Enhance AssistantConfig and pipeline for FastGPT integration

- Add new fields in AssistantConfig for FastGPT connection details, including `fastgpt_api_url`, `fastgpt_api_key`, and `fastgpt_app_id`.
- Update the pipeline to utilize the new FastGPT configuration, ensuring proper integration with external services.
- Introduce type handling for different assistant types, including support for realtime modes and external brain management.
- Refactor frontend components to include hints for FastGPT configuration inputs, improving user guidance during setup.

backend/services/pipecat/pipeline.py

@@ -11,7 +11,12 @@ from uuid import uuid4
 import config
 from loguru import logger
 from models import AssistantConfig
-from services.pipecat.service_factory import create_realtime_service, create_services
+from services.brains import build_brain
+from services.pipecat.service_factory import (
+    create_realtime_service,
+    create_stt,
+    create_tts,
+)
 from services.workflow_engine import WorkflowEngine
 
 from pipecat.adapters.schemas.function_schema import FunctionSchema
@@ -207,13 +212,23 @@ async def run_pipeline(transport, cfg: AssistantConfig) -> None:
                    只要有 .input() / .output() / event_handler 即可。
         cfg:       助手配置(随请求内联传入)。
     """
-    logger.info(f"启动管线: assistant={cfg.name} mode={cfg.runtimeMode}")
+    logger.info(f"启动管线: assistant={cfg.name} type={cfg.type} mode={cfg.runtimeMode}")
+
+    # 大脑:按类型决定 LLM 槽/开场白/上下文归属。每通电话一个实例(可持会话状态)。
+    brain = build_brain(cfg)
+    if (
+        cfg.runtimeMode == "realtime"
+        and "realtime" not in brain.spec.supported_runtime_modes
+    ):
+        logger.warning(f"类型 {cfg.type} 不支持 realtime,回退 cascade")
+        cfg.runtimeMode = "pipeline"
 
     if cfg.runtimeMode == "realtime":
         await run_realtime_pipeline(transport, cfg)
         return
 
-    stt, llm, tts = create_services(cfg)
+    stt = create_stt(cfg)
+    tts = create_tts(cfg)
 
     # ---- workflow 图引擎(可选)----
     # 有节点图时按图驱动:开场白/系统提示来自起始节点,每轮回复后按条件路由。
@@ -240,11 +255,17 @@ async def run_pipeline(transport, cfg: AssistantConfig) -> None:
         logger.info(
             f"工作流模式启用: 起始节点={engine.name(wf_state['current'])}"
         )
-    else:
+    elif brain.spec.owns_context:
         greeting = cfg.greeting
         system_content = cfg.prompt
+    else:
+        # 外部托管(fastgpt 等):开场白来自对方后台,系统提示/上下文不归我们维护
+        greeting = await brain.greeting(cfg)
+        system_content = ""
 
     context = LLMContext(messages=[{"role": "system", "content": system_content}])
+    # LLM 槽由大脑提供:内部类型=OpenAI 兼容服务;fastgpt=包 SDK 的伪 LLM。
+    llm = brain.build_llm(cfg, context)
     user_aggregator = LLMUserAggregator(
         context,
         params=LLMUserAggregatorParams(
@@ -539,7 +560,9 @@ async def run_pipeline(transport, cfg: AssistantConfig) -> None:
     @transport.event_handler("on_client_connected")
     async def on_client_connected(_transport, _client):
         if greeting:
-            context.add_message({"role": "assistant", "content": greeting})
+            # 外部托管类型的上下文由对方服务端维护,开场白不写入本地 context
+            if brain.spec.owns_context:
+                context.add_message({"role": "assistant", "content": greeting})
             await worker.queue_frame(TTSSpeakFrame(greeting, append_to_context=False))
         # 工作流:点亮当前(开始)节点。开始节点即首个会话节点。
         if workflow_active:

backend/services/pipecat/service_factory.py

@@ -132,16 +132,6 @@ def create_tts(cfg: AssistantConfig):
     )
 
 
-def create_services(cfg: AssistantConfig):
-    logger.info(
-        f"创建服务: stt={cfg.stt_interface_type}/{cfg.asr or config.STT_MODEL} "
-        f"llm={cfg.model or config.LLM_MODEL} "
-        f"tts={cfg.tts_interface_type}/{cfg.tts_model or config.TTS_MODEL} "
-        f"voice={cfg.voice or config.TTS_VOICE}"
-    )
-    return create_stt(cfg), create_llm(cfg), create_tts(cfg)
-
-
 def create_realtime_service(cfg: AssistantConfig):
     """Create a speech-to-speech service that owns STT, LLM, and TTS."""
     if cfg.realtime_interface_type == "stepfun-realtime":