feat(orchestrator): Implement Synthesizer node for final worker results integration

backend/src/ea_chatbot/graph/nodes/synthesizer.py

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+from ea_chatbot.graph.state import AgentState
+from ea_chatbot.config import Settings
+from ea_chatbot.utils.llm_factory import get_llm_model
+from ea_chatbot.utils.logging import get_logger, LangChainLoggingHandler
+from ea_chatbot.graph.prompts.synthesizer import SYNTHESIZER_PROMPT
+
+def synthesizer_node(state: AgentState) -> dict:
+    """Synthesize the results from multiple workers into a final answer."""
+    question = state["question"]
+    history = state.get("messages", [])
+    
+    # We look for the 'summary' from the last worker which might have cumulative info
+    # Or we can look at all messages in history bubbled up from workers.
+    # For now, let's assume the history contains all the worker summaries.
+    
+    settings = Settings()
+    logger = get_logger("orchestrator:synthesizer")
+    
+    logger.info("Synthesizing final answer from worker results...")
+    
+    llm = get_llm_model(
+        settings.summarizer_llm,
+        callbacks=[LangChainLoggingHandler(logger=logger)]
+    )
+    
+    # We provide the full history and the original question
+    messages = SYNTHESIZER_PROMPT.format_messages(
+        question=question,
+        history=history,
+        worker_results="Review the worker summaries provided in the message history."
+    )
+    
+    try:
+        response = llm.invoke(messages)
+        logger.info("[bold green]Final synthesis complete.[/bold green]")
+        
+        # Return the final message to be added to the state
+        return {
+            "messages": [response],
+            "next_action": "end"
+        }
+    except Exception as e:
+        logger.error(f"Failed to synthesize final answer: {str(e)}")
+        raise e

backend/src/ea_chatbot/graph/prompts/synthesizer.py

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+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+
+SYNTHESIZER_SYSTEM = """You are a Lead Orchestrator for an Election Analytics Chatbot.
+You have coordinated several specialized workers (Data Analysts, Researchers) to answer a user's complex query.
+
+Your goal is to synthesize their individual findings into a single, cohesive, and comprehensive final response for the user.
+
+**Guidelines:**
+- Do NOT mention the internal 'workers' or 'checklist' names.
+- Combine the data insights (from Data Analysts) and factual research (from Researchers) into a natural narrative.
+- Ensure all numbers, dates, and names from the worker reports are included accurately.
+- If any part of the plan failed, explain the status honestly but professionally.
+- Present data in clear formats (tables, bullet points) where appropriate."""
+
+SYNTHESIZER_USER = """USER QUESTION:
+{question}
+
+EXECUTION SUMMARY (Results from specialized workers):
+{worker_results}
+
+Provide the final integrated response:"""
+
+SYNTHESIZER_PROMPT = ChatPromptTemplate.from_messages([
+    ("system", SYNTHESIZER_SYSTEM),
+    MessagesPlaceholder(variable_name="history"),
+    ("human", SYNTHESIZER_USER),
+])

backend/tests/test_orchestrator_synthesizer.py

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+from unittest.mock import MagicMock, patch
+from ea_chatbot.graph.nodes.synthesizer import synthesizer_node
+from ea_chatbot.graph.state import AgentState
+from langchain_core.messages import AIMessage
+
+def test_synthesizer_node_success():
+    """Verify that the synthesizer node produces a final response."""
+    state = AgentState(
+        messages=[AIMessage(content="Worker 1 found data."), AIMessage(content="Worker 2 searched web.")],
+        question="What are the results?",
+        checklist=[],
+        current_step=0,
+        iterations=0,
+        vfs={},
+        plots=[],
+        dfs={},
+        next_action="",
+        analysis={}
+    )
+    
+    # Mocking the LLM
+    with patch("ea_chatbot.graph.nodes.synthesizer.get_llm_model") as mock_get_llm:
+        mock_llm = MagicMock()
+        mock_llm.invoke.return_value = AIMessage(content="Final synthesized answer.")
+        mock_get_llm.return_value = mock_llm
+        
+        result = synthesizer_node(state)
+        
+        assert len(result["messages"]) == 1
+        assert result["messages"][0].content == "Final synthesized answer."
+        assert result["next_action"] == "end"