feat: LLM classification for project name matching — _call_llm_raw with temp=0

wwwroot/api/cockpit_chat.dspy

@@ -115,6 +115,20 @@ async def _sel_model(sor, pref):
     recs = await sor.sqlExe("SELECT id,name,provider,model_id,api_base,api_key FROM llm WHERE status='active' LIMIT 1",{})
     return recs[0] if recs else None
 
+async def _call_llm_raw(prompt, temp=0.0):
+    """Lightweight LLM call for classification tasks. Uses first available model."""
+    to = aiohttp.ClientTimeout(total=30)
+    async with DBPools().sqlorContext(dbname) as sor:
+        model = await _sel_model(sor, None)
+    if not model:
+        return "不存在"
+    hdrs = {"Authorization": f"Bearer {model.api_key or ''}", "Content-Type": "application/json"}
+    payload = {"model": model.model_id, "messages": [{"role":"user","content":prompt}], "temperature": temp}
+    async with aiohttp.ClientSession(timeout=to) as s:
+        async with s.post(f"{model.api_base.rstrip('/')}/chat/completions", headers=hdrs, json=payload) as r:
+            if r.status != 200: raise ValueError(f"LLM {r.status}")
+            return (await r.json())["choices"][0]["message"]["content"]
+
 async def _call_llm(model, msgs, temp):
     hdrs = {"Authorization": f"Bearer {model.api_key or ''}", "Content-Type": "application/json"}
     payload = {"model": model.model_id, "messages": msgs, "temperature": temp}
@@ -206,8 +220,21 @@ async def _exec_tool(sor, tool, params, ctx, uid, org_id):
                 return '可用项目:\n'+'\n'.join(lines)
             proj = await _find_project(sor, name)
             if not proj:
-                allp = await sor.sqlExe("SELECT name FROM sd_projects ORDER BY created_at DESC LIMIT 15",{})
+                # LLM classification: ask LLM to match user input against project list
-                return 'FAIL: 未找到。可用: '+', '.join([getattr(r,'name','') for r in (allp or [])])
+                allp = await sor.sqlExe("SELECT id,name FROM sd_projects ORDER BY created_at DESC LIMIT 20",{})
+                pnames = [getattr(r,'name','') for r in (allp or [])]
+                classify_prompt = f"用户输入: {name}\n项目列表: {', '.join(pnames)}\n\n判断用户想要哪个项目。只回复项目名或\"不存在\"。"
+                try:
+                    raw = await _call_llm_raw(classify_prompt, 0.0)
+                    matched = raw.strip().strip('"').strip("'")
+                    for r in (allp or []):
+                        if getattr(r,'name','') == matched:
+                            proj = r; break
+                except:
+                    pass
+            if not proj:
+                allp2 = await sor.sqlExe("SELECT name FROM sd_projects ORDER BY created_at DESC LIMIT 15",{})
+                return 'FAIL: 未找到。可用: '+', '.join([getattr(r,'name','') for r in (allp2 or [])])
             if proj.id == ctx['pid']:
                 return f'已在「{proj.name}」项目中，无需切换'
             await _save_ctx(sor, uid, proj.id)
@@ -445,10 +472,7 @@ if action == 'send_message':
                 return
 
             repos_str = ', '.join([r['n'] for r in ctx['repos']]) or '无'
-            # Load all project names so LLM can match user's input
+            env_text = f"【当前项目: {ctx['pname'] or '未选择'}】— 所有操作在当前项目内完成\n工作空间: {ctx['ws'] or '未设置'} | 仓库: {repos_str}"
-            all_projects = await sor.sqlExe("SELECT name FROM sd_projects ORDER BY created_at DESC LIMIT 20",{})
-            proj_list = ', '.join([getattr(p,'name','') for p in (all_projects or [])])
-            env_text = f"【当前项目: {ctx['pname'] or '未选择'}】\n所有项目: {proj_list}\n工作空间: {ctx['ws'] or '未设置'} | 仓库: {repos_str}"
             system = AGENT_PROMPT.replace('__TOOLS__',TOOLS_TEXT).replace('__ENV__',env_text)
             msgs = [{"role":"system","content":system}]
             for h in (await sor.sqlExe("SELECT role,content FROM pipeline_conversations WHERE created_by=${u}$ ORDER BY created_at ASC LIMIT 20",{"u":uid}) or []):