feat(llm): 新增 IR 类型层 + 切换 LlmProvider trait 签名
引入跨 Provider 统一的新类型系统： - Message 扁平大枚举（System/User/UserImage/Assistant/ToolResult） - MessageRequest + MessageResponse + StreamEvent（高精度 IR） - PartialMessageResponse 含 apply_to/finalize 汇聚算法 - ProviderType enum 扩展 + ProviderCapabilities 切换 LlmProvider trait 签名为 chat(MessageRequest) → MessageResponse， chat_stream 返回 Stream<Item = Result<StreamEvent, LlmError>>，新增 capabilities()。 StreamEvent 命名冲突处理：旧变体迁移至 old_stream::LegacyStreamEvent， stream.rs 通过 pub use 重新导出新高精度事件。 OpenaiProvider 添加 Phase 0 临时桥接（MessageRequest ↔ OpenaiChatRequest 转换）， 标注 ponytail: Phase 0 临时桥接 标记，Phase 1 重写时移除。 测试 147 passed / 0 failed（新增 31 个新类型测试）。
This commit is contained in:
徐涛
+283
-177
@@ -16,11 +16,16 @@ use crate::llm::compact::{should_compact, microcompact, CompactConfig, CompactSt
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use crate::llm::cycle::retry::should_retry;
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use crate::llm::error::LlmError;
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use crate::llm::hooks::{HookContext, HookExecutor};
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use crate::llm::provider::LlmProvider;
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use crate::llm::provider::{LlmProvider, ProviderCapabilities, ProviderFeatures};
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use crate::llm::stream::StreamEvent;
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use crate::llm::types::message::{ContentBlock, Message};
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use crate::llm::types::openai_message::{
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ContentField, OpenaiChatMessage, OpenaiContentPart,
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};
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use crate::llm::types::request_v2::MessageRequest;
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use crate::llm::types::response_v2::{MessageResponse, StopReason};
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use crate::llm::types::{
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ChatRequest, ChatResponse, FinishReason, OpenaiChatMessage, OpenaiTool, OpenaiToolCall,
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ToolChoice, ToolDefinition,
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FinishReason, FunctionCall, OpenaiToolCall, ToolChoice, ToolDefinition,
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};
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/// LLM 调用周期配置。
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@@ -155,27 +160,18 @@ impl LlmCycle {
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&mut self,
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messages: Vec<OpenaiChatMessage>,
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tools: Vec<ToolDefinition>,
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) -> Result<ChatResponse, LlmError> {
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let openai_tools: Option<Vec<OpenaiTool>> = if tools.is_empty() {
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None
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} else {
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Some(
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tools
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.iter()
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.map(|t| OpenaiTool::Function {
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function: t.clone(),
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})
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.collect(),
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)
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};
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) -> Result<MessageResponse, LlmError> {
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let ir_messages: Vec<Message> =
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messages.iter().map(chat_message_to_ir_message).collect();
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let ir_tools: Vec<ToolDefinition> = tools.clone();
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let request = ChatRequest {
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let request = MessageRequest {
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model: self.config.model.clone(),
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messages,
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messages: ir_messages,
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tools: ir_tools,
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tool_choice: ToolChoice::Auto,
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max_tokens: self.config.max_tokens,
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temperature: self.config.temperature,
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tools: openai_tools,
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tool_choice: Some(ToolChoice::Auto),
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..Default::default()
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};
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@@ -198,11 +194,7 @@ impl LlmCycle {
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match self.provider.chat(request).await {
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Ok(response) => {
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if let Some(ref executor) = self.hook_executor {
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let post_request = ChatRequest {
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model: self.config.model.clone(),
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messages: vec![],
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..Default::default()
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};
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let post_request = MessageRequest::default();
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let ctx = HookContext::new(crate::llm::hooks::HookEvent::PostRequest)
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.with_request(&post_request);
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executor
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@@ -229,7 +221,7 @@ impl LlmCycle {
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&mut self,
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prompt: String,
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tools: Vec<ToolDefinition>,
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) -> Result<ChatResponse, LlmError> {
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) -> Result<MessageResponse, LlmError> {
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self.messages.push(OpenaiChatMessage::user_text(prompt));
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if let Some(ref config) = self.compact_config
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@@ -273,7 +265,9 @@ impl LlmCycle {
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.await;
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}
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self.messages.push(response.message.clone());
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// ponytail: Phase 0 中内部消息存储仍为 Vec<OpenaiChatMessage>，
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// 把 IR Message 转回旧 wire 格式以便存储。Phase 2 切换后移除。
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self.messages.push(message_to_chat_message(&response.message));
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self.usage.add(&response.usage);
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return Ok(response);
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@@ -346,59 +340,28 @@ impl LlmCycle {
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}
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}
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let chunk_stream = self.provider.chat_stream(request).await?;
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let ir_event_stream = self.provider.chat_stream(request).await?;
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let hook_executor = self.hook_executor.clone();
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let post_request = self.build_request(&tools);
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// ponytail: Phase 0 中 Provider 临时桥接层把 IR 事件再回填成本 phase 的
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// 信息冗余（OpenaiProvider 的 chat_stream 现在已输出 IR 事件）。当前实现
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// 直接转发 IR 事件，仅在 `MessageComplete` 到达时收尾。
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Ok(Box::pin(stream! {
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use futures_util::StreamExt;
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let mut chunk_stream = chunk_stream;
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let mut ir_event_stream = ir_event_stream;
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while let Some(result) = chunk_stream.next().await {
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while let Some(result) = ir_event_stream.next().await {
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match result {
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Ok(chunk) => {
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let mut assistant_text = String::new();
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let mut tool_started: Option<(String, String, String)> = None;
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for choice in &chunk.choices {
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let delta = &choice.delta;
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if let Some(content) = &delta.content {
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assistant_text.push_str(content);
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}
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if let Some(tool_calls) = &delta.tool_calls
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&& let Some(tc) = tool_calls.first()
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{
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let crate::llm::types::OpenaiToolCall::Function { id, function } = tc;
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tool_started = Some((id.clone(), function.name.clone(), function.arguments.clone()));
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}
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Ok(event) => {
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let is_terminal = matches!(event, StreamEvent::MessageComplete { .. });
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let is_error = matches!(event, StreamEvent::Error { .. });
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yield event;
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if is_terminal {
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break;
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}
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if !assistant_text.is_empty() {
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yield StreamEvent::AssistantTextDelta { text: assistant_text };
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}
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if let Some((tool_call_id, tool_name, arguments)) = tool_started {
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let args: serde_json::Value = serde_json::from_str(&arguments)
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.unwrap_or(serde_json::Value::Null);
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yield StreamEvent::ToolExecutionStarted {
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tool_name,
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input: args,
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tool_call_id,
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};
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}
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for choice in &chunk.choices {
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if let Some(finish_reason) = &choice.finish_reason {
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yield StreamEvent::TurnComplete {
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reason: *finish_reason,
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};
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}
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}
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if let Some(usage_info) = &chunk.usage {
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yield StreamEvent::CostUpdate { usage: *usage_info };
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if is_error {
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break;
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}
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}
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Err(e) => {
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@@ -429,37 +392,30 @@ impl LlmCycle {
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}))
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}
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fn build_request(&self, tools: &[ToolDefinition]) -> ChatRequest {
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let mut messages = self.messages.clone();
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fn build_request(&self, tools: &[ToolDefinition]) -> MessageRequest {
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let messages = self.messages.clone();
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let ir_messages: Vec<Message> = messages
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.iter()
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.map(chat_message_to_ir_message)
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.collect();
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let mut ir_messages = ir_messages;
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if let Some(sys_prompt) = &self.system_prompt
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&& !messages
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&& !ir_messages
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.iter()
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.any(|m| matches!(m, OpenaiChatMessage::System { .. }))
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.any(|m| matches!(m, Message::System { .. }))
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{
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messages.insert(0, OpenaiChatMessage::system_text(sys_prompt));
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ir_messages.insert(0, Message::system(sys_prompt.as_str()));
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}
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let openai_tools: Option<Vec<OpenaiTool>> = if tools.is_empty() {
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None
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} else {
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Some(
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tools
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.iter()
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.map(|t| OpenaiTool::Function {
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function: t.clone(),
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})
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.collect(),
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)
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};
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ChatRequest {
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MessageRequest {
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model: self.config.model.clone(),
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messages,
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messages: ir_messages,
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tools: tools.to_vec(),
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tool_choice: ToolChoice::Auto,
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max_tokens: self.config.max_tokens,
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temperature: self.config.temperature,
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tools: openai_tools,
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tool_choice: Some(ToolChoice::Auto),
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..Default::default()
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}
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}
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@@ -470,7 +426,7 @@ impl LlmCycle {
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async fn submit_request(
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&mut self,
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tools: &[ToolDefinition],
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) -> Result<ChatResponse, LlmError> {
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) -> Result<MessageResponse, LlmError> {
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let mut attempts = 0;
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loop {
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@@ -548,7 +504,7 @@ impl LlmCycle {
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&mut self,
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prompt: String,
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registry: &crate::tools::ToolRegistry,
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) -> Result<ChatResponse, LlmError> {
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) -> Result<MessageResponse, LlmError> {
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let tools = registry.definitions();
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let max_turns = self.config.max_tool_turns.unwrap_or(10);
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let tool_timeout = self.config.tool_timeout_secs;
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@@ -570,24 +526,25 @@ impl LlmCycle {
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let response = self.submit_request(&tools).await?;
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// 判断是否需要执行工具
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let should_execute = matches!(response.stop_reason, Some(FinishReason::ToolCalls))
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&& has_tool_calls_in_message(&response.message);
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let should_execute = matches!(response.stop_reason, StopReason::ToolUse)
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&& has_tool_calls_in_response(&response);
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// 将 Assistant 响应（含 tool_calls 或最终文本）追加到消息历史
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self.messages.push(response.message.clone());
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// ponytail: Phase 0 中内部消息存储仍为 Vec<OpenaiChatMessage>，
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// 把 IR Message 转回旧 wire 格式以便存储。Phase 2 切换后移除。
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self.messages.push(message_to_chat_message(&response.message));
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if !should_execute {
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return Ok(response);
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}
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// 解析 tool_calls 并执行
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let tool_calls = extract_tool_calls_from_message(&response.message);
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let tool_calls = extract_tool_calls_from_response(&response);
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let calls: Vec<(String, serde_json::Value)> = tool_calls
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.into_iter()
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.map(|(_id, name, args)| {
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let args: serde_json::Value =
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let value: serde_json::Value =
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serde_json::from_str(&args).unwrap_or(serde_json::Value::Null);
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(name, args)
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(name, value)
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})
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.collect();
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@@ -642,37 +599,185 @@ impl LlmCycle {
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}
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/// 判断 Assistant 消息是否包含 tool_calls。
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fn has_tool_calls_in_message(msg: &OpenaiChatMessage) -> bool {
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matches!(
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msg,
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OpenaiChatMessage::Assistant {
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tool_calls: Some(calls),
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..
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} if !calls.is_empty()
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)
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fn has_tool_calls_in_response(response: &MessageResponse) -> bool {
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match &response.message {
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Message::Assistant { content } => content
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.iter()
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.any(|b| matches!(b, ContentBlock::ToolUse { .. })),
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_ => false,
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}
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}
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/// 提取 Assistant 消息中的 tool_calls。
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///
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/// 返回 `(tool_call_id, tool_name, arguments_json_string)` 列表。
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fn extract_tool_calls_from_message(
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msg: &OpenaiChatMessage,
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///
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/// ponytail: 当前 Phase 0 实现，`arguments_json_string` 内含 JSON 序列化的 input。
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/// 消费方在调用 `registry.invoke_all()` 时反序列化一次。该小段冗余序列化
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/// 在 Phase 2 切换为 `Vec<Message>` 后可整体消除。
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fn extract_tool_calls_from_response(
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response: &MessageResponse,
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) -> Vec<(String, String, String)> {
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if let OpenaiChatMessage::Assistant {
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tool_calls: Some(calls),
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..
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} = msg
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{
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calls
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.iter()
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.map(|c| match c {
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OpenaiToolCall::Function { id, function } => {
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(id.clone(), function.name.clone(), function.arguments.clone())
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let mut out = Vec::new();
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if let Message::Assistant { content } = &response.message {
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for block in content {
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if let ContentBlock::ToolUse { id, name, input } = block {
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let args = serde_json::to_string(input).unwrap_or_else(|_| "null".to_string());
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out.push((id.clone(), name.clone(), args));
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}
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}
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}
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out
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}
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/// 把 `OpenaiChatMessage` 转换为 IR `Message`。
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///
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/// ponytail: Phase 0 转换层 —— build_request 中需要把存储用的
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/// `Vec<OpenaiChatMessage>` 转为 `MessageRequest.messages`。Phase 2 中
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/// `self.messages` 直接改为 `Vec<Message>` 时此函数整体删除。
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fn chat_message_to_ir_message(msg: &OpenaiChatMessage) -> Message {
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match msg {
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OpenaiChatMessage::Developer { content, .. }
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| OpenaiChatMessage::System { content, .. } => Message::System {
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content: content_field_to_blocks(content),
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},
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OpenaiChatMessage::User { content, .. } => Message::User {
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content: content_field_to_blocks(content),
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},
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OpenaiChatMessage::Assistant {
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content,
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tool_calls,
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..
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} => {
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let mut blocks = content_field_to_blocks(content);
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if let Some(calls) = tool_calls {
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for call in calls {
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if let OpenaiToolCall::Function { id, function } = call {
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let input: serde_json::Value = serde_json::from_str(&function.arguments)
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.unwrap_or(serde_json::Value::Null);
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blocks.push(ContentBlock::ToolUse {
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id: id.clone(),
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name: function.name.clone(),
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input,
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});
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}
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}
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}
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Message::Assistant { content: blocks }
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}
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OpenaiChatMessage::Tool {
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content,
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tool_call_id,
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} => Message::ToolResult {
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tool_call_id: tool_call_id.clone(),
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content: content_field_to_blocks(content),
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is_error: false,
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},
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OpenaiChatMessage::Function { content, name } => Message::ToolResult {
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tool_call_id: name.clone(),
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content: content_field_to_blocks(content),
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is_error: false,
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},
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}
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}
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/// 把 IR `Message` 转换为 `OpenaiChatMessage`（用于 `self.messages` 持久化）。
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///
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/// ponytail: Phase 0 转换层 —— submit 后 `response.message: Message` 需要
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/// 转回 `OpenaiChatMessage` 才能追加到旧 store。Phase 2 中移除。
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fn message_to_chat_message(msg: &Message) -> OpenaiChatMessage {
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match msg {
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Message::System { content } => OpenaiChatMessage::System {
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content: blocks_to_content_field(content),
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name: None,
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},
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Message::User { content } => OpenaiChatMessage::User {
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content: blocks_to_content_field(content),
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name: None,
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},
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// ponytail: UserImage 当前无 OpenAI 对应，回退为空的 User 消息（图片数据丢失）。
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// Phase 2 切换后此函数整体移除。
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Message::UserImage { .. } => OpenaiChatMessage::User {
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content: ContentField::Array(vec![]),
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name: None,
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},
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Message::Assistant { content } => {
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let mut text_blocks: Vec<String> = Vec::new();
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let mut tool_call_blocks: Vec<OpenaiToolCall> = Vec::new();
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for block in content {
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match block {
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ContentBlock::Text { text } => text_blocks.push(text.clone()),
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ContentBlock::ToolUse { id, name, input } => {
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tool_call_blocks.push(OpenaiToolCall::Function {
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id: id.clone(),
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function: FunctionCall {
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name: name.clone(),
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arguments: serde_json::to_string(input)
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.unwrap_or_else(|_| "null".to_string()),
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||||
},
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||||
});
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}
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// ponytail: thinking/refusal/image/audio/file 在 Phase 0 持久化时被丢弃。
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// Phase 2 切换后此函数整体移除，自然修复。
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_ => {}
|
||||
}
|
||||
}
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let text: String = text_blocks.into_iter().collect();
|
||||
OpenaiChatMessage::Assistant {
|
||||
content: if text.is_empty() {
|
||||
ContentField::Array(vec![])
|
||||
} else {
|
||||
ContentField::String(text)
|
||||
},
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||||
refusal: None,
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||||
name: None,
|
||||
tool_calls: if tool_call_blocks.is_empty() {
|
||||
None
|
||||
} else {
|
||||
Some(tool_call_blocks)
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||||
},
|
||||
}
|
||||
}
|
||||
Message::ToolResult {
|
||||
tool_call_id,
|
||||
content,
|
||||
is_error: _,
|
||||
} => OpenaiChatMessage::Tool {
|
||||
content: blocks_to_content_field(content),
|
||||
tool_call_id: tool_call_id.clone(),
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
fn blocks_to_content_field(blocks: &[ContentBlock]) -> ContentField {
|
||||
if let [ContentBlock::Text { text }] = blocks {
|
||||
return ContentField::String(text.clone());
|
||||
}
|
||||
let parts: Vec<OpenaiContentPart> = blocks
|
||||
.iter()
|
||||
.filter_map(|b| match b {
|
||||
ContentBlock::Text { text } => Some(OpenaiContentPart::Text { text: text.clone() }),
|
||||
_ => None,
|
||||
})
|
||||
.collect();
|
||||
ContentField::Array(parts)
|
||||
}
|
||||
|
||||
fn content_field_to_blocks(field: &ContentField) -> Vec<ContentBlock> {
|
||||
match field {
|
||||
ContentField::String(s) => vec![ContentBlock::Text { text: s.clone() }],
|
||||
ContentField::Array(parts) => parts
|
||||
.iter()
|
||||
.filter_map(|p| match p {
|
||||
OpenaiContentPart::Text { text } => {
|
||||
Some(ContentBlock::Text { text: text.clone() })
|
||||
}
|
||||
OpenaiContentPart::Refusal { refusal } => {
|
||||
Some(ContentBlock::Text { text: refusal.clone() })
|
||||
}
|
||||
_ => None,
|
||||
})
|
||||
.collect()
|
||||
} else {
|
||||
Vec::new()
|
||||
.collect(),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -691,19 +796,20 @@ fn truncate_tool_result(s: &str, max_bytes: usize) -> String {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::llm::types::{ContentField, OpenaiContentPart};
|
||||
use crate::tools::{BaseTool, ToolRegistry};
|
||||
use async_trait::async_trait;
|
||||
use futures_core::Stream;
|
||||
use serde_json::{json, Value};
|
||||
use std::pin::Pin;
|
||||
|
||||
/// 模拟 Provider —— 预定义响应序列，按调用顺序返回。
|
||||
struct MockProvider {
|
||||
responses: std::sync::Mutex<Vec<ChatResponse>>,
|
||||
responses: std::sync::Mutex<Vec<MessageResponse>>,
|
||||
call_count: std::sync::Mutex<u32>,
|
||||
}
|
||||
|
||||
impl MockProvider {
|
||||
fn new(responses: Vec<ChatResponse>) -> Self {
|
||||
fn new(responses: Vec<MessageResponse>) -> Self {
|
||||
Self {
|
||||
responses: std::sync::Mutex::new(responses),
|
||||
call_count: std::sync::Mutex::new(0),
|
||||
@@ -713,7 +819,7 @@ mod tests {
|
||||
|
||||
#[async_trait]
|
||||
impl LlmProvider for MockProvider {
|
||||
async fn chat(&self, _request: ChatRequest) -> Result<ChatResponse, LlmError> {
|
||||
async fn chat(&self, _request: MessageRequest) -> Result<MessageResponse, LlmError> {
|
||||
let mut count = self.call_count.lock().unwrap();
|
||||
*count += 1;
|
||||
let mut responses = self.responses.lock().unwrap();
|
||||
@@ -722,45 +828,61 @@ mod tests {
|
||||
}
|
||||
Ok(responses.remove(0))
|
||||
}
|
||||
async fn chat_stream(
|
||||
&self,
|
||||
_request: MessageRequest,
|
||||
) -> Result<Pin<Box<dyn Stream<Item = Result<StreamEvent, LlmError>> + Send>>, LlmError>
|
||||
{
|
||||
unimplemented!()
|
||||
}
|
||||
fn capabilities(&self) -> ProviderCapabilities {
|
||||
ProviderCapabilities {
|
||||
provider_name: "mock",
|
||||
supported_models: None,
|
||||
features: ProviderFeatures::default(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn empty_usage() -> crate::llm::types::Usage {
|
||||
crate::llm::types::Usage::default()
|
||||
}
|
||||
|
||||
fn assistant_text_response(text: &str) -> ChatResponse {
|
||||
ChatResponse {
|
||||
message: OpenaiChatMessage::assistant_text(text),
|
||||
fn assistant_text_response(text: &str) -> MessageResponse {
|
||||
MessageResponse {
|
||||
id: String::new(),
|
||||
model: String::new(),
|
||||
message: Message::Assistant {
|
||||
content: vec![ContentBlock::Text {
|
||||
text: text.into(),
|
||||
}],
|
||||
},
|
||||
usage: empty_usage(),
|
||||
stop_reason: Some(FinishReason::Stop),
|
||||
stop_reason: StopReason::Stop,
|
||||
extra: std::collections::HashMap::new(),
|
||||
}
|
||||
}
|
||||
|
||||
fn assistant_tool_call_response(
|
||||
calls: Vec<(&str, &str, &str)>,
|
||||
) -> ChatResponse {
|
||||
use crate::llm::types::{OpenaiToolCall, FunctionCall};
|
||||
let tool_calls: Vec<OpenaiToolCall> = calls
|
||||
fn assistant_tool_call_response(calls: Vec<(&str, &str, &str)>) -> MessageResponse {
|
||||
let tool_blocks: Vec<ContentBlock> = calls
|
||||
.into_iter()
|
||||
.map(|(id, name, args)| OpenaiToolCall::Function {
|
||||
id: id.to_string(),
|
||||
function: FunctionCall {
|
||||
.map(|(id, name, args)| {
|
||||
let input: serde_json::Value =
|
||||
serde_json::from_str(args).unwrap_or(serde_json::Value::Null);
|
||||
ContentBlock::ToolUse {
|
||||
id: id.to_string(),
|
||||
name: name.to_string(),
|
||||
arguments: args.to_string(),
|
||||
},
|
||||
input,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
ChatResponse {
|
||||
message: OpenaiChatMessage::Assistant {
|
||||
content: ContentField::Array(vec![OpenaiContentPart::Text {
|
||||
text: String::new(),
|
||||
}]),
|
||||
refusal: None,
|
||||
name: None,
|
||||
tool_calls: Some(tool_calls),
|
||||
},
|
||||
MessageResponse {
|
||||
id: String::new(),
|
||||
model: String::new(),
|
||||
message: Message::Assistant { content: tool_blocks },
|
||||
usage: empty_usage(),
|
||||
stop_reason: Some(FinishReason::ToolCalls),
|
||||
stop_reason: StopReason::ToolUse,
|
||||
extra: std::collections::HashMap::new(),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -790,7 +912,6 @@ mod tests {
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_submit_with_tools_single_turn() {
|
||||
// 第一轮：返回 tool_call；第二轮：返回最终文本
|
||||
let responses = vec![
|
||||
assistant_tool_call_response(vec![("call_1", "add", r#"{"a":1,"b":2}"#)]),
|
||||
assistant_text_response("答案是 3"),
|
||||
@@ -805,13 +926,11 @@ mod tests {
|
||||
.submit_with_tools("1+2=?".to_string(), ®istry)
|
||||
.await
|
||||
.unwrap();
|
||||
// 验证最终响应是文本响应
|
||||
assert!(matches!(
|
||||
response.message,
|
||||
OpenaiChatMessage::Assistant { .. }
|
||||
));
|
||||
// 最终响应是 Assistant 消息
|
||||
assert!(matches!(response.message, Message::Assistant { .. }));
|
||||
|
||||
// 验证消息历史：user, assistant(tool_calls), tool, assistant(text)
|
||||
// 验证消息历史（Vec<OpenaiChatMessage>，Phase 2 才切到 Vec<Message>）：
|
||||
// user, assistant(tool_calls → 转回后含 tool_use), tool, assistant(text)
|
||||
let messages = cycle.messages();
|
||||
assert_eq!(messages.len(), 4);
|
||||
assert!(matches!(messages[0], OpenaiChatMessage::User { .. }));
|
||||
@@ -822,7 +941,6 @@ mod tests {
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_submit_with_tools_multi_turn() {
|
||||
// 3 轮 tool 调用后给出最终答案
|
||||
let responses = vec![
|
||||
assistant_tool_call_response(vec![("call_1", "add", r#"{"a":1,"b":2}"#)]),
|
||||
assistant_tool_call_response(vec![("call_2", "add", r#"{"a":3,"b":4}"#)]),
|
||||
@@ -839,10 +957,7 @@ mod tests {
|
||||
.submit_with_tools("计算总和".to_string(), ®istry)
|
||||
.await
|
||||
.unwrap();
|
||||
assert!(matches!(
|
||||
response.message,
|
||||
OpenaiChatMessage::Assistant { .. }
|
||||
));
|
||||
assert!(matches!(response.message, Message::Assistant { .. }));
|
||||
|
||||
// user + 3*(assistant + tool) + final assistant = 8
|
||||
let messages = cycle.messages();
|
||||
@@ -851,10 +966,8 @@ mod tests {
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_submit_with_tools_max_turns_exceeded() {
|
||||
// 配置 max_tool_turns = 2
|
||||
let mut config = CycleConfig::default();
|
||||
config.max_tool_turns = Some(2);
|
||||
// 4 轮 tool 调用 + 终止
|
||||
let responses = vec![
|
||||
assistant_tool_call_response(vec![("c1", "add", r#"{"a":1,"b":1}"#)]),
|
||||
assistant_tool_call_response(vec![("c2", "add", r#"{"a":1,"b":1}"#)]),
|
||||
@@ -867,15 +980,12 @@ mod tests {
|
||||
let mut registry = ToolRegistry::new();
|
||||
registry.register(std::sync::Arc::new(AddTool)).unwrap();
|
||||
|
||||
let result = cycle
|
||||
.submit_with_tools("test".to_string(), ®istry)
|
||||
.await;
|
||||
let result = cycle.submit_with_tools("test".to_string(), ®istry).await;
|
||||
assert!(matches!(result, Err(LlmError::Other(msg)) if msg.contains("达到最大工具循环轮次")));
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_submit_with_tools_no_tool_call_response() {
|
||||
// LLM 直接给出最终响应（不调用工具）
|
||||
let responses = vec![assistant_text_response("直接回答")];
|
||||
let provider = Box::new(MockProvider::new(responses));
|
||||
let mut cycle = LlmCycle::new(provider, CycleConfig::default());
|
||||
@@ -887,10 +997,7 @@ mod tests {
|
||||
.submit_with_tools("直接回答".to_string(), ®istry)
|
||||
.await
|
||||
.unwrap();
|
||||
assert!(matches!(
|
||||
response.message,
|
||||
OpenaiChatMessage::Assistant { .. }
|
||||
));
|
||||
assert!(matches!(response.message, Message::Assistant { .. }));
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -911,7 +1018,6 @@ mod tests {
|
||||
fn test_truncate_tool_result_chinese_chars() {
|
||||
let s = "中".repeat(100);
|
||||
let truncated = truncate_tool_result(&s, 50);
|
||||
// 不会在字符中间截断
|
||||
assert!(truncated.starts_with("中"));
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user