solve some comments.

dbgpt/rag/transformer/intent_interpreter.py

@@ -87,8 +87,8 @@ def drop(self):
         """Do nothing by default."""
 
     def _parse_response(self, text: str) -> Dict:
-        """Parse llm response."""
         """
+        Parse llm response.
         The returned diction should contain the following content.
         {
             "category": "Type of the given question.",

dbgpt/rag/transformer/text2gql.py

@@ -2,7 +2,7 @@
 import json
 import logging
 import re
-from typing import Dict, List
+from typing import Dict, List, Union
 
 from dbgpt.core import BaseMessage, HumanPromptTemplate, LLMClient
 from dbgpt.rag.transformer.llm_translator import LLMTranslator
@@ -69,12 +69,12 @@ def __init__(self, llm_client: LLMClient, model_name: str):
 
     def _format_messages(self, text: str, history: str = None) -> List[BaseMessage]:
         # translate intention to gql with single prompt only.
-        intention = json.loads(text)
-        question = intention["rewritten_question"]
-        category = intention["category"]
-        entities = intention["entities"]
-        relations = intention["relations"]
-        schema = intention["schema"]
+        intention: Dict[str, Union[str, List[str]]] = json.loads(text)
+        question = intention.get("rewritten_question")
+        category = intention.get("category")
+        entities = intention.get("entities")
+        relations = intention.get("relations")
+        schema = intention.get("schema")
 
         template = HumanPromptTemplate.from_template(self._prompt_template)
 

dbgpt/storage/knowledge_graph/community_summary.py

@@ -553,7 +553,7 @@ async def asimilar_search_with_scores(
                 json.dumps(intention)
             )
             try:
-                query = translation["query"]
+                query = translation.get("query")
                 if "LIMIT" not in query:
                     query += f" LIMIT {self._text_search_topk}"
                 subgraph = self._graph_store_apdater.query(query=query)