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Introduce signals change (#655)
* 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
GitHub
parent
57327ba667
commit
b4543ba56c
17 changed files with 3972 additions and 191 deletions
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@ -24,6 +24,150 @@ use crate::transforms::*;
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type AnthropicMessagesRequest = MessagesRequest;
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// ============================================================================
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// RESPONSES API INPUT CONVERSION
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// ============================================================================
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/// Helper struct for converting ResponsesAPI input to OpenAI messages
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pub struct ResponsesInputConverter {
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pub input: InputParam,
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pub instructions: Option<String>,
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}
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impl TryFrom<ResponsesInputConverter> for Vec<Message> {
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type Error = TransformError;
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fn try_from(converter: ResponsesInputConverter) -> Result<Self, Self::Error> {
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// Convert input to messages
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match converter.input {
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InputParam::Text(text) => {
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// Simple text input becomes a user message
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let mut messages = Vec::new();
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// Add instructions as system message if present
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if let Some(instructions) = converter.instructions {
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messages.push(Message {
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role: Role::System,
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content: MessageContent::Text(instructions),
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name: None,
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tool_call_id: None,
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tool_calls: None,
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});
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}
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// Add the user message
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messages.push(Message {
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role: Role::User,
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content: MessageContent::Text(text),
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name: None,
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tool_call_id: None,
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tool_calls: None,
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});
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Ok(messages)
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}
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InputParam::Items(items) => {
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// Convert input items to messages
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let mut converted_messages = Vec::new();
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// Add instructions as system message if present
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if let Some(instructions) = converter.instructions {
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converted_messages.push(Message {
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role: Role::System,
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content: MessageContent::Text(instructions),
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name: None,
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tool_call_id: None,
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tool_calls: None,
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});
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}
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// Convert each input item
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for item in items {
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if let InputItem::Message(input_msg) = item {
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let role = match input_msg.role {
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MessageRole::User => Role::User,
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MessageRole::Assistant => Role::Assistant,
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MessageRole::System => Role::System,
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MessageRole::Developer => Role::System, // Map developer to system
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};
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// Convert content based on MessageContent type
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let content = match &input_msg.content {
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crate::apis::openai_responses::MessageContent::Text(text) => {
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// Simple text content
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MessageContent::Text(text.clone())
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}
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crate::apis::openai_responses::MessageContent::Items(content_items) => {
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// Check if it's a single text item (can use simple text format)
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if content_items.len() == 1 {
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if let InputContent::InputText { text } = &content_items[0] {
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MessageContent::Text(text.clone())
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} else {
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// Single non-text item - use parts format
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MessageContent::Parts(
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content_items.iter()
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.filter_map(|c| match c {
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InputContent::InputText { text } => {
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Some(crate::apis::openai::ContentPart::Text { text: text.clone() })
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}
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InputContent::InputImage { image_url, .. } => {
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Some(crate::apis::openai::ContentPart::ImageUrl {
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image_url: crate::apis::openai::ImageUrl {
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url: image_url.clone(),
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detail: None,
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}
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})
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}
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InputContent::InputFile { .. } => None, // Skip files for now
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InputContent::InputAudio { .. } => None, // Skip audio for now
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})
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.collect()
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)
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}
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} else {
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// Multiple content items - convert to parts
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MessageContent::Parts(
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content_items
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.iter()
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.filter_map(|c| match c {
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InputContent::InputText { text } => {
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Some(crate::apis::openai::ContentPart::Text {
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text: text.clone(),
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})
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}
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InputContent::InputImage { image_url, .. } => Some(
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crate::apis::openai::ContentPart::ImageUrl {
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image_url: crate::apis::openai::ImageUrl {
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url: image_url.clone(),
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detail: None,
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},
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},
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),
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InputContent::InputFile { .. } => None, // Skip files for now
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InputContent::InputAudio { .. } => None, // Skip audio for now
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})
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.collect(),
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)
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}
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}
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};
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converted_messages.push(Message {
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role,
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content,
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name: None,
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tool_call_id: None,
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tool_calls: None,
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});
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}
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}
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Ok(converted_messages)
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}
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}
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}
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}
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// ============================================================================
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// MAIN REQUEST TRANSFORMATIONS
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// ============================================================================
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@ -253,117 +397,12 @@ impl TryFrom<ResponsesAPIRequest> for ChatCompletionsRequest {
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type Error = TransformError;
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fn try_from(req: ResponsesAPIRequest) -> Result<Self, Self::Error> {
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// Convert input to messages
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let messages = match req.input {
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InputParam::Text(text) => {
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// Simple text input becomes a user message
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vec![Message {
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role: Role::User,
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content: MessageContent::Text(text),
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name: None,
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tool_call_id: None,
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tool_calls: None,
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}]
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}
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InputParam::Items(items) => {
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// Convert input items to messages
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let mut converted_messages = Vec::new();
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// Add instructions as system message if present
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if let Some(instructions) = &req.instructions {
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converted_messages.push(Message {
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role: Role::System,
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content: MessageContent::Text(instructions.clone()),
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name: None,
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tool_call_id: None,
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tool_calls: None,
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});
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}
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// Convert each input item
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for item in items {
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if let InputItem::Message(input_msg) = item {
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let role = match input_msg.role {
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MessageRole::User => Role::User,
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MessageRole::Assistant => Role::Assistant,
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MessageRole::System => Role::System,
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MessageRole::Developer => Role::System, // Map developer to system
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};
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// Convert content based on MessageContent type
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let content = match &input_msg.content {
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crate::apis::openai_responses::MessageContent::Text(text) => {
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// Simple text content
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MessageContent::Text(text.clone())
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}
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crate::apis::openai_responses::MessageContent::Items(content_items) => {
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// Check if it's a single text item (can use simple text format)
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if content_items.len() == 1 {
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if let InputContent::InputText { text } = &content_items[0] {
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MessageContent::Text(text.clone())
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} else {
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// Single non-text item - use parts format
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MessageContent::Parts(
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content_items.iter()
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.filter_map(|c| match c {
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InputContent::InputText { text } => {
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Some(crate::apis::openai::ContentPart::Text { text: text.clone() })
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}
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InputContent::InputImage { image_url, .. } => {
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Some(crate::apis::openai::ContentPart::ImageUrl {
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image_url: crate::apis::openai::ImageUrl {
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url: image_url.clone(),
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detail: None,
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}
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})
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}
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InputContent::InputFile { .. } => None, // Skip files for now
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InputContent::InputAudio { .. } => None, // Skip audio for now
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})
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.collect()
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)
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}
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} else {
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// Multiple content items - convert to parts
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MessageContent::Parts(
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content_items
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.iter()
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.filter_map(|c| match c {
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InputContent::InputText { text } => {
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Some(crate::apis::openai::ContentPart::Text {
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text: text.clone(),
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})
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}
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InputContent::InputImage { image_url, .. } => Some(
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crate::apis::openai::ContentPart::ImageUrl {
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image_url: crate::apis::openai::ImageUrl {
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url: image_url.clone(),
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detail: None,
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},
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},
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),
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InputContent::InputFile { .. } => None, // Skip files for now
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InputContent::InputAudio { .. } => None, // Skip audio for now
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})
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.collect(),
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)
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}
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}
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};
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converted_messages.push(Message {
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role,
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content,
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name: None,
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tool_call_id: None,
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tool_calls: None,
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});
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}
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}
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converted_messages
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}
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// Convert input to messages using the shared converter
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let converter = ResponsesInputConverter {
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input: req.input,
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instructions: req.instructions.clone(),
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};
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let messages: Vec<Message> = converter.try_into()?;
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// Build the ChatCompletionsRequest
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Ok(ChatCompletionsRequest {
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