feat(thread): add support for ThreadAssistantMessageReadingCode event

This commit introduces a new event type `ThreadAssistantMessageReadingCode` to handle code reading states in the thread. It also updates the `use-thread-run` hook to ignore unknown event types instead of throwing an error.

ee/tabby-schema/src/schema/thread/types.rs

@@ -250,6 +250,13 @@ pub struct ThreadAssistantMessageCreated {
     pub id: ID,
 }
 
+#[derive(GraphQLObject, Clone, Debug)]
+pub struct ThreadAssistantMessageReadingCode {
+    pub snippet: bool,
+    pub file_list: bool,
+    // pub commit_history: bool
+}
+
 #[derive(GraphQLObject)]
 pub struct ThreadAssistantMessageAttachmentsCode {
     pub code_source_id: String,
@@ -282,6 +289,7 @@ pub enum ThreadRunItem {
     ThreadRelevantQuestions(ThreadRelevantQuestions),
     ThreadUserMessageCreated(ThreadUserMessageCreated),
     ThreadAssistantMessageCreated(ThreadAssistantMessageCreated),
+    ThreadAssistantMessageReadingCode(ThreadAssistantMessageReadingCode),
     ThreadAssistantMessageAttachmentsCode(ThreadAssistantMessageAttachmentsCode),
     ThreadAssistantMessageAttachmentsDoc(ThreadAssistantMessageAttachmentsDoc),
     ThreadAssistantMessageContentDelta(ThreadAssistantMessageContentDelta),

ee/tabby-ui/lib/hooks/use-thread-run.ts

@@ -288,7 +288,8 @@ export function useThreadRun({
         x.completed = true
         break
       default:
-        throw new Error('Unknown event ' + JSON.stringify(x))
+        // Ignore unknown event type.
+        break
     }
 
     return x

ee/tabby-webserver/src/service/answer.rs

@@ -20,10 +20,7 @@ use async_openai_alt::{
 };
 use async_stream::stream;
 use futures::stream::BoxStream;
-use prompt_tools::{
-    pipeline_decide_need_codebase_context,
-    pipeline_related_questions,
-};
+use prompt_tools::{pipeline_decide_need_codebase_context, pipeline_related_questions};
 use tabby_common::{
     api::{
         code::{
@@ -116,6 +113,7 @@ impl AnswerService {
             if let Some(code_query) = options.code_query.as_ref() {
                 if let Some(repository) = self.find_repository(&context_info_helper, code_query, policy.clone()).await {
                     let need_codebase_context = pipeline_decide_need_codebase_context(self.chat.clone(), &query.content).await?;
+                    yield Ok(ThreadRunItem::ThreadAssistantMessageReadingCode(need_codebase_context.clone()));
                     if need_codebase_context.file_list {
                         // List at most 300 files in the repository.
                         match self.repository.list_files(&policy, &repository.kind, &repository.id, None, Some(300)).await {

ee/tabby-webserver/src/service/answer/prompt_tools.rs

@@ -6,6 +6,7 @@ use async_openai_alt::types::{
     CreateChatCompletionRequestArgs,
 };
 use tabby_inference::ChatCompletionStream;
+use tabby_schema::thread::ThreadAssistantMessageReadingCode;
 use tracing::debug;
 
 async fn request_llm(chat: Arc<dyn ChatCompletionStream>, prompt: &str) -> Result<String> {
@@ -77,17 +78,11 @@ fn detect_content(content: &str, check: &str) -> bool {
     content.to_lowercase().contains(check)
 }
 
-#[derive(Debug)]
-pub struct CodebaseContext {
-    pub snippet: bool,
-    pub file_list: bool,
-}
-
 /// Decide whether the question requires knowledge from codebase content.
 pub async fn pipeline_decide_need_codebase_context(
     chat: Arc<dyn ChatCompletionStream>,
     question: &str,
-) -> Result<CodebaseContext> {
+) -> Result<ThreadAssistantMessageReadingCode> {
     let prompt = format!(
         r#"You are a helpful assistant that helps the user to decide the types of context needed to answer the question. Currently, the following two kinds of context are supported:
 SNIPPET: Snippets searched from codebase given the question.
@@ -99,14 +94,15 @@ Here's a few examples:
 "How to implement an embedding api?" -> SNIPPET
 "Which file contains http api definitions" -> SNIPPET,FILE_LIST
 "How many python files is in the codebase?" -> FILE_LIST
+"Which file contains main function?" -> SNIPPET
 
 Here's the original question:
 {question}
 "#
     );
 
     let content = request_llm(chat, &prompt).await?;
-    let context = CodebaseContext {
+    let context = ThreadAssistantMessageReadingCode {
         snippet: detect_content(&content, "snippet"),
         file_list: detect_content(&content, "file_list"),
     };