mirror of
https://github.com/katanemo/plano.git
synced 2026-06-17 15:25:17 +02:00
fixing the messages construction and using the trait for signals
This commit is contained in:
Salman Paracha
parent
8d5e083ae9
commit
78f90234da
3 changed files with 160 additions and 198 deletions
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@ -112,7 +112,7 @@ pub async fn llm_chat(
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.map(|msg| truncate_message(&msg, 50));
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// Extract messages for signal analysis (clone before moving client_request)
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let messages_for_signals = extract_messages_for_signals(&client_request);
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let messages_for_signals = client_request.get_messages();
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client_request.set_model(resolved_model.clone());
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if client_request.remove_metadata_key("archgw_preference_config") {
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@ -298,8 +298,8 @@ pub async fn llm_chat(
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);
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// Add messages for signal analysis if available
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if let Some(messages) = messages_for_signals {
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base_processor = base_processor.with_messages(messages);
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if !messages_for_signals.is_empty() {
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base_processor = base_processor.with_messages(messages_for_signals);
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}
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// === v1/responses state management: Wrap with ResponsesStateProcessor ===
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@ -499,14 +499,3 @@ async fn get_provider_info(
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(hermesllm::ProviderId::OpenAI, None)
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}
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}
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/// Extract messages from ProviderRequestType for signal analysis
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/// Returns None for non-ChatCompletions requests
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fn extract_messages_for_signals(
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request: &ProviderRequestType,
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) -> Option<Vec<hermesllm::apis::openai::Message>> {
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match request {
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ProviderRequestType::ChatCompletionsRequest(chat_req) => Some(chat_req.messages.clone()),
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_ => None,
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}
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}
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@ -1127,82 +1127,16 @@ impl ProviderRequest for ResponsesAPIRequest {
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}
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fn get_messages(&self) -> Vec<crate::apis::openai::Message> {
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use crate::apis::openai::{Message, MessageContent, Role};
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use crate::transforms::request::from_openai::ResponsesInputConverter;
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let mut openai_messages = Vec::new();
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// Use the shared converter to get the full conversion with image support
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let converter = ResponsesInputConverter {
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input: self.input.clone(),
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instructions: self.instructions.clone(),
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};
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// Add instructions as system message if present
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if let Some(instructions) = &self.instructions {
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openai_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_calls: None,
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tool_call_id: None,
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});
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}
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// Convert input to messages
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match &self.input {
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InputParam::Text(text) => {
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openai_messages.push(Message {
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role: Role::User,
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content: MessageContent::Text(text.clone()),
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name: None,
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tool_calls: None,
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tool_call_id: None,
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});
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}
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InputParam::Items(items) => {
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for item in items {
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match item {
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InputItem::Message(msg) => {
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// Convert message role
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let role = match 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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// Extract text from message content
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let content = match &msg.content {
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crate::apis::openai_responses::MessageContent::Text(text) => {
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text.clone()
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}
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crate::apis::openai_responses::MessageContent::Items(items) => {
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items
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.iter()
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.filter_map(|c| {
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if let InputContent::InputText { text } = c {
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Some(text.clone())
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} else {
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None
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}
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})
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.collect::<Vec<_>>()
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.join("\n")
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}
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};
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openai_messages.push(Message {
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role,
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content: MessageContent::Text(content),
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name: None,
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tool_calls: None,
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tool_call_id: None,
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});
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}
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// Skip other input item types for now
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InputItem::ItemReference { .. } | InputItem::FunctionCallOutput { .. } => {
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// These are not yet supported in agent framework
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}
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}
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}
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}
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}
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openai_messages
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// Convert and return, falling back to empty vec on error
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converter.try_into().unwrap_or_else(|_| Vec::new())
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}
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fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) {
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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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