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Introduce signals change (#655)

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* adding support for signals

* reducing false positives for signals like positive interaction

* adding docs. Still need to fix the messages list, but waiting on PR #621

* Improve frustration detection: normalize contractions and refine punctuation

* Further refine test cases with longer messages

* minor doc changes

* fixing echo statement for build

* fixing the messages construction and using the trait for signals

* update signals docs

* fixed some minor doc changes

* added more tests and fixed docuemtnation. PR 100% ready

* made fixes based on PR comments

* Optimize latency

1. replace sliding window approach with trigram containment check
2. add code to pre-compute ngrams for patterns

* removed some debug statements to make tests easier to read

* PR comments to make ObservableStreamProcessor accept optonal Vec<Messagges>

* fixed PR comments

---------

Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-342.local>
Co-authored-by: MeiyuZhong <mariazhong9612@gmail.com>
Co-authored-by: nehcgs <54548843+nehcgs@users.noreply.github.com>
This commit is contained in:
Salman Paracha 2026-01-07 11:20:44 -08:00 • committed by GitHub
parent 57327ba667
commit b4543ba56c
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17 changed files with 3972 additions and 191 deletions
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82
crates/hermesllm/src/apis/openai_responses.rs
View file

@ -1127,82 +1127,16 @@ impl ProviderRequest for ResponsesAPIRequest {
}
fn get_messages(&self) -> Vec<crate::apis::openai::Message> {
use crate::apis::openai::{Message, MessageContent, Role};
use crate::transforms::request::from_openai::ResponsesInputConverter;
let mut openai_messages = Vec::new();
// Use the shared converter to get the full conversion with image support
let converter = ResponsesInputConverter {
input: self.input.clone(),
instructions: self.instructions.clone(),
};
// Add instructions as system message if present
if let Some(instructions) = &self.instructions {
openai_messages.push(Message {
role: Role::System,
content: MessageContent::Text(instructions.clone()),
name: None,
tool_calls: None,
tool_call_id: None,
});
}
// Convert input to messages
match &self.input {
InputParam::Text(text) => {
openai_messages.push(Message {
role: Role::User,
content: MessageContent::Text(text.clone()),
name: None,
tool_calls: None,
tool_call_id: None,
});
}
InputParam::Items(items) => {
for item in items {
match item {
InputItem::Message(msg) => {
// Convert message role
let role = match msg.role {
MessageRole::User => Role::User,
MessageRole::Assistant => Role::Assistant,
MessageRole::System => Role::System,
MessageRole::Developer => Role::System, // Map developer to system
};
// Extract text from message content
let content = match &msg.content {
crate::apis::openai_responses::MessageContent::Text(text) => {
text.clone()
}
crate::apis::openai_responses::MessageContent::Items(items) => {
items
.iter()
.filter_map(|c| {
if let InputContent::InputText { text } = c {
Some(text.clone())
} else {
None
}
})
.collect::<Vec<_>>()
.join("\n")
}
};
openai_messages.push(Message {
role,
content: MessageContent::Text(content),
name: None,
tool_calls: None,
tool_call_id: None,
});
}
// Skip other input item types for now
InputItem::ItemReference { .. } | InputItem::FunctionCallOutput { .. } => {
// These are not yet supported in agent framework
}
}
}
}
}
openai_messages
// Convert and return, falling back to empty vec on error
converter.try_into().unwrap_or_else(|_| Vec::new())
}
fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) {