speed optim

backend/app/agent/chart.py

@@ -9,8 +9,8 @@ if str(PROJECT_ROOT) not in sys.path:
     sys.path.append(str(PROJECT_ROOT))
 
 from nanobot.providers.litellm_provider import LiteLLMProvider
-from app.api.llm import _load_data as load_llm_config
 from app.schemas.chart import ChartGenerationResponse
+from app.services.llm_cache import get_active_llm_config
 
 CHART_INSTRUCTIONS = """
 ### INSTRUCTIONS ###
@@ -133,9 +133,7 @@ CHART_EXAMPLES = """
 """
 
 async def generate_chart(data: List[Dict[str, Any]], query: str) -> ChartGenerationResponse:
-    # 1. Initialize Provider
-    llm_configs = load_llm_config()
-    active_config = next((c for c in llm_configs if c.get("is_active")), None)
+    active_config = get_active_llm_config()
     
     if not active_config:
         return ChartGenerationResponse(
@@ -178,7 +176,7 @@ async def generate_chart(data: List[Dict[str, Any]], query: str) -> ChartGenerat
         columns = list(data[0].keys())
     
     # 3. Construct Prompt
-    schema_json = json.dumps(ChartGenerationResponse.model_json_schema(), indent=2)
+    schema_json = json.dumps(ChartGenerationResponse.model_json_schema(), ensure_ascii=False, separators=(",", ":"))
     
     system_prompt = f"""You are a data analyst great at visualizing data using vega-lite! Given the user's question, sample data and sample column values, you need to generate vega-lite schema in JSON and provide suitable chart type.
 Besides, you need to give a concise and easy-to-understand reasoning to describe why you provide such vega-lite schema based on the question, sample data and sample column values.
@@ -201,7 +199,7 @@ Please provide your chain of thought reasoning, chart type and the vega-lite sch
     user_prompt = f"""
 ### INPUT ###
 Question: {query}
-Sample Data: {json.dumps(sample_data, indent=2, default=str)}
+Sample Data: {json.dumps(sample_data, ensure_ascii=False, separators=(",", ":"), default=str)}
 Sample Column Values: {columns}
 Language: Chinese (Simplified)
 

backend/app/agent/nl2sql.py

@@ -18,13 +18,13 @@ from nanobot.providers.litellm_provider import LiteLLMProvider
 from app.connectors.postgres import postgres_connector
 from app.connectors.clickhouse import clickhouse_connector
 from app.connectors.factory import get_connector
-from app.api.llm import _load_data as load_llm_config
 from app.schemas.chart import ChartGenerationResponse
 from app.agent.chart import generate_chart
 from app.database import SessionLocal
 from app.models.datasource import DataSource
 from app.core.files import resolve_upload_file_path
 from app.services.mdl import MDLService
+from app.services.llm_cache import get_active_llm_config
 
 SCHEMA_CACHE_TTL_SECONDS = 300
 CONNECTION_CACHE_TTL_SECONDS = 30
@@ -41,6 +41,7 @@ class NL2SQLRequest(BaseModel):
     source: str = Field(..., description="Data source to query (postgres, clickhouse, upload, ds:{id})")
     file_url: Optional[str] = Field(None, description="Uploaded file URL when source is upload")
     session_id: Optional[str] = Field(None, description="Conversation session identifier")
+    generate_chart: bool = Field(False, description="Whether to generate chart specification")
 
 class NL2SQLResponse(BaseModel):
     sql: str
@@ -246,7 +247,7 @@ async def process_nl2sql(request: NL2SQLRequest) -> NL2SQLResponse:
          schema = connector.get_schema()
          _set_cached_schema(request.source, connector, schema)
          
-    schema_str = json.dumps(schema, indent=2)
+    schema_str = json.dumps(schema, ensure_ascii=False, separators=(",", ":"))
 
     # Try to load MDL context
     mdl_context = ""
@@ -280,8 +281,7 @@ async def process_nl2sql(request: NL2SQLRequest) -> NL2SQLResponse:
             print(f"Failed to load MDL: {e}")
 
     # 2. Get the active LLM config
-    llm_configs = load_llm_config()
-    active_config = next((c for c in llm_configs if c.get("is_active")), None)
+    active_config = get_active_llm_config()
     
     if not active_config:
         return NL2SQLResponse(sql="", result=[], error="No active LLM configuration found")
@@ -383,10 +383,8 @@ Let's think step by step.
 
         # 7. Generate Chart
         chart_response = None
-        if formatted_results:
-             # Only try to generate chart if we have results
-             # Convert to list of dicts if possible, or pass as is
-             chart_response = await generate_chart(formatted_results, request.query)
+        if request.generate_chart and formatted_results:
+            chart_response = await generate_chart(formatted_results, request.query)
 
         return NL2SQLResponse(sql=sql_query, result=formatted_results, chart=chart_response)
     except Exception as e: