Use FastGPT app opener for greetings

engine/config.py

@@ -147,7 +147,6 @@ class LLMConfig:
     variables: dict[str, str] = field(default_factory=dict)
     detail: bool = False
     timeout_sec: float = 60.0
-    send_system_prompt: bool = False
 
     @property
     def is_fastgpt(self) -> bool:
@@ -160,7 +159,7 @@ class LLMConfig:
     @property
     def uses_local_context_history(self) -> bool:
         """Whether the pipeline should seed and maintain local LLM context history."""
-        return not self.is_fastgpt or self.send_system_prompt
+        return not self.is_fastgpt
 
 
 @dataclass(frozen=True)
@@ -233,9 +232,11 @@ def config_from_dict(data: dict) -> EngineConfig:
     agent = _dict(data.get("agent"))
     if agent.get("greeting") == "":
         agent["greeting"] = None
-    if agent.get("greeting_mode") not in (None, "generated", "fixed", "off"):
-        raise ValueError("agent.greeting_mode must be one of: generated, fixed, off")
-    response_state = ResponseStateConfig(**_dict(agent.pop("response_state")))
+    if agent.get("greeting_mode") not in (None, "generated", "fixed", "off", "fastgpt_opener"):
+        raise ValueError(
+            "agent.greeting_mode must be one of: generated, fixed, off, fastgpt_opener"
+        )
+    response_state = ResponseStateConfig(**_dict(agent.pop("response_state", None)))
     if response_state.max_prefix_chars < 1:
         raise ValueError("agent.response_state.max_prefix_chars must be greater than 0")
     if not response_state.tag:
@@ -255,6 +256,10 @@ def config_from_dict(data: dict) -> EngineConfig:
         llm["app_id"] = None
     if not isinstance(llm.get("variables"), dict):
         llm["variables"] = {}
+    if agent.get("greeting_mode") == "fastgpt_opener" and llm["provider"] != "fastgpt":
+        raise ValueError(
+            "agent.greeting_mode='fastgpt_opener' requires services.llm.provider='fastgpt'"
+        )
 
     turn = _dict(data.get("turn"))
     vad = _dict(turn.get("vad"))