/chat?ref=kpXDe6) _(ref link with a $10 bonus for you)_ runs inference on Venice-controlled GPUs or zero-data-retention partner infrastructure and stores no prompts or responses, so your conversations don't linger anywhere. It serves both frontier proprietary models and the latest open-source ones.
- 🆔 Identifier: `venice`
- 🔗 Links: [🏠 Home page](https://venice.ai/chat?ref=kpXDe6), [👤 Sign up](https://venice.ai/chat?ref=kpXDe6), [📋 Models list](https://docs.venice.ai/models/overview)

4
src/agent/provider/anthropic/controller.rs
View File

@@ -15,7 +15,7 @@ use crate::agent::provider::{
};
use crate::conversation::llm::{
Author as LLMAuthor, Conversation as LLMConversation, Message as LLMMessage,
MessageContent as LLMMessageContent, shorten_messages_list_to_context_size,
MessageContent as LLMMessageContent, TokenEstimate, shorten_messages_list_to_context_size,
};
use crate::strings;
@@ -130,7 +130,7 @@ impl ControllerTrait for Controller {
tracing::trace!("Shortening messages list to context size");
conversation_messages = shorten_messages_list_to_context_size(
&text_generation_config.model_id,
TokenEstimate::Approximate,
&prompt_message,
conversation_messages,
Some(text_generation_config.max_response_tokens),

4
src/agent/provider/openai/controller.rs
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@@ -24,7 +24,7 @@ use crate::{
},
conversation::llm::{
Author as LLMAuthor, Conversation as LLMConversation, Message as LLMMessage,
MessageContent as LLMMessageContent, shorten_messages_list_to_context_size,
MessageContent as LLMMessageContent, TokenEstimate, shorten_messages_list_to_context_size,
},
utils::base64::base64_decode,
};
@@ -117,7 +117,7 @@ impl ControllerTrait for Controller {
tracing::trace!("Shortening messages list to context size");
conversation_messages = shorten_messages_list_to_context_size(
&text_generation_config.model_id,
TokenEstimate::Tiktoken(&text_generation_config.model_id),
&prompt_message,
conversation_messages,
text_generation_config.max_response_tokens,

4
src/agent/provider/openai_compat/controller.rs
View File

@@ -15,7 +15,7 @@ use crate::{
},
conversation::llm::{
Author as LLMAuthor, Conversation as LLMConversation, Message as LLMMessage,
MessageContent as LLMMessageContent, shorten_messages_list_to_context_size,
MessageContent as LLMMessageContent, TokenEstimate, shorten_messages_list_to_context_size,
},
};
use crate::{
@@ -114,7 +114,7 @@ impl ControllerTrait for Controller {
tracing::trace!("Shortening messages list to context size");
conversation_messages = shorten_messages_list_to_context_size(
&text_generation_config.model_id,
TokenEstimate::Approximate,
&prompt_message,
conversation_messages,
text_generation_config.max_response_tokens,

4
src/agent/provider/venice/chat.rs
View File

@@ -8,7 +8,7 @@ use crate::agent::AgentPurpose;
use crate::agent::provider::entity::{TextGenerationParams, TextGenerationResult};
use crate::conversation::llm::{
Author as LLMAuthor, Conversation as LLMConversation, Message as LLMMessage,
MessageContent as LLMMessageContent, shorten_messages_list_to_context_size,
MessageContent as LLMMessageContent, TokenEstimate, shorten_messages_list_to_context_size,
};
use crate::strings;
@@ -64,7 +64,7 @@ pub async fn generate_text(
if params.context_management_enabled {
conversation_messages = shorten_messages_list_to_context_size(
&text_generation_config.model_id,
TokenEstimate::Approximate,
&prompt_message,
conversation_messages,
text_generation_config.max_response_tokens,

2
src/conversation/llm/mod.rs
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@@ -6,5 +6,5 @@ mod utils;
mod tests;
pub use entity::*;
pub use tokenization::shorten_messages_list_to_context_size;
pub use tokenization::{TokenEstimate, shorten_messages_list_to_context_size};
pub use utils::*;

248
src/conversation/llm/tokenization.rs
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@@ -4,6 +4,22 @@ use tiktoken_rs::tokenizer;
use super::{Author, Message, MessageContent};
/// How to count the tokens in a conversation when trimming it to fit the context window.
pub enum TokenEstimate<'a> {
/// Count via the [tiktoken-rs](https://github.com/zurawiki/tiktoken-rs) library.
/// Accurate for OpenAI models; every other model falls back to the gpt-4
/// tokenizer, which misreads it (badly so for non-English text). Use this only
/// for the OpenAI provider.
Tiktoken(&'a str),
/// Provider-neutral approximation that needs no per-model tokenizer. Expect it
/// to land within roughly 10-20% of the real count for typical text, leaning
/// slightly high: over-counting trims a little extra history, while
/// under-counting would overflow the model's real context window. Use this for
/// every non-OpenAI provider.
Approximate,
}
fn get_bpe_for_model(model: &str) -> &'static CoreBPE {
let tokenizer = tokenizer::get_tokenizer(model)
.or_else(|| tokenizer::get_tokenizer("gpt-4"))
@@ -13,21 +29,27 @@ fn get_bpe_for_model(model: &str) -> &'static CoreBPE {
}
pub fn shorten_messages_list_to_context_size(
model: &str,
estimate: TokenEstimate<'_>,
prompt_message: &Option<Message>,
mut messages: Vec<Message>,
max_response_tokens: Option<u32>,
max_context_tokens: u32,
) -> Vec<Message> {
// Loading the tokenization data is an expensive process, so
// se construct the BPE instance once and then use it for all messages.
let bpe = get_bpe_for_model(model);
// Loading the tiktoken data is expensive, so we resolve the counter once up
// front and reuse it for every message.
let tiktoken = match estimate {
TokenEstimate::Tiktoken(model) => Some((get_bpe_for_model(model), model)),
TokenEstimate::Approximate => None,
};
let count = |message: &Message| match tiktoken {
Some((bpe, model)) => tiktoken_token_size_for_message(bpe, model, message),
None => approximate_token_size_for_message(message),
};
// We want to retain the prompt in all cases, so we always count it first.
// We also always reserve enough tokens for the maximum response we expect.
let mut current_context_length: u32 = if let Some(prompt_message) = prompt_message {
calculate_token_size_for_message(bpe, model, prompt_message)
+ max_response_tokens.unwrap_or(0)
count(prompt_message) + max_response_tokens.unwrap_or(0)
} else {
0
};
@@ -37,7 +59,7 @@ pub fn shorten_messages_list_to_context_size(
let mut messages_to_keep: Vec<Message> = Vec::new();
for message in messages {
let tokens_for_message = calculate_token_size_for_message(bpe, model, &message);
let tokens_for_message = count(&message);
if current_context_length + tokens_for_message > max_context_tokens {
break;
@@ -48,14 +70,26 @@ pub fn shorten_messages_list_to_context_size(
messages_to_keep.push(message);
}
// Cut on a turn boundary: the loop may stop right after an assistant reply
// whose triggering user message did not fit, which would leave the kept window
// starting on an orphaned reply. `messages_to_keep` is newest-first here, so
// the oldest kept messages are at the end; drop any trailing assistant messages
// until the window begins at the start of a turn (a user message).
while matches!(
messages_to_keep.last().map(|message| &message.author),
Some(Author::Assistant)
) {
messages_to_keep.pop();
}
messages_to_keep.reverse();
messages_to_keep
}
/// Calculate the token size of a message for a given model, with a preloaded CoreBPE object.
/// Related to `calculate_token_size_for_model_message`.
fn calculate_token_size_for_message(bpe: &CoreBPE, model: &str, message: &Message) -> u32 {
/// Token size of a message via tiktoken, for a preloaded CoreBPE object.
/// Accurate only for OpenAI models (see [`TokenEstimate::Tiktoken`]).
fn tiktoken_token_size_for_message(bpe: &CoreBPE, model: &str, message: &Message) -> u32 {
let (tokens_per_message, tokens_per_name) = if model.starts_with("gpt-3.5") {
(
4, // every message follows <im_start>{role/name}\n{content}<im_end>\n
@@ -80,6 +114,52 @@ fn calculate_token_size_for_message(bpe: &CoreBPE, model: &str, message: &Messag
(text_length + role_length + tokens_per_message + tokens_per_name) as u32
}
/// ASCII text averages about four characters per token.
const ASCII_TOKENS_PER_CHAR: f32 = 0.25;
/// Non-ASCII scripts (Cyrillic, CJK, and others) pack more information per
/// character: real tokenizers land around two characters per token for them, so
/// each counts as half a token. CJK runs a touch denser than that, so its estimate
/// can read slightly low, still within the tolerance this approximation targets.
const WIDE_TOKENS_PER_CHAR: f32 = 0.5;
/// Structural per-message overhead (role marker plus message framing), mirroring
/// the small constant the tiktoken path adds.
const APPROX_TOKENS_PER_MESSAGE: u32 = 4;
/// Provider-neutral, tokenizer-free token size of a message
/// (see [`TokenEstimate::Approximate`]).
fn approximate_token_size_for_message(message: &Message) -> u32 {
let text_tokens = match &message.content {
MessageContent::Text(text) => approximate_token_size_for_text(text),
// Images and files are not counted as text, matching the tiktoken path.
MessageContent::Image(..) | MessageContent::File(..) => 0,
};
text_tokens + APPROX_TOKENS_PER_MESSAGE
}
/// Rough token estimate for a piece of text, with no tokenizer.
///
/// ASCII characters count as a quarter-token each (~4 chars/token); characters
/// outside ASCII count as half a token each (~2 chars/token), matching how real
/// tokenizers treat Cyrillic and CJK. Weighting non-ASCII up keeps the estimate
/// from badly under-counting non-English text, the case the tiktoken fallback gets
/// most wrong.
fn approximate_token_size_for_text(text: &str) -> u32 {
let mut estimate = 0.0_f32;
for character in text.chars() {
estimate += if character.is_ascii() {
ASCII_TOKENS_PER_CHAR
} else {
WIDE_TOKENS_PER_CHAR
};
}
estimate.ceil() as u32
}
pub mod test {
#[test]
fn message_size_counting_works() {
@@ -94,7 +174,7 @@ pub mod test {
timestamp: chrono::Utc::now(),
};
let tokens = super::calculate_token_size_for_message(bpe, model, &message);
let tokens = super::tiktoken_token_size_for_message(bpe, model, &message);
assert_eq!(8, tokens);
}
@@ -118,7 +198,7 @@ pub mod test {
assert_eq!(
prompt_length,
super::calculate_token_size_for_message(bpe, model, &prompt)
super::tiktoken_token_size_for_message(bpe, model, &prompt)
);
let mut conversation_messages = Vec::new();
@@ -133,7 +213,7 @@ pub mod test {
assert_eq!(
first_length,
super::calculate_token_size_for_message(bpe, model, &first)
super::tiktoken_token_size_for_message(bpe, model, &first)
);
conversation_messages.push(first);
@@ -148,7 +228,7 @@ pub mod test {
assert_eq!(
second_length,
super::calculate_token_size_for_message(bpe, model, &second)
super::tiktoken_token_size_for_message(bpe, model, &second)
);
conversation_messages.push(second);
@@ -165,7 +245,7 @@ pub mod test {
assert_eq!(
third_length,
super::calculate_token_size_for_message(bpe, model, &third)
super::tiktoken_token_size_for_message(bpe, model, &third)
);
conversation_messages.push(third.clone());
@@ -182,7 +262,7 @@ pub mod test {
assert_eq!(
forth_length,
super::calculate_token_size_for_message(bpe, model, &forth)
super::tiktoken_token_size_for_message(bpe, model, &forth)
);
conversation_messages.push(forth.clone());
@@ -190,7 +270,7 @@ pub mod test {
assert_eq!(4, conversation_messages.len());
let new_conversation_messages = super::shorten_messages_list_to_context_size(
model,
super::TokenEstimate::Tiktoken(model),
&Some(prompt),
conversation_messages,
max_response_tokens,
@@ -229,7 +309,7 @@ pub mod test {
assert_eq!(
prompt_length,
super::calculate_token_size_for_message(bpe, model, &prompt)
super::tiktoken_token_size_for_message(bpe, model, &prompt)
);
let mut conversation_messages = Vec::new();
@@ -244,7 +324,7 @@ pub mod test {
assert_eq!(
first_length,
super::calculate_token_size_for_message(bpe, model, &first)
super::tiktoken_token_size_for_message(bpe, model, &first)
);
conversation_messages.push(first);
@@ -259,7 +339,7 @@ pub mod test {
assert_eq!(
second_length,
super::calculate_token_size_for_message(bpe, model, &second)
super::tiktoken_token_size_for_message(bpe, model, &second)
);
conversation_messages.push(second);
@@ -276,7 +356,7 @@ pub mod test {
assert_eq!(
third_length,
super::calculate_token_size_for_message(bpe, model, &third)
super::tiktoken_token_size_for_message(bpe, model, &third)
);
conversation_messages.push(third.clone());
@@ -293,7 +373,7 @@ pub mod test {
assert_eq!(
forth_length,
super::calculate_token_size_for_message(bpe, model, &forth)
super::tiktoken_token_size_for_message(bpe, model, &forth)
);
conversation_messages.push(forth.clone());
@@ -301,7 +381,7 @@ pub mod test {
assert_eq!(4, conversation_messages.len());
let new_conversation_messages = super::shorten_messages_list_to_context_size(
model,
super::TokenEstimate::Tiktoken(model),
&Some(prompt),
conversation_messages,
max_response_tokens,
@@ -320,4 +400,126 @@ pub mod test {
forth.content
);
}
#[test]
fn approximate_counting_weights_ascii_and_wide_scripts() {
// 12 ASCII characters at ~4 chars/token = 3 text tokens.
assert_eq!(3, super::approximate_token_size_for_text("Hello there!"));
// 5 CJK characters at ~0.5 token/char = 3 text tokens. The ASCII rate would
// have under-counted these to 2, the failure mode this path avoids.
assert_eq!(3, super::approximate_token_size_for_text("こんにちは"));
let message = super::Message {
author: super::Author::User,
sender_id: None,
content: super::MessageContent::Text("Hello there!".to_string()),
timestamp: chrono::Utc::now(),
};
// 3 text tokens plus the per-message overhead (4).
assert_eq!(7, super::approximate_token_size_for_message(&message));
}
#[test]
fn approximate_shortening_trims_to_budget() {
let prompt = super::Message {
author: super::Author::Prompt,
sender_id: None,
content: super::MessageContent::Text("You are a bot!".to_string()),
timestamp: chrono::Utc::now(),
};
let older = super::Message {
author: super::Author::User,
sender_id: None,
content: super::MessageContent::Text("This is the older message.".to_string()),
timestamp: chrono::Utc::now(),
};
let newer = super::Message {
// A user message, so it is a valid window start: keeping a lone
// assistant reply would be an orphan and get trimmed (see
// `shortening_cuts_on_a_turn_boundary`).
author: super::Author::User,
sender_id: None,
content: super::MessageContent::Text("This is the newer message.".to_string()),
timestamp: chrono::Utc::now(),
};
// Budget room for the prompt and only the newest message.
let max_context_tokens = super::approximate_token_size_for_message(&prompt)
+ super::approximate_token_size_for_message(&newer);
let new_conversation_messages = super::shorten_messages_list_to_context_size(
super::TokenEstimate::Approximate,
&Some(prompt),
vec![older, newer.clone()],
None,
max_context_tokens,
);
assert_eq!(1, new_conversation_messages.len());
assert_eq!(
new_conversation_messages.first().unwrap().content,
newer.content
);
}
#[test]
fn shortening_cuts_on_a_turn_boundary() {
// A four-message conversation of two full turns. All four messages are the
// same length, so they cost the same number of tokens.
let prompt = super::Message {
author: super::Author::Prompt,
sender_id: None,
content: super::MessageContent::Text("system".to_string()),
timestamp: chrono::Utc::now(),
};
let user_one = super::Message {
author: super::Author::User,
sender_id: None,
content: super::MessageContent::Text("user msg 1".to_string()),
timestamp: chrono::Utc::now(),
};
let asst_one = super::Message {
author: super::Author::Assistant,
sender_id: None,
content: super::MessageContent::Text("asst msg 1".to_string()),
timestamp: chrono::Utc::now(),
};
let user_two = super::Message {
author: super::Author::User,
sender_id: None,
content: super::MessageContent::Text("user msg 2".to_string()),
timestamp: chrono::Utc::now(),
};
let asst_two = super::Message {
author: super::Author::Assistant,
sender_id: None,
content: super::MessageContent::Text("asst msg 2".to_string()),
timestamp: chrono::Utc::now(),
};
let per_message = super::approximate_token_size_for_message(&user_one);
// Budget fits the prompt plus three messages. By raw token budget the loop
// would keep asst_two, user_two, and asst_one, but asst_one's own user
// message (user_one) does not fit, so it must be dropped too rather than
// left as an orphaned reply.
let max_context_tokens =
super::approximate_token_size_for_message(&prompt) + (per_message * 3);
let kept = super::shorten_messages_list_to_context_size(
super::TokenEstimate::Approximate,
&Some(prompt),
vec![user_one, asst_one, user_two.clone(), asst_two.clone()],
None,
max_context_tokens,
);
// Only the last whole turn survives; the orphaned asst_one is dropped.
assert_eq!(2, kept.len());
assert_eq!(kept.first().unwrap().content, user_two.content);
assert_eq!(kept.last().unwrap().content, asst_two.content);
}
}

2
src/strings/help/cfg.rs
View File

@@ -128,7 +128,7 @@ pub fn text_generation_context_management_intro() -> String {
format!(
"{}\n{}",
"Controls the bot's ability to **intelligently drop old messages from the conversation context** when it gets too large.",
"This feature relies on [tokenization](https://en.wikipedia.org/wiki/Large_language_model#Tokenization) performed by the [tiktoken-rs](https://github.com/zurawiki/tiktoken-rs) library which is [poorly well-maintained](https://github.com/zurawiki/tiktoken-rs/issues/50) and only works well for [OpenAI](./providers.md#openai) models.",
"Counting tokens precisely needs the model's own tokenizer. For [OpenAI](./providers.md#openai) models the bot uses the [tiktoken-rs](https://github.com/zurawiki/tiktoken-rs) library; for every other provider (including the recommended [Venice](./providers.md#venice)) it falls back to a provider-neutral **approximation** (ASCII counted at ~4 characters per token, other scripts at ~2), within roughly 10-20% of the real count for typical text.",
)
}