mirror of
https://github.com/katanemo/plano.git
synced 2026-05-08 15:22:43 +02:00
Use mcp tools for filter chain (#621)
* agents framework demo * more changes * add more changes * pending changes * fix tests * fix more * rebase with main and better handle error from mcp * add trace for filters * add test for client error, server error and for mcp error * update schema validate code and rename kind => type in agent_filter * fix agent description and pre-commit * fix tests * add provider specific request parsing in agents chat * fix precommit and tests * cleanup demo * update readme * fix pre-commit * refactor tracing * fix fmt * fix: handle MessageContent enum in responses API conversion - Update request.rs to handle new MessageContent enum structure from main - MessageContent can now be Text(String) or Items(Vec<InputContent>) - Handle new InputItem variants (ItemReference, FunctionCallOutput) - Fixes compilation error after merging latest main (#632) * address pr feedback * fix span * fix build * update openai version
This commit is contained in:
Adil Hafeez
GitHub
parent
cb82a83c7b
commit
2f9121407b
40 changed files with 4886 additions and 190 deletions
|
|
@ -233,6 +233,104 @@ impl ProviderRequest for ConverseRequest {
|
|||
fn get_temperature(&self) -> Option<f32> {
|
||||
self.inference_config.as_ref()?.temperature
|
||||
}
|
||||
|
||||
fn get_messages(&self) -> Vec<crate::apis::openai::Message> {
|
||||
use crate::apis::openai::{Message, MessageContent, Role};
|
||||
|
||||
let mut openai_messages = Vec::new();
|
||||
|
||||
// Add system messages if present
|
||||
if let Some(system) = &self.system {
|
||||
for sys_block in system {
|
||||
match sys_block {
|
||||
SystemContentBlock::Text { text } => {
|
||||
openai_messages.push(Message {
|
||||
role: Role::System,
|
||||
content: MessageContent::Text(text.clone()),
|
||||
name: None,
|
||||
tool_calls: None,
|
||||
tool_call_id: None,
|
||||
});
|
||||
}
|
||||
_ => {} // Skip other system content types
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Convert conversation messages
|
||||
if let Some(messages) = &self.messages {
|
||||
for msg in messages {
|
||||
let role = match msg.role {
|
||||
ConversationRole::User => Role::User,
|
||||
ConversationRole::Assistant => Role::Assistant,
|
||||
};
|
||||
|
||||
// Extract text from content blocks
|
||||
let content = msg.content.iter()
|
||||
.filter_map(|block| {
|
||||
if let ContentBlock::Text { text } = block {
|
||||
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,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
openai_messages
|
||||
}
|
||||
|
||||
fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) {
|
||||
// Convert OpenAI messages to Bedrock format
|
||||
use crate::apis::amazon_bedrock::{ContentBlock, ConversationRole, SystemContentBlock};
|
||||
|
||||
let mut system_blocks = Vec::new();
|
||||
let mut bedrock_messages = Vec::new();
|
||||
|
||||
for msg in messages {
|
||||
match msg.role {
|
||||
crate::apis::openai::Role::System => {
|
||||
if let crate::apis::openai::MessageContent::Text(text) = &msg.content {
|
||||
system_blocks.push(SystemContentBlock::Text { text: text.clone() });
|
||||
}
|
||||
}
|
||||
crate::apis::openai::Role::User | crate::apis::openai::Role::Assistant => {
|
||||
let role = match msg.role {
|
||||
crate::apis::openai::Role::User => ConversationRole::User,
|
||||
crate::apis::openai::Role::Assistant => ConversationRole::Assistant,
|
||||
_ => continue,
|
||||
};
|
||||
|
||||
let content = if let crate::apis::openai::MessageContent::Text(text) = &msg.content {
|
||||
vec![ContentBlock::Text { text: text.clone() }]
|
||||
} else {
|
||||
vec![]
|
||||
};
|
||||
|
||||
bedrock_messages.push(crate::apis::amazon_bedrock::Message {
|
||||
role,
|
||||
content,
|
||||
});
|
||||
}
|
||||
_ => {}
|
||||
}
|
||||
}
|
||||
|
||||
if !system_blocks.is_empty() {
|
||||
self.system = Some(system_blocks);
|
||||
}
|
||||
self.messages = Some(bedrock_messages);
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
|
|
|
|||
|
|
@ -541,6 +541,65 @@ impl ProviderRequest for MessagesRequest {
|
|||
fn get_temperature(&self) -> Option<f32> {
|
||||
self.temperature
|
||||
}
|
||||
|
||||
fn get_messages(&self) -> Vec<crate::apis::openai::Message> {
|
||||
use crate::apis::openai::Message;
|
||||
|
||||
let mut openai_messages = Vec::new();
|
||||
|
||||
// Add system prompt as system message if present
|
||||
if let Some(system) = &self.system {
|
||||
openai_messages.push(system.clone().into());
|
||||
}
|
||||
|
||||
// Convert each Anthropic message to OpenAI format
|
||||
for msg in &self.messages {
|
||||
if let Ok(converted_msgs) = TryInto::<Vec<Message>>::try_into(msg.clone()) {
|
||||
openai_messages.extend(converted_msgs);
|
||||
}
|
||||
}
|
||||
|
||||
openai_messages
|
||||
}
|
||||
|
||||
fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) {
|
||||
// Convert OpenAI messages to Anthropic format
|
||||
// Separate system messages from regular messages
|
||||
let mut system_messages = Vec::new();
|
||||
let mut regular_messages = Vec::new();
|
||||
|
||||
for msg in messages {
|
||||
if msg.role == crate::apis::openai::Role::System {
|
||||
system_messages.push(msg.clone());
|
||||
} else {
|
||||
regular_messages.push(msg.clone());
|
||||
}
|
||||
}
|
||||
|
||||
// Set system prompt if there are system messages
|
||||
if !system_messages.is_empty() {
|
||||
// Combine all system messages into one
|
||||
let system_text = system_messages.iter()
|
||||
.filter_map(|msg| {
|
||||
if let crate::apis::openai::MessageContent::Text(text) = &msg.content {
|
||||
Some(text.as_str())
|
||||
} else {
|
||||
None
|
||||
}
|
||||
})
|
||||
.collect::<Vec<_>>()
|
||||
.join("\n");
|
||||
|
||||
self.system = Some(crate::apis::anthropic::MessagesSystemPrompt::Single(system_text));
|
||||
}
|
||||
|
||||
// Convert regular messages
|
||||
self.messages = regular_messages.iter()
|
||||
.filter_map(|msg| {
|
||||
msg.clone().try_into().ok()
|
||||
})
|
||||
.collect();
|
||||
}
|
||||
}
|
||||
|
||||
impl MessagesResponse {
|
||||
|
|
|
|||
|
|
@ -735,6 +735,14 @@ impl ProviderRequest for ChatCompletionsRequest {
|
|||
fn get_temperature(&self) -> Option<f32> {
|
||||
self.temperature
|
||||
}
|
||||
|
||||
fn get_messages(&self) -> Vec<crate::apis::openai::Message> {
|
||||
self.messages.clone()
|
||||
}
|
||||
|
||||
fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) {
|
||||
self.messages = messages.to_vec();
|
||||
}
|
||||
}
|
||||
|
||||
/// Implementation of ProviderResponse for ChatCompletionsResponse
|
||||
|
|
|
|||
|
|
@ -1134,6 +1134,140 @@ impl ProviderRequest for ResponsesAPIRequest {
|
|||
fn get_temperature(&self) -> Option<f32> {
|
||||
self.temperature
|
||||
}
|
||||
|
||||
fn get_messages(&self) -> Vec<crate::apis::openai::Message> {
|
||||
use crate::apis::openai::{Message, MessageContent, Role};
|
||||
|
||||
let mut openai_messages = Vec::new();
|
||||
|
||||
// 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
|
||||
}
|
||||
|
||||
fn set_messages(&mut self, messages: &[crate::apis::openai::Message]) {
|
||||
// For ResponsesAPI, we need to convert messages back to input format
|
||||
// Extract system messages as instructions
|
||||
let system_text = messages.iter()
|
||||
.filter(|msg| msg.role == crate::apis::openai::Role::System)
|
||||
.filter_map(|msg| {
|
||||
if let crate::apis::openai::MessageContent::Text(text) = &msg.content {
|
||||
Some(text.as_str())
|
||||
} else {
|
||||
None
|
||||
}
|
||||
})
|
||||
.collect::<Vec<_>>()
|
||||
.join("\n");
|
||||
|
||||
if !system_text.is_empty() {
|
||||
self.instructions = Some(system_text);
|
||||
}
|
||||
|
||||
// Convert user/assistant messages to InputParam
|
||||
// For simplicity, we'll use the last user message as the input
|
||||
// or combine all non-system messages
|
||||
let input_messages: Vec<_> = messages.iter()
|
||||
.filter(|msg| msg.role != crate::apis::openai::Role::System)
|
||||
.collect();
|
||||
|
||||
if !input_messages.is_empty() {
|
||||
// If there's only one message, use Text format
|
||||
if input_messages.len() == 1 {
|
||||
if let crate::apis::openai::MessageContent::Text(text) = &input_messages[0].content {
|
||||
self.input = crate::apis::openai_responses::InputParam::Text(text.clone());
|
||||
}
|
||||
} else {
|
||||
// Multiple messages - combine them as text for now
|
||||
// A more sophisticated approach would use InputParam::Items
|
||||
let combined_text = input_messages.iter()
|
||||
.filter_map(|msg| {
|
||||
if let crate::apis::openai::MessageContent::Text(text) = &msg.content {
|
||||
Some(format!("{}: {}",
|
||||
match msg.role {
|
||||
crate::apis::openai::Role::User => "User",
|
||||
crate::apis::openai::Role::Assistant => "Assistant",
|
||||
_ => "Unknown",
|
||||
},
|
||||
text
|
||||
))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
})
|
||||
.collect::<Vec<_>>()
|
||||
.join("\n");
|
||||
|
||||
self.input = crate::apis::openai_responses::InputParam::Text(combined_text);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue