speed acc

backend/app/agent/chart.py

@@ -12,6 +12,10 @@ from nanobot.providers.litellm_provider import LiteLLMProvider
 from app.schemas.chart import ChartGenerationResponse
 from app.services.llm_cache import get_active_llm_config
 
+CHART_MAX_TOKENS = 700
+CHART_TEMPERATURE = 0.2
+CHART_REASONING_EFFORT = "low"
+
 CHART_INSTRUCTIONS = """
 ### INSTRUCTIONS ###
 
@@ -202,8 +206,6 @@ Question: {query}
 Sample Data: {json.dumps(sample_data, ensure_ascii=False, separators=(",", ":"), default=str)}
 Sample Column Values: {columns}
 Language: Chinese (Simplified)
-
-Please think step by step
 """
 
     messages = [
@@ -213,7 +215,12 @@ Please think step by step
 
     # 4. Call LLM
     try:
-        response = await provider.chat(messages=messages)
+        response = await provider.chat(
+            messages=messages,
+            max_tokens=CHART_MAX_TOKENS,
+            temperature=CHART_TEMPERATURE,
+            reasoning_effort=CHART_REASONING_EFFORT,
+        )
         content = response.content
         
         # Clean up code blocks

backend/app/agent/nl2sql.py

@@ -4,7 +4,7 @@ import json
 import time
 import threading
 from pathlib import Path
-from typing import List, Optional, Dict, Any
+from typing import List, Optional, Dict, Any, Callable, Awaitable
 from pydantic import BaseModel, Field
 import duckdb
 import pandas as pd
@@ -30,6 +30,9 @@ SCHEMA_CACHE_TTL_SECONDS = 300
 CONNECTION_CACHE_TTL_SECONDS = 30
 UPLOAD_CACHE_TTL_SECONDS = 900
 MAX_UPLOAD_CACHE_ITEMS = 8
+NL2SQL_MAX_TOKENS = 900
+NL2SQL_TEMPERATURE = 0.1
+NL2SQL_REASONING_EFFORT = "low"
 
 _schema_cache: Dict[str, Dict[str, Any]] = {}
 _connection_cache: Dict[str, Dict[str, Any]] = {}
@@ -84,7 +87,7 @@ DEFAULT_TEXT_TO_SQL_RULES = """
 SQL_GENERATION_SYSTEM_PROMPT = f"""
 You are a helpful assistant that converts natural language queries into ANSI SQL queries.
 
-Given user's question, database schema, etc., you should think deeply and carefully and generate the SQL query based on the given reasoning plan step by step.
+Given user's question and database schema, generate accurate ANSI SQL directly and concisely.
 
 ### GENERAL RULES ###
 
@@ -195,7 +198,15 @@ def _check_connection_with_cache(source: str, connector: Any) -> bool:
         _connection_cache[cache_key] = {"ok": ok, "expires_at": now + CONNECTION_CACHE_TTL_SECONDS}
     return ok
 
-async def process_nl2sql(request: NL2SQLRequest) -> NL2SQLResponse:
+async def process_nl2sql(
+    request: NL2SQLRequest,
+    on_progress: Callable[[str], Awaitable[None]] | None = None,
+) -> NL2SQLResponse:
+    async def emit_progress(content: str) -> None:
+        if on_progress and content:
+            await on_progress(content)
+
+    total_started = time.perf_counter()
     # 1. Get the connector and schema
     connector = None
     schema = {}
@@ -207,13 +218,16 @@ async def process_nl2sql(request: NL2SQLRequest) -> NL2SQLResponse:
         connector = clickhouse_connector
     elif request.source == "upload":
         try:
+            upload_started = time.perf_counter()
             upload_payload = _get_upload_payload(request.file_url)
             upload_df = upload_payload["df"]
             schema = upload_payload["schema"]
+            await emit_progress(f"上传文件加载完成 ({time.perf_counter() - upload_started:.2f}s)")
         except Exception as e:
             return NL2SQLResponse(sql="", result=[], error=f"Failed to load uploaded file: {e}")
     elif request.source.startswith("ds:"):
         try:
+            ds_started = time.perf_counter()
             ds_id = int(request.source.split(":")[1])
             db = SessionLocal()
             try:
@@ -223,6 +237,7 @@ async def process_nl2sql(request: NL2SQLRequest) -> NL2SQLResponse:
                 connector = get_connector(ds)
             finally:
                 db.close()
+            await emit_progress(f"数据源配置读取完成 ({time.perf_counter() - ds_started:.2f}s)")
         except ValueError:
              return NL2SQLResponse(sql="", result=[], error=f"Invalid data source ID: {request.source}")
         except Exception as e:
@@ -231,21 +246,29 @@ async def process_nl2sql(request: NL2SQLRequest) -> NL2SQLResponse:
         return NL2SQLResponse(sql="", result=[], error=f"Unsupported data source: {request.source}")
 
     if connector:
+        await emit_progress("正在检测数据源连通性")
         cached_schema = _get_cached_schema(request.source, connector)
         if cached_schema:
             schema = cached_schema
+            await emit_progress(f"命中 Schema 缓存，已加载 {len(schema)} 张表")
         else:
+            conn_started = time.perf_counter()
             if not _check_connection_with_cache(request.source, connector):
                 return NL2SQLResponse(sql="", result=[], error=f"Failed to connect to {request.source}")
+            await emit_progress(f"连接检测完成 ({time.perf_counter() - conn_started:.2f}s)")
+            schema_started = time.perf_counter()
             schema = connector.get_schema()
             _set_cached_schema(request.source, connector, schema)
+            await emit_progress(f"Schema 拉取完成，共 {len(schema)} 张表 ({time.perf_counter() - schema_started:.2f}s)")
             
     if connector and not schema:
+         retry_started = time.perf_counter()
          # Double check in case schema was empty but connection is ok (e.g. empty db)
          if not _check_connection_with_cache(request.source, connector):
               return NL2SQLResponse(sql="", result=[], error=f"Failed to connect to {request.source}")
          schema = connector.get_schema()
          _set_cached_schema(request.source, connector, schema)
+         await emit_progress(f"Schema 二次拉取完成，共 {len(schema)} 张表 ({time.perf_counter() - retry_started:.2f}s)")
          
     schema_str = json.dumps(schema, ensure_ascii=False, separators=(",", ":"))
 
@@ -307,8 +330,6 @@ async def process_nl2sql(request: NL2SQLRequest) -> NL2SQLResponse:
 ### INPUTS ###
 User's Question: {request.query}
 Language: Chinese (Simplified)
-
-Let's think step by step.
 """
 
     messages = [
@@ -318,7 +339,14 @@ Let's think step by step.
 
     # 5. Call LLM
     try:
-        response = await provider.chat(messages=messages)
+        llm_started = time.perf_counter()
+        await emit_progress("正在生成 SQL")
+        response = await provider.chat(
+            messages=messages,
+            max_tokens=NL2SQL_MAX_TOKENS,
+            temperature=NL2SQL_TEMPERATURE,
+            reasoning_effort=NL2SQL_REASONING_EFFORT,
+        )
         content = response.content.strip()
         
         # Clean up code blocks
@@ -336,12 +364,15 @@ Let's think step by step.
         except json.JSONDecodeError:
             # Fallback if LLM doesn't return valid JSON despite instructions
             sql_query = content
+        await emit_progress(f"SQL 生成完成 ({time.perf_counter() - llm_started:.2f}s)")
             
     except Exception as e:
         return NL2SQLResponse(sql="", result=[], error=f"LLM generation failed: {e}")
 
     # 6. Execute SQL
     try:
+        sql_exec_started = time.perf_counter()
+        await emit_progress("正在执行 SQL 查询")
         if request.source == "upload":
             if upload_df is None:
                 upload_df = _get_upload_payload(request.file_url)["df"]
@@ -380,11 +411,16 @@ Let's think step by step.
             else:
                  # Unknown format, try to return as is or empty
                  formatted_results = []
+        await emit_progress(f"SQL 执行完成，返回 {len(formatted_results)} 行 ({time.perf_counter() - sql_exec_started:.2f}s)")
 
         # 7. Generate Chart
         chart_response = None
         if request.generate_chart and formatted_results:
+            chart_started = time.perf_counter()
+            await emit_progress("正在生成可视化方案")
             chart_response = await generate_chart(formatted_results, request.query)
+            await emit_progress(f"可视化方案生成完成 ({time.perf_counter() - chart_started:.2f}s)")
+        await emit_progress(f"NL2SQL 总耗时 {time.perf_counter() - total_started:.2f}s")
 
         return NL2SQLResponse(sql=sql_query, result=formatted_results, chart=chart_response)
     except Exception as e:

backend/main.py

@@ -250,14 +250,37 @@ async def nanobot_chat_stream(request: ChatRequest):
             use_nl2sql, route_reason, resolved_source = _should_use_nl2sql(request)
             yield f"data: {json.dumps({'type': 'routing', 'selected': 'sql' if use_nl2sql else 'chat', 'reason': route_reason}, ensure_ascii=False)}\n\n"
             if use_nl2sql:
-                nl2sql_result = await process_nl2sql(
+                yield f"data: {json.dumps({'type': 'progress', 'content': '已识别为数据分析请求，正在连接数据源'}, ensure_ascii=False)}\n\n"
+                sql_progress_queue: asyncio.Queue[str] = asyncio.Queue()
+
+                async def _on_sql_progress(content: str) -> None:
+                    if content:
+                        await sql_progress_queue.put(content)
+
+                sql_task = asyncio.create_task(
+                    process_nl2sql(
                         NL2SQLRequest(
                             query=request.message,
                             source=resolved_source,
                             file_url=request.file_url,
                             generate_chart=request.prefer_sql_chart or _looks_like_visual_intent(request.message),
+                        ),
+                        on_progress=_on_sql_progress,
                     )
                 )
+                while True:
+                    if sql_task.done() and sql_progress_queue.empty():
+                        break
+                    try:
+                        progress = await asyncio.wait_for(sql_progress_queue.get(), timeout=0.2)
+                        yield f"data: {json.dumps({'type': 'progress', 'content': progress}, ensure_ascii=False)}\n\n"
+                    except asyncio.TimeoutError:
+                        continue
+                nl2sql_result = await sql_task
+                if nl2sql_result.error:
+                    yield f"data: {json.dumps({'type': 'progress', 'content': '数据查询阶段返回错误，正在整理结果'}, ensure_ascii=False)}\n\n"
+                else:
+                    yield f"data: {json.dumps({'type': 'progress', 'content': 'SQL 已执行完成，正在整理回答'}, ensure_ascii=False)}\n\n"
                 persisted_viz_payload = _build_sql_chart_viz(nl2sql_result)
                 viz_payload = {
                     "type": "viz",
@@ -284,13 +307,14 @@ async def nanobot_chat_stream(request: ChatRequest):
                     on_progress=_on_progress,
                 )
             )
+            yield f"data: {json.dumps({'type': 'progress', 'content': '已发送给模型，正在分析问题'}, ensure_ascii=False)}\n\n"
             text = ""
             while True:
                 if task.done() and progress_queue.empty():
                     break
                 try:
                     progress = await asyncio.wait_for(progress_queue.get(), timeout=0.2)
-                    yield f"data: {json.dumps({'type': 'delta', 'content': progress}, ensure_ascii=False)}\n\n"
+                    yield f"data: {json.dumps({'type': 'progress', 'content': progress}, ensure_ascii=False)}\n\n"
                 except asyncio.TimeoutError:
                     continue
             response = await task

frontend/src/components/ChatInterface.tsx

@@ -21,6 +21,8 @@ interface Message {
   content: string;
   awaitingFirstToken?: boolean;
   viz?: MessageViz;
+  progressLogs?: string[];
+  routeInfo?: string;
 }
 
 interface MessageViz {
@@ -403,9 +405,23 @@ export function ChatInterface() {
           id: assistantId,
           role: "assistant",
           content: "",
-          awaitingFirstToken: true
+          awaitingFirstToken: true,
+          progressLogs: ["请求已提交，准备路由..."],
        }]);
 
+      const pushProgressLog = (text: string) => {
+        if (!text.trim()) return;
+        setMessagesForSession(targetSessionKey, (prev) =>
+          prev.map((msg) => {
+            if (msg.id !== assistantId) return msg;
+            const current = msg.progressLogs || [];
+            if (current[current.length - 1] === text) return msg;
+            const next = [...current, text].slice(-8);
+            return { ...msg, progressLogs: next };
+          })
+        );
+      };
+
        const token = localStorage.getItem("token");
        const effectiveModelId = selectedModelId || currentModel?.id || "";
        
@@ -497,6 +513,8 @@ export function ChatInterface() {
             sql?: string;
             result?: unknown;
             error?: string;
+            selected?: string;
+            reason?: string;
             chart?: { chart_spec?: ChartSpec | null; reasoning?: string; can_visualize?: boolean; chart_type?: string } | null;
           };
 
@@ -505,10 +523,26 @@ export function ChatInterface() {
             flushAssistant(false);
            }
 
+          if (payload.type === "routing") {
+            const selected = payload.selected === "sql" ? "SQL 分析" : "通用对话";
+            const reason = payload.reason ? `（${payload.reason}）` : "";
+            pushProgressLog(`路由：${selected}${reason}`);
+            setMessagesForSession(targetSessionKey, (prev) =>
+              prev.map((msg) =>
+                msg.id === assistantId ? { ...msg, routeInfo: `${selected}${reason}` } : msg
+              )
+            );
+          }
+
+          if (payload.type === "progress" && payload.content) {
+            pushProgressLog(payload.content);
+          }
+
            if (payload.type === "final" && payload.content) {
             hasFinalPayload = true;
              streamedText = payload.content;
             flushAssistant(true);
+            pushProgressLog("回答生成完成");
              setMessagesForSession(targetSessionKey, (prev) =>
                prev.map((msg) =>
                 msg.id === assistantId ? { ...msg, content: payload.content || "", awaitingFirstToken: false, viz: streamedViz ?? msg.viz } : msg
@@ -525,6 +559,7 @@ export function ChatInterface() {
            }
 
           if (payload.type === "viz") {
+            pushProgressLog("可视化结果已生成");
             streamedViz = buildMessageViz(payload);
             setMessagesForSession(targetSessionKey, (prev) =>
               prev.map((msg) =>
@@ -805,7 +840,23 @@ export function ChatInterface() {
                     }`}
                   >
                     {msg.role === "assistant" ? (
-                      msg.awaitingFirstToken && !msg.content ? (
+                      <>
+                        {msg.progressLogs && msg.progressLogs.length > 0 ? (
+                          <div className="mb-2 rounded-xl border border-zinc-100 bg-zinc-50/70 px-3 py-2">
+                            <div className="flex items-center gap-2 text-zinc-500 text-xs">
+                              {msg.awaitingFirstToken ? <Loader2 className="h-3.5 w-3.5 animate-spin" /> : <CheckCircle2 className="h-3.5 w-3.5 text-emerald-500" />}
+                              <span>{msg.awaitingFirstToken ? "正在处理中" : "处理完成"}</span>
+                            </div>
+                            <div className="mt-1.5 space-y-1">
+                              {msg.progressLogs.map((log, idx) => (
+                                <div key={`${msg.id}-log-${idx}`} className="text-[12px] text-zinc-500 leading-5">
+                                  {idx + 1}. {log}
+                                </div>
+                              ))}
+                            </div>
+                          </div>
+                        ) : null}
+                        {msg.awaitingFirstToken && !msg.content ? (
                           <div className="flex items-center gap-2 text-zinc-500 text-sm py-1">
                             <Loader2 className="h-4 w-4 animate-spin" />
                             <span>模型思考中，请稍候...</span>
@@ -823,7 +874,8 @@ export function ChatInterface() {
                               </div>
                             ) : null}
                           </>
-                      )
+                        )}
+                      </>
                     ) : (
                       msg.content
                     )}