feature: nl2sql first successful

backend/app/agent/nl2sql.py

@@ -4,6 +4,8 @@ import json
 from pathlib import Path
 from typing import List, Optional, Dict, Any
 from pydantic import BaseModel, Field
+import duckdb
+import pandas as pd
 
 # Add project root to sys.path to allow importing nanobot
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
@@ -19,7 +21,8 @@ from app.agent.chart import generate_chart
 
 class NL2SQLRequest(BaseModel):
     query: str = Field(..., description="User's natural language query")
-    source: str = Field(..., description="Data source to query (postgres, clickhouse)")
+    source: str = Field(..., description="Data source to query (postgres, clickhouse, upload)")
+    file_url: Optional[str] = Field(None, description="Uploaded file URL when source is upload")
 
 class NL2SQLResponse(BaseModel):
     sql: str
@@ -80,20 +83,63 @@ The final answer must be a ANSI SQL query in JSON format:
 }}
 """
 
+def _resolve_upload_file_path(file_url: Optional[str]) -> Path:
+    if not file_url or not file_url.startswith("local://"):
+        raise ValueError("Invalid uploaded file URL")
+    raw_name = file_url.replace("local://", "", 1)
+    safe_name = os.path.basename(raw_name)
+    upload_dir = Path(__file__).resolve().parents[2] / "data" / "uploads"
+    file_path = upload_dir / safe_name
+    if not file_path.exists():
+        raise ValueError(f"Uploaded file not found: {safe_name}")
+    return file_path
+
+def _load_upload_dataframe(file_url: Optional[str]) -> pd.DataFrame:
+    file_path = _resolve_upload_file_path(file_url)
+    suffix = file_path.suffix.lower()
+    if suffix == ".csv":
+        return pd.read_csv(file_path)
+    if suffix in [".xls", ".xlsx"]:
+        return pd.read_excel(file_path)
+    raise ValueError(f"Unsupported uploaded file type: {suffix}")
+
+def _get_upload_schema(file_url: Optional[str]) -> Dict[str, List[str]]:
+    df = _load_upload_dataframe(file_url)
+    conn = duckdb.connect(":memory:")
+    conn.register("uploaded_file", df)
+    columns = conn.execute("DESCRIBE uploaded_file").fetchall()
+    schema = {"uploaded_file": [f"{col[0]} ({col[1]})" for col in columns]}
+    conn.close()
+    return schema
+
+def _execute_upload_sql(sql_query: str, file_url: Optional[str]) -> List[Dict[str, Any]]:
+    df = _load_upload_dataframe(file_url)
+    conn = duckdb.connect(":memory:")
+    conn.register("uploaded_file", df)
+    result_df = conn.execute(sql_query).df()
+    conn.close()
+    return result_df.to_dict(orient="records")
+
 async def process_nl2sql(request: NL2SQLRequest) -> NL2SQLResponse:
     # 1. Get the connector and schema
     connector = None
+    schema = {}
     if request.source == "postgres":
         connector = postgres_connector
     elif request.source == "clickhouse":
         connector = clickhouse_connector
+    elif request.source == "upload":
+        try:
+            schema = _get_upload_schema(request.file_url)
+        except Exception as e:
+            return NL2SQLResponse(sql="", result=[], error=f"Failed to load uploaded file: {e}")
     else:
         return NL2SQLResponse(sql="", result=[], error=f"Unsupported data source: {request.source}")
 
-    if not connector.test_connection():
-         return NL2SQLResponse(sql="", result=[], error=f"Failed to connect to {request.source}")
-
-    schema = connector.get_schema()
+    if connector:
+        if not connector.test_connection():
+             return NL2SQLResponse(sql="", result=[], error=f"Failed to connect to {request.source}")
+        schema = connector.get_schema()
     schema_str = json.dumps(schema, indent=2)
 
     # 2. Get the active LLM config
@@ -158,19 +204,22 @@ Let's think step by step.
 
     # 6. Execute SQL
     try:
-        results = connector.execute_query(sql_query)
+        if request.source == "upload":
+            formatted_results = _execute_upload_sql(sql_query, request.file_url)
+        else:
+            results = connector.execute_query(sql_query)
         # Convert results to list of dicts if not already (Postgres returns list of dicts, ClickHouse returns list of tuples)
-        formatted_results = []
-        if request.source == "postgres":
-             formatted_results = results
-        elif request.source == "clickhouse":
+            formatted_results = []
+            if request.source == "postgres":
+                 formatted_results = results
+            elif request.source == "clickhouse":
             # ClickHouse returns list of tuples, we need column names
             # But execute_query in ClickHouseConnector just returns raw results from client.execute
             # client.execute(query, with_column_types=True) might be better but let's stick to simple for now
             # Actually, without column names it's hard to format as dict.
             # Let's assume we can just return the raw tuples for now or try to fetch column names.
             # For now, let's just return as list of lists/tuples if it's not a dict
-            formatted_results = [list(row) for row in results]
+                formatted_results = [list(row) for row in results]
         
         # 7. Generate Chart
         chart_response = None

backend/main.py

@@ -67,6 +67,9 @@ class ChatRequest(BaseModel):
     session_id: str = "api:default"
     skill_ids: Optional[List[str]] = None
     model_id: Optional[str] = None
+    source: str = "postgres"
+    prefer_sql_chart: bool = False
+    file_url: Optional[str] = None
 
 
 class SessionAliasUpdateRequest(BaseModel):
@@ -77,6 +80,27 @@ class SessionAliasUpdateRequest(BaseModel):
 @app.post("/nanobot/chat")
 async def nanobot_chat(request: ChatRequest):
     try:
+        if request.prefer_sql_chart:
+            nl2sql_result = await process_nl2sql(
+                NL2SQLRequest(query=request.message, source=request.source, file_url=request.file_url)
+            )
+            chart = nl2sql_result.chart
+            can_visualize = bool(chart and chart.can_visualize and chart.chart_spec)
+            text = (
+                f"已为你生成 SQL 并查询到 {len(nl2sql_result.result)} 行数据。"
+                f"{'可视化面板已同步更新图表。' if can_visualize else '本次结果不适合图表展示。'}"
+            )
+            if chart and chart.reasoning:
+                text = f"{text}\n\n可视化说明：{chart.reasoning}"
+            return {
+                "response": text,
+                "viz": {
+                    "sql": nl2sql_result.sql,
+                    "result": nl2sql_result.result,
+                    "chart": chart.model_dump() if chart else None,
+                    "error": nl2sql_result.error,
+                },
+            }
         response = await nanobot_service.process_message(
             request.message,
             session_id=request.session_id,
@@ -91,6 +115,29 @@ async def nanobot_chat(request: ChatRequest):
 async def nanobot_chat_stream(request: ChatRequest):
     async def event_generator():
         try:
+            if request.prefer_sql_chart:
+                nl2sql_result = await process_nl2sql(
+                    NL2SQLRequest(query=request.message, source=request.source, file_url=request.file_url)
+                )
+                chart = nl2sql_result.chart
+                viz_payload = {
+                    "type": "viz",
+                    "sql": nl2sql_result.sql,
+                    "result": nl2sql_result.result,
+                    "chart": chart.model_dump() if chart else None,
+                    "error": nl2sql_result.error,
+                }
+                yield f"data: {json.dumps(viz_payload, ensure_ascii=False)}\n\n"
+                can_visualize = bool(chart and chart.can_visualize and chart.chart_spec)
+                text = (
+                    f"已为你生成 SQL 并查询到 {len(nl2sql_result.result)} 行数据。"
+                    f"{'可视化面板已同步更新图表。' if can_visualize else '本次结果不适合图表展示。'}"
+                )
+                if chart and chart.reasoning:
+                    text = f"{text}\n\n可视化说明：{chart.reasoning}"
+                yield f"data: {json.dumps({'type': 'final', 'content': text}, ensure_ascii=False)}\n\n"
+                yield f"data: {json.dumps({'type': 'done'}, ensure_ascii=False)}\n\n"
+                return
             response = await nanobot_service.process_message(
                 request.message,
                 session_id=request.session_id,