V4.14.4 dev (#6058)

* perf: faq

* index

* delete dataset

* delete dataset

* perf: delete dataset

* init

* fix: outLink UID (#6048)

* perf: query extension

* fix: s3 configs (#6050)

* fix: s3 configs

* s3

---------

Co-authored-by: archer <545436317@qq.com>

* s3 valid string check

* perf: completion api

* fix: model test

* perf: init

* fix: init

* fix: init shell

* fix: faq

---------

Co-authored-by: Roy <whoeverimf5@gmail.com>

packages/service/core/ai/functions/queryExtension.ts

@@ -13,7 +13,7 @@ import { useTextCosine } from '../hooks/useTextCosine';
   This module can eliminate referential ambiguity and expand queries based on context to improve retrieval.
   Submodular Optimization Mode: Generate multiple candidate queries, then use submodular algorithm to select the optimal query combination
 */
-const title = global.feConfigs?.systemTitle || 'FastAI';
+const title = global.feConfigs?.systemTitle || 'Nginx';
 const defaultPrompt = `## 你的任务
 你作为一个向量检索助手，你的任务是结合历史记录，为"原问题"生成{{count}}个不同版本的"检索词"。这些检索词应该从不同角度探索主题，以提高向量检索的语义丰富度和精度。
 
@@ -230,7 +230,7 @@ assistant: ${chatBg}
     .replace(/  /g, '');
 
   try {
-    const queries = json5.parse(jsonStr) as string[];
+    let queries = json5.parse(jsonStr) as string[];
 
     if (!Array.isArray(queries) || queries.length === 0) {
       return {
@@ -248,6 +248,8 @@ assistant: ${chatBg}
     const { lazyGreedyQuerySelection, embeddingModel: useEmbeddingModel } = useTextCosine({
       embeddingModel
     });
+    queries = queries.map((item) => String(item));
+
     const { selectedData: selectedQueries, embeddingTokens } = await lazyGreedyQuerySelection({
       originalText: query,
       candidates: queries,

packages/service/core/ai/llm/request.ts

@@ -81,7 +81,7 @@ export const createLLMResponse = async <T extends CompletionsBodyType>(
     return requestMessages;
   })();
 
-  const requestBody = await llmCompletionsBodyFormat({
+  const { requestBody, modelData } = await llmCompletionsBodyFormat({
     ...body,
     messages: rewriteMessages
   });
@@ -89,6 +89,7 @@ export const createLLMResponse = async <T extends CompletionsBodyType>(
   // console.log(JSON.stringify(requestBody, null, 2));
   const { response, isStreamResponse, getEmptyResponseTip } = await createChatCompletion({
     body: requestBody,
+    modelData,
     userKey,
     options: {
       headers: {
@@ -491,10 +492,16 @@ const llmCompletionsBodyFormat = async <T extends CompletionsBodyType>({
   parallel_tool_calls,
   toolCallMode,
   ...body
-}: LLMRequestBodyType<T>): Promise<InferCompletionsBody<T>> => {
+}: LLMRequestBodyType<T>): Promise<{
+  requestBody: InferCompletionsBody<T>;
+  modelData: LLMModelItemType;
+}> => {
   const modelData = getLLMModel(body.model);
   if (!modelData) {
-    return body as unknown as InferCompletionsBody<T>;
+    return {
+      requestBody: body as unknown as InferCompletionsBody<T>,
+      modelData
+    };
   }
 
   const response_format = (() => {
@@ -548,7 +555,10 @@ const llmCompletionsBodyFormat = async <T extends CompletionsBodyType>({
     });
   }
 
-  return requestBody as unknown as InferCompletionsBody<T>;
+  return {
+    requestBody: requestBody as unknown as InferCompletionsBody<T>,
+    modelData
+  };
 };
 const createChatCompletion = async ({
   modelData,
@@ -579,6 +589,7 @@ const createChatCompletion = async ({
   try {
     // Rewrite model
     const modelConstantsData = modelData || getLLMModel(body.model);
+
     if (!modelConstantsData) {
       return Promise.reject(`${body.model} not found`);
     }