feat: Enhance ProvideFinalAnswerComponent to improve context formatti…

…ng and final answer generation

src/backend/base/langflow/components/agents/write_final_answer.py

@@ -1,7 +1,8 @@
 from typing import TYPE_CHECKING
 
 from langflow.custom import Component
-from langflow.io import HandleInput, MessageTextInput, Output
+from langflow.io import MessageTextInput, Output
+from langflow.schema.data import Data
 from langflow.schema.message import Message
 
 if TYPE_CHECKING:
@@ -10,10 +11,9 @@
 
 class ProvideFinalAnswerComponent(Component):
     display_name = "Provide Final Answer"
-    description = "Generates the final answer based on the agent's context."
+    description = "Provides a final answer based on the context and actions taken."
 
     inputs = [
-        HandleInput(name="agent_context", display_name="Agent Context", input_types=["AgentContext"], required=True),
         MessageTextInput(
             name="prompt",
             display_name="Prompt",
@@ -22,13 +22,50 @@ class ProvideFinalAnswerComponent(Component):
         ),
     ]
 
-    outputs = [Output(name="final_answer", method="get_final_answer")]
+    outputs = [Output(name="final_answer", display_name="Final Answer", method="get_final_answer")]
+
+    def _format_context(self) -> str:
+        ctx = self.ctx
+        context_parts = []
+
+        # Add thought if exists
+        if ctx.get("thought"):
+            context_parts.append(f"Last Thought: {ctx['thought']}")
+
+        # Add last action and result if they exist
+        if ctx.get("last_action"):
+            context_parts.append(f"Last Action: {ctx['last_action']}")
+            if ctx.get("last_action_result"):
+                context_parts.append(f"Action Result: {ctx['last_action_result']}")
+
+        # Add initial message for context
+        if ctx.get("initial_message"):
+            context_parts.append(f"\nInitial Query: {ctx['initial_message']}")
+
+        return "\n".join(context_parts)
 
     def get_final_answer(self) -> Message:
-        # Append the prompt after the accumulated context following ReAct format
-        full_prompt = f"{self.agent_context.get_full_context()}\n{self.prompt}\nFinal Answer:"
-        final_answer: AIMessage = self.agent_context.llm.invoke(full_prompt)
-        self.agent_context.final_answer = final_answer
-        self.agent_context.update_context("Final Answer", final_answer)
-        self.status = self.agent_context.to_data_repr()
-        return Message(text=final_answer.content)
+        # Format the full context
+        full_prompt = f"{self._format_context()}\n{self.prompt}\nFinal Answer:"
+
+        # Generate final answer using LLM
+        response: AIMessage = self.ctx["llm"].invoke(full_prompt)
+
+        # Update context with final answer
+        self.update_ctx({"final_answer": response.content})
+
+        # Create status data
+        self.status = [
+            Data(
+                name="Final Answer",
+                value=f"""
+Context Used:
+{self._format_context()}
+
+Final Answer:
+{response.content}
+""",
+            )
+        ]
+
+        return Message(text=response.content)