Update LLM configuration to support FastGPT integration. Modify requirements to include fastgpt-python-sdk, enhance greeting messages, and adjust LLM service creation to handle app_id. Implement welcome text fetching for FastGPT and improve context handling in the pipeline based on LLM provider. Update related configurations and properties for better integration.

engine/config.py

@@ -4,6 +4,9 @@ import json
 from dataclasses import dataclass, field
 from pathlib import Path
 
+SUPPORTED_LLM_PROVIDERS = frozenset({"openai", "fastgpt"})
+_LLM_PROVIDER_ALIASES = {"llm": "openai", "openai": "openai", "fastgpt": "fastgpt"}
+
 
 @dataclass(frozen=True)
 class ServerConfig:
@@ -99,10 +102,17 @@ class AgentConfig:
 
 @dataclass(frozen=True)
 class LLMConfig:
+    """LLM backend selection via ``provider``.
+
+    Set ``provider`` to ``"openai"`` (alias ``"llm"``) for OpenAI-compatible chat
+    completions, or ``"fastgpt"`` for FastGPT server-side memory via ``chat_id``.
+    """
+
     provider: str = "openai"
     api_key: str = ""
     base_url: str | None = None
     model: str = "gpt-4o-mini"
+    app_id: str | None = None
     temperature: float | None = 0.7
     chat_id: str | None = None
     variables: dict[str, str] = field(default_factory=dict)
@@ -110,6 +120,19 @@ class LLMConfig:
     timeout_sec: float = 60.0
     send_system_prompt: bool = False
 
+    @property
+    def is_fastgpt(self) -> bool:
+        return self.provider == "fastgpt"
+
+    @property
+    def is_openai(self) -> bool:
+        return self.provider == "openai"
+
+    @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
+
 
 @dataclass(frozen=True)
 class STTConfig:
@@ -186,8 +209,11 @@ def config_from_dict(data: dict) -> EngineConfig:
         stt["language"] = None
 
     llm = _dict(services.get("llm"))
+    llm["provider"] = _normalize_llm_provider(llm.get("provider", LLMConfig().provider))
     if llm.get("chat_id") == "":
         llm["chat_id"] = None
+    if llm.get("app_id") == "":
+        llm["app_id"] = None
     if not isinstance(llm.get("variables"), dict):
         llm["variables"] = {}
 
@@ -227,3 +253,14 @@ def config_from_dict(data: dict) -> EngineConfig:
 
 def _dict(value: object) -> dict:
     return dict(value) if isinstance(value, dict) else {}
+
+
+def _normalize_llm_provider(value: object) -> str:
+    provider = str(value or LLMConfig().provider).strip().lower()
+    normalized = _LLM_PROVIDER_ALIASES.get(provider)
+    if normalized is None:
+        supported = ", ".join(sorted(SUPPORTED_LLM_PROVIDERS | {"llm"}))
+        raise ValueError(
+            f"services.llm.provider must be one of: {supported}; got {value!r}"
+        )
+    return normalized