Feat: support tree structured deep-research policy. (#12559)

### What problem does this PR solve?

#12558
### Type of change

- [x] New Feature (non-breaking change which adds functionality)

rag/prompts/multi_queries_gen.md

@@ -0,0 +1,41 @@
+You are a query optimization expert. 
+The user's original query failed to retrieve sufficient information; 
+please generate multiple complementary improved questions and corresponding queries.
+
+Original query:
+{{ original_query }}
+
+Original question:
+{{ original_question }}
+
+Currently, retrieved content:
+{{ retrieved_docs }}
+
+Missing information:
+{{ missing_info }}
+
+Please generate 2-3 complementary queries to help find the missing information. These queries should:
+1. Focus on different missing information points.
+2. Use different expressions.
+3. Avoid being identical to the original query.
+4. Remain concise and clear.
+
+Output format (JSON):
+```json
+{
+    "reasoning": "Explanation of query generation strategy",
+    "questions": [
+        {"question": "Improved question 1", "query": "Improved query 1"},
+        {"question": "Improved question 2", "query": "Improved query 2"},
+        {"question": "Improved question 3", "query": "Improved query 3"}
+    ]
+}
+```
+
+Requirements:
+1. Questions array contains 1-3 questions and corresponding queries.
+2. Each question length is between 5-200 characters.
+3. Each query length is between 1-5 keywords.
+4. Each query MUST be in the same language as the retrieved content in. 
+5. DO NOT generate question and query that is similar to the original query. 
+6. Reasoning explains the generation strategy.