Compeleted Task B-15

backend/api/chat.py

@@ -17,6 +17,7 @@
     SendMessageResponse,
 )
 from services.project_service import get_project_service
+from services.llm_service import llm_service
 
 router = APIRouter(prefix="/chat", tags=["chat"])
 project_service = get_project_service()
@@ -262,8 +263,24 @@ async def send_message(
         created_at=datetime.utcnow().isoformat() + "Z",
     )
 
-    # Generate mock query result
-    query_result = generate_mock_query_result(request.message, project_id)
+    # Use LLMService for AI response, fallback to mock if not configured
+    try:
+        ai_content = llm_service.run(request.message)
+        # For now, just echo the LLM response as the AI message content
+        query_result = QueryResult(
+            id=str(uuid.uuid4()),
+            query=request.message,
+            sql_query="",  # To be filled by future agent logic
+            result_type="summary",
+            data=[],
+            execution_time=0.0,
+            row_count=0,
+            chart_config=None,
+        )
+    except Exception as e:
+        # Fallback to mock logic if LLM not available
+        ai_content = f"[MOCK] Here are the results for your query: '{request.message}'"
+        query_result = generate_mock_query_result(request.message, project_id)
 
     # Store message in mock database
     if project_id not in MOCK_CHAT_MESSAGES:
@@ -275,7 +292,7 @@ async def send_message(
         id=str(uuid.uuid4()),
         project_id=project_id,
         user_id="assistant",
-        content=f"Here are the results for your query: '{request.message}'",
+        content=ai_content,
         role="assistant",
         created_at=datetime.utcnow().isoformat() + "Z",
         metadata={"query_result_id": query_result.id},

backend/requirements.txt

@@ -35,6 +35,6 @@ google-auth==2.25.2
 email-validator==2.1.0
 
 # Future dependencies (commented for now, will be added in later tasks)
-# langchain==0.1.0
-# openai==1.3.0
-# duckdb==0.9.2 
+langchain==0.1.0
+openai==1.3.0
+duckdb==0.9.2 

backend/services/__init__.py

@@ -1 +1,2 @@
 # Services package for SmartQuery backend
+# This file intentionally left blank for service package initialization.

backend/services/llm_service.py

@@ -0,0 +1,28 @@
+import os
+from langchain.llms import OpenAI
+from langchain.agents import initialize_agent, Tool
+from langchain.agents import AgentType
+
+class LLMService:
+    """Service for managing LangChain LLM agent for query processing."""
+
+    def __init__(self):
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        if not self.openai_api_key:
+            raise ValueError("OPENAI_API_KEY environment variable not set.")
+        self.llm = OpenAI(openai_api_key=self.openai_api_key, temperature=0)
+        # Placeholder: Add tools as needed for agent
+        self.tools = []
+        self.agent = initialize_agent(
+            self.tools,
+            self.llm,
+            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+            verbose=False,
+        )
+
+    def run(self, prompt: str) -> str:
+        """Run the agent with a given prompt and return the response."""
+        return self.agent.run(prompt)
+
+# Singleton instance for import
+llm_service = LLMService()