Route to sql_generator service if MCP is disabled

Author: kostisd

src/datu/app_config.py

@@ -99,7 +99,7 @@ class DatuConfig(BaseSettings):
     schema_categorical_detection: bool = True
     schema_sample_limit: int = 1000
     schema_categorical_threshold: int = 10
-    enable_mcp: bool = True
+    enable_mcp: bool = False
     mcp: MCPConfig | None = Field(
         default_factory=MCPConfig,
         description="Configuration settings for MCP integration.",

src/datu/llm_clients/openai_client.py

@@ -66,6 +66,7 @@ class OpenAIClient(BaseLLMClient):
     """
 
     def __init__(self):
+        """Initializes the OpenAIClient with the configured model and API key."""
         super().__init__()
         self.model = getattr(settings, "openai_model", "gpt-4o-mini")
         self.client = ChatOpenAI(
@@ -74,6 +75,7 @@ def __init__(self):
             temperature=settings.llm_temperature,
         )
         self.history = ChatMessageHistory()
+        self.agent = None
         if settings.enable_mcp:
             if not self.mcp_client:
                 raise RuntimeError("MCP is enabled but mcp_client was not initialized. ")
@@ -90,13 +92,28 @@ def __init__(self):
                 raise
 
     async def chat(self, input_text: str) -> str:
+        """Sends a chat message to the MCP agent and returns the response.
+        Args:
+            input_text (str): The input text to send to the agent.
+        Returns:
+            str: The response from the agent."""
+
+        if not settings.enable_mcp or self.agent is None:
+            raise RuntimeError("chat() requires MCP enabled and an initialized agent.")
         response = await self.agent.run(
             input_text,
             max_steps=30,
         )
         return response
 
     async def chat_completion(self, messages: list[BaseMessage], system_prompt: str | None = None) -> str:
+        """Generates a chat completion response based on the provided messages and system prompt.
+        Args:
+            messages (list[BaseMessage]): A list of messages to send to the LLM.
+            system_prompt (str | None): An optional system prompt to guide the LLM's response.
+        Returns:
+            str: The generated response from the LLM.
+        """
         if settings.simulate_llm_response:
             return create_simulated_llm_response()
         if not messages:
@@ -141,7 +158,12 @@ async def chat_completion(self, messages: list[BaseMessage], system_prompt: str
         # Adjust this if your llm_with_tools expects different format
         input_text = "\n".join(msg.content for msg in self.history.messages if hasattr(msg, "content"))
 
-        response = await self.chat(input_text)
+        if settings.enable_mcp:
+            # uses MCP agent
+            response = await self.chat(input_text)
+        else:
+            # direct LLM call without MCP
+            response = await self.client.ainvoke(self.history.messages)
 
         # Assuming response is a BaseMessage or similar with 'content'
         if hasattr(response, "content"):

src/datu/routers/chat.py

@@ -14,6 +14,7 @@
     QueryDetails,
     estimate_query_complexity,
     extract_sql_blocks,
+    generate_sql_core,
     get_query_execution_time_estimate,
 )
 from datu.services.sql_generator.normalizer import normalize_for_preview
@@ -38,6 +39,9 @@ async def chat_with_llm(request: ChatRequest):
         }
     """
     try:
+        if not settings.enable_mcp:
+            return await generate_sql_core(request)
+
         if not request.system_prompt:
             system_prompt = """
 You have access to MCP tools (e.g., SQL generation, web browsing, file operations, data retrieval).