update env variables

app/config/settings.py

@@ -18,7 +18,6 @@ class Settings(BaseSettings):
     CEREBRAS_API_KEY: str = ""
 
     LLM_MODEL: str = "gpt-4o"
-    LLM_ROUTER_MODEL: str = "gpt-4o-mini"
     LLM_EMBED_MODEL: str = "text-embedding-3-small"
 
     # GitHub Copilot OAuth token storage directory.
@@ -43,7 +42,7 @@ class Settings(BaseSettings):
 
     LANGFUSE_SECRET_KEY: str = ""
     LANGFUSE_PUBLIC_KEY: str = ""
-    LANGFUSE_HOST: str = "https://cloud.langfuse.com"
+    LANGFUSE_BASE_URL: str = "https://cloud.langfuse.com"
 
     ENV: Literal["dev", "prod"] = "dev"
 

app/core/langfuse_client.py

@@ -67,9 +67,9 @@ def get_langfuse() -> Any | None:
         _client = Langfuse(
             secret_key=settings.LANGFUSE_SECRET_KEY,
             public_key=settings.LANGFUSE_PUBLIC_KEY,
-            host=settings.LANGFUSE_HOST,
+            host=settings.LANGFUSE_BASE_URL,
         )
-        logger.info("langfuse: client initialized host=%s", settings.LANGFUSE_HOST)
+        logger.info("langfuse: client initialized host=%s", settings.LANGFUSE_BASE_URL)
     except Exception as exc:
         logger.warning("langfuse: failed to initialize: %s", exc)
         _client = None

app/core/llm.py

@@ -1,6 +1,6 @@
 """LLM factory — centralised model instantiation via LiteLLM.
 
-Every agent and the orchestrator call ``get_llm()`` or ``get_router_llm()``
+Every agent and the orchestrator call ``get_llm()``
 instead of directly constructing a provider-specific class.  The model string
 follows the `LiteLLM model naming convention
 <https://docs.litellm.ai/docs/providers>`_:
@@ -11,7 +11,7 @@ follows the `LiteLLM model naming convention
 * Ollama:     ``ollama/llama3``
 * Bedrock:    ``bedrock/anthropic.claude-v2``
 
-Switch providers by changing **LLM_MODEL** / **LLM_ROUTER_MODEL** in ``.env``
+Switch providers by changing **LLM_MODEL** in ``.env``
 — no code changes required.
 """
 
@@ -95,14 +95,6 @@ def get_llm(
     )
 
 
-def get_router_llm(
-    *,
-    temperature: float = 0,
-) -> ChatOpenAI | ChatLiteLLM:
-    """Return the lighter model used for intent classification / routing."""
-    return get_llm(model=settings.LLM_ROUTER_MODEL, temperature=temperature)
-
-
 async def embed(text: str) -> list[float]:
     """Return an embedding vector for *text*.