Fix fastgpt client tool 3 rounds bugs

api/app/models.py

@@ -133,6 +133,7 @@ class Assistant(Base):
     config_mode: Mapped[str] = mapped_column(String(32), default="platform")
     api_url: Mapped[Optional[str]] = mapped_column(String(255), nullable=True)
     api_key: Mapped[Optional[str]] = mapped_column(String(255), nullable=True)
+    app_id: Mapped[Optional[str]] = mapped_column(String(255), nullable=True)
     # 模型关联
     llm_model_id: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
     asr_model_id: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)

api/app/routers/assistants.py

@@ -129,6 +129,9 @@ def _ensure_assistant_schema(db: Session) -> None:
     if "asr_interim_enabled" not in columns:
         db.execute(text("ALTER TABLE assistants ADD COLUMN asr_interim_enabled BOOLEAN DEFAULT 0"))
         altered = True
+    if "app_id" not in columns:
+        db.execute(text("ALTER TABLE assistants ADD COLUMN app_id VARCHAR(255)"))
+        altered = True
 
     if altered:
         db.commit()
@@ -297,7 +300,7 @@ def _resolve_runtime_metadata(db: Session, assistant: Assistant) -> tuple[Dict[s
 
     config_mode = str(assistant.config_mode or "platform").strip().lower()
 
-    if config_mode in {"dify", "fastgpt"}:
+    if config_mode == "dify":
         metadata["services"]["llm"] = {
             "provider": "openai",
             "model": "",
@@ -308,6 +311,19 @@ def _resolve_runtime_metadata(db: Session, assistant: Assistant) -> tuple[Dict[s
             warnings.append(f"External LLM API URL is empty for mode: {assistant.config_mode}")
         if not (assistant.api_key or "").strip():
             warnings.append(f"External LLM API key is empty for mode: {assistant.config_mode}")
+    elif config_mode == "fastgpt":
+        metadata["services"]["llm"] = {
+            "provider": "fastgpt",
+            "model": "fastgpt",
+            "apiKey": assistant.api_key,
+            "baseUrl": assistant.api_url,
+        }
+        if (assistant.app_id or "").strip():
+            metadata["services"]["llm"]["appId"] = assistant.app_id
+        if not (assistant.api_url or "").strip():
+            warnings.append(f"FastGPT API URL is empty for mode: {assistant.config_mode}")
+        if not (assistant.api_key or "").strip():
+            warnings.append(f"FastGPT API key is empty for mode: {assistant.config_mode}")
     elif assistant.llm_model_id:
         llm = db.query(LLMModel).filter(LLMModel.id == assistant.llm_model_id).first()
         if llm:
@@ -450,6 +466,7 @@ def assistant_to_dict(assistant: Assistant) -> dict:
         "configMode": assistant.config_mode,
         "apiUrl": assistant.api_url,
         "apiKey": assistant.api_key,
+        "appId": assistant.app_id,
         "llmModelId": assistant.llm_model_id,
         "asrModelId": assistant.asr_model_id,
         "embeddingModelId": assistant.embedding_model_id,
@@ -472,6 +489,7 @@ def _apply_assistant_update(assistant: Assistant, update_data: dict) -> None:
         "generatedOpenerEnabled": "generated_opener_enabled",
         "apiUrl": "api_url",
         "apiKey": "api_key",
+        "appId": "app_id",
         "llmModelId": "llm_model_id",
         "asrModelId": "asr_model_id",
         "embeddingModelId": "embedding_model_id",
@@ -666,6 +684,7 @@ def create_assistant(data: AssistantCreate, db: Session = Depends(get_db)):
         config_mode=data.configMode,
         api_url=data.apiUrl,
         api_key=data.apiKey,
+        app_id=data.appId,
         llm_model_id=data.llmModelId,
         asr_model_id=data.asrModelId,
         embedding_model_id=data.embeddingModelId,

api/app/schemas.py

@@ -298,6 +298,7 @@ class AssistantBase(BaseModel):
     configMode: str = "platform"
     apiUrl: Optional[str] = None
     apiKey: Optional[str] = None
+    appId: Optional[str] = None
     # 模型关联
     llmModelId: Optional[str] = None
     asrModelId: Optional[str] = None
@@ -330,6 +331,7 @@ class AssistantUpdate(BaseModel):
     configMode: Optional[str] = None
     apiUrl: Optional[str] = None
     apiKey: Optional[str] = None
+    appId: Optional[str] = None
     llmModelId: Optional[str] = None
     asrModelId: Optional[str] = None
     embeddingModelId: Optional[str] = None

api/tests/test_assistants.py

@@ -29,6 +29,7 @@ class TestAssistantAPI:
         assert data["generatedOpenerEnabled"] is False
         assert data["asrInterimEnabled"] is False
         assert data["botCannotBeInterrupted"] is False
+        assert data["appId"] is None
         assert "id" in data
         assert data["callCount"] == 0
 
@@ -419,3 +420,21 @@ class TestAssistantAPI:
         assert metadata["greeting"] == ""
         assert metadata["bargeIn"]["enabled"] is False
         assert metadata["bargeIn"]["minDurationMs"] == 900
+
+    def test_fastgpt_app_id_persists_and_flows_to_runtime(self, client, sample_assistant_data):
+        sample_assistant_data.update({
+            "configMode": "fastgpt",
+            "apiUrl": "https://cloud.fastgpt.cn/api",
+            "apiKey": "fastgpt-key",
+            "appId": "app-fastgpt-123",
+        })
+        assistant_resp = client.post("/api/assistants", json=sample_assistant_data)
+        assert assistant_resp.status_code == 200
+        assistant_id = assistant_resp.json()["id"]
+        assert assistant_resp.json()["appId"] == "app-fastgpt-123"
+
+        runtime_resp = client.get(f"/api/assistants/{assistant_id}/runtime-config")
+        assert runtime_resp.status_code == 200
+        metadata = runtime_resp.json()["sessionStartMetadata"]
+        assert metadata["services"]["llm"]["provider"] == "fastgpt"
+        assert metadata["services"]["llm"]["appId"] == "app-fastgpt-123"

engine/Dockerfile

@@ -2,6 +2,11 @@ FROM python:3.12-slim
 
 WORKDIR /app
 
+# Build this image from the project parent directory so both
+# engine-v3/engine and fastgpt-python-sdk are available in the context.
+# Example:
+#   docker build -f engine-v3/engine/Dockerfile -t engine-v3 .
+
 # Install system dependencies for audio processing
 RUN apt-get update && apt-get install -y --no-install-recommends \
     build-essential \
@@ -12,11 +17,13 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     && rm -rf /var/lib/apt/lists/*
 
 # Install Python dependencies
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+COPY engine-v3/engine/requirements.txt /tmp/requirements.txt
+COPY fastgpt-python-sdk /deps/fastgpt-python-sdk
+RUN pip install --no-cache-dir -r /tmp/requirements.txt \
+    && pip install --no-cache-dir /deps/fastgpt-python-sdk
 
 # Copy application code
-COPY . .
+COPY engine-v3/engine /app
 
 # Create necessary directories
 RUN mkdir -p /app/logs /app/data/vad

engine/adapters/control_plane/backend.py

@@ -214,6 +214,8 @@ class LocalYamlAssistantConfigAdapter(NullBackendAdapter):
                 llm_runtime["apiKey"] = cls._as_str(llm.get("api_key"))
             if cls._as_str(llm.get("api_url")):
                 llm_runtime["baseUrl"] = cls._as_str(llm.get("api_url"))
+            if cls._as_str(llm.get("app_id")):
+                llm_runtime["appId"] = cls._as_str(llm.get("app_id"))
             if llm_runtime:
                 runtime["services"]["llm"] = llm_runtime
 

engine/app/config.py

@@ -62,7 +62,7 @@ class Settings(BaseSettings):
     # LLM Configuration
     llm_provider: str = Field(
         default="openai",
-        description="LLM provider (openai, openai_compatible, siliconflow)"
+        description="LLM provider (openai, openai_compatible, siliconflow, fastgpt)"
     )
     llm_api_url: Optional[str] = Field(default=None, description="LLM provider API base URL")
     llm_model: str = Field(default="gpt-4o-mini", description="LLM model name")

engine/app/main.py

@@ -30,6 +30,18 @@ from runtime.events import get_event_bus, reset_event_bus
 _HEARTBEAT_CHECK_INTERVAL_SEC = 5
 
 
+def _inactivity_deadline(
+    *,
+    last_received_at: float,
+    inactivity_timeout_sec: int,
+    pending_client_tool_deadline: Optional[float] = None,
+) -> float:
+    deadline = float(last_received_at) + float(inactivity_timeout_sec)
+    if pending_client_tool_deadline is not None:
+        deadline = max(deadline, float(pending_client_tool_deadline))
+    return deadline
+
+
 async def heartbeat_and_timeout_task(
     transport: BaseTransport,
     session: Session,
@@ -48,8 +60,22 @@ async def heartbeat_and_timeout_task(
         if transport.is_closed:
             break
         now = time.monotonic()
-        if now - last_received_at[0] > inactivity_timeout_sec:
-            logger.info(f"Session {session_id}: {inactivity_timeout_sec}s no message, closing")
+        pending_client_tool_deadline = session.pipeline.pending_client_tool_deadline()
+        idle_deadline = _inactivity_deadline(
+            last_received_at=last_received_at[0],
+            inactivity_timeout_sec=inactivity_timeout_sec,
+            pending_client_tool_deadline=pending_client_tool_deadline,
+        )
+        if now > idle_deadline:
+            if pending_client_tool_deadline is not None and pending_client_tool_deadline >= (
+                last_received_at[0] + inactivity_timeout_sec
+            ):
+                logger.info(
+                    "Session {}: no message before pending client tool deadline, closing",
+                    session_id,
+                )
+            else:
+                logger.info(f"Session {session_id}: {inactivity_timeout_sec}s no message, closing")
             await session.cleanup()
             break
         if now - last_heartbeat_at[0] >= heartbeat_interval_sec:

engine/config/agents/dashscope.yaml

@@ -40,7 +40,7 @@ agent:
 
   duplex:
     enabled: true
-    system_prompt: You are a helpful, friendly voice assistant. Keep your responses concise and conversational.
+    system_prompt: 你是一个人工智能助手，你用简答语句回答，避免使用标点符号和emoji。
 
   barge_in:
     min_duration_ms: 200

engine/config/agents/dashscope_ontest.yaml

@@ -0,0 +1,47 @@
+# Agent behavior configuration for DashScope realtime ASR/TTS.
+# This file only controls agent-side behavior (VAD/LLM/TTS/ASR providers).
+# Infra/server/network settings should stay in .env.
+
+agent:
+  vad:
+    type: silero
+    model_path: data/vad/silero_vad.onnx
+    threshold: 0.5
+    min_speech_duration_ms: 100
+    eou_threshold_ms: 800
+
+  llm:
+    # provider: openai | openai_compatible | siliconflow
+    provider: openai_compatible
+    model: deepseek-v3
+    temperature: 0.7
+    api_key: sk-fc4d59b360475f53401a864db8ce0985010acc4e696723d20a90d6569f38d80a
+    api_url: https://api.qnaigc.com/v1
+
+  tts:
+    provider: dashscope
+    api_key: sk-391f5126d18345d497c6e8717c8c9ad7
+    api_url: wss://dashscope.aliyuncs.com/api-ws/v1/realtime
+    model: qwen3-tts-flash-realtime
+    voice: Cherry
+    dashscope_mode: commit
+    speed: 1.0
+
+  asr:
+    provider: dashscope
+    api_key: sk-391f5126d18345d497c6e8717c8c9ad7
+    api_url: wss://dashscope.aliyuncs.com/api-ws/v1/realtime
+    model: qwen3-asr-flash-realtime
+    interim_interval_ms: 500
+    min_audio_ms: 300
+    start_min_speech_ms: 160
+    pre_speech_ms: 240
+    final_tail_ms: 120
+
+  duplex:
+    enabled: true
+    system_prompt: 你是一个人工智能助手，你用简答语句回答，避免使用标点符号和emoji。
+
+  barge_in:
+    min_duration_ms: 200
+    silence_tolerance_ms: 60

engine/config/agents/example.yaml

@@ -11,7 +11,7 @@ agent:
     eou_threshold_ms: 800
 
   llm:
-    # provider: openai | openai_compatible | siliconflow
+    # provider: openai | openai_compatible | siliconflow | fastgpt
     provider: openai_compatible
     model: deepseek-v3
     temperature: 0.7
@@ -73,3 +73,4 @@ agent:
   barge_in:
     min_duration_ms: 200
     silence_tolerance_ms: 60
+

engine/config/agents/volcengine_ontest.yaml

@@ -0,0 +1,67 @@
+# Agent behavior configuration (safe to edit per profile)
+# This file only controls agent-side behavior (VAD/LLM/TTS/ASR providers).
+# Infra/server/network settings should stay in .env.
+
+agent:
+  vad:
+    type: silero
+    model_path: data/vad/silero_vad.onnx
+    threshold: 0.5
+    min_speech_duration_ms: 100
+    eou_threshold_ms: 800
+
+  llm:
+    # provider: openai | openai_compatible | siliconflow
+    provider: openai_compatible
+    model: deepseek-v3
+    temperature: 0.7
+    # Required: no fallback. You can still reference env explicitly.
+    api_key: sk-fc4d59b360475f53401a864db8ce0985010acc4e696723d20a90d6569f38d80a
+    # Optional for OpenAI-compatible endpoints:
+    api_url: https://api.qnaigc.com/v1
+
+  tts:
+    # provider: edge | openai_compatible | siliconflow | dashscope
+    # dashscope defaults (if omitted):
+    #   api_url: wss://dashscope.aliyuncs.com/api-ws/v1/realtime
+    #   model: qwen3-tts-flash-realtime
+    #   dashscope_mode: commit (engine splits) | server_commit (dashscope splits)
+    #   note: dashscope_mode/mode is ONLY used when provider=dashscope.
+    # volcengine defaults (if omitted):
+    provider: volcengine
+    api_url: https://openspeech.bytedance.com/api/v3/tts/unidirectional
+    resource_id: seed-tts-2.0
+    app_id: 2931820332
+    api_key: 4ustCTIpdCq8dE_msFrZvFn4nDpioIVo
+    speed: 1.1
+    voice: zh_female_vv_uranus_bigtts
+
+  asr:
+    provider: volcengine
+    api_url: wss://openspeech.bytedance.com/api/v3/sauc/bigmodel
+    app_id: 8607675070
+    api_key: QiO0AptfmU0GLTSitwn7t5-zeo4gJ6K1
+    resource_id: volc.bigasr.sauc.duration
+    uid: caller-1
+    model: bigmodel
+    request_params:
+      end_window_size: 800
+      force_to_speech_time: 1000
+      enable_punc: true
+      enable_itn: false
+      enable_ddc: false
+      show_utterance: true
+      result_type: single
+    interim_interval_ms: 500
+    min_audio_ms: 300
+    start_min_speech_ms: 160
+    pre_speech_ms: 240
+    final_tail_ms: 120
+
+  duplex:
+    enabled: true
+    system_prompt: 你是一个人工智能助手，你用简答语句回答，避免使用标点符号和emoji。
+
+  barge_in:
+    min_duration_ms: 200
+    silence_tolerance_ms: 60

engine/examples/wav_client.py

@@ -3,13 +3,15 @@
 WAV file client for testing duplex voice conversation.
 
 This client reads audio from a WAV file, sends it to the server,
-and saves the AI's voice response to an output WAV file.
+and saves a stereo WAV file with the input audio on the left channel
+and the AI's voice response on the right channel.
 
 Usage:
     python examples/wav_client.py --input input.wav --output response.wav
     python examples/wav_client.py --input input.wav --output response.wav --url ws://localhost:8000/ws
     python examples/wav_client.py --input input.wav --output response.wav --wait-time 10
     python wav_client.py --input ../data/audio_examples/two_utterances.wav -o response.wav
+
 Requirements:
     pip install soundfile websockets numpy
 """
@@ -45,14 +47,14 @@ except ImportError:
 class WavFileClient:
     """
     WAV file client for voice conversation testing.
-    
+
     Features:
     - Read audio from WAV file
     - Send audio to WebSocket server
-    - Receive and save response audio
+    - Receive and save stereo conversation audio
     - Event logging
     """
-    
+
     def __init__(
         self,
         url: str,
@@ -69,7 +71,7 @@ class WavFileClient:
     ):
         """
         Initialize WAV file client.
-        
+
         Args:
             url: WebSocket server URL
             input_file: Input WAV file path
@@ -92,48 +94,51 @@ class WavFileClient:
         self.track_debug = track_debug
         self.tail_silence_ms = max(0, int(tail_silence_ms))
         self.frame_bytes = 640  # 16k mono pcm_s16le, 20ms
-        
+
         # WebSocket connection
         self.ws = None
         self.running = False
-        
+
         # Audio buffers
+        self.input_audio = np.array([], dtype=np.int16)
         self.received_audio = bytearray()
-        
+        self.output_segments: list[dict[str, object]] = []
+        self.current_output_segment: bytearray | None = None
+
         # Statistics
         self.bytes_sent = 0
         self.bytes_received = 0
-        
+
         # TTFB tracking (per response)
         self.send_start_time = None
-        self.response_start_time = None  # set on each trackStart
+        self.response_start_time = None  # set on each output.audio.start
         self.waiting_for_first_audio = False
         self.ttfb_ms = None  # last TTFB for summary
         self.ttfb_list = []  # TTFB for each response
-        
+
         # State tracking
         self.track_started = False
         self.track_ended = False
         self.send_completed = False
         self.session_ready = False
-        
+
         # Events log
         self.events_log = []
-    
-    def log_event(self, direction: str, message: str):
+
+    def log_event(self, direction: str, message: str) -> None:
         """Log an event with timestamp."""
         timestamp = time.time()
-        self.events_log.append({
-            "timestamp": timestamp,
-            "direction": direction,
-            "message": message
-        })
-        # Handle encoding errors on Windows
+        self.events_log.append(
+            {
+                "timestamp": timestamp,
+                "direction": direction,
+                "message": message,
+            }
+        )
         try:
             print(f"{direction} {message}")
         except UnicodeEncodeError:
-            # Replace problematic characters for console output
-            safe_message = message.encode('ascii', errors='replace').decode('ascii')
+            safe_message = message.encode("ascii", errors="replace").decode("ascii")
             print(f"{direction} {safe_message}")
 
     @staticmethod
@@ -152,119 +157,160 @@ class WavFileClient:
         query = dict(parse_qsl(parts.query, keep_blank_values=True))
         query["assistant_id"] = self.assistant_id
         return urlunsplit((parts.scheme, parts.netloc, parts.path, urlencode(query), parts.fragment))
-    
+
+    def _current_timeline_sample(self) -> int:
+        """Return current sample position relative to input send start."""
+        if self.send_start_time is None:
+            return 0
+        elapsed_seconds = max(0.0, time.time() - self.send_start_time)
+        return int(round(elapsed_seconds * self.sample_rate))
+
+    def _start_output_segment(self) -> None:
+        """Create a new assistant-audio segment if one is not active."""
+        if self.current_output_segment is not None:
+            return
+        self.current_output_segment = bytearray()
+        self.output_segments.append(
+            {
+                "start_sample": self._current_timeline_sample(),
+                "audio": self.current_output_segment,
+            }
+        )
+
+    def _close_output_segment(self) -> None:
+        """Close the active assistant-audio segment, if any."""
+        self.current_output_segment = None
+
+    def _build_input_track(self) -> np.ndarray:
+        """Build the saved left channel using the streamed input audio."""
+        input_track = self.input_audio.astype(np.int16, copy=True)
+        tail_samples = int(round(self.sample_rate * self.tail_silence_ms / 1000.0))
+        if tail_samples <= 0:
+            return input_track
+        if input_track.size == 0:
+            return np.zeros(tail_samples, dtype=np.int16)
+        return np.concatenate((input_track, np.zeros(tail_samples, dtype=np.int16)))
+
+    def _build_output_track(self) -> np.ndarray:
+        """Build the saved right channel using received assistant audio."""
+        if not self.output_segments:
+            return np.zeros(0, dtype=np.int16)
+
+        total_samples = max(
+            int(segment["start_sample"]) + (len(segment["audio"]) // 2)
+            for segment in self.output_segments
+        )
+        mixed_track = np.zeros(total_samples, dtype=np.int32)
+
+        for segment in self.output_segments:
+            start_sample = int(segment["start_sample"])
+            segment_audio = np.frombuffer(bytes(segment["audio"]), dtype=np.int16).astype(np.int32)
+            if segment_audio.size == 0:
+                continue
+            end_sample = start_sample + segment_audio.size
+            mixed_track[start_sample:end_sample] += segment_audio
+
+        np.clip(mixed_track, -32768, 32767, out=mixed_track)
+        return mixed_track.astype(np.int16)
+
     async def connect(self) -> None:
         """Connect to WebSocket server."""
         session_url = self._session_url()
-        self.log_event("→", f"Connecting to {session_url}...")
+        self.log_event("->", f"Connecting to {session_url}...")
         self.ws = await websockets.connect(session_url)
         self.running = True
-        self.log_event("←", "Connected!")
+        self.log_event("->", "Connected!")
+
+        await self.send_command(
+            {
+                "type": "session.start",
+                "audio": {
+                    "encoding": "pcm_s16le",
+                    "sample_rate_hz": self.sample_rate,
+                    "channels": 1,
+                },
+                "metadata": {
+                    "channel": self.channel,
+                    "source": "wav_client",
+                },
+            }
+        )
 
-        await self.send_command({
-            "type": "session.start",
-            "audio": {
-                "encoding": "pcm_s16le",
-                "sample_rate_hz": self.sample_rate,
-                "channels": 1
-            },
-            "metadata": {
-                "channel": self.channel,
-                "source": "wav_client",
-            },
-        })
-    
     async def send_command(self, cmd: dict) -> None:
         """Send JSON command to server."""
         if self.ws:
             await self.ws.send(json.dumps(cmd))
-            self.log_event("→", f"Command: {cmd.get('type', 'unknown')}")
-    
+            self.log_event("->", f"Command: {cmd.get('type', 'unknown')}")
+
     async def send_hangup(self, reason: str = "Session complete") -> None:
         """Send hangup command."""
-        await self.send_command({
-            "type": "session.stop",
-            "reason": reason
-        })
-    
+        await self.send_command({"type": "session.stop", "reason": reason})
+
     def load_wav_file(self) -> tuple[np.ndarray, int]:
         """
         Load and prepare WAV file for sending.
-        
+
         Returns:
             Tuple of (audio_data as int16 numpy array, original sample rate)
         """
         if not self.input_file.exists():
             raise FileNotFoundError(f"Input file not found: {self.input_file}")
-        
-        # Load audio file
+
         audio_data, file_sample_rate = sf.read(self.input_file)
-        self.log_event("→", f"Loaded: {self.input_file}")
-        self.log_event("→", f"  Original sample rate: {file_sample_rate} Hz")
-        self.log_event("→", f"  Duration: {len(audio_data) / file_sample_rate:.2f}s")
-        
-        # Convert stereo to mono if needed
+        self.log_event("->", f"Loaded: {self.input_file}")
+        self.log_event("->", f"  Original sample rate: {file_sample_rate} Hz")
+        self.log_event("->", f"  Duration: {len(audio_data) / file_sample_rate:.2f}s")
+
         if len(audio_data.shape) > 1:
             audio_data = audio_data.mean(axis=1)
-            self.log_event("→", "  Converted stereo to mono")
-        
-        # Resample if needed
+            self.log_event("->", "  Converted stereo to mono")
+
         if file_sample_rate != self.sample_rate:
-            # Simple resampling using numpy
             duration = len(audio_data) / file_sample_rate
             num_samples = int(duration * self.sample_rate)
             indices = np.linspace(0, len(audio_data) - 1, num_samples)
             audio_data = np.interp(indices, np.arange(len(audio_data)), audio_data)
-            self.log_event("→", f"  Resampled to {self.sample_rate} Hz")
-        
-        # Convert to int16
+            self.log_event("->", f"  Resampled to {self.sample_rate} Hz")
+
         if audio_data.dtype != np.int16:
-            # Normalize to [-1, 1] if needed
             max_val = np.max(np.abs(audio_data))
             if max_val > 1.0:
                 audio_data = audio_data / max_val
             audio_data = (audio_data * 32767).astype(np.int16)
-        
-        self.log_event("→", f"  Prepared: {len(audio_data)} samples ({len(audio_data)/self.sample_rate:.2f}s)")
-        
+
+        self.log_event("->", f"  Prepared: {len(audio_data)} samples ({len(audio_data) / self.sample_rate:.2f}s)")
+        self.input_audio = audio_data.copy()
         return audio_data, file_sample_rate
-    
+
     async def audio_sender(self, audio_data: np.ndarray) -> None:
         """Send audio data to server in chunks."""
         total_samples = len(audio_data)
         chunk_size = self.chunk_samples
         sent_samples = 0
-        
+
         self.send_start_time = time.time()
-        self.log_event("→", f"Starting audio transmission ({total_samples} samples)...")
-        
+        self.log_event("->", f"Starting audio transmission ({total_samples} samples)...")
+
         while sent_samples < total_samples and self.running:
-            # Get next chunk
             end_sample = min(sent_samples + chunk_size, total_samples)
             chunk = audio_data[sent_samples:end_sample]
             chunk_bytes = chunk.tobytes()
             if len(chunk_bytes) % self.frame_bytes != 0:
-                # v1 audio framing requires 640-byte (20ms) PCM units.
                 pad = self.frame_bytes - (len(chunk_bytes) % self.frame_bytes)
                 chunk_bytes += b"\x00" * pad
-            
-            # Send to server
+
             if self.ws:
                 await self.ws.send(chunk_bytes)
                 self.bytes_sent += len(chunk_bytes)
-            
+
             sent_samples = end_sample
-            
-            # Progress logging (every 500ms worth of audio)
+
             if self.verbose and sent_samples % (self.sample_rate // 2) == 0:
                 progress = (sent_samples / total_samples) * 100
                 print(f"  Sending: {progress:.0f}%", end="\r")
-            
-            # Delay to simulate real-time streaming
-            # Server expects audio at real-time pace for VAD/ASR to work properly
+
             await asyncio.sleep(self.chunk_duration_ms / 1000)
 
-        # Add a short silence tail to help VAD/EOU close the final utterance.
         if self.tail_silence_ms > 0 and self.ws:
             tail_frames = max(1, self.tail_silence_ms // 20)
             silence = b"\x00" * self.frame_bytes
@@ -272,56 +318,53 @@ class WavFileClient:
                 await self.ws.send(silence)
                 self.bytes_sent += len(silence)
                 await asyncio.sleep(0.02)
-            self.log_event("→", f"Sent trailing silence: {self.tail_silence_ms}ms")
-        
+            self.log_event("->", f"Sent trailing silence: {self.tail_silence_ms}ms")
+
         self.send_completed = True
         elapsed = time.time() - self.send_start_time
-        self.log_event("→", f"Audio transmission complete ({elapsed:.2f}s, {self.bytes_sent/1024:.1f} KB)")
-    
+        self.log_event("->", f"Audio transmission complete ({elapsed:.2f}s, {self.bytes_sent / 1024:.1f} KB)")
+
     async def receiver(self) -> None:
         """Receive messages from server."""
         try:
             while self.running:
                 try:
                     message = await asyncio.wait_for(self.ws.recv(), timeout=0.1)
-                    
+
                     if isinstance(message, bytes):
-                        # Audio data received
                         self.bytes_received += len(message)
                         self.received_audio.extend(message)
-                        
-                        # Calculate TTFB on first audio of each response
+                        self._start_output_segment()
+                        self.current_output_segment.extend(message)
+
                         if self.waiting_for_first_audio and self.response_start_time is not None:
                             ttfb_ms = (time.time() - self.response_start_time) * 1000
                             self.ttfb_ms = ttfb_ms
                             self.ttfb_list.append(ttfb_ms)
                             self.waiting_for_first_audio = False
-                            self.log_event("←", f"[TTFB] First audio latency: {ttfb_ms:.0f}ms")
-                        
-                        # Log progress
+                            self.log_event("<-", f"[TTFB] First audio latency: {ttfb_ms:.0f}ms")
+
                         duration_ms = len(message) / (self.sample_rate * 2) * 1000
                         total_ms = len(self.received_audio) / (self.sample_rate * 2) * 1000
                         if self.verbose:
-                            print(f"← Audio: +{duration_ms:.0f}ms (total: {total_ms:.0f}ms)", end="\r")
-                        
+                            print(f"<- Audio: +{duration_ms:.0f}ms (total: {total_ms:.0f}ms)", end="\r")
                     else:
-                        # JSON event
                         event = json.loads(message)
                         await self._handle_event(event)
-                        
+
                 except asyncio.TimeoutError:
                     continue
                 except websockets.ConnectionClosed:
-                    self.log_event("←", "Connection closed")
+                    self.log_event("<-", "Connection closed")
                     self.running = False
                     break
-                    
+
         except asyncio.CancelledError:
             pass
-        except Exception as e:
-            self.log_event("!", f"Receiver error: {e}")
+        except Exception as exc:
+            self.log_event("!", f"Receiver error: {exc}")
             self.running = False
-    
+
     async def _handle_event(self, event: dict) -> None:
         """Handle incoming event."""
         event_type = event.get("type", "unknown")
@@ -331,14 +374,14 @@ class WavFileClient:
 
         if event_type == "session.started":
             self.session_ready = True
-            self.log_event("←", f"Session ready!{ids}")
+            self.log_event("<-", f"Session ready!{ids}")
         elif event_type == "config.resolved":
             config = event.get("config", {})
-            self.log_event("←", f"Config resolved (output={config.get('output', {})}){ids}")
+            self.log_event("<-", f"Config resolved (output={config.get('output', {})}){ids}")
         elif event_type == "input.speech_started":
-            self.log_event("←", f"Speech detected{ids}")
+            self.log_event("<-", f"Speech detected{ids}")
         elif event_type == "input.speech_stopped":
-            self.log_event("←", f"Silence detected{ids}")
+            self.log_event("<-", f"Silence detected{ids}")
         elif event_type == "transcript.delta":
             text = event.get("text", "")
             display_text = text[:60] + "..." if len(text) > 60 else text
@@ -346,125 +389,128 @@ class WavFileClient:
         elif event_type == "transcript.final":
             text = event.get("text", "")
             print(" " * 80, end="\r")
-            self.log_event("←", f"→ You: {text}{ids}")
+            self.log_event("<-", f"You: {text}{ids}")
         elif event_type == "metrics.ttfb":
             latency_ms = event.get("latencyMs", 0)
-            self.log_event("←", f"[TTFB] Server latency: {latency_ms}ms")
+            self.log_event("<-", f"[TTFB] Server latency: {latency_ms}ms")
         elif event_type == "assistant.response.delta":
             text = event.get("text", "")
             if self.verbose and text:
-                self.log_event("←", f"LLM: {text}{ids}")
+                self.log_event("<-", f"LLM: {text}{ids}")
         elif event_type == "assistant.response.final":
             text = event.get("text", "")
             if text:
-                self.log_event("←", f"LLM Response (final): {text[:100]}{'...' if len(text) > 100 else ''}{ids}")
+                summary = text[:100] + ("..." if len(text) > 100 else "")
+                self.log_event("<-", f"LLM Response (final): {summary}{ids}")
         elif event_type == "output.audio.start":
             self.track_started = True
             self.response_start_time = time.time()
             self.waiting_for_first_audio = True
-            self.log_event("←", f"Bot started speaking{ids}")
+            self._close_output_segment()
+            self.log_event("<-", f"Bot started speaking{ids}")
         elif event_type == "output.audio.end":
             self.track_ended = True
-            self.log_event("←", f"Bot finished speaking{ids}")
+            self._close_output_segment()
+            self.log_event("<-", f"Bot finished speaking{ids}")
         elif event_type == "response.interrupted":
-            self.log_event("←", f"Bot interrupted!{ids}")
+            self._close_output_segment()
+            self.log_event("<-", f"Bot interrupted!{ids}")
         elif event_type == "error":
             self.log_event("!", f"Error: {event.get('message')}{ids}")
         elif event_type == "session.stopped":
-            self.log_event("←", f"Session stopped: {event.get('reason')}{ids}")
+            self.log_event("<-", f"Session stopped: {event.get('reason')}{ids}")
             self.running = False
         else:
-            self.log_event("←", f"Event: {event_type}{ids}")
-    
+            self.log_event("<-", f"Event: {event_type}{ids}")
+
     def save_output_wav(self) -> None:
-        """Save received audio to output WAV file."""
-        if not self.received_audio:
-            self.log_event("!", "No audio received to save")
+        """Save the conversation to a stereo WAV file."""
+        input_track = self._build_input_track()
+        output_track = self._build_output_track()
+
+        if input_track.size == 0 and output_track.size == 0:
+            self.log_event("!", "No audio available to save")
             return
-        
-        # Convert bytes to numpy array
-        audio_data = np.frombuffer(bytes(self.received_audio), dtype=np.int16)
-        
-        # Ensure output directory exists
+
+        if not self.received_audio:
+            self.log_event("!", "No assistant audio received; saving silent right channel")
+
+        total_samples = max(input_track.size, output_track.size)
+        if input_track.size < total_samples:
+            input_track = np.pad(input_track, (0, total_samples - input_track.size))
+        if output_track.size < total_samples:
+            output_track = np.pad(output_track, (0, total_samples - output_track.size))
+
+        stereo_audio = np.column_stack((input_track, output_track)).astype(np.int16, copy=False)
+
         self.output_file.parent.mkdir(parents=True, exist_ok=True)
-        
-        # Save using wave module for compatibility
-        with wave.open(str(self.output_file), 'wb') as wav_file:
-            wav_file.setnchannels(1)
+
+        with wave.open(str(self.output_file), "wb") as wav_file:
+            wav_file.setnchannels(2)
             wav_file.setsampwidth(2)  # 16-bit
             wav_file.setframerate(self.sample_rate)
-            wav_file.writeframes(audio_data.tobytes())
-        
-        duration = len(audio_data) / self.sample_rate
-        self.log_event("→", f"Saved output: {self.output_file}")
-        self.log_event("→", f"  Duration: {duration:.2f}s ({len(audio_data)} samples)")
-        self.log_event("→", f"  Size: {len(self.received_audio)/1024:.1f} KB")
-    
+            wav_file.writeframes(stereo_audio.tobytes())
+
+        duration = total_samples / self.sample_rate
+        self.log_event("->", f"Saved stereo output: {self.output_file}")
+        self.log_event("->", f"  Duration: {duration:.2f}s ({total_samples} samples/channel)")
+        self.log_event("->", "  Channels: left=input, right=assistant")
+        self.log_event("->", f"  Size: {stereo_audio.nbytes / 1024:.1f} KB")
+
     async def run(self) -> None:
         """Run the WAV file test."""
         try:
-            # Load input WAV file
             audio_data, _ = self.load_wav_file()
-            
-            # Connect to server
+
             await self.connect()
-            
-            # Start receiver task
+
             receiver_task = asyncio.create_task(self.receiver())
 
-            # Wait for session.started before streaming audio
             ready_start = time.time()
             while self.running and not self.session_ready:
                 if time.time() - ready_start > 8.0:
                     raise TimeoutError("Timeout waiting for session.started")
                 await asyncio.sleep(0.05)
-            
-            # Send audio
+
             await self.audio_sender(audio_data)
-            
-            # Wait for response
-            self.log_event("→", f"Waiting {self.wait_time}s for response...")
-            
+
+            self.log_event("->", f"Waiting {self.wait_time}s for response...")
+
             wait_start = time.time()
             while self.running and (time.time() - wait_start) < self.wait_time:
-                # Check if track has ended (response complete)
                 if self.track_ended and self.send_completed:
-                    # Give a little extra time for any remaining audio
                     await asyncio.sleep(1.0)
                     break
                 await asyncio.sleep(0.1)
-            
-            # Cleanup
+
             self.running = False
             receiver_task.cancel()
-            
+
             try:
                 await receiver_task
             except asyncio.CancelledError:
                 pass
-            
-            # Save output
+
             self.save_output_wav()
-            
-            # Print summary
             self._print_summary()
-            
-        except FileNotFoundError as e:
-            print(f"Error: {e}")
+
+        except FileNotFoundError as exc:
+            print(f"Error: {exc}")
             sys.exit(1)
         except ConnectionRefusedError:
             print(f"Error: Could not connect to {self.url}")
             print("Make sure the server is running.")
             sys.exit(1)
-        except Exception as e:
-            print(f"Error: {e}")
+        except Exception as exc:
+            print(f"Error: {exc}")
             import traceback
+
             traceback.print_exc()
             sys.exit(1)
         finally:
             await self.close()
-    
-    def _print_summary(self):
+
+    def _print_summary(self) -> None:
         """Print session summary."""
         print("\n" + "=" * 50)
         print("Session Summary")
@@ -477,19 +523,20 @@ class WavFileClient:
             if len(self.ttfb_list) == 1:
                 print(f"  TTFB:           {self.ttfb_list[0]:.0f} ms")
             else:
-                print(f"  TTFB (per response): {', '.join(f'{t:.0f}ms' for t in self.ttfb_list)}")
+                values = ", ".join(f"{ttfb:.0f}ms" for ttfb in self.ttfb_list)
+                print(f"  TTFB (per response): {values}")
         if self.received_audio:
             duration = len(self.received_audio) / (self.sample_rate * 2)
             print(f"  Response duration: {duration:.2f}s")
         print("=" * 50)
-    
+
     async def close(self) -> None:
         """Close the connection."""
         self.running = False
         if self.ws:
             try:
                 await self.ws.close()
-            except:
+            except Exception:
                 pass
 
 
@@ -498,67 +545,71 @@ async def main():
         description="WAV file client for testing duplex voice conversation"
     )
     parser.add_argument(
-        "--input", "-i",
+        "--input",
+        "-i",
         required=True,
-        help="Input WAV file path"
+        help="Input WAV file path",
     )
     parser.add_argument(
-        "--output", "-o",
+        "--output",
+        "-o",
         required=True,
-        help="Output WAV file path for response"
+        help="Output WAV file path for stereo conversation audio",
     )
     parser.add_argument(
         "--url",
         default="ws://localhost:8000/ws",
-        help="WebSocket server URL (default: ws://localhost:8000/ws)"
+        help="WebSocket server URL (default: ws://localhost:8000/ws)",
     )
     parser.add_argument(
         "--sample-rate",
         type=int,
         default=16000,
-        help="Target sample rate for audio (default: 16000)"
+        help="Target sample rate for audio (default: 16000)",
     )
     parser.add_argument(
         "--assistant-id",
         default="default",
-        help="Assistant identifier used in websocket query parameter"
+        help="Assistant identifier used in websocket query parameter",
     )
     parser.add_argument(
         "--channel",
         default="wav_client",
-        help="Client channel name"
+        help="Client channel name",
     )
     parser.add_argument(
         "--chunk-duration",
         type=int,
         default=20,
-        help="Chunk duration in ms for sending (default: 20)"
+        help="Chunk duration in ms for sending (default: 20)",
     )
     parser.add_argument(
-        "--wait-time", "-w",
+        "--wait-time",
+        "-w",
         type=float,
         default=15.0,
-        help="Time to wait for response after sending (default: 15.0)"
+        help="Time to wait for response after sending (default: 15.0)",
     )
     parser.add_argument(
-        "--verbose", "-v",
+        "--verbose",
+        "-v",
         action="store_true",
-        help="Enable verbose output"
+        help="Enable verbose output",
     )
     parser.add_argument(
         "--track-debug",
         action="store_true",
-        help="Print event trackId for protocol debugging"
+        help="Print event trackId for protocol debugging",
     )
     parser.add_argument(
         "--tail-silence-ms",
         type=int,
         default=800,
-        help="Trailing silence to send after WAV playback for EOU detection (default: 800)"
+        help="Trailing silence to send after WAV playback for EOU detection (default: 800)",
     )
-    
+
     args = parser.parse_args()
-    
+
     client = WavFileClient(
         url=args.url,
         input_file=args.input,
@@ -572,7 +623,7 @@ async def main():
         track_debug=args.track_debug,
         tail_silence_ms=args.tail_silence_ms,
     )
-    
+
     await client.run()
 
 
@@ -580,4 +631,4 @@ if __name__ == "__main__":
     try:
         asyncio.run(main())
     except KeyboardInterrupt:
-        print("\nInterrupted by user")
+        print("\nInterrupted by user")

engine/providers/factory/default.py

@@ -28,7 +28,7 @@ from providers.tts.volcengine import VolcengineTTSService
 _OPENAI_COMPATIBLE_PROVIDERS = {"openai_compatible", "openai-compatible", "siliconflow"}
 _DASHSCOPE_PROVIDERS = {"dashscope"}
 _VOLCENGINE_PROVIDERS = {"volcengine"}
-_SUPPORTED_LLM_PROVIDERS = {"openai", *_OPENAI_COMPATIBLE_PROVIDERS}
+_SUPPORTED_LLM_PROVIDERS = {"openai", "fastgpt", *_OPENAI_COMPATIBLE_PROVIDERS}
 
 
 class DefaultRealtimeServiceFactory(RealtimeServiceFactory):
@@ -58,7 +58,18 @@ class DefaultRealtimeServiceFactory(RealtimeServiceFactory):
 
     def create_llm_service(self, spec: LLMServiceSpec) -> LLMPort:
         provider = self._normalize_provider(spec.provider)
-        if provider in _SUPPORTED_LLM_PROVIDERS and spec.api_key:
+        if provider == "fastgpt" and spec.api_key and spec.base_url:
+            from providers.llm.fastgpt import FastGPTLLMService
+
+            return FastGPTLLMService(
+                api_key=spec.api_key,
+                base_url=spec.base_url,
+                app_id=spec.app_id,
+                model=spec.model,
+                system_prompt=spec.system_prompt,
+            )
+
+        if provider in _SUPPORTED_LLM_PROVIDERS and provider != "fastgpt" and spec.api_key:
             return OpenAILLMService(
                 api_key=spec.api_key,
                 base_url=spec.base_url,

engine/providers/llm/__init__.py

@@ -1 +1,14 @@
 """LLM providers."""
+
+from providers.llm.openai import MockLLMService, OpenAILLMService
+
+try:  # pragma: no cover - import depends on optional sibling SDK
+    from providers.llm.fastgpt import FastGPTLLMService
+except Exception:  # pragma: no cover - provider remains lazily available via factory
+    FastGPTLLMService = None  # type: ignore[assignment]
+
+__all__ = [
+    "FastGPTLLMService",
+    "MockLLMService",
+    "OpenAILLMService",
+]

engine/providers/llm/fastgpt.py

@@ -0,0 +1,553 @@
+"""FastGPT-backed LLM provider."""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import uuid
+from typing import Any, AsyncIterator, Dict, List, Optional
+
+from loguru import logger
+
+from providers.common.base import BaseLLMService, LLMMessage, LLMStreamEvent, ServiceState
+from providers.llm.fastgpt_types import (
+    FastGPTConversationState,
+    FastGPTField,
+    FastGPTInteractivePrompt,
+    FastGPTOption,
+    FastGPTPendingInteraction,
+)
+
+try:
+    from fastgpt_client import AsyncChatClient, aiter_stream_events
+except Exception as exc:  # pragma: no cover - exercised indirectly via connect()
+    AsyncChatClient = None  # type: ignore[assignment]
+    aiter_stream_events = None  # type: ignore[assignment]
+    _FASTGPT_IMPORT_ERROR: Optional[Exception] = exc
+else:  # pragma: no cover - import success depends on local environment
+    _FASTGPT_IMPORT_ERROR = None
+
+
+class FastGPTLLMService(BaseLLMService):
+    """LLM provider that delegates orchestration to FastGPT."""
+
+    INTERACTIVE_TOOL_NAME = "fastgpt.interactive"
+    INTERACTIVE_TIMEOUT_MS = 300000
+
+    def __init__(
+        self,
+        *,
+        api_key: str,
+        base_url: str,
+        app_id: Optional[str] = None,
+        model: str = "fastgpt",
+        system_prompt: Optional[str] = None,
+    ):
+        super().__init__(model=model or "fastgpt")
+        self.api_key = api_key
+        self.base_url = str(base_url or "").rstrip("/")
+        self.app_id = str(app_id or "").strip()
+        self.system_prompt = system_prompt or ""
+        self.client: Any = None
+        self._cancel_event = asyncio.Event()
+        self._state = FastGPTConversationState()
+        self._knowledge_config: Dict[str, Any] = {}
+        self._tool_schemas: List[Dict[str, Any]] = []
+
+    async def connect(self) -> None:
+        if AsyncChatClient is None or aiter_stream_events is None:
+            raise RuntimeError(
+                "fastgpt_client package is not available. "
+                "Install the sibling fastgpt-python-sdk package first."
+            ) from _FASTGPT_IMPORT_ERROR
+        if not self.api_key:
+            raise ValueError("FastGPT API key not provided")
+        if not self.base_url:
+            raise ValueError("FastGPT base URL not provided")
+        self.client = AsyncChatClient(api_key=self.api_key, base_url=self.base_url)
+        self.state = ServiceState.CONNECTED
+        logger.info("FastGPT LLM service connected: base_url={}", self.base_url)
+
+    async def disconnect(self) -> None:
+        if self.client and hasattr(self.client, "close"):
+            await self.client.close()
+        self.client = None
+        self._state.pending_interaction = None
+        self.state = ServiceState.DISCONNECTED
+        logger.info("FastGPT LLM service disconnected")
+
+    def cancel(self) -> None:
+        self._cancel_event.set()
+        self._state.pending_interaction = None
+
+    def set_knowledge_config(self, config: Optional[Dict[str, Any]]) -> None:
+        # FastGPT owns KB orchestration in this provider mode.
+        self._knowledge_config = dict(config or {})
+
+    def set_tool_schemas(self, schemas: Optional[List[Dict[str, Any]]]) -> None:
+        # FastGPT owns workflow and tool orchestration in this provider mode.
+        self._tool_schemas = list(schemas or [])
+
+    def handles_client_tool(self, tool_name: str) -> bool:
+        return str(tool_name or "").strip() == self.INTERACTIVE_TOOL_NAME
+
+    async def get_initial_greeting(self) -> Optional[str]:
+        if not self.client or not self.app_id:
+            return None
+
+        response = await self.client.get_chat_init(
+            appId=self.app_id,
+            chatId=self._ensure_chat_id(),
+        )
+        raise_for_status = getattr(response, "raise_for_status", None)
+        if callable(raise_for_status):
+            raise_for_status()
+        elif int(getattr(response, "status_code", 200) or 200) >= 400:
+            raise RuntimeError(f"FastGPT chat init failed: HTTP {getattr(response, 'status_code', 'unknown')}")
+
+        payload = response.json() if hasattr(response, "json") else {}
+        return self._extract_initial_greeting(payload)
+
+    async def generate(
+        self,
+        messages: List[LLMMessage],
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None,
+    ) -> str:
+        parts: List[str] = []
+        async for event in self.generate_stream(messages, temperature=temperature, max_tokens=max_tokens):
+            if event.type == "text_delta" and event.text:
+                parts.append(event.text)
+            if event.type == "tool_call":
+                break
+        return "".join(parts)
+
+    async def generate_stream(
+        self,
+        messages: List[LLMMessage],
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None,
+    ) -> AsyncIterator[LLMStreamEvent]:
+        del temperature, max_tokens
+        if not self.client:
+            raise RuntimeError("LLM service not connected")
+
+        self._cancel_event.clear()
+        request_messages = self._build_request_messages(messages)
+        response = await self.client.create_chat_completion(
+            messages=request_messages,
+            chatId=self._ensure_chat_id(),
+            detail=True,
+            stream=True,
+        )
+        try:
+            async for event in aiter_stream_events(response):
+                if self._cancel_event.is_set():
+                    logger.info("FastGPT stream cancelled")
+                    break
+
+                stop_after_event = False
+                for mapped in self._map_stream_event(event):
+                    if mapped.type == "tool_call":
+                        stop_after_event = True
+                    yield mapped
+                if stop_after_event:
+                    break
+        finally:
+            await self._close_stream_response(response)
+
+    async def resume_after_client_tool_result(
+        self,
+        tool_call_id: str,
+        result: Dict[str, Any],
+    ) -> AsyncIterator[LLMStreamEvent]:
+        if not self.client:
+            raise RuntimeError("LLM service not connected")
+
+        pending = self._require_pending_interaction(tool_call_id)
+        follow_up_text = self._build_resume_text(pending, result)
+        self._state.pending_interaction = None
+
+        if not follow_up_text:
+            yield LLMStreamEvent(type="done")
+            return
+
+        self._cancel_event.clear()
+        response = await self.client.create_chat_completion(
+            messages=[{"role": "user", "content": follow_up_text}],
+            chatId=pending.chat_id,
+            detail=True,
+            stream=True,
+        )
+        try:
+            async for event in aiter_stream_events(response):
+                if self._cancel_event.is_set():
+                    logger.info("FastGPT resume stream cancelled")
+                    break
+
+                stop_after_event = False
+                for mapped in self._map_stream_event(event):
+                    if mapped.type == "tool_call":
+                        stop_after_event = True
+                    yield mapped
+                if stop_after_event:
+                    break
+        finally:
+            await self._close_stream_response(response)
+
+    async def _close_stream_response(self, response: Any) -> None:
+        if response is None:
+            return
+
+        # httpx async streaming responses must use `aclose()`.
+        aclose = getattr(response, "aclose", None)
+        if callable(aclose):
+            await aclose()
+            return
+
+        close = getattr(response, "close", None)
+        if callable(close):
+            maybe_awaitable = close()
+            if hasattr(maybe_awaitable, "__await__"):
+                await maybe_awaitable
+
+    def _ensure_chat_id(self) -> str:
+        chat_id = str(self._state.chat_id or "").strip()
+        if not chat_id:
+            chat_id = f"fastgpt_{uuid.uuid4().hex}"
+            self._state.chat_id = chat_id
+        return chat_id
+
+    def _build_request_messages(self, messages: List[LLMMessage]) -> List[Dict[str, Any]]:
+        non_empty = [msg for msg in messages if str(msg.content or "").strip()]
+        if not non_empty:
+            return [{"role": "user", "content": ""}]
+
+        latest_user = next((msg for msg in reversed(non_empty) if msg.role == "user"), None)
+        trailing_system = non_empty[-1] if non_empty and non_empty[-1].role == "system" else None
+
+        request: List[Dict[str, Any]] = []
+        if trailing_system and trailing_system is not latest_user:
+            request.append({"role": "system", "content": trailing_system.content.strip()})
+        if latest_user and str(latest_user.content or "").strip():
+            request.append({"role": "user", "content": latest_user.content.strip()})
+            return request
+
+        last_message = non_empty[-1]
+        payload = last_message.to_dict()
+        payload["content"] = str(payload.get("content") or "").strip()
+        return [payload]
+
+    def _extract_initial_greeting(self, payload: Any) -> Optional[str]:
+        if not isinstance(payload, dict):
+            return None
+
+        candidates: List[Any] = [
+            payload.get("app"),
+            payload.get("data"),
+        ]
+        for container in candidates:
+            if not isinstance(container, dict):
+                continue
+            nested_app = container.get("app") if isinstance(container.get("app"), dict) else None
+            if nested_app:
+                text = self._welcome_text_from_app(nested_app)
+                if text:
+                    return text
+            text = self._welcome_text_from_app(container)
+            if text:
+                return text
+
+        return None
+
+    @staticmethod
+    def _welcome_text_from_app(app_payload: Dict[str, Any]) -> Optional[str]:
+        chat_config = app_payload.get("chatConfig") if isinstance(app_payload.get("chatConfig"), dict) else {}
+        text = str(
+            chat_config.get("welcomeText")
+            or app_payload.get("welcomeText")
+            or ""
+        ).strip()
+        return text or None
+
+    def _map_stream_event(self, event: Any) -> List[LLMStreamEvent]:
+        kind = str(getattr(event, "kind", "") or "")
+        data = getattr(event, "data", {})
+        if not isinstance(data, dict):
+            data = {}
+
+        if kind in {"data", "answer", "fastAnswer"}:
+            chunks = self._extract_text_chunks(kind, data)
+            return [LLMStreamEvent(type="text_delta", text=chunk) for chunk in chunks if chunk]
+
+        if kind == "interactive":
+            return [self._build_interactive_tool_event(data)]
+
+        if kind == "error":
+            message = str(data.get("message") or data.get("error") or "FastGPT streaming error")
+            raise RuntimeError(message)
+
+        if kind == "done":
+            return [LLMStreamEvent(type="done")]
+
+        return []
+
+    @staticmethod
+    def _normalize_interactive_payload(payload: Dict[str, Any]) -> Dict[str, Any]:
+        normalized = payload
+        wrapped = normalized.get("interactive")
+        if isinstance(wrapped, dict):
+            normalized = wrapped
+
+        interaction_type = str(normalized.get("type") or "").strip()
+        if interaction_type == "toolChildrenInteractive":
+            params = normalized.get("params") if isinstance(normalized.get("params"), dict) else {}
+            children_response = params.get("childrenResponse")
+            if isinstance(children_response, dict):
+                normalized = children_response
+
+        return normalized
+
+    def _extract_text_chunks(self, kind: str, data: Dict[str, Any]) -> List[str]:
+        if kind in {"answer", "fastAnswer"}:
+            text = str(data.get("text") or "")
+            if text:
+                return [text]
+
+        choices = data.get("choices") if isinstance(data.get("choices"), list) else []
+        if not choices:
+            text = str(data.get("text") or "")
+            return [text] if text else []
+
+        first = choices[0] if isinstance(choices[0], dict) else {}
+        delta = first.get("delta") if isinstance(first.get("delta"), dict) else {}
+        if isinstance(delta.get("content"), str) and delta.get("content"):
+            return [str(delta.get("content"))]
+        message = first.get("message") if isinstance(first.get("message"), dict) else {}
+        if isinstance(message.get("content"), str) and message.get("content"):
+            return [str(message.get("content"))]
+        return []
+
+    def _build_interactive_tool_event(self, payload: Dict[str, Any]) -> LLMStreamEvent:
+        normalized_payload = self._normalize_interactive_payload(payload)
+        prompt = self._parse_interactive_prompt(normalized_payload)
+        call_id = f"fgi_{uuid.uuid4().hex[:12]}"
+        pending = FastGPTPendingInteraction(
+            tool_call_id=call_id,
+            chat_id=self._ensure_chat_id(),
+            prompt=prompt,
+            timeout_ms=self.INTERACTIVE_TIMEOUT_MS,
+            fastgpt_event=dict(normalized_payload),
+        )
+        self._state.pending_interaction = pending
+        arguments = prompt.to_ws_arguments(chat_id=pending.chat_id)
+        tool_call = {
+            "id": call_id,
+            "type": "function",
+            "executor": "client",
+            "wait_for_response": True,
+            "timeout_ms": pending.timeout_ms,
+            "display_name": prompt.title or prompt.description or prompt.prompt or "FastGPT Interactive",
+            "function": {
+                "name": self.INTERACTIVE_TOOL_NAME,
+                "arguments": json.dumps(arguments, ensure_ascii=False),
+            },
+        }
+        return LLMStreamEvent(type="tool_call", tool_call=tool_call)
+
+    def _parse_interactive_prompt(self, payload: Dict[str, Any]) -> FastGPTInteractivePrompt:
+        params = payload.get("params") if isinstance(payload.get("params"), dict) else {}
+        kind = str(payload.get("type") or "userSelect").strip() or "userSelect"
+        title = str(
+            payload.get("title")
+            or params.get("title")
+            or payload.get("nodeName")
+            or payload.get("label")
+            or ""
+        ).strip()
+        description = str(
+            payload.get("description")
+            or payload.get("desc")
+            or params.get("description")
+            or params.get("desc")
+            or ""
+        ).strip()
+        prompt_text = str(
+            payload.get("opener")
+            or params.get("opener")
+            or payload.get("intro")
+            or params.get("intro")
+            or payload.get("prompt")
+            or params.get("prompt")
+            or payload.get("text")
+            or params.get("text")
+            or title
+            or description
+        ).strip()
+        required = self._coerce_bool(payload.get("required"), default=True)
+        multiple = self._coerce_bool(params.get("multiple") or payload.get("multiple"), default=False)
+        submit_label = str(params.get("submitText") or payload.get("submitText") or "Continue").strip() or "Continue"
+        cancel_label = str(params.get("cancelText") or payload.get("cancelText") or "Cancel").strip() or "Cancel"
+
+        options: List[FastGPTOption] = []
+        raw_options = params.get("userSelectOptions") if isinstance(params.get("userSelectOptions"), list) else []
+        for index, raw_option in enumerate(raw_options):
+            if isinstance(raw_option, str):
+                value = raw_option.strip()
+                if not value:
+                    continue
+                options.append(FastGPTOption(id=f"option_{index}", label=value, value=value))
+                continue
+            if not isinstance(raw_option, dict):
+                continue
+            label = str(raw_option.get("label") or raw_option.get("value") or raw_option.get("id") or "").strip()
+            value = str(raw_option.get("value") or raw_option.get("label") or raw_option.get("id") or "").strip()
+            option_id = str(raw_option.get("id") or value or f"option_{index}").strip()
+            if not label and not value:
+                continue
+            options.append(
+                FastGPTOption(
+                    id=option_id or f"option_{index}",
+                    label=label or value,
+                    value=value or label,
+                    description=str(
+                        raw_option.get("description")
+                        or raw_option.get("desc")
+                        or raw_option.get("intro")
+                        or raw_option.get("summary")
+                        or ""
+                    ).strip(),
+                )
+            )
+
+        form: List[FastGPTField] = []
+        raw_form = params.get("inputForm") if isinstance(params.get("inputForm"), list) else []
+        for index, raw_field in enumerate(raw_form):
+            if not isinstance(raw_field, dict):
+                continue
+            field_options: List[FastGPTOption] = []
+            nested_options = raw_field.get("options") if isinstance(raw_field.get("options"), list) else []
+            for opt_index, option in enumerate(nested_options):
+                if isinstance(option, str):
+                    value = option.strip()
+                    if not value:
+                        continue
+                    field_options.append(FastGPTOption(id=f"field_{index}_opt_{opt_index}", label=value, value=value))
+                    continue
+                if not isinstance(option, dict):
+                    continue
+                label = str(option.get("label") or option.get("value") or option.get("id") or "").strip()
+                value = str(option.get("value") or option.get("label") or option.get("id") or "").strip()
+                option_id = str(option.get("id") or value or f"field_{index}_opt_{opt_index}").strip()
+                if not label and not value:
+                    continue
+                field_options.append(
+                    FastGPTOption(
+                        id=option_id or f"field_{index}_opt_{opt_index}",
+                        label=label or value,
+                        value=value or label,
+                        description=str(
+                            option.get("description")
+                            or option.get("desc")
+                            or option.get("intro")
+                            or option.get("summary")
+                            or ""
+                        ).strip(),
+                    )
+                )
+            name = str(raw_field.get("key") or raw_field.get("name") or raw_field.get("label") or f"field_{index}").strip()
+            label = str(raw_field.get("label") or raw_field.get("name") or name).strip()
+            form.append(
+                FastGPTField(
+                    name=name or f"field_{index}",
+                    label=label or name or f"field_{index}",
+                    input_type=str(raw_field.get("type") or raw_field.get("inputType") or "text").strip() or "text",
+                    required=self._coerce_bool(raw_field.get("required"), default=False),
+                    placeholder=str(
+                        raw_field.get("placeholder")
+                        or raw_field.get("description")
+                        or raw_field.get("desc")
+                        or ""
+                    ).strip(),
+                    default=raw_field.get("defaultValue", raw_field.get("default")),
+                    options=field_options,
+                )
+            )
+
+        return FastGPTInteractivePrompt(
+            kind="userInput" if kind == "userInput" else "userSelect",
+            title=title,
+            description=description,
+            prompt=prompt_text,
+            required=required,
+            multiple=multiple,
+            submit_label=submit_label,
+            cancel_label=cancel_label,
+            options=options,
+            form=form,
+            raw=dict(payload),
+        )
+
+    def _require_pending_interaction(self, tool_call_id: str) -> FastGPTPendingInteraction:
+        pending = self._state.pending_interaction
+        if pending is None or pending.tool_call_id != tool_call_id:
+            raise ValueError(f"FastGPT interaction not pending for tool call: {tool_call_id}")
+        return pending
+
+    def _build_resume_text(self, pending: FastGPTPendingInteraction, result: Dict[str, Any]) -> str:
+        status = result.get("status") if isinstance(result.get("status"), dict) else {}
+        status_code = self._safe_int(status.get("code"), default=0)
+        output = result.get("output") if isinstance(result.get("output"), dict) else {}
+        action = str(output.get("action") or "").strip().lower()
+
+        if action == "cancel" or status_code == 499:
+            return ""
+        if status_code == 422:
+            raise ValueError("Invalid FastGPT interactive payload from client")
+        if status_code and not 200 <= status_code < 300:
+            raise ValueError(f"FastGPT interactive result rejected with status {status_code}")
+        if action and action != "submit":
+            raise ValueError(f"Unsupported FastGPT interactive action: {action}")
+
+        payload = output.get("result") if isinstance(output.get("result"), dict) else output
+        if not isinstance(payload, dict):
+            raise ValueError("FastGPT interactive client result must be an object")
+
+        if pending.prompt.kind == "userSelect":
+            selected = str(payload.get("selected") or "").strip()
+            if selected:
+                return selected
+            selected_values = payload.get("selected_values") if isinstance(payload.get("selected_values"), list) else []
+            values = [str(item).strip() for item in selected_values if str(item).strip()]
+            if values:
+                return ", ".join(values)
+            text_value = str(payload.get("text") or "").strip()
+            return text_value
+
+        text_value = str(payload.get("text") or "").strip()
+        if text_value:
+            return text_value
+        fields = payload.get("fields") if isinstance(payload.get("fields"), dict) else {}
+        compact_fields = {str(key): value for key, value in fields.items()}
+        if compact_fields:
+            return json.dumps(compact_fields, ensure_ascii=False)
+        return ""
+
+    @staticmethod
+    def _coerce_bool(value: Any, *, default: bool) -> bool:
+        if isinstance(value, bool):
+            return value
+        if isinstance(value, str):
+            normalized = value.strip().lower()
+            if normalized in {"true", "1", "yes", "on"}:
+                return True
+            if normalized in {"false", "0", "no", "off"}:
+                return False
+        return default
+
+    @staticmethod
+    def _safe_int(value: Any, *, default: int) -> int:
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return default

engine/providers/llm/fastgpt_types.py

@@ -0,0 +1,95 @@
+"""FastGPT-specific provider types."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Literal, Optional
+
+InteractiveKind = Literal["userSelect", "userInput"]
+
+
+@dataclass(frozen=True)
+class FastGPTOption:
+    id: str
+    label: str
+    value: str
+    description: str = ""
+
+
+@dataclass(frozen=True)
+class FastGPTField:
+    name: str
+    label: str
+    input_type: str = "text"
+    required: bool = False
+    placeholder: str = ""
+    default: Any = None
+    options: List[FastGPTOption] = field(default_factory=list)
+
+
+@dataclass(frozen=True)
+class FastGPTInteractivePrompt:
+    kind: InteractiveKind
+    title: str = ""
+    description: str = ""
+    prompt: str = ""
+    required: bool = True
+    multiple: bool = False
+    submit_label: str = "Continue"
+    cancel_label: str = "Cancel"
+    options: List[FastGPTOption] = field(default_factory=list)
+    form: List[FastGPTField] = field(default_factory=list)
+    raw: Dict[str, Any] = field(default_factory=dict)
+
+    def to_ws_arguments(
+        self,
+        *,
+        turn_id: Optional[str] = None,
+        response_id: Optional[str] = None,
+        chat_id: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        context: Dict[str, Any] = {}
+        if turn_id:
+            context["turn_id"] = turn_id
+        if response_id:
+            context["response_id"] = response_id
+        if chat_id:
+            context["chat_id"] = chat_id
+        return {
+            "provider": "fastgpt",
+            "version": "fastgpt_interactive_v1",
+            "interaction": {
+                "type": self.kind,
+                "title": self.title,
+                "description": self.description,
+                "prompt": self.prompt,
+                "required": self.required,
+                "multiple": self.multiple,
+                "submit_label": self.submit_label,
+                "cancel_label": self.cancel_label,
+                "options": [vars(item) for item in self.options],
+                "form": [
+                    {
+                        **vars(item),
+                        "options": [vars(option) for option in item.options],
+                    }
+                    for item in self.form
+                ],
+            },
+            "context": context,
+        }
+
+
+@dataclass
+class FastGPTPendingInteraction:
+    tool_call_id: str
+    chat_id: str
+    prompt: FastGPTInteractivePrompt
+    timeout_ms: int
+    fastgpt_event: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class FastGPTConversationState:
+    chat_id: Optional[str] = None
+    pending_interaction: Optional[FastGPTPendingInteraction] = None

engine/requirements.txt

@@ -33,3 +33,6 @@ dashscope>=1.25.11
 sounddevice>=0.4.6
 soundfile>=0.12.1
 pyaudio>=0.2.13  # More reliable audio on Windows
+
+# FastGPT runtime support is installed from the sibling fastgpt-python-sdk package.
+# Local dev: pip install -e ..\\fastgpt-python-sdk

engine/runtime/pipeline/duplex.py

@@ -73,6 +73,8 @@ class DuplexPipeline:
     _MIN_SPLIT_SPOKEN_CHARS = 6
     _TOOL_WAIT_TIMEOUT_SECONDS = 60.0
     _SERVER_TOOL_TIMEOUT_SECONDS = 15.0
+    _MAX_LLM_ROUNDS = 3
+    _MAX_PROVIDER_MANAGED_ROUNDS = 24
     TRACK_AUDIO_IN = "audio_in"
     TRACK_AUDIO_OUT = "audio_out"
     TRACK_CONTROL = "control"
@@ -408,6 +410,7 @@ class DuplexPipeline:
         self._runtime_tool_display_names: Dict[str, str] = {}
         self._runtime_tool_wait_for_response: Dict[str, bool] = {}
         self._pending_tool_waiters: Dict[str, asyncio.Future] = {}
+        self._pending_tool_deadlines: Dict[str, float] = {}
         self._early_tool_results: Dict[str, Dict[str, Any]] = {}
         self._completed_tool_call_ids: set[str] = set()
         self._pending_client_tool_call_ids: set[str] = set()
@@ -594,6 +597,7 @@ class DuplexPipeline:
                     "provider": llm_provider,
                     "model": str(self._runtime_llm.get("model") or settings.llm_model),
                     "baseUrl": llm_base_url,
+                    "appId": str(self._runtime_llm.get("appId") or ""),
                 },
                 "asr": {
                     "provider": asr_provider,
@@ -937,6 +941,19 @@ class DuplexPipeline:
             return None
         return text.strip().strip('"').strip("'")
 
+    async def _resolve_provider_initial_greeting(self) -> Optional[str]:
+        if not self.llm_service or not hasattr(self.llm_service, "get_initial_greeting"):
+            return None
+
+        try:
+            greeting = await self.llm_service.get_initial_greeting()
+        except Exception as exc:
+            logger.warning("Failed to load provider initial greeting: {}", exc)
+            return None
+
+        text = str(greeting or "").strip()
+        return text or None
+
     async def start(self) -> None:
         """Start the pipeline and connect services."""
         try:
@@ -956,6 +973,7 @@ class DuplexPipeline:
                         model=str(llm_model),
                         api_key=str(llm_api_key).strip() if llm_api_key else None,
                         base_url=str(llm_base_url).strip() if llm_base_url else None,
+                        app_id=str(self._runtime_llm.get("appId")).strip() if self._runtime_llm.get("appId") else None,
                         system_prompt=self.conversation.system_prompt,
                         temperature=settings.llm_temperature,
                         knowledge_config=self._resolved_knowledge_config(),
@@ -1096,7 +1114,11 @@ class DuplexPipeline:
         if not self._bot_starts_first():
             return
 
-        if self._generated_opener_enabled() and self._resolved_tool_schemas():
+        provider_greeting = await self._resolve_provider_initial_greeting()
+        if provider_greeting:
+            self.conversation.greeting = provider_greeting
+
+        if not provider_greeting and self._generated_opener_enabled() and self._resolved_tool_schemas():
             # Run generated opener as a normal tool-capable assistant turn.
             # Use an empty user input so the opener can be driven by system prompt policy.
             if self._current_turn_task and not self._current_turn_task.done():
@@ -1107,13 +1129,13 @@ class DuplexPipeline:
             return
 
         manual_opener_execution: Dict[str, List[Dict[str, Any]]] = {"toolCalls": [], "toolResults": []}
-        if not self._generated_opener_enabled() and self._resolved_manual_opener_tool_calls():
+        if not provider_greeting and not self._generated_opener_enabled() and self._resolved_manual_opener_tool_calls():
             self._start_turn()
             self._start_response()
             manual_opener_execution = await self._execute_manual_opener_tool_calls()
 
         greeting_to_speak = self.conversation.greeting
-        if self._generated_opener_enabled():
+        if not provider_greeting and self._generated_opener_enabled():
             generated_greeting = await self._generate_runtime_greeting()
             if generated_greeting:
                 greeting_to_speak = generated_greeting
@@ -1954,12 +1976,35 @@ class DuplexPipeline:
         return bool(self._runtime_tool_wait_for_response.get(normalized, False))
 
     def _tool_executor(self, tool_call: Dict[str, Any]) -> str:
+        explicit_executor = str(tool_call.get("executor") or "").strip().lower()
+        if explicit_executor in {"client", "server"}:
+            return explicit_executor
         name = self._tool_name(tool_call)
         if name and name in self._runtime_tool_executor:
             return self._runtime_tool_executor[name]
         # Default to server execution unless explicitly marked as client.
         return "server"
 
+    def _tool_wait_for_response_for_call(self, tool_name: str, tool_call: Dict[str, Any]) -> bool:
+        explicit_wait = tool_call.get("wait_for_response")
+        if explicit_wait is None:
+            explicit_wait = tool_call.get("waitForResponse")
+        if isinstance(explicit_wait, bool):
+            return explicit_wait
+        return self._tool_wait_for_response(tool_name)
+
+    def _tool_timeout_ms(self, tool_call: Dict[str, Any]) -> int:
+        raw_timeout = tool_call.get("timeout_ms")
+        if raw_timeout is None:
+            raw_timeout = tool_call.get("timeoutMs")
+        try:
+            timeout_ms = int(raw_timeout)
+        except (TypeError, ValueError):
+            timeout_ms = 0
+        if timeout_ms > 0:
+            return timeout_ms
+        return int(self._TOOL_WAIT_TIMEOUT_SECONDS * 1000)
+
     def _tool_arguments(self, tool_call: Dict[str, Any]) -> Dict[str, Any]:
         fn = tool_call.get("function")
         if not isinstance(fn, dict):
@@ -2179,7 +2224,7 @@ class DuplexPipeline:
             self._early_tool_results[call_id] = item
             self._completed_tool_call_ids.add(call_id)
 
-    async def _wait_for_single_tool_result(self, call_id: str) -> Dict[str, Any]:
+    async def _wait_for_single_tool_result(self, call_id: str, timeout_seconds: Optional[float] = None) -> Dict[str, Any]:
         if call_id in self._completed_tool_call_ids and call_id not in self._early_tool_results:
             return {
                 "tool_call_id": call_id,
@@ -2193,8 +2238,10 @@ class DuplexPipeline:
         loop = asyncio.get_running_loop()
         future = loop.create_future()
         self._pending_tool_waiters[call_id] = future
+        timeout = timeout_seconds if isinstance(timeout_seconds, (int, float)) and timeout_seconds > 0 else self._TOOL_WAIT_TIMEOUT_SECONDS
+        self._pending_tool_deadlines[call_id] = time.monotonic() + timeout
         try:
-            return await asyncio.wait_for(future, timeout=self._TOOL_WAIT_TIMEOUT_SECONDS)
+            return await asyncio.wait_for(future, timeout=timeout)
         except asyncio.TimeoutError:
             self._completed_tool_call_ids.add(call_id)
             return {
@@ -2204,8 +2251,14 @@ class DuplexPipeline:
             }
         finally:
             self._pending_tool_waiters.pop(call_id, None)
+            self._pending_tool_deadlines.pop(call_id, None)
             self._pending_client_tool_call_ids.discard(call_id)
 
+    def pending_client_tool_deadline(self) -> Optional[float]:
+        if not self._pending_tool_deadlines:
+            return None
+        return max(self._pending_tool_deadlines.values())
+
     def _normalize_stream_event(self, item: Any) -> LLMStreamEvent:
         if isinstance(item, LLMStreamEvent):
             return item
@@ -2246,7 +2299,8 @@ class DuplexPipeline:
             messages = self.conversation.get_messages()
             if system_context and system_context.strip():
                 messages = [*messages, LLMMessage(role="system", content=system_context.strip())]
-            max_rounds = 3
+            llm_rounds = 0
+            provider_rounds_remaining = self._MAX_PROVIDER_MANAGED_ROUNDS
 
             await self.conversation.start_assistant_turn()
             self._is_bot_speaking = True
@@ -2256,10 +2310,28 @@ class DuplexPipeline:
             first_audio_sent = False
             self._pending_llm_delta = ""
             self._last_llm_delta_emit_ms = 0.0
-            for _ in range(max_rounds):
+            pending_provider_stream = None
+            while True:
                 if self._interrupt_event.is_set():
                     break
 
+                if pending_provider_stream is not None:
+                    if provider_rounds_remaining <= 0:
+                        logger.warning(
+                            "Provider-managed tool chain exceeded {} rounds; ending turn early",
+                            self._MAX_PROVIDER_MANAGED_ROUNDS,
+                        )
+                        break
+                    provider_rounds_remaining -= 1
+                else:
+                    if llm_rounds >= self._MAX_LLM_ROUNDS:
+                        logger.warning(
+                            "LLM tool planning exceeded {} rounds; ending turn early",
+                            self._MAX_LLM_ROUNDS,
+                        )
+                        break
+                    llm_rounds += 1
+
                 sentence_buffer = ""
                 pending_punctuation = ""
                 round_response = ""
@@ -2267,7 +2339,10 @@ class DuplexPipeline:
                 allow_text_output = True
                 use_engine_sentence_split = self._use_engine_sentence_split_for_tts()
 
-                async for raw_event in self.llm_service.generate_stream(messages):
+                stream_iter = pending_provider_stream if pending_provider_stream is not None else self.llm_service.generate_stream(messages)
+                pending_provider_stream = None
+
+                async for raw_event in stream_iter:
                     if self._interrupt_event.is_set():
                         break
 
@@ -2282,14 +2357,21 @@ class DuplexPipeline:
                         if not tool_call:
                             continue
                         allow_text_output = False
+                        tool_name = self._tool_name(tool_call) or "unknown_tool"
                         executor = self._tool_executor(tool_call)
                         enriched_tool_call = dict(tool_call)
                         enriched_tool_call["executor"] = executor
-                        tool_name = self._tool_name(enriched_tool_call) or "unknown_tool"
                         tool_id = self._tool_id_for_name(tool_name)
-                        tool_display_name = self._tool_display_name(tool_name) or tool_name
-                        wait_for_response = self._tool_wait_for_response(tool_name)
+                        tool_display_name = str(
+                            enriched_tool_call.get("displayName")
+                            or enriched_tool_call.get("display_name")
+                            or self._tool_display_name(tool_name)
+                            or tool_name
+                        ).strip()
+                        wait_for_response = self._tool_wait_for_response_for_call(tool_name, enriched_tool_call)
                         enriched_tool_call["wait_for_response"] = wait_for_response
+                        timeout_ms = self._tool_timeout_ms(enriched_tool_call)
+                        enriched_tool_call["timeout_ms"] = timeout_ms
                         call_id = str(enriched_tool_call.get("id") or "").strip()
                         fn_payload = (
                             dict(enriched_tool_call.get("function"))
@@ -2298,6 +2380,15 @@ class DuplexPipeline:
                         )
                         raw_args = str(fn_payload.get("arguments") or "") if isinstance(fn_payload, dict) else ""
                         tool_arguments = self._tool_arguments(enriched_tool_call)
+                        if tool_name == "fastgpt.interactive":
+                            context_payload = (
+                                dict(tool_arguments.get("context"))
+                                if isinstance(tool_arguments.get("context"), dict)
+                                else {}
+                            )
+                            context_payload.setdefault("turn_id", turn_id)
+                            context_payload.setdefault("response_id", response_id)
+                            tool_arguments["context"] = context_payload
                         merged_tool_arguments = self._apply_tool_default_args(tool_name, tool_arguments)
                         try:
                             merged_args_text = json.dumps(merged_tool_arguments, ensure_ascii=False)
@@ -2324,9 +2415,9 @@ class DuplexPipeline:
                                     tool_id=tool_id,
                                     tool_display_name=tool_display_name,
                                     wait_for_response=wait_for_response,
-                                    arguments=tool_arguments,
+                                    arguments=merged_tool_arguments,
                                     executor=executor,
-                                    timeout_ms=int(self._TOOL_WAIT_TIMEOUT_SECONDS * 1000),
+                                    timeout_ms=timeout_ms,
                                     tool_call=enriched_tool_call,
                                 )
                             },
@@ -2457,6 +2548,8 @@ class DuplexPipeline:
                     break
 
                 tool_results: List[Dict[str, Any]] = []
+                provider_managed_tool = False
+                provider_resumed = False
                 for call in tool_calls:
                     call_id = str(call.get("id") or "").strip()
                     if not call_id:
@@ -2466,9 +2559,27 @@ class DuplexPipeline:
                     tool_id = self._tool_id_for_name(tool_name)
                     logger.info(f"[Tool] execute start name={tool_name} call_id={call_id} executor={executor}")
                     if executor == "client":
-                        result = await self._wait_for_single_tool_result(call_id)
+                        timeout_ms = self._tool_timeout_ms(call)
+                        result = await self._wait_for_single_tool_result(
+                            call_id,
+                            timeout_seconds=(timeout_ms / 1000.0),
+                        )
                         await self._emit_tool_result(result, source="client")
                         tool_results.append(result)
+                        if (
+                            hasattr(self.llm_service, "handles_client_tool")
+                            and hasattr(self.llm_service, "resume_after_client_tool_result")
+                            and self.llm_service.handles_client_tool(tool_name)
+                        ):
+                            provider_managed_tool = True
+                            status = result.get("status") if isinstance(result.get("status"), dict) else {}
+                            status_code = int(status.get("code") or 0) if status else 0
+                            output = result.get("output") if isinstance(result.get("output"), dict) else {}
+                            action = str(output.get("action") or "").strip().lower()
+                            if 200 <= status_code < 300 and action != "cancel":
+                                pending_provider_stream = self.llm_service.resume_after_client_tool_result(call_id, result)
+                                provider_resumed = True
+                            break
                         continue
 
                     call_for_executor = dict(call)
@@ -2495,6 +2606,11 @@ class DuplexPipeline:
                     await self._emit_tool_result(result, source="server")
                     tool_results.append(result)
 
+                if provider_resumed:
+                    continue
+                if provider_managed_tool:
+                    break
+
                 messages = [
                     *messages,
                     LLMMessage(

engine/runtime/ports/__init__.py

@@ -14,7 +14,13 @@ from runtime.ports.control_plane import (
     KnowledgeRetriever,
     ToolCatalog,
 )
-from runtime.ports.llm import LLMCancellable, LLMPort, LLMRuntimeConfigurable, LLMServiceSpec
+from runtime.ports.llm import (
+    LLMCancellable,
+    LLMClientToolResumable,
+    LLMPort,
+    LLMRuntimeConfigurable,
+    LLMServiceSpec,
+)
 from runtime.ports.service_factory import RealtimeServiceFactory
 from runtime.ports.tts import TTSPort, TTSServiceSpec
 
@@ -30,6 +36,7 @@ __all__ = [
     "KnowledgeRetriever",
     "ToolCatalog",
     "LLMCancellable",
+    "LLMClientToolResumable",
     "LLMPort",
     "LLMRuntimeConfigurable",
     "LLMServiceSpec",

engine/runtime/ports/llm.py

@@ -18,6 +18,7 @@ class LLMServiceSpec:
     model: str
     api_key: Optional[str] = None
     base_url: Optional[str] = None
+    app_id: Optional[str] = None
     system_prompt: Optional[str] = None
     temperature: float = 0.7
     knowledge_config: Dict[str, Any] = field(default_factory=dict)
@@ -65,3 +66,17 @@ class LLMRuntimeConfigurable(Protocol):
 
     def set_tool_schemas(self, schemas: Optional[List[Dict[str, Any]]]) -> None:
         """Apply runtime tool schemas used for tool calling."""
+
+
+class LLMClientToolResumable(Protocol):
+    """Optional extension for providers that pause on client-side tool results."""
+
+    def handles_client_tool(self, tool_name: str) -> bool:
+        """Return True when the provider owns the lifecycle of this client tool."""
+
+    def resume_after_client_tool_result(
+        self,
+        tool_call_id: str,
+        result: Dict[str, Any],
+    ) -> AsyncIterator[LLMStreamEvent]:
+        """Resume the provider stream after a correlated client-side tool result."""

engine/tests/test_backend_adapters.py

@@ -283,6 +283,30 @@ def test_translate_agent_schema_maps_volcengine_fields():
     }
 
 
+def test_translate_agent_schema_maps_llm_app_id():
+    payload = {
+        "agent": {
+            "llm": {
+                "provider": "fastgpt",
+                "model": "fastgpt",
+                "api_key": "llm-key",
+                "api_url": "https://cloud.fastgpt.cn/api",
+                "app_id": "app-fastgpt-123",
+            },
+        }
+    }
+
+    translated = LocalYamlAssistantConfigAdapter._translate_agent_schema("assistant_demo", payload)
+    assert translated is not None
+    assert translated["services"]["llm"] == {
+        "provider": "fastgpt",
+        "model": "fastgpt",
+        "apiKey": "llm-key",
+        "baseUrl": "https://cloud.fastgpt.cn/api",
+        "appId": "app-fastgpt-123",
+    }
+
+
 @pytest.mark.asyncio
 async def test_backend_mode_disabled_uses_local_assistant_config_even_with_url(monkeypatch, tmp_path):
     class _FailIfCalledClientSession:

engine/tests/test_fastgpt_provider.py

@@ -0,0 +1,411 @@
+import json
+from types import SimpleNamespace
+from typing import Any, Dict, List
+
+import pytest
+
+from providers.common.base import LLMMessage
+from providers.llm.fastgpt import FastGPTLLMService
+
+
+class _FakeResponse:
+    def __init__(self, events: List[Any]):
+        self.events = events
+        self.closed = False
+
+    async def close(self) -> None:
+        self.closed = True
+
+
+class _FakeJSONResponse:
+    def __init__(self, payload: Dict[str, Any], status_code: int = 200):
+        self._payload = payload
+        self.status_code = status_code
+
+    def json(self) -> Dict[str, Any]:
+        return dict(self._payload)
+
+    def raise_for_status(self) -> None:
+        if self.status_code >= 400:
+            raise RuntimeError(f"HTTP {self.status_code}")
+
+
+class _FakeAsyncStreamResponse(_FakeResponse):
+    def __init__(self, events: List[Any]):
+        super().__init__(events)
+        self.aclosed = False
+
+    def close(self) -> None:
+        raise AssertionError("sync close should not be used for async stream responses")
+
+    async def aclose(self) -> None:
+        self.aclosed = True
+
+
+class _FakeAsyncChatClient:
+    responses: List[_FakeResponse] = []
+    init_payload: Dict[str, Any] | None = None
+
+    def __init__(self, api_key: str, base_url: str):
+        self.api_key = api_key
+        self.base_url = base_url
+        self.requests: List[Dict[str, Any]] = []
+        self.init_requests: List[Dict[str, Any]] = []
+
+    async def create_chat_completion(self, **kwargs):
+        self.requests.append(dict(kwargs))
+        if not self.responses:
+            raise AssertionError("No fake FastGPT response queued")
+        return self.responses.pop(0)
+
+    async def get_chat_init(self, **kwargs):
+        self.init_requests.append(dict(kwargs))
+        return _FakeJSONResponse(
+            self.init_payload or {"data": {"app": {"chatConfig": {"welcomeText": ""}}}},
+        )
+
+    async def close(self) -> None:
+        return None
+
+
+async def _fake_aiter_stream_events(response: _FakeResponse):
+    for event in response.events:
+        yield event
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_streams_text_from_data_event(monkeypatch):
+    monkeypatch.setattr("providers.llm.fastgpt.AsyncChatClient", _FakeAsyncChatClient)
+    monkeypatch.setattr("providers.llm.fastgpt.aiter_stream_events", _fake_aiter_stream_events)
+
+    _FakeAsyncChatClient.responses = [
+        _FakeResponse(
+            [
+                SimpleNamespace(
+                    kind="data",
+                    data={"choices": [{"delta": {"content": "Hello from FastGPT."}}]},
+                ),
+                SimpleNamespace(kind="done", data={}),
+            ]
+        )
+    ]
+
+    service = FastGPTLLMService(api_key="key", base_url="https://fastgpt.example")
+    await service.connect()
+
+    events = [event async for event in service.generate_stream([LLMMessage(role="user", content="Hi")])]
+
+    assert [event.type for event in events] == ["text_delta", "done"]
+    assert events[0].text == "Hello from FastGPT."
+    assert service.client.requests[0]["messages"] == [{"role": "user", "content": "Hi"}]
+    assert service.client.requests[0]["chatId"] == service._state.chat_id
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_streams_text_from_answer_delta_event(monkeypatch):
+    monkeypatch.setattr("providers.llm.fastgpt.AsyncChatClient", _FakeAsyncChatClient)
+    monkeypatch.setattr("providers.llm.fastgpt.aiter_stream_events", _fake_aiter_stream_events)
+
+    _FakeAsyncChatClient.responses = [
+        _FakeResponse(
+            [
+                SimpleNamespace(
+                    kind="answer",
+                    data={"choices": [{"delta": {"content": "Hello from answer delta."}}]},
+                ),
+                SimpleNamespace(kind="done", data={}),
+            ]
+        )
+    ]
+
+    service = FastGPTLLMService(api_key="key", base_url="https://fastgpt.example")
+    await service.connect()
+
+    events = [event async for event in service.generate_stream([LLMMessage(role="user", content="Hi")])]
+
+    assert [event.type for event in events] == ["text_delta", "done"]
+    assert events[0].text == "Hello from answer delta."
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_uses_async_close_for_stream_responses(monkeypatch):
+    monkeypatch.setattr("providers.llm.fastgpt.AsyncChatClient", _FakeAsyncChatClient)
+    monkeypatch.setattr("providers.llm.fastgpt.aiter_stream_events", _fake_aiter_stream_events)
+
+    response = _FakeAsyncStreamResponse(
+        [
+            SimpleNamespace(
+                kind="data",
+                data={"choices": [{"delta": {"content": "Hello from FastGPT."}}]},
+            ),
+            SimpleNamespace(kind="done", data={}),
+        ]
+    )
+    _FakeAsyncChatClient.responses = [response]
+
+    service = FastGPTLLMService(api_key="key", base_url="https://fastgpt.example")
+    await service.connect()
+
+    events = [event async for event in service.generate_stream([LLMMessage(role="user", content="Hi")])]
+
+    assert [event.type for event in events] == ["text_delta", "done"]
+    assert response.aclosed is True
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_loads_initial_greeting_from_chat_init(monkeypatch):
+    monkeypatch.setattr("providers.llm.fastgpt.AsyncChatClient", _FakeAsyncChatClient)
+    monkeypatch.setattr("providers.llm.fastgpt.aiter_stream_events", _fake_aiter_stream_events)
+
+    _FakeAsyncChatClient.init_payload = {
+        "data": {
+            "app": {
+                "chatConfig": {
+                    "welcomeText": "Hello from FastGPT init.",
+                }
+            }
+        }
+    }
+
+    service = FastGPTLLMService(
+        api_key="key",
+        base_url="https://fastgpt.example",
+        app_id="app-123",
+    )
+    await service.connect()
+
+    greeting = await service.get_initial_greeting()
+
+    assert greeting == "Hello from FastGPT init."
+    assert service.client.init_requests[0] == {
+        "appId": "app-123",
+        "chatId": service._state.chat_id,
+    }
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_maps_interactive_event_to_client_tool(monkeypatch):
+    monkeypatch.setattr("providers.llm.fastgpt.AsyncChatClient", _FakeAsyncChatClient)
+    monkeypatch.setattr("providers.llm.fastgpt.aiter_stream_events", _fake_aiter_stream_events)
+
+    _FakeAsyncChatClient.responses = [
+        _FakeResponse(
+            [
+                SimpleNamespace(
+                    kind="interactive",
+                    data={
+                        "type": "userSelect",
+                        "title": "Choose a plan",
+                        "params": {
+                            "description": "Pick the best plan for your team.",
+                            "userSelectOptions": [
+                                {"id": "basic", "label": "Basic", "value": "basic", "desc": "Starter tier"},
+                                {"id": "pro", "label": "Pro", "value": "pro", "description": "Advanced tier"},
+                            ]
+                        },
+                    },
+                )
+            ]
+        )
+    ]
+
+    service = FastGPTLLMService(api_key="key", base_url="https://fastgpt.example")
+    await service.connect()
+
+    events = [event async for event in service.generate_stream([LLMMessage(role="user", content="Start")])]
+
+    assert len(events) == 1
+    assert events[0].type == "tool_call"
+    tool_call = events[0].tool_call
+    assert tool_call["executor"] == "client"
+    assert tool_call["wait_for_response"] is True
+    assert tool_call["timeout_ms"] == 300000
+    assert tool_call["function"]["name"] == "fastgpt.interactive"
+
+    arguments = json.loads(tool_call["function"]["arguments"])
+    assert arguments["provider"] == "fastgpt"
+    assert arguments["version"] == "fastgpt_interactive_v1"
+    assert arguments["interaction"]["type"] == "userSelect"
+    assert arguments["interaction"]["description"] == "Pick the best plan for your team."
+    assert arguments["interaction"]["options"][0]["description"] == "Starter tier"
+    assert arguments["interaction"]["options"][1]["value"] == "pro"
+    assert arguments["interaction"]["options"][1]["description"] == "Advanced tier"
+    assert arguments["context"]["chat_id"] == service._state.chat_id
+    assert service._state.pending_interaction is not None
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_unwraps_nested_tool_children_interactive(monkeypatch):
+    monkeypatch.setattr("providers.llm.fastgpt.AsyncChatClient", _FakeAsyncChatClient)
+    monkeypatch.setattr("providers.llm.fastgpt.aiter_stream_events", _fake_aiter_stream_events)
+
+    _FakeAsyncChatClient.responses = [
+        _FakeResponse(
+            [
+                SimpleNamespace(
+                    kind="interactive",
+                    data={
+                        "interactive": {
+                            "type": "toolChildrenInteractive",
+                            "params": {
+                                "childrenResponse": {
+                                    "type": "userSelect",
+                                    "params": {
+                                        "description": "Please choose a workflow branch.",
+                                        "userSelectOptions": [
+                                            {"value": "A", "description": "Branch A"},
+                                            {"value": "B", "description": "Branch B"},
+                                        ],
+                                    },
+                                }
+                            },
+                        }
+                    },
+                )
+            ]
+        )
+    ]
+
+    service = FastGPTLLMService(api_key="key", base_url="https://fastgpt.example")
+    await service.connect()
+
+    events = [event async for event in service.generate_stream([LLMMessage(role="user", content="Start")])]
+
+    assert len(events) == 1
+    arguments = json.loads(events[0].tool_call["function"]["arguments"])
+    assert arguments["interaction"]["type"] == "userSelect"
+    assert arguments["interaction"]["description"] == "Please choose a workflow branch."
+    assert arguments["interaction"]["options"][0]["description"] == "Branch A"
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_uses_opener_for_interactive_prompt_when_prompt_missing(monkeypatch):
+    monkeypatch.setattr("providers.llm.fastgpt.AsyncChatClient", _FakeAsyncChatClient)
+    monkeypatch.setattr("providers.llm.fastgpt.aiter_stream_events", _fake_aiter_stream_events)
+
+    _FakeAsyncChatClient.responses = [
+        _FakeResponse(
+            [
+                SimpleNamespace(
+                    kind="interactive",
+                    data={
+                        "type": "userSelect",
+                        "opener": "请确认您是否满意本次服务。",
+                        "params": {
+                            "userSelectOptions": [
+                                {"value": "是"},
+                                {"value": "否"},
+                            ]
+                        },
+                    },
+                )
+            ]
+        )
+    ]
+
+    service = FastGPTLLMService(api_key="key", base_url="https://fastgpt.example")
+    await service.connect()
+
+    events = [event async for event in service.generate_stream([LLMMessage(role="user", content="Start")])]
+
+    assert len(events) == 1
+    tool_call = events[0].tool_call
+    arguments = json.loads(tool_call["function"]["arguments"])
+    assert tool_call["display_name"] == "请确认您是否满意本次服务。"
+    assert arguments["interaction"]["prompt"] == "请确认您是否满意本次服务。"
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_resumes_same_chat_after_client_result(monkeypatch):
+    monkeypatch.setattr("providers.llm.fastgpt.AsyncChatClient", _FakeAsyncChatClient)
+    monkeypatch.setattr("providers.llm.fastgpt.aiter_stream_events", _fake_aiter_stream_events)
+
+    _FakeAsyncChatClient.responses = [
+        _FakeResponse(
+            [
+                SimpleNamespace(
+                    kind="interactive",
+                    data={
+                        "type": "userSelect",
+                        "params": {"userSelectOptions": [{"label": "Pro", "value": "pro"}]},
+                    },
+                )
+            ]
+        ),
+        _FakeResponse(
+            [
+                SimpleNamespace(kind="answer", data={"text": "Resumed answer."}),
+                SimpleNamespace(kind="done", data={}),
+            ]
+        ),
+    ]
+
+    service = FastGPTLLMService(api_key="key", base_url="https://fastgpt.example")
+    await service.connect()
+
+    initial_events = [event async for event in service.generate_stream([LLMMessage(role="user", content="Start")])]
+    call_id = initial_events[0].tool_call["id"]
+
+    resumed_events = [
+        event
+        async for event in service.resume_after_client_tool_result(
+            call_id,
+            {
+                "tool_call_id": call_id,
+                "name": "fastgpt.interactive",
+                "output": {
+                    "action": "submit",
+                    "result": {"type": "userSelect", "selected": "pro"},
+                },
+                "status": {"code": 200, "message": "ok"},
+            },
+        )
+    ]
+
+    assert [event.type for event in resumed_events] == ["text_delta", "done"]
+    assert resumed_events[0].text == "Resumed answer."
+    assert service.client.requests[1]["chatId"] == service.client.requests[0]["chatId"]
+    assert service.client.requests[1]["messages"] == [{"role": "user", "content": "pro"}]
+    assert service._state.pending_interaction is None
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_cancel_result_clears_pending_interaction(monkeypatch):
+    monkeypatch.setattr("providers.llm.fastgpt.AsyncChatClient", _FakeAsyncChatClient)
+    monkeypatch.setattr("providers.llm.fastgpt.aiter_stream_events", _fake_aiter_stream_events)
+
+    _FakeAsyncChatClient.responses = [
+        _FakeResponse(
+            [
+                SimpleNamespace(
+                    kind="interactive",
+                    data={
+                        "type": "userInput",
+                        "params": {"inputForm": [{"name": "name", "label": "Name"}]},
+                    },
+                )
+            ]
+        )
+    ]
+
+    service = FastGPTLLMService(api_key="key", base_url="https://fastgpt.example")
+    await service.connect()
+
+    initial_events = [event async for event in service.generate_stream([LLMMessage(role="user", content="Start")])]
+    call_id = initial_events[0].tool_call["id"]
+
+    resumed_events = [
+        event
+        async for event in service.resume_after_client_tool_result(
+            call_id,
+            {
+                "tool_call_id": call_id,
+                "name": "fastgpt.interactive",
+                "output": {"action": "cancel", "result": {}},
+                "status": {"code": 499, "message": "user_cancelled"},
+            },
+        )
+    ]
+
+    assert [event.type for event in resumed_events] == ["done"]
+    assert service._state.pending_interaction is None

engine/tests/test_session_timeout.py

@@ -0,0 +1,13 @@
+from app.main import _inactivity_deadline
+
+
+def test_inactivity_deadline_uses_default_timeout_without_pending_tool():
+    assert _inactivity_deadline(last_received_at=100.0, inactivity_timeout_sec=60) == 160.0
+
+
+def test_inactivity_deadline_extends_while_waiting_for_client_tool():
+    assert _inactivity_deadline(
+        last_received_at=100.0,
+        inactivity_timeout_sec=60,
+        pending_client_tool_deadline=340.0,
+    ) == 340.0

engine/tests/test_tool_call_flow.py

@@ -1,5 +1,6 @@
 import asyncio
 import json
+import time
 from typing import Any, Dict, List
 
 import pytest
@@ -109,6 +110,22 @@ class _CaptureGenerateLLM:
         yield LLMStreamEvent(type="done")
 
 
+class _InitGreetingLLM:
+    def __init__(self, greeting: str):
+        self.greeting = greeting
+        self.init_calls = 0
+
+    async def generate(self, _messages, temperature=0.7, max_tokens=None):
+        return ""
+
+    async def generate_stream(self, _messages, temperature=0.7, max_tokens=None):
+        yield LLMStreamEvent(type="done")
+
+    async def get_initial_greeting(self):
+        self.init_calls += 1
+        return self.greeting
+
+
 def _build_pipeline(monkeypatch, llm_rounds: List[List[LLMStreamEvent]]) -> tuple[DuplexPipeline, List[Dict[str, Any]]]:
     monkeypatch.setattr("runtime.pipeline.duplex.SileroVAD", _DummySileroVAD)
     monkeypatch.setattr("runtime.pipeline.duplex.VADProcessor", _DummyVADProcessor)
@@ -306,6 +323,21 @@ async def test_generated_opener_uses_tool_capable_turn_when_tools_available(monk
     assert called.get("user_text") == ""
 
 
+@pytest.mark.asyncio
+async def test_provider_initial_greeting_takes_precedence_over_local_opener(monkeypatch):
+    llm = _InitGreetingLLM("FastGPT init greeting")
+    pipeline, events = _build_pipeline_with_custom_llm(monkeypatch, llm)
+    pipeline.apply_runtime_overrides({"output": {"mode": "text"}})
+    pipeline.conversation.greeting = "local fallback greeting"
+
+    await pipeline.emit_initial_greeting()
+
+    finals = [event for event in events if event.get("type") == "assistant.response.final"]
+    assert finals
+    assert finals[-1]["text"] == "FastGPT init greeting"
+    assert llm.init_calls == 1
+
+
 @pytest.mark.asyncio
 async def test_manual_opener_tool_calls_emit_assistant_tool_call(monkeypatch):
     pipeline, events = _build_pipeline(monkeypatch, [[LLMStreamEvent(type="done")]])
@@ -736,3 +768,268 @@ async def test_eou_early_return_clears_stale_asr_capture(monkeypatch):
     assert pipeline._asr_capture_active is False
     assert pipeline._asr_capture_started_ms == 0.0
     assert pipeline._pending_speech_audio == b""
+
+class _FakeResumableLLM:
+    def __init__(self, *, timeout_ms: int = 300000):
+        self.timeout_ms = timeout_ms
+        self.generate_stream_calls = 0
+        self.resumed_results: List[Dict[str, Any]] = []
+
+    async def generate(self, _messages, temperature=0.7, max_tokens=None):
+        return ""
+
+    async def generate_stream(self, _messages, temperature=0.7, max_tokens=None):
+        self.generate_stream_calls += 1
+        yield LLMStreamEvent(
+            type="tool_call",
+            tool_call={
+                "id": "call_fastgpt_1",
+                "executor": "client",
+                "wait_for_response": True,
+                "timeout_ms": self.timeout_ms,
+                "display_name": "Choose a plan",
+                "type": "function",
+                "function": {
+                    "name": "fastgpt.interactive",
+                    "arguments": json.dumps(
+                        {
+                            "provider": "fastgpt",
+                            "version": "fastgpt_interactive_v1",
+                            "interaction": {
+                                "type": "userSelect",
+                                "title": "Choose a plan",
+                                "options": [
+                                    {"id": "basic", "label": "Basic", "value": "basic"},
+                                    {"id": "pro", "label": "Pro", "value": "pro"},
+                                ],
+                                "form": [],
+                            },
+                            "context": {"chat_id": "fastgpt_chat_1"},
+                        },
+                        ensure_ascii=False,
+                    ),
+                },
+            },
+        )
+        yield LLMStreamEvent(type="done")
+
+    def handles_client_tool(self, tool_name: str) -> bool:
+        return tool_name == "fastgpt.interactive"
+
+    async def resume_after_client_tool_result(self, tool_call_id: str, result: Dict[str, Any]):
+        self.resumed_results.append({"tool_call_id": tool_call_id, "result": dict(result)})
+        yield LLMStreamEvent(type="text_delta", text="provider resumed answer.")
+        yield LLMStreamEvent(type="done")
+
+
+class _FakeChainedResumableLLM:
+    def __init__(self, call_ids: List[str], *, timeout_ms: int = 300000):
+        self.call_ids = call_ids
+        self.timeout_ms = timeout_ms
+        self.generate_stream_calls = 0
+        self.resumed_results: List[Dict[str, Any]] = []
+
+    def _tool_call_event(self, call_id: str) -> LLMStreamEvent:
+        return LLMStreamEvent(
+            type="tool_call",
+            tool_call={
+                "id": call_id,
+                "executor": "client",
+                "wait_for_response": True,
+                "timeout_ms": self.timeout_ms,
+                "display_name": f"Collect {call_id}",
+                "type": "function",
+                "function": {
+                    "name": "fastgpt.interactive",
+                    "arguments": json.dumps(
+                        {
+                            "provider": "fastgpt",
+                            "version": "fastgpt_interactive_v1",
+                            "interaction": {
+                                "type": "userInput",
+                                "title": "",
+                                "description": f"Prompt for {call_id}",
+                                "prompt": f"Prompt for {call_id}",
+                                "form": [{"name": "result", "label": "result", "input_type": "input"}],
+                                "options": [],
+                            },
+                            "context": {"chat_id": "fastgpt_chat_chain"},
+                        },
+                        ensure_ascii=False,
+                    ),
+                },
+            },
+        )
+
+    async def generate(self, _messages, temperature=0.7, max_tokens=None):
+        return ""
+
+    async def generate_stream(self, _messages, temperature=0.7, max_tokens=None):
+        self.generate_stream_calls += 1
+        yield self._tool_call_event(self.call_ids[0])
+        yield LLMStreamEvent(type="done")
+
+    def handles_client_tool(self, tool_name: str) -> bool:
+        return tool_name == "fastgpt.interactive"
+
+    async def resume_after_client_tool_result(self, tool_call_id: str, result: Dict[str, Any]):
+        self.resumed_results.append({"tool_call_id": tool_call_id, "result": dict(result)})
+        next_index = len(self.resumed_results)
+        if next_index < len(self.call_ids):
+            yield self._tool_call_event(self.call_ids[next_index])
+        else:
+            yield LLMStreamEvent(type="text_delta", text="completed after third interactive input.")
+        yield LLMStreamEvent(type="done")
+
+
+def _build_pipeline_with_custom_llm(monkeypatch, llm_service) -> tuple[DuplexPipeline, List[Dict[str, Any]]]:
+    monkeypatch.setattr("runtime.pipeline.duplex.SileroVAD", _DummySileroVAD)
+    monkeypatch.setattr("runtime.pipeline.duplex.VADProcessor", _DummyVADProcessor)
+    monkeypatch.setattr("runtime.pipeline.duplex.EouDetector", _DummyEouDetector)
+
+    pipeline = DuplexPipeline(
+        transport=_FakeTransport(),
+        session_id="s_fastgpt",
+        llm_service=llm_service,
+        tts_service=_FakeTTS(),
+        asr_service=_FakeASR(),
+    )
+    events: List[Dict[str, Any]] = []
+
+    async def _capture_event(event: Dict[str, Any], priority: int = 20):
+        events.append(event)
+
+    async def _noop_speak(_text: str, *args, **kwargs):
+        return None
+
+    monkeypatch.setattr(pipeline, "_send_event", _capture_event)
+    monkeypatch.setattr(pipeline, "_speak_sentence", _noop_speak)
+    return pipeline, events
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_managed_tool_resumes_provider_stream(monkeypatch):
+    llm = _FakeResumableLLM(timeout_ms=300000)
+    pipeline, events = _build_pipeline_with_custom_llm(monkeypatch, llm)
+    pipeline.apply_runtime_overrides({"output": {"mode": "text"}})
+
+    task = asyncio.create_task(pipeline._handle_turn("start fastgpt"))
+    for _ in range(200):
+        if any(event.get("type") == "assistant.tool_call" for event in events):
+            break
+        await asyncio.sleep(0.005)
+
+    tool_event = next(event for event in events if event.get("type") == "assistant.tool_call")
+    assert tool_event.get("executor") == "client"
+    assert tool_event.get("tool_name") == "fastgpt.interactive"
+    assert tool_event.get("timeout_ms") == 300000
+    assert tool_event.get("arguments", {}).get("context", {}).get("turn_id")
+    assert tool_event.get("arguments", {}).get("context", {}).get("response_id")
+
+    await pipeline.handle_tool_call_results(
+        [
+            {
+                "tool_call_id": "call_fastgpt_1",
+                "name": "fastgpt.interactive",
+                "output": {
+                    "action": "submit",
+                    "result": {"type": "userSelect", "selected": "pro"},
+                },
+                "status": {"code": 200, "message": "ok"},
+            }
+        ]
+    )
+    await task
+
+    finals = [event for event in events if event.get("type") == "assistant.response.final"]
+    assert finals
+    assert "provider resumed answer" in finals[-1].get("text", "")
+    assert llm.generate_stream_calls == 1
+    assert len(llm.resumed_results) == 1
+    assert llm.resumed_results[0]["tool_call_id"] == "call_fastgpt_1"
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_managed_tool_timeout_stops_without_generic_tool_prompt(monkeypatch):
+    llm = _FakeResumableLLM(timeout_ms=10)
+    pipeline, events = _build_pipeline_with_custom_llm(monkeypatch, llm)
+    pipeline.apply_runtime_overrides({"output": {"mode": "text"}})
+
+    await pipeline._handle_turn("start fastgpt")
+
+    tool_results = [event for event in events if event.get("type") == "assistant.tool_result"]
+    assert tool_results
+    assert tool_results[-1].get("result", {}).get("status", {}).get("code") == 504
+    finals = [event for event in events if event.get("type") == "assistant.response.final"]
+    assert not finals
+    assert llm.generate_stream_calls == 1
+    assert llm.resumed_results == []
+
+
+@pytest.mark.asyncio
+async def test_fastgpt_provider_managed_tool_chain_can_continue_after_third_result(monkeypatch):
+    llm = _FakeChainedResumableLLM(["call_fastgpt_1", "call_fastgpt_2", "call_fastgpt_3"])
+    pipeline, events = _build_pipeline_with_custom_llm(monkeypatch, llm)
+    pipeline.apply_runtime_overrides({"output": {"mode": "text"}})
+
+    task = asyncio.create_task(pipeline._handle_turn("start chained fastgpt"))
+
+    expected_call_ids = ["call_fastgpt_1", "call_fastgpt_2", "call_fastgpt_3"]
+    for idx, call_id in enumerate(expected_call_ids, start=1):
+        for _ in range(200):
+            seen_call_ids = [event.get("tool_call_id") for event in events if event.get("type") == "assistant.tool_call"]
+            if call_id in seen_call_ids:
+                break
+            await asyncio.sleep(0.005)
+
+        await pipeline.handle_tool_call_results(
+            [
+                {
+                    "tool_call_id": call_id,
+                    "name": "fastgpt.interactive",
+                    "output": {
+                        "action": "submit",
+                        "result": {"type": "userInput", "fields": {"result": f"value-{idx}"}},
+                    },
+                    "status": {"code": 200, "message": "ok"},
+                }
+            ]
+        )
+
+    await task
+
+    finals = [event for event in events if event.get("type") == "assistant.response.final"]
+    assert finals
+    assert "completed after third interactive input" in finals[-1].get("text", "")
+    assert llm.generate_stream_calls == 1
+    assert len(llm.resumed_results) == 3
+
+
+@pytest.mark.asyncio
+async def test_pending_client_tool_deadline_tracks_waiting_result(monkeypatch):
+    pipeline, _events = _build_pipeline(monkeypatch, [[LLMStreamEvent(type="done")]])
+
+    waiter = asyncio.create_task(pipeline._wait_for_single_tool_result("call_deadline", timeout_seconds=30))
+    for _ in range(50):
+        deadline = pipeline.pending_client_tool_deadline()
+        if deadline is not None:
+            break
+        await asyncio.sleep(0.001)
+
+    deadline = pipeline.pending_client_tool_deadline()
+    assert deadline is not None
+    assert deadline > time.monotonic() + 25
+
+    await pipeline.handle_tool_call_results(
+        [
+            {
+                "tool_call_id": "call_deadline",
+                "name": "fastgpt.interactive",
+                "output": {"action": "submit", "result": {"type": "userInput", "fields": {"name": "Alice"}}},
+                "status": {"code": 200, "message": "ok"},
+            }
+        ]
+    )
+    await waiter
+
+    assert pipeline.pending_client_tool_deadline() is None

web/components.json

@@ -0,0 +1,25 @@
+{
+  "$schema": "https://ui.shadcn.com/schema.json",
+  "style": "base-nova",
+  "rsc": false,
+  "tsx": true,
+  "tailwind": {
+    "config": "",
+    "css": "index.css",
+    "baseColor": "neutral",
+    "cssVariables": true,
+    "prefix": ""
+  },
+  "iconLibrary": "lucide",
+  "rtl": false,
+  "aliases": {
+    "components": "@/components",
+    "utils": "@/lib/utils",
+    "ui": "@/components/ui",
+    "lib": "@/lib",
+    "hooks": "@/hooks"
+  },
+  "menuColor": "default",
+  "menuAccent": "subtle",
+  "registries": {}
+}

web/components/UI.tsx

@@ -1,63 +1,37 @@
-
 import React from 'react';
 import { X } from 'lucide-react';
 
-// Button
+// Shadcn UI Imports
+import { Button as ShadcnButton } from './ui/button';
+import { Input as ShadcnInput } from './ui/input';
+import { Switch as ShadcnSwitch } from './ui/switch';
+import { Card as ShadcnCard } from './ui/card';
+import { Badge as ShadcnBadge } from './ui/badge';
+import { TableHeader as ShadcnTableHeader, TableRow as ShadcnTableRow, TableHead as ShadcnTableHead, TableCell as ShadcnTableCell } from './ui/table';
+import { Sheet, SheetContent, SheetHeader, SheetTitle } from './ui/sheet';
+import { Dialog as ShadcnDialog, DialogContent, DialogHeader, DialogTitle, DialogFooter } from './ui/dialog';
+
+// Button Wrapper to match old API
 interface ButtonProps extends React.ButtonHTMLAttributes<HTMLButtonElement> {
   variant?: 'primary' | 'secondary' | 'outline' | 'ghost' | 'destructive';
   size?: 'sm' | 'md' | 'lg' | 'icon';
 }
+export const Button: React.FC<ButtonProps> = ({ variant = 'primary', size = 'md', className, ...props }) => {
+  const vMap: any = { primary: 'default', secondary: 'secondary', outline: 'outline', ghost: 'ghost', destructive: 'destructive' };
+  const sMap: any = { sm: 'sm', md: 'default', lg: 'lg', icon: 'icon' };
+  return <ShadcnButton variant={vMap[variant] || 'default'} size={sMap[size] || 'default'} className={className} {...props} />;
+}
 
-export const Button: React.FC<ButtonProps> = ({ 
-  className = '', 
-  variant = 'primary', 
-  size = 'md', 
-  children, 
-  ...props 
-}) => {
-  const baseStyles = "inline-flex items-center justify-center rounded-md text-sm font-medium transition-all focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50 active:scale-95";
-  
-  const variants = {
-    // Primary: Glow effect
-    primary: "bg-primary text-primary-foreground shadow-[0_0_10px_rgba(6,182,212,0.5)] hover:bg-primary/90 hover:shadow-[0_0_15px_rgba(6,182,212,0.6)]",
-    secondary: "bg-secondary text-secondary-foreground shadow-sm hover:bg-secondary/80",
-    outline: "border border-input bg-transparent shadow-sm hover:bg-accent hover:text-accent-foreground hover:border-primary/50",
-    ghost: "hover:bg-accent hover:text-accent-foreground",
-    destructive: "bg-destructive text-destructive-foreground shadow-sm hover:bg-destructive/90",
-  };
-
-  const sizes = {
-    sm: "h-8 px-3 text-xs",
-    md: "h-9 px-4 py-2",
-    lg: "h-10 px-8",
-    icon: "h-9 w-9",
-  };
-
-  return (
-    <button className={`${baseStyles} ${variants[variant]} ${sizes[size]} ${className}`} {...props}>
-      {children}
-    </button>
-  );
-};
-
-// Input - Removed border, added subtle background
-interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-export const Input: React.FC<InputProps> = ({ className = '', ...props }) => {
-  return (
-    <input
-      className={`flex h-9 w-full rounded-md bg-white/5 px-3 py-1 text-sm shadow-sm transition-colors file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-primary/50 focus-visible:bg-white/10 disabled:cursor-not-allowed disabled:opacity-50 ${className}`}
-      {...props}
-    />
-  );
-};
-
-interface SelectProps extends React.SelectHTMLAttributes<HTMLSelectElement> {}
+// Input and Switch match seamlessly
+export const Input = ShadcnInput;
+export const Switch = ShadcnSwitch;
 
+// Native Select Wrapper to avoid breaking consumers expecting <select><option></select>
+interface SelectProps extends React.SelectHTMLAttributes<HTMLSelectElement> { }
 export const Select: React.FC<SelectProps> = ({ className = '', children, ...props }) => {
   return (
     <select
-      className={`flex h-9 w-full rounded-md border-0 bg-white/5 px-3 py-1 text-sm shadow-sm transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-primary/50 [&>option]:bg-card text-foreground disabled:cursor-not-allowed disabled:opacity-50 ${className}`}
+      className={`flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-1 text-base shadow-sm transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:cursor-not-allowed disabled:opacity-50 md:text-sm [&>option]:bg-card [&>option]:text-foreground ${className}`}
       {...props}
     >
       {children}
@@ -65,143 +39,40 @@ export const Select: React.FC<SelectProps> = ({ className = '', children, ...pro
   );
 };
 
-interface SwitchProps extends Omit<React.ButtonHTMLAttributes<HTMLButtonElement>, 'onChange'> {
-  checked: boolean;
-  onCheckedChange: (checked: boolean) => void;
-}
-
-export const Switch: React.FC<SwitchProps> = ({
-  checked,
-  onCheckedChange,
-  className = '',
-  disabled,
-  ...props
-}) => {
-  return (
-    <button
-      type="button"
-      role="switch"
-      aria-checked={checked}
-      disabled={disabled}
-      onClick={() => {
-        if (!disabled) onCheckedChange(!checked);
-      }}
-      className={`relative h-6 w-11 rounded-full transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-primary/60 focus-visible:ring-offset-1 focus-visible:ring-offset-background disabled:cursor-not-allowed disabled:opacity-50 ${checked ? 'bg-emerald-500/80' : 'bg-white/20'} ${className}`}
-      {...props}
-    >
-      <span
-        className={`absolute left-0.5 top-1/2 h-5 w-5 -translate-y-1/2 rounded-full bg-white shadow transition-transform ${checked ? 'translate-x-5' : 'translate-x-0'}`}
-      />
-    </button>
-  );
-};
-
-// Card - Glassmorphism style, very subtle border
-interface CardProps extends React.HTMLAttributes<HTMLDivElement> {
-  children: React.ReactNode;
-  className?: string;
-}
+// Card Wrapper
+interface CardProps extends React.HTMLAttributes<HTMLDivElement> { children: React.ReactNode; }
 export const Card: React.FC<CardProps> = ({ children, className = '', ...props }) => (
-  <div className={`rounded-xl border border-white/5 bg-card/40 backdrop-blur-md text-card-foreground shadow-sm ${className}`} {...props}>
+  <ShadcnCard className={`bg-card/40 backdrop-blur-md ${className}`} {...props}>
     {children}
-  </div>
+  </ShadcnCard>
 );
 
-// Badge
+// Badge Wrapper for old variants
 interface BadgeProps {
   children: React.ReactNode;
   variant?: 'default' | 'success' | 'warning' | 'outline';
   className?: string;
 }
 export const Badge: React.FC<BadgeProps> = ({ children, variant = 'default', className = '' }) => {
-  const styles = {
-    default: "border-transparent bg-primary/20 text-primary hover:bg-primary/30 border border-primary/20",
-    success: "border-transparent bg-green-500/20 text-green-400 border border-green-500/20",
-    warning: "border-transparent bg-yellow-500/20 text-yellow-400 border border-yellow-500/20",
-    outline: "text-foreground border border-white/10 hover:bg-accent hover:text-accent-foreground",
-  };
-  return (
-    <div className={`inline-flex items-center rounded-md px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 ${styles[variant]} ${className}`}>
-      {children}
-    </div>
-  );
+  let cName = className;
+  let shadcnVariant: any = variant === 'outline' ? 'outline' : 'default';
+
+  if (variant === 'success') {
+    cName += ' border-transparent bg-emerald-500/20 text-emerald-400 hover:bg-emerald-500/30';
+  } else if (variant === 'warning') {
+    cName += ' border-transparent bg-yellow-500/20 text-yellow-400 hover:bg-yellow-500/30';
+  }
+
+  return <ShadcnBadge variant={shadcnVariant} className={cName}>{children}</ShadcnBadge>;
 };
 
-// Table - Subtle borders
-export const TableHeader: React.FC<{ children: React.ReactNode }> = ({ children }) => <thead className="[&_tr]:border-b [&_tr]:border-white/5">{children}</thead>;
+// Table Exports 
+export const TableHeader = ShadcnTableHeader;
+export const TableRow = ShadcnTableRow;
+export const TableHead = ShadcnTableHead;
+export const TableCell = ShadcnTableCell;
 
-interface TableRowProps extends React.HTMLAttributes<HTMLTableRowElement> {
-    children: React.ReactNode;
-    className?: string;
-}
-export const TableRow: React.FC<TableRowProps> = ({ children, className = '', ...props }) => <tr className={`border-b border-white/5 transition-colors hover:bg-white/5 data-[state=selected]:bg-muted ${className}`} {...props}>{children}</tr>;
-
-interface TableHeadProps extends React.ThHTMLAttributes<HTMLTableCellElement> {
-    children: React.ReactNode;
-    className?: string;
-}
-export const TableHead: React.FC<TableHeadProps> = ({ children, className = '', ...props }) => <th className={`h-10 px-4 text-left align-middle text-sm font-medium text-muted-foreground [&:has([role=checkbox])]:pr-0 ${className}`} {...props}>{children}</th>;
-
-interface TableCellProps extends React.TdHTMLAttributes<HTMLTableCellElement> {
-    children: React.ReactNode;
-    className?: string;
-}
-export const TableCell: React.FC<TableCellProps> = ({ children, className = '', ...props }) => <td className={`p-4 align-middle text-sm [&:has([role=checkbox])]:pr-0 ${className}`} {...props}>{children}</td>;
-
-interface LibraryPageShellProps {
-  title: string;
-  primaryAction: React.ReactNode;
-  filterBar: React.ReactNode;
-  children: React.ReactNode;
-}
-
-export const LibraryPageShell: React.FC<LibraryPageShellProps> = ({ title, primaryAction, filterBar, children }) => {
-  return (
-    <div className="space-y-6 animate-in fade-in py-4 pb-10">
-      <div className="flex items-center justify-between">
-        <h1 className="text-2xl font-bold tracking-tight text-white">{title}</h1>
-        {primaryAction}
-      </div>
-      <div className="grid grid-cols-1 md:grid-cols-4 gap-4 bg-card/50 p-4 rounded-lg border border-white/5 shadow-sm">
-        {filterBar}
-      </div>
-      {children}
-    </div>
-  );
-};
-
-interface TableStatusRowProps {
-  colSpan: number;
-  text: string;
-}
-
-export const TableStatusRow: React.FC<TableStatusRowProps> = ({ colSpan, text }) => {
-  return (
-    <TableRow>
-      <TableCell colSpan={colSpan} className="text-center py-8 text-muted-foreground">
-        {text}
-      </TableCell>
-    </TableRow>
-  );
-};
-
-interface LibraryActionCellProps {
-  previewAction?: React.ReactNode;
-  editAction: React.ReactNode;
-  deleteAction: React.ReactNode;
-}
-
-export const LibraryActionCell: React.FC<LibraryActionCellProps> = ({ previewAction, editAction, deleteAction }) => {
-  return (
-    <TableCell className="text-right">
-      {previewAction}
-      {editAction}
-      {deleteAction}
-    </TableCell>
-  );
-};
-
-// Drawer (Side Sheet)
+// Drawer (Side Sheet Wrapper)
 interface DrawerProps {
   isOpen: boolean;
   onClose: () => void;
@@ -209,32 +80,25 @@ interface DrawerProps {
   className?: string;
   children: React.ReactNode;
 }
-
 export const Drawer: React.FC<DrawerProps> = ({ isOpen, onClose, title, className, children }) => {
-  if (!isOpen) return null;
+  // Pass `!w-[85vw]` logic directly down from the parent to naturally override Shadcn specificities safely.
+  const containerClasses = className || 'w-full max-w-md sm:max-w-lg';
 
   return (
-    <div className="fixed inset-0 z-50 flex">
-      {/* Backdrop */}
-      <div className="fixed inset-0 bg-black/60 backdrop-blur-sm transition-opacity" onClick={onClose} />
-      
-      {/* Drawer Content */}
-      <div className={`relative ml-auto flex h-full w-full flex-col bg-background/95 border-l border-white/10 p-6 shadow-2xl animate-in slide-in-from-right ${className || 'max-w-md sm:max-w-lg'}`}>
-        <div className="flex items-center justify-between mb-4 shrink-0">
-          <h2 className="text-lg font-semibold text-foreground">{title}</h2>
-          <Button variant="ghost" size="icon" onClick={onClose}>
-            <X className="h-4 w-4" />
-          </Button>
-        </div>
-        <div className="flex-1 min-h-0 overflow-y-auto">
+    <Sheet open={isOpen} onOpenChange={(open) => { if (!open) onClose(); }}>
+      <SheetContent className={`flex flex-col p-6 bg-background/95 backdrop-blur-md border-l border-white/10 shadow-2xl [&>button]:top-5 [&>button]:right-5 ${containerClasses}`}>
+        <SheetHeader className="mb-2 shrink-0 p-0 text-left">
+          <SheetTitle className="text-lg font-semibold">{title}</SheetTitle>
+        </SheetHeader>
+        <div className="flex-1 min-h-0 overflow-y-auto custom-scrollbar flex flex-col">
           {children}
         </div>
-      </div>
-    </div>
+      </SheetContent>
+    </Sheet>
   );
 };
 
-// Dialog (Modal)
+// Dialog (Modal Wrapper)
 interface DialogProps {
   isOpen: boolean;
   onClose: () => void;
@@ -243,33 +107,66 @@ interface DialogProps {
   footer?: React.ReactNode;
   contentClassName?: string;
 }
-
 export const Dialog: React.FC<DialogProps> = ({ isOpen, onClose, title, children, footer, contentClassName }) => {
-  if (!isOpen) return null;
-
   return (
-    <div className="fixed inset-0 z-50 flex items-center justify-center p-4">
-       <div className="fixed inset-0 bg-black/80 backdrop-blur-sm transition-opacity animate-in fade-in" onClick={onClose} />
-       <div className={`relative z-50 w-full max-w-lg rounded-xl border border-white/10 bg-card p-6 shadow-2xl animate-in zoom-in-95 duration-200 ${contentClassName || ''}`}>
-          <div className="flex flex-col space-y-1.5 text-center sm:text-left mb-4">
-             <h2 className="text-lg font-semibold leading-none tracking-tight">{title}</h2>
-          </div>
-          <div className="py-4">
-            {children}
-          </div>
-          {footer && (
-             <div className="flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2 mt-2">
-                {footer}
-             </div>
-          )}
-          <button 
-             onClick={onClose}
-             className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none disabled:pointer-events-none data-[state=open]:bg-accent data-[state=open]:text-muted-foreground"
-          >
-             <X className="h-4 w-4" />
-             <span className="sr-only">Close</span>
-          </button>
-       </div>
-    </div>
+    <ShadcnDialog open={isOpen} onOpenChange={(open) => { if (!open) onClose(); }}>
+      <DialogContent className={`max-h-[95vh] flex flex-col ${contentClassName || ''}`}>
+        <DialogHeader>
+          <DialogTitle>{title}</DialogTitle>
+        </DialogHeader>
+        <div className="py-2 flex-1 min-h-0 overflow-y-auto pr-2 custom-scrollbar">
+          {children}
+        </div>
+        {footer && <DialogFooter>{footer}</DialogFooter>}
+      </DialogContent>
+    </ShadcnDialog>
   );
 };
+
+
+// ---------------------------------------------
+// Custom Application Layout Components
+// ---------------------------------------------
+interface LibraryPageShellProps {
+  title: string;
+  primaryAction: React.ReactNode;
+  filterBar: React.ReactNode;
+  children: React.ReactNode;
+}
+export const LibraryPageShell: React.FC<LibraryPageShellProps> = ({ title, primaryAction, filterBar, children }) => (
+  <div className="space-y-6 animate-in fade-in py-4 pb-10">
+    <div className="flex items-center justify-between">
+      <h1 className="text-2xl font-bold tracking-tight text-foreground">{title}</h1>
+      {primaryAction}
+    </div>
+    <div className="grid grid-cols-1 md:grid-cols-4 gap-4 bg-card/50 p-4 rounded-lg border border-border shadow-sm">
+      {filterBar}
+    </div>
+    {children}
+  </div>
+);
+
+interface TableStatusRowProps {
+  colSpan: number;
+  text: string;
+}
+export const TableStatusRow: React.FC<TableStatusRowProps> = ({ colSpan, text }) => (
+  <TableRow>
+    <TableCell colSpan={colSpan} className="text-center py-8 text-muted-foreground">
+      {text}
+    </TableCell>
+  </TableRow>
+);
+
+interface LibraryActionCellProps {
+  previewAction?: React.ReactNode;
+  editAction: React.ReactNode;
+  deleteAction: React.ReactNode;
+}
+export const LibraryActionCell: React.FC<LibraryActionCellProps> = ({ previewAction, editAction, deleteAction }) => (
+  <TableCell className="text-right whitespace-nowrap">
+    {previewAction}
+    {editAction}
+    {deleteAction}
+  </TableCell>
+);

web/components/ui/badge.tsx

@@ -0,0 +1,52 @@
+import { mergeProps } from "@base-ui/react/merge-props"
+import { useRender } from "@base-ui/react/use-render"
+import { cva, type VariantProps } from "class-variance-authority"
+
+import { cn } from "@/lib/utils"
+
+const badgeVariants = cva(
+  "group/badge inline-flex h-5 w-fit shrink-0 items-center justify-center gap-1 overflow-hidden rounded-4xl border border-transparent px-2 py-0.5 text-xs font-medium whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3!",
+  {
+    variants: {
+      variant: {
+        default: "bg-primary text-primary-foreground [a]:hover:bg-primary/80",
+        secondary:
+          "bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80",
+        destructive:
+          "bg-destructive/10 text-destructive focus-visible:ring-destructive/20 dark:bg-destructive/20 dark:focus-visible:ring-destructive/40 [a]:hover:bg-destructive/20",
+        outline:
+          "border-border text-foreground [a]:hover:bg-muted [a]:hover:text-muted-foreground",
+        ghost:
+          "hover:bg-muted hover:text-muted-foreground dark:hover:bg-muted/50",
+        link: "text-primary underline-offset-4 hover:underline",
+      },
+    },
+    defaultVariants: {
+      variant: "default",
+    },
+  }
+)
+
+function Badge({
+  className,
+  variant = "default",
+  render,
+  ...props
+}: useRender.ComponentProps<"span"> & VariantProps<typeof badgeVariants>) {
+  return useRender({
+    defaultTagName: "span",
+    props: mergeProps<"span">(
+      {
+        className: cn(badgeVariants({ variant }), className),
+      },
+      props
+    ),
+    render,
+    state: {
+      slot: "badge",
+      variant,
+    },
+  })
+}
+
+export { Badge, badgeVariants }

web/components/ui/button.tsx

@@ -0,0 +1,58 @@
+import { Button as ButtonPrimitive } from "@base-ui/react/button"
+import { cva, type VariantProps } from "class-variance-authority"
+
+import { cn } from "@/lib/utils"
+
+const buttonVariants = cva(
+  "group/button inline-flex shrink-0 items-center justify-center rounded-lg border border-transparent bg-clip-padding text-sm font-medium whitespace-nowrap transition-all outline-none select-none focus-visible:border-ring focus-visible:ring-3 focus-visible:ring-ring/50 disabled:pointer-events-none disabled:opacity-50 aria-invalid:border-destructive aria-invalid:ring-3 aria-invalid:ring-destructive/20 dark:aria-invalid:border-destructive/50 dark:aria-invalid:ring-destructive/40 [&_svg]:pointer-events-none [&_svg]:shrink-0 [&_svg:not([class*='size-'])]:size-4",
+  {
+    variants: {
+      variant: {
+        default: "bg-primary text-primary-foreground [a]:hover:bg-primary/80",
+        outline:
+          "border-border bg-background hover:bg-muted hover:text-foreground aria-expanded:bg-muted aria-expanded:text-foreground dark:border-input dark:bg-input/30 dark:hover:bg-input/50",
+        secondary:
+          "bg-secondary text-secondary-foreground hover:bg-secondary/80 aria-expanded:bg-secondary aria-expanded:text-secondary-foreground",
+        ghost:
+          "hover:bg-muted hover:text-foreground aria-expanded:bg-muted aria-expanded:text-foreground dark:hover:bg-muted/50",
+        destructive:
+          "bg-destructive/10 text-destructive hover:bg-destructive/20 focus-visible:border-destructive/40 focus-visible:ring-destructive/20 dark:bg-destructive/20 dark:hover:bg-destructive/30 dark:focus-visible:ring-destructive/40",
+        link: "text-primary underline-offset-4 hover:underline",
+      },
+      size: {
+        default:
+          "h-8 gap-1.5 px-2.5 has-data-[icon=inline-end]:pr-2 has-data-[icon=inline-start]:pl-2",
+        xs: "h-6 gap-1 rounded-[min(var(--radius-md),10px)] px-2 text-xs in-data-[slot=button-group]:rounded-lg has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 [&_svg:not([class*='size-'])]:size-3",
+        sm: "h-7 gap-1 rounded-[min(var(--radius-md),12px)] px-2.5 text-[0.8rem] in-data-[slot=button-group]:rounded-lg has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 [&_svg:not([class*='size-'])]:size-3.5",
+        lg: "h-9 gap-1.5 px-2.5 has-data-[icon=inline-end]:pr-3 has-data-[icon=inline-start]:pl-3",
+        icon: "size-8",
+        "icon-xs":
+          "size-6 rounded-[min(var(--radius-md),10px)] in-data-[slot=button-group]:rounded-lg [&_svg:not([class*='size-'])]:size-3",
+        "icon-sm":
+          "size-7 rounded-[min(var(--radius-md),12px)] in-data-[slot=button-group]:rounded-lg",
+        "icon-lg": "size-9",
+      },
+    },
+    defaultVariants: {
+      variant: "default",
+      size: "default",
+    },
+  }
+)
+
+function Button({
+  className,
+  variant = "default",
+  size = "default",
+  ...props
+}: ButtonPrimitive.Props & VariantProps<typeof buttonVariants>) {
+  return (
+    <ButtonPrimitive
+      data-slot="button"
+      className={cn(buttonVariants({ variant, size, className }))}
+      {...props}
+    />
+  )
+}
+
+export { Button, buttonVariants }

web/components/ui/card.tsx

@@ -0,0 +1,103 @@
+import * as React from "react"
+
+import { cn } from "@/lib/utils"
+
+function Card({
+  className,
+  size = "default",
+  ...props
+}: React.ComponentProps<"div"> & { size?: "default" | "sm" }) {
+  return (
+    <div
+      data-slot="card"
+      data-size={size}
+      className={cn(
+        "group/card flex flex-col gap-4 overflow-hidden rounded-xl bg-card py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function CardHeader({ className, ...props }: React.ComponentProps<"div">) {
+  return (
+    <div
+      data-slot="card-header"
+      className={cn(
+        "group/card-header @container/card-header grid auto-rows-min items-start gap-1 rounded-t-xl px-4 group-data-[size=sm]/card:px-3 has-data-[slot=card-action]:grid-cols-[1fr_auto] has-data-[slot=card-description]:grid-rows-[auto_auto] [.border-b]:pb-4 group-data-[size=sm]/card:[.border-b]:pb-3",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function CardTitle({ className, ...props }: React.ComponentProps<"div">) {
+  return (
+    <div
+      data-slot="card-title"
+      className={cn(
+        "text-base leading-snug font-medium group-data-[size=sm]/card:text-sm",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function CardDescription({ className, ...props }: React.ComponentProps<"div">) {
+  return (
+    <div
+      data-slot="card-description"
+      className={cn("text-sm text-muted-foreground", className)}
+      {...props}
+    />
+  )
+}
+
+function CardAction({ className, ...props }: React.ComponentProps<"div">) {
+  return (
+    <div
+      data-slot="card-action"
+      className={cn(
+        "col-start-2 row-span-2 row-start-1 self-start justify-self-end",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function CardContent({ className, ...props }: React.ComponentProps<"div">) {
+  return (
+    <div
+      data-slot="card-content"
+      className={cn("px-4 group-data-[size=sm]/card:px-3", className)}
+      {...props}
+    />
+  )
+}
+
+function CardFooter({ className, ...props }: React.ComponentProps<"div">) {
+  return (
+    <div
+      data-slot="card-footer"
+      className={cn(
+        "flex items-center rounded-b-xl border-t bg-muted/50 p-4 group-data-[size=sm]/card:p-3",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+export {
+  Card,
+  CardHeader,
+  CardFooter,
+  CardTitle,
+  CardAction,
+  CardDescription,
+  CardContent,
+}

web/components/ui/dialog.tsx

@@ -0,0 +1,157 @@
+"use client"
+
+import * as React from "react"
+import { Dialog as DialogPrimitive } from "@base-ui/react/dialog"
+
+import { cn } from "@/lib/utils"
+import { Button } from "@/components/ui/button"
+import { XIcon } from "lucide-react"
+
+function Dialog({ ...props }: DialogPrimitive.Root.Props) {
+  return <DialogPrimitive.Root data-slot="dialog" {...props} />
+}
+
+function DialogTrigger({ ...props }: DialogPrimitive.Trigger.Props) {
+  return <DialogPrimitive.Trigger data-slot="dialog-trigger" {...props} />
+}
+
+function DialogPortal({ ...props }: DialogPrimitive.Portal.Props) {
+  return <DialogPrimitive.Portal data-slot="dialog-portal" {...props} />
+}
+
+function DialogClose({ ...props }: DialogPrimitive.Close.Props) {
+  return <DialogPrimitive.Close data-slot="dialog-close" {...props} />
+}
+
+function DialogOverlay({
+  className,
+  ...props
+}: DialogPrimitive.Backdrop.Props) {
+  return (
+    <DialogPrimitive.Backdrop
+      data-slot="dialog-overlay"
+      className={cn(
+        "fixed inset-0 isolate z-50 bg-black/10 duration-100 supports-backdrop-filter:backdrop-blur-xs data-open:animate-in data-open:fade-in-0 data-closed:animate-out data-closed:fade-out-0",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function DialogContent({
+  className,
+  children,
+  showCloseButton = true,
+  ...props
+}: DialogPrimitive.Popup.Props & {
+  showCloseButton?: boolean
+}) {
+  return (
+    <DialogPortal>
+      <DialogOverlay />
+      <DialogPrimitive.Popup
+        data-slot="dialog-content"
+        className={cn(
+          "fixed top-1/2 left-1/2 z-50 grid w-full max-w-[calc(100%-2rem)] -translate-x-1/2 -translate-y-1/2 gap-4 rounded-xl bg-background p-4 text-sm ring-1 ring-foreground/10 duration-100 outline-none sm:max-w-sm data-open:animate-in data-open:fade-in-0 data-open:zoom-in-95 data-closed:animate-out data-closed:fade-out-0 data-closed:zoom-out-95",
+          className
+        )}
+        {...props}
+      >
+        {children}
+        {showCloseButton && (
+          <DialogPrimitive.Close
+            data-slot="dialog-close"
+            render={
+              <Button
+                variant="ghost"
+                className="absolute top-2 right-2"
+                size="icon-sm"
+              />
+            }
+          >
+            <XIcon
+            />
+            <span className="sr-only">Close</span>
+          </DialogPrimitive.Close>
+        )}
+      </DialogPrimitive.Popup>
+    </DialogPortal>
+  )
+}
+
+function DialogHeader({ className, ...props }: React.ComponentProps<"div">) {
+  return (
+    <div
+      data-slot="dialog-header"
+      className={cn("flex flex-col gap-2", className)}
+      {...props}
+    />
+  )
+}
+
+function DialogFooter({
+  className,
+  showCloseButton = false,
+  children,
+  ...props
+}: React.ComponentProps<"div"> & {
+  showCloseButton?: boolean
+}) {
+  return (
+    <div
+      data-slot="dialog-footer"
+      className={cn(
+        "-mx-4 -mb-4 flex flex-col-reverse gap-2 rounded-b-xl border-t bg-muted/50 p-4 sm:flex-row sm:justify-end",
+        className
+      )}
+      {...props}
+    >
+      {children}
+      {showCloseButton && (
+        <DialogPrimitive.Close render={<Button variant="outline" />}>
+          Close
+        </DialogPrimitive.Close>
+      )}
+    </div>
+  )
+}
+
+function DialogTitle({ className, ...props }: DialogPrimitive.Title.Props) {
+  return (
+    <DialogPrimitive.Title
+      data-slot="dialog-title"
+      className={cn("text-base leading-none font-medium", className)}
+      {...props}
+    />
+  )
+}
+
+function DialogDescription({
+  className,
+  ...props
+}: DialogPrimitive.Description.Props) {
+  return (
+    <DialogPrimitive.Description
+      data-slot="dialog-description"
+      className={cn(
+        "text-sm text-muted-foreground *:[a]:underline *:[a]:underline-offset-3 *:[a]:hover:text-foreground",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+export {
+  Dialog,
+  DialogClose,
+  DialogContent,
+  DialogDescription,
+  DialogFooter,
+  DialogHeader,
+  DialogOverlay,
+  DialogPortal,
+  DialogTitle,
+  DialogTrigger,
+}

web/components/ui/input.tsx

@@ -0,0 +1,20 @@
+import * as React from "react"
+import { Input as InputPrimitive } from "@base-ui/react/input"
+
+import { cn } from "@/lib/utils"
+
+function Input({ className, type, ...props }: React.ComponentProps<"input">) {
+  return (
+    <InputPrimitive
+      type={type}
+      data-slot="input"
+      className={cn(
+        "h-8 w-full min-w-0 rounded-lg border border-input bg-transparent px-2.5 py-1 text-base transition-colors outline-none file:inline-flex file:h-6 file:border-0 file:bg-transparent file:text-sm file:font-medium file:text-foreground placeholder:text-muted-foreground focus-visible:border-ring focus-visible:ring-3 focus-visible:ring-ring/50 disabled:pointer-events-none disabled:cursor-not-allowed disabled:bg-input/50 disabled:opacity-50 aria-invalid:border-destructive aria-invalid:ring-3 aria-invalid:ring-destructive/20 md:text-sm dark:bg-input/30 dark:disabled:bg-input/80 dark:aria-invalid:border-destructive/50 dark:aria-invalid:ring-destructive/40",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+export { Input }

web/components/ui/select.tsx

@@ -0,0 +1,201 @@
+"use client"
+
+import * as React from "react"
+import { Select as SelectPrimitive } from "@base-ui/react/select"
+
+import { cn } from "@/lib/utils"
+import { ChevronDownIcon, CheckIcon, ChevronUpIcon } from "lucide-react"
+
+const Select = SelectPrimitive.Root
+
+function SelectGroup({ className, ...props }: SelectPrimitive.Group.Props) {
+  return (
+    <SelectPrimitive.Group
+      data-slot="select-group"
+      className={cn("scroll-my-1 p-1", className)}
+      {...props}
+    />
+  )
+}
+
+function SelectValue({ className, ...props }: SelectPrimitive.Value.Props) {
+  return (
+    <SelectPrimitive.Value
+      data-slot="select-value"
+      className={cn("flex flex-1 text-left", className)}
+      {...props}
+    />
+  )
+}
+
+function SelectTrigger({
+  className,
+  size = "default",
+  children,
+  ...props
+}: SelectPrimitive.Trigger.Props & {
+  size?: "sm" | "default"
+}) {
+  return (
+    <SelectPrimitive.Trigger
+      data-slot="select-trigger"
+      data-size={size}
+      className={cn(
+        "flex w-fit items-center justify-between gap-1.5 rounded-lg border border-input bg-transparent py-2 pr-2 pl-2.5 text-sm whitespace-nowrap transition-colors outline-none select-none focus-visible:border-ring focus-visible:ring-3 focus-visible:ring-ring/50 disabled:cursor-not-allowed disabled:opacity-50 aria-invalid:border-destructive aria-invalid:ring-3 aria-invalid:ring-destructive/20 data-placeholder:text-muted-foreground data-[size=default]:h-8 data-[size=sm]:h-7 data-[size=sm]:rounded-[min(var(--radius-md),10px)] *:data-[slot=select-value]:line-clamp-1 *:data-[slot=select-value]:flex *:data-[slot=select-value]:items-center *:data-[slot=select-value]:gap-1.5 dark:bg-input/30 dark:hover:bg-input/50 dark:aria-invalid:border-destructive/50 dark:aria-invalid:ring-destructive/40 [&_svg]:pointer-events-none [&_svg]:shrink-0 [&_svg:not([class*='size-'])]:size-4",
+        className
+      )}
+      {...props}
+    >
+      {children}
+      <SelectPrimitive.Icon
+        render={
+          <ChevronDownIcon className="pointer-events-none size-4 text-muted-foreground" />
+        }
+      />
+    </SelectPrimitive.Trigger>
+  )
+}
+
+function SelectContent({
+  className,
+  children,
+  side = "bottom",
+  sideOffset = 4,
+  align = "center",
+  alignOffset = 0,
+  alignItemWithTrigger = true,
+  ...props
+}: SelectPrimitive.Popup.Props &
+  Pick<
+    SelectPrimitive.Positioner.Props,
+    "align" | "alignOffset" | "side" | "sideOffset" | "alignItemWithTrigger"
+  >) {
+  return (
+    <SelectPrimitive.Portal>
+      <SelectPrimitive.Positioner
+        side={side}
+        sideOffset={sideOffset}
+        align={align}
+        alignOffset={alignOffset}
+        alignItemWithTrigger={alignItemWithTrigger}
+        className="isolate z-50"
+      >
+        <SelectPrimitive.Popup
+          data-slot="select-content"
+          data-align-trigger={alignItemWithTrigger}
+          className={cn("relative isolate z-50 max-h-(--available-height) w-(--anchor-width) min-w-36 origin-(--transform-origin) overflow-x-hidden overflow-y-auto rounded-lg bg-popover text-popover-foreground shadow-md ring-1 ring-foreground/10 duration-100 data-[align-trigger=true]:animate-none data-[side=bottom]:slide-in-from-top-2 data-[side=inline-end]:slide-in-from-left-2 data-[side=inline-start]:slide-in-from-right-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 data-open:animate-in data-open:fade-in-0 data-open:zoom-in-95 data-closed:animate-out data-closed:fade-out-0 data-closed:zoom-out-95", className )}
+          {...props}
+        >
+          <SelectScrollUpButton />
+          <SelectPrimitive.List>{children}</SelectPrimitive.List>
+          <SelectScrollDownButton />
+        </SelectPrimitive.Popup>
+      </SelectPrimitive.Positioner>
+    </SelectPrimitive.Portal>
+  )
+}
+
+function SelectLabel({
+  className,
+  ...props
+}: SelectPrimitive.GroupLabel.Props) {
+  return (
+    <SelectPrimitive.GroupLabel
+      data-slot="select-label"
+      className={cn("px-1.5 py-1 text-xs text-muted-foreground", className)}
+      {...props}
+    />
+  )
+}
+
+function SelectItem({
+  className,
+  children,
+  ...props
+}: SelectPrimitive.Item.Props) {
+  return (
+    <SelectPrimitive.Item
+      data-slot="select-item"
+      className={cn(
+        "relative flex w-full cursor-default items-center gap-1.5 rounded-md py-1 pr-8 pl-1.5 text-sm outline-hidden select-none focus:bg-accent focus:text-accent-foreground not-data-[variant=destructive]:focus:**:text-accent-foreground data-disabled:pointer-events-none data-disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:shrink-0 [&_svg:not([class*='size-'])]:size-4 *:[span]:last:flex *:[span]:last:items-center *:[span]:last:gap-2",
+        className
+      )}
+      {...props}
+    >
+      <SelectPrimitive.ItemText className="flex flex-1 shrink-0 gap-2 whitespace-nowrap">
+        {children}
+      </SelectPrimitive.ItemText>
+      <SelectPrimitive.ItemIndicator
+        render={
+          <span className="pointer-events-none absolute right-2 flex size-4 items-center justify-center" />
+        }
+      >
+        <CheckIcon className="pointer-events-none" />
+      </SelectPrimitive.ItemIndicator>
+    </SelectPrimitive.Item>
+  )
+}
+
+function SelectSeparator({
+  className,
+  ...props
+}: SelectPrimitive.Separator.Props) {
+  return (
+    <SelectPrimitive.Separator
+      data-slot="select-separator"
+      className={cn("pointer-events-none -mx-1 my-1 h-px bg-border", className)}
+      {...props}
+    />
+  )
+}
+
+function SelectScrollUpButton({
+  className,
+  ...props
+}: React.ComponentProps<typeof SelectPrimitive.ScrollUpArrow>) {
+  return (
+    <SelectPrimitive.ScrollUpArrow
+      data-slot="select-scroll-up-button"
+      className={cn(
+        "top-0 z-10 flex w-full cursor-default items-center justify-center bg-popover py-1 [&_svg:not([class*='size-'])]:size-4",
+        className
+      )}
+      {...props}
+    >
+      <ChevronUpIcon
+      />
+    </SelectPrimitive.ScrollUpArrow>
+  )
+}
+
+function SelectScrollDownButton({
+  className,
+  ...props
+}: React.ComponentProps<typeof SelectPrimitive.ScrollDownArrow>) {
+  return (
+    <SelectPrimitive.ScrollDownArrow
+      data-slot="select-scroll-down-button"
+      className={cn(
+        "bottom-0 z-10 flex w-full cursor-default items-center justify-center bg-popover py-1 [&_svg:not([class*='size-'])]:size-4",
+        className
+      )}
+      {...props}
+    >
+      <ChevronDownIcon
+      />
+    </SelectPrimitive.ScrollDownArrow>
+  )
+}
+
+export {
+  Select,
+  SelectContent,
+  SelectGroup,
+  SelectItem,
+  SelectLabel,
+  SelectScrollDownButton,
+  SelectScrollUpButton,
+  SelectSeparator,
+  SelectTrigger,
+  SelectValue,
+}

web/components/ui/sheet.tsx

@@ -0,0 +1,133 @@
+import * as React from "react"
+import { Dialog as SheetPrimitive } from "@base-ui/react/dialog"
+
+import { cn } from "@/lib/utils"
+import { Button } from "@/components/ui/button"
+import { XIcon } from "lucide-react"
+
+function Sheet({ ...props }: SheetPrimitive.Root.Props) {
+  return <SheetPrimitive.Root data-slot="sheet" {...props} />
+}
+
+function SheetTrigger({ ...props }: SheetPrimitive.Trigger.Props) {
+  return <SheetPrimitive.Trigger data-slot="sheet-trigger" {...props} />
+}
+
+function SheetClose({ ...props }: SheetPrimitive.Close.Props) {
+  return <SheetPrimitive.Close data-slot="sheet-close" {...props} />
+}
+
+function SheetPortal({ ...props }: SheetPrimitive.Portal.Props) {
+  return <SheetPrimitive.Portal data-slot="sheet-portal" {...props} />
+}
+
+function SheetOverlay({ className, ...props }: SheetPrimitive.Backdrop.Props) {
+  return (
+    <SheetPrimitive.Backdrop
+      data-slot="sheet-overlay"
+      className={cn(
+        "fixed inset-0 z-50 bg-black/10 transition-opacity duration-150 data-ending-style:opacity-0 data-starting-style:opacity-0 supports-backdrop-filter:backdrop-blur-xs",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function SheetContent({
+  className,
+  children,
+  side = "right",
+  showCloseButton = true,
+  ...props
+}: SheetPrimitive.Popup.Props & {
+  side?: "top" | "right" | "bottom" | "left"
+  showCloseButton?: boolean
+}) {
+  return (
+    <SheetPortal>
+      <SheetOverlay />
+      <SheetPrimitive.Popup
+        data-slot="sheet-content"
+        data-side={side}
+        className={cn(
+          "fixed z-50 flex flex-col gap-4 bg-background bg-clip-padding text-sm shadow-lg transition duration-200 ease-in-out data-ending-style:opacity-0 data-starting-style:opacity-0 data-[side=bottom]:inset-x-0 data-[side=bottom]:bottom-0 data-[side=bottom]:h-auto data-[side=bottom]:border-t data-[side=bottom]:data-ending-style:translate-y-[2.5rem] data-[side=bottom]:data-starting-style:translate-y-[2.5rem] data-[side=left]:inset-y-0 data-[side=left]:left-0 data-[side=left]:h-full data-[side=left]:w-3/4 data-[side=left]:border-r data-[side=left]:data-ending-style:translate-x-[-2.5rem] data-[side=left]:data-starting-style:translate-x-[-2.5rem] data-[side=right]:inset-y-0 data-[side=right]:right-0 data-[side=right]:h-full data-[side=right]:w-3/4 data-[side=right]:border-l data-[side=right]:data-ending-style:translate-x-[2.5rem] data-[side=right]:data-starting-style:translate-x-[2.5rem] data-[side=top]:inset-x-0 data-[side=top]:top-0 data-[side=top]:h-auto data-[side=top]:border-b data-[side=top]:data-ending-style:translate-y-[-2.5rem] data-[side=top]:data-starting-style:translate-y-[-2.5rem] data-[side=left]:sm:max-w-sm data-[side=right]:sm:max-w-sm",
+          className
+        )}
+        {...props}
+      >
+        {children}
+        {showCloseButton && (
+          <SheetPrimitive.Close
+            data-slot="sheet-close"
+            render={
+              <Button
+                variant="ghost"
+                className="absolute top-3 right-3"
+                size="icon-sm"
+              />
+            }
+          >
+            <XIcon
+            />
+            <span className="sr-only">Close</span>
+          </SheetPrimitive.Close>
+        )}
+      </SheetPrimitive.Popup>
+    </SheetPortal>
+  )
+}
+
+function SheetHeader({ className, ...props }: React.ComponentProps<"div">) {
+  return (
+    <div
+      data-slot="sheet-header"
+      className={cn("flex flex-col gap-0.5 p-4", className)}
+      {...props}
+    />
+  )
+}
+
+function SheetFooter({ className, ...props }: React.ComponentProps<"div">) {
+  return (
+    <div
+      data-slot="sheet-footer"
+      className={cn("mt-auto flex flex-col gap-2 p-4", className)}
+      {...props}
+    />
+  )
+}
+
+function SheetTitle({ className, ...props }: SheetPrimitive.Title.Props) {
+  return (
+    <SheetPrimitive.Title
+      data-slot="sheet-title"
+      className={cn("text-base font-medium text-foreground", className)}
+      {...props}
+    />
+  )
+}
+
+function SheetDescription({
+  className,
+  ...props
+}: SheetPrimitive.Description.Props) {
+  return (
+    <SheetPrimitive.Description
+      data-slot="sheet-description"
+      className={cn("text-sm text-muted-foreground", className)}
+      {...props}
+    />
+  )
+}
+
+export {
+  Sheet,
+  SheetTrigger,
+  SheetClose,
+  SheetContent,
+  SheetHeader,
+  SheetFooter,
+  SheetTitle,
+  SheetDescription,
+}

web/components/ui/switch.tsx

@@ -0,0 +1,30 @@
+import { Switch as SwitchPrimitive } from "@base-ui/react/switch"
+
+import { cn } from "@/lib/utils"
+
+function Switch({
+  className,
+  size = "default",
+  ...props
+}: SwitchPrimitive.Root.Props & {
+  size?: "sm" | "default"
+}) {
+  return (
+    <SwitchPrimitive.Root
+      data-slot="switch"
+      data-size={size}
+      className={cn(
+        "peer group/switch relative inline-flex shrink-0 items-center rounded-full border border-transparent transition-all outline-none after:absolute after:-inset-x-3 after:-inset-y-2 focus-visible:border-ring focus-visible:ring-3 focus-visible:ring-ring/50 aria-invalid:border-destructive aria-invalid:ring-3 aria-invalid:ring-destructive/20 data-[size=default]:h-[18.4px] data-[size=default]:w-[32px] data-[size=sm]:h-[14px] data-[size=sm]:w-[24px] dark:aria-invalid:border-destructive/50 dark:aria-invalid:ring-destructive/40 data-checked:bg-primary data-unchecked:bg-input dark:data-unchecked:bg-input/80 data-disabled:cursor-not-allowed data-disabled:opacity-50",
+        className
+      )}
+      {...props}
+    >
+      <SwitchPrimitive.Thumb
+        data-slot="switch-thumb"
+        className="pointer-events-none block rounded-full bg-background ring-0 transition-transform group-data-[size=default]/switch:size-4 group-data-[size=sm]/switch:size-3 group-data-[size=default]/switch:data-checked:translate-x-[calc(100%-2px)] group-data-[size=sm]/switch:data-checked:translate-x-[calc(100%-2px)] dark:data-checked:bg-primary-foreground group-data-[size=default]/switch:data-unchecked:translate-x-0 group-data-[size=sm]/switch:data-unchecked:translate-x-0 dark:data-unchecked:bg-foreground"
+      />
+    </SwitchPrimitive.Root>
+  )
+}
+
+export { Switch }

web/components/ui/table.tsx

@@ -0,0 +1,116 @@
+"use client"
+
+import * as React from "react"
+
+import { cn } from "@/lib/utils"
+
+function Table({ className, ...props }: React.ComponentProps<"table">) {
+  return (
+    <div
+      data-slot="table-container"
+      className="relative w-full overflow-x-auto"
+    >
+      <table
+        data-slot="table"
+        className={cn("w-full caption-bottom text-sm", className)}
+        {...props}
+      />
+    </div>
+  )
+}
+
+function TableHeader({ className, ...props }: React.ComponentProps<"thead">) {
+  return (
+    <thead
+      data-slot="table-header"
+      className={cn("[&_tr]:border-b", className)}
+      {...props}
+    />
+  )
+}
+
+function TableBody({ className, ...props }: React.ComponentProps<"tbody">) {
+  return (
+    <tbody
+      data-slot="table-body"
+      className={cn("[&_tr:last-child]:border-0", className)}
+      {...props}
+    />
+  )
+}
+
+function TableFooter({ className, ...props }: React.ComponentProps<"tfoot">) {
+  return (
+    <tfoot
+      data-slot="table-footer"
+      className={cn(
+        "border-t bg-muted/50 font-medium [&>tr]:last:border-b-0",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function TableRow({ className, ...props }: React.ComponentProps<"tr">) {
+  return (
+    <tr
+      data-slot="table-row"
+      className={cn(
+        "border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function TableHead({ className, ...props }: React.ComponentProps<"th">) {
+  return (
+    <th
+      data-slot="table-head"
+      className={cn(
+        "h-10 px-2 text-left align-middle font-medium whitespace-nowrap text-foreground [&:has([role=checkbox])]:pr-0",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function TableCell({ className, ...props }: React.ComponentProps<"td">) {
+  return (
+    <td
+      data-slot="table-cell"
+      className={cn(
+        "p-2 align-middle whitespace-nowrap [&:has([role=checkbox])]:pr-0",
+        className
+      )}
+      {...props}
+    />
+  )
+}
+
+function TableCaption({
+  className,
+  ...props
+}: React.ComponentProps<"caption">) {
+  return (
+    <caption
+      data-slot="table-caption"
+      className={cn("mt-4 text-sm text-muted-foreground", className)}
+      {...props}
+    />
+  )
+}
+
+export {
+  Table,
+  TableHeader,
+  TableBody,
+  TableFooter,
+  TableHead,
+  TableRow,
+  TableCell,
+  TableCaption,
+}

web/index.css

@@ -0,0 +1,158 @@
+@import "tailwindcss";
+@import "tw-animate-css";
+@import "shadcn/tailwind.css";
+@import "@fontsource-variable/geist";
+
+@custom-variant dark (&:is(.dark *));
+
+:root {
+    --background: oklch(1 0 0);
+    --foreground: oklch(0.145 0 0);
+    --card: oklch(1 0 0);
+    --card-foreground: oklch(0.145 0 0);
+    --popover: oklch(1 0 0);
+    --popover-foreground: oklch(0.145 0 0);
+    --primary: oklch(0.205 0 0);
+    --primary-foreground: oklch(0.985 0 0);
+    --secondary: oklch(0.97 0 0);
+    --secondary-foreground: oklch(0.205 0 0);
+    --muted: oklch(0.97 0 0);
+    --muted-foreground: oklch(0.556 0 0);
+    --accent: oklch(0.97 0 0);
+    --accent-foreground: oklch(0.205 0 0);
+    --destructive: oklch(0.58 0.22 27);
+    --border: oklch(0.922 0 0);
+    --input: oklch(0.922 0 0);
+    --ring: oklch(0.708 0 0);
+    --chart-1: oklch(0.809 0.105 251.813);
+    --chart-2: oklch(0.623 0.214 259.815);
+    --chart-3: oklch(0.546 0.245 262.881);
+    --chart-4: oklch(0.488 0.243 264.376);
+    --chart-5: oklch(0.424 0.199 265.638);
+    --radius: 0.625rem;
+    --sidebar: oklch(0.985 0 0);
+    --sidebar-foreground: oklch(0.145 0 0);
+    --sidebar-primary: oklch(0.205 0 0);
+    --sidebar-primary-foreground: oklch(0.985 0 0);
+    --sidebar-accent: oklch(0.97 0 0);
+    --sidebar-accent-foreground: oklch(0.205 0 0);
+    --sidebar-border: oklch(0.922 0 0);
+    --sidebar-ring: oklch(0.708 0 0);
+}
+
+.dark {
+    /* Using the exact colors from your previous custom theme */
+    --background: hsl(224, 71%, 4%);
+    --foreground: hsl(210, 40%, 98%);
+    --card: hsl(224, 71%, 5%);
+    --card-foreground: hsl(210, 40%, 98%);
+    --popover: hsl(224, 71%, 4%);
+    --popover-foreground: hsl(210, 40%, 98%);
+    --primary: hsl(196, 100%, 50%);
+    --primary-foreground: hsl(222.2, 47.4%, 11.2%);
+    --secondary: hsl(217.2, 32.6%, 17.5%);
+    --secondary-foreground: hsl(210, 40%, 98%);
+    --muted: hsl(217.2, 32.6%, 17.5%);
+    --muted-foreground: hsl(215, 20.2%, 65.1%);
+    --accent: hsl(217.2, 32.6%, 17.5%);
+    --accent-foreground: hsl(210, 40%, 98%);
+    --destructive: hsl(0, 62.8%, 30.6%);
+    --destructive-foreground: hsl(210, 40%, 98%);
+    --border: hsl(217.2, 32.6%, 17.5%);
+    --input: hsl(217.2, 32.6%, 17.5%);
+    --ring: hsl(196, 100%, 50%);
+    --chart-1: oklch(0.809 0.105 251.813);
+    --chart-2: oklch(0.623 0.214 259.815);
+    --chart-3: oklch(0.546 0.245 262.881);
+    --chart-4: oklch(0.488 0.243 264.376);
+    --chart-5: oklch(0.424 0.199 265.638);
+    --sidebar: hsl(224, 71%, 5%);
+    --sidebar-foreground: hsl(210, 40%, 98%);
+    --sidebar-primary: hsl(196, 100%, 50%);
+    --sidebar-primary-foreground: hsl(222.2, 47.4%, 11.2%);
+    --sidebar-accent: hsl(217.2, 32.6%, 17.5%);
+    --sidebar-accent-foreground: hsl(210, 40%, 98%);
+    --sidebar-border: hsl(217.2, 32.6%, 17.5%);
+    --sidebar-ring: hsl(196, 100%, 50%);
+}
+
+@theme inline {
+    --font-sans: 'Geist Variable', sans-serif;
+    --color-sidebar-ring: var(--sidebar-ring);
+    --color-sidebar-border: var(--sidebar-border);
+    --color-sidebar-accent-foreground: var(--sidebar-accent-foreground);
+    --color-sidebar-accent: var(--sidebar-accent);
+    --color-sidebar-primary-foreground: var(--sidebar-primary-foreground);
+    --color-sidebar-primary: var(--sidebar-primary);
+    --color-sidebar-foreground: var(--sidebar-foreground);
+    --color-sidebar: var(--sidebar);
+    --color-chart-5: var(--chart-5);
+    --color-chart-4: var(--chart-4);
+    --color-chart-3: var(--chart-3);
+    --color-chart-2: var(--chart-2);
+    --color-chart-1: var(--chart-1);
+    --color-ring: var(--ring);
+    --color-input: var(--input);
+    --color-border: var(--border);
+    --color-destructive: var(--destructive);
+    --color-accent-foreground: var(--accent-foreground);
+    --color-accent: var(--accent);
+    --color-muted-foreground: var(--muted-foreground);
+    --color-muted: var(--muted);
+    --color-secondary-foreground: var(--secondary-foreground);
+    --color-secondary: var(--secondary);
+    --color-primary-foreground: var(--primary-foreground);
+    --color-primary: var(--primary);
+    --color-popover-foreground: var(--popover-foreground);
+    --color-popover: var(--popover);
+    --color-card-foreground: var(--card-foreground);
+    --color-card: var(--card);
+    --color-foreground: var(--foreground);
+    --color-background: var(--background);
+    --radius-sm: calc(var(--radius) * 0.6);
+    --radius-md: calc(var(--radius) * 0.8);
+    --radius-lg: var(--radius);
+    --radius-xl: calc(var(--radius) * 1.4);
+    --radius-2xl: calc(var(--radius) * 1.8);
+    --radius-3xl: calc(var(--radius) * 2.2);
+    --radius-4xl: calc(var(--radius) * 2.6);
+}
+
+@layer base {
+    * {
+        @apply border-border outline-ring/50;
+    }
+
+    body {
+        @apply bg-background text-foreground;
+        /* Subtle Grid Pattern */
+        background-image:
+            linear-gradient(rgba(255, 255, 255, 0.03) 1px, transparent 1px),
+            linear-gradient(90deg, rgba(255, 255, 255, 0.03) 1px, transparent 1px);
+        background-size: 40px 40px;
+        min-height: 100vh;
+    }
+
+    html {
+        @apply font-sans;
+    }
+}
+
+/* Custom Scrollbar styled for dark mode */
+::-webkit-scrollbar {
+    width: 8px;
+    height: 8px;
+}
+
+::-webkit-scrollbar-track {
+    background: transparent;
+}
+
+::-webkit-scrollbar-thumb {
+    background: var(--muted);
+    border-radius: 4px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+    background: var(--primary);
+}

web/index.html

@@ -1,122 +1,11 @@
 
 <!DOCTYPE html>
-<html lang="en">
+<html lang="en" class="dark">
   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>AI视频助手</title>
-    <script src="https://cdn.tailwindcss.com"></script>
-    <script>
-      tailwind.config = {
-        darkMode: 'class',
-        theme: {
-          extend: {
-            colors: {
-              border: "hsl(var(--border))",
-              input: "hsl(var(--input))",
-              ring: "hsl(var(--ring))",
-              background: "hsl(var(--background))",
-              foreground: "hsl(var(--foreground))",
-              primary: {
-                DEFAULT: "hsl(var(--primary))",
-                foreground: "hsl(var(--primary-foreground))",
-              },
-              secondary: {
-                DEFAULT: "hsl(var(--secondary))",
-                foreground: "hsl(var(--secondary-foreground))",
-              },
-              destructive: {
-                DEFAULT: "hsl(var(--destructive))",
-                foreground: "hsl(var(--destructive-foreground))",
-              },
-              muted: {
-                DEFAULT: "hsl(var(--muted))",
-                foreground: "hsl(var(--muted-foreground))",
-              },
-              accent: {
-                DEFAULT: "hsl(var(--accent))",
-                foreground: "hsl(var(--accent-foreground))",
-              },
-              card: {
-                DEFAULT: "hsl(var(--card))",
-                foreground: "hsl(var(--card-foreground))",
-              },
-              popover: {
-                DEFAULT: "hsl(var(--popover))",
-                foreground: "hsl(var(--popover-foreground))",
-              },
-            },
-            borderRadius: {
-              lg: "var(--radius)",
-              md: "calc(var(--radius) - 2px)",
-              sm: "calc(var(--radius) - 4px)",
-            },
-          },
-        },
-      }
-    </script>
-    <style>
-      :root {
-        /* Tech/Dark Theme Palette */
-        --background: 224 71% 4%; /* Very Dark Blue #020817 */
-        --foreground: 210 40% 98%; /* Light Gray/White */
-        
-        --card: 224 71% 5%; /* Slightly lighter than bg, will use transparency */
-        --card-foreground: 210 40% 98%;
- 
-        --popover: 224 71% 4%;
-        --popover-foreground: 210 40% 98%;
- 
-        --primary: 196 100% 50%; /* Cyan/Electric Blue for high tech feel */
-        --primary-foreground: 222.2 47.4% 11.2%;
- 
-        --secondary: 217.2 32.6% 17.5%;
-        --secondary-foreground: 210 40% 98%;
- 
-        --muted: 217.2 32.6% 17.5%;
-        --muted-foreground: 215 20.2% 65.1%;
- 
-        --accent: 217.2 32.6% 17.5%;
-        --accent-foreground: 210 40% 98%;
- 
-        --destructive: 0 62.8% 30.6%;
-        --destructive-foreground: 210 40% 98%;
- 
-        --border: 217.2 32.6% 17.5%;
-        --input: 217.2 32.6% 17.5%;
-        --ring: 196 100% 50%;
-        
-        --radius: 0.5rem;
-      }
-      
-      body {
-        font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
-        background-color: hsl(var(--background));
-        color: hsl(var(--foreground));
-        /* Subtle Grid Pattern */
-        background-image: 
-            linear-gradient(rgba(255, 255, 255, 0.03) 1px, transparent 1px), 
-            linear-gradient(90deg, rgba(255, 255, 255, 0.03) 1px, transparent 1px);
-        background-size: 40px 40px;
-        min-height: 100vh;
-      }
 
-      /* Custom Scrollbar */
-      ::-webkit-scrollbar {
-        width: 8px;
-        height: 8px;
-      }
-      ::-webkit-scrollbar-track {
-        background: transparent; 
-      }
-      ::-webkit-scrollbar-thumb {
-        background: hsl(var(--muted)); 
-        border-radius: 4px;
-      }
-      ::-webkit-scrollbar-thumb:hover {
-        background: hsl(var(--primary)); 
-      }
-    </style>
   <script type="importmap">
 {
   "imports": {

web/index.tsx

@@ -3,6 +3,7 @@ import ReactDOM from 'react-dom/client';
 import { QueryClientProvider } from '@tanstack/react-query';
 import App from './App';
 import { queryClient } from './services/queryClient';
+import './index.css';
 
 const rootElement = document.getElementById('root');
 if (!rootElement) {

web/lib/utils.ts

@@ -0,0 +1,6 @@
+import { clsx, type ClassValue } from "clsx"
+import { twMerge } from "tailwind-merge"
+
+export function cn(...inputs: ClassValue[]) {
+  return twMerge(clsx(inputs))
+}

web/package.json

@@ -9,17 +9,28 @@
     "preview": "vite preview"
   },
   "dependencies": {
-    "@tanstack/react-query": "^5.90.2",
-    "lucide-react": "^0.563.0",
-    "zustand": "^5.0.8",
-    "react-router-dom": "^7.13.0",
+    "@base-ui/react": "^1.2.0",
+    "@fontsource-variable/geist": "^5.2.8",
     "@google/genai": "^1.39.0",
+    "@tailwindcss/vite": "^4.2.1",
+    "@tanstack/react-query": "^5.90.2",
+    "class-variance-authority": "^0.7.1",
+    "clsx": "^2.1.1",
+    "lucide-react": "^0.563.0",
     "react": "^19.2.4",
-    "react-dom": "^19.2.4"
+    "react-dom": "^19.2.4",
+    "react-router-dom": "^7.13.0",
+    "shadcn": "^4.0.2",
+    "tailwind-merge": "^3.5.0",
+    "tw-animate-css": "^1.4.0",
+    "zustand": "^5.0.8"
   },
   "devDependencies": {
     "@types/node": "^22.14.0",
     "@vitejs/plugin-react": "^5.0.0",
+    "autoprefixer": "^10.4.27",
+    "postcss": "^8.5.8",
+    "tailwindcss": "^4.2.1",
     "typescript": "~5.8.2",
     "vite": "^6.2.0"
   }

web/services/backendApi.ts

@@ -95,6 +95,7 @@ const mapAssistant = (raw: AnyRecord): Assistant => ({
   configMode: readField(raw, ['configMode', 'config_mode'], 'platform') as 'platform' | 'dify' | 'fastgpt' | 'none',
   apiUrl: readField(raw, ['apiUrl', 'api_url'], ''),
   apiKey: readField(raw, ['apiKey', 'api_key'], ''),
+  appId: readField(raw, ['appId', 'app_id'], ''),
   llmModelId: readField(raw, ['llmModelId', 'llm_model_id'], ''),
   asrModelId: readField(raw, ['asrModelId', 'asr_model_id'], ''),
   embeddingModelId: readField(raw, ['embeddingModelId', 'embedding_model_id'], ''),
@@ -302,6 +303,7 @@ export const createAssistant = async (data: Partial<Assistant>): Promise<Assista
     configMode: data.configMode || 'platform',
     apiUrl: data.apiUrl || '',
     apiKey: data.apiKey || '',
+    appId: data.appId || '',
     llmModelId: data.llmModelId || '',
     asrModelId: data.asrModelId || '',
     embeddingModelId: data.embeddingModelId || '',
@@ -335,6 +337,7 @@ export const updateAssistant = async (id: string, data: Partial<Assistant>): Pro
     configMode: data.configMode,
     apiUrl: data.apiUrl,
     apiKey: data.apiKey,
+    appId: data.appId,
     llmModelId: data.llmModelId,
     asrModelId: data.asrModelId,
     embeddingModelId: data.embeddingModelId,

web/types.ts

@@ -25,6 +25,7 @@ export interface Assistant {
   configMode?: 'platform' | 'dify' | 'fastgpt' | 'none';
   apiUrl?: string;
   apiKey?: string;
+  appId?: string;
   llmModelId?: string;
   asrModelId?: string;
   embeddingModelId?: string;

web/vite.config.ts

@@ -1,23 +1,24 @@
 import path from 'path';
 import { defineConfig, loadEnv } from 'vite';
 import react from '@vitejs/plugin-react';
+import tailwindcss from '@tailwindcss/vite';
 
 export default defineConfig(({ mode }) => {
-    const env = loadEnv(mode, '.', '');
-    return {
-      server: {
-        port: 3000,
-        host: '0.0.0.0',
-      },
-      plugins: [react()],
-      define: {
-        'process.env.API_KEY': JSON.stringify(env.GEMINI_API_KEY),
-        'process.env.GEMINI_API_KEY': JSON.stringify(env.GEMINI_API_KEY)
-      },
-      resolve: {
-        alias: {
-          '@': path.resolve(__dirname, '.'),
-        }
+  const env = loadEnv(mode, '.', '');
+  return {
+    server: {
+      port: 3000,
+      host: '0.0.0.0',
+    },
+    plugins: [react(), tailwindcss()],
+    define: {
+      'process.env.API_KEY': JSON.stringify(env.GEMINI_API_KEY),
+      'process.env.GEMINI_API_KEY': JSON.stringify(env.GEMINI_API_KEY)
+    },
+    resolve: {
+      alias: {
+        '@': path.resolve(__dirname, '.'),
       }
-    };
+    }
+  };
 });