Refactor and test final answer prompts (#595)

docs/source/en/reference/agents.md

@@ -65,3 +65,5 @@ _This class is deprecated since 1.8.0: now you simply need to pass attributes `n
 [[autodoc]] smolagents.agents.PlanningPromptTemplate
 
 [[autodoc]] smolagents.agents.ManagedAgentPromptTemplate
+
+[[autodoc]] smolagents.agents.FinalAnswerPromptTemplate

docs/source/hi/reference/agents.md

@@ -163,3 +163,5 @@ model = OpenAIServerModel(
 [[autodoc]] smolagents.agents.PlanningPromptTemplate
 
 [[autodoc]] smolagents.agents.ManagedAgentPromptTemplate
+
+[[autodoc]] smolagents.agents.FinalAnswerPromptTemplate

docs/source/zh/reference/agents.md

@@ -155,3 +155,5 @@ print(model(messages))
 [[autodoc]] smolagents.agents.PlanningPromptTemplate
 
 [[autodoc]] smolagents.agents.ManagedAgentPromptTemplate
+
+[[autodoc]] smolagents.agents.FinalAnswerPromptTemplate

src/smolagents/agents.py

@@ -117,19 +117,34 @@ class ManagedAgentPromptTemplate(TypedDict):
     report: str
 
 
+class FinalAnswerPromptTemplate(TypedDict):
+    """
+    Prompt templates for the final answer.
+
+    Args:
+        pre_messages (`str`): Pre-messages prompt.
+        post_messages (`str`): Post-messages prompt.
+    """
+
+    pre_messages: str
+    post_messages: str
+
+
 class PromptTemplates(TypedDict):
     """
     Prompt templates for the agent.
 
     Args:
         system_prompt (`str`): System prompt.
-        planning ([`~agents.PlanningPromptTemplate`]): Planning prompt template.
+        planning ([`~agents.PlanningPromptTemplate`]): Planning prompt templates.
-        managed_agent ([`~agents.ManagedAgentPromptTemplate`]): Managed agent prompt template.
+        managed_agent ([`~agents.ManagedAgentPromptTemplate`]): Managed agent prompt templates.
+        final_answer ([`~agents.FinalAnswerPromptTemplate`]): Final answer prompt templates.
     """
 
     system_prompt: str
     planning: PlanningPromptTemplate
     managed_agent: ManagedAgentPromptTemplate
+    final_answer: FinalAnswerPromptTemplate
 
 
 EMPTY_PROMPT_TEMPLATES = PromptTemplates(
@@ -143,6 +158,7 @@ EMPTY_PROMPT_TEMPLATES = PromptTemplates(
         update_plan_post_messages="",
     ),
     managed_agent=ManagedAgentPromptTemplate(task="", report=""),
+    final_answer=FinalAnswerPromptTemplate(pre_messages="", post_messages=""),
 )
 
 
@@ -290,46 +306,33 @@ class MultiStepAgent:
         Returns:
             `str`: Final answer to the task.
         """
-        messages = [{"role": MessageRole.SYSTEM, "content": []}]
+        messages = [
+            {
+                "role": MessageRole.SYSTEM,
+                "content": [
+                    {
+                        "type": "text",
+                        "text": self.prompt_templates["final_answer"]["pre_messages"],
+                    }
+                ],
+            }
+        ]
         if images:
-            messages[0]["content"] = [
-                {
-                    "type": "text",
-                    "text": "An agent tried to answer a user query but it got stuck and failed to do so. You are tasked with providing an answer instead. Here is the agent's memory:",
-                }
-            ]
             messages[0]["content"].append({"type": "image"})
-            messages += self.write_memory_to_messages()[1:]
+        messages += self.write_memory_to_messages()[1:]
-            messages += [
+        messages += [
-                {
+            {
-                    "role": MessageRole.USER,
+                "role": MessageRole.USER,
-                    "content": [
+                "content": [
-                        {
+                    {
-                            "type": "text",
+                        "type": "text",
-                            "text": f"Based on the above, please provide an answer to the following user request:\n{task}",
+                        "text": populate_template(
-                        }
+                            self.prompt_templates["final_answer"]["post_messages"], variables={"task": task}
-                    ],
+                        ),
-                }
+                    }
-            ]
+                ],
-        else:
+            }
-            messages[0]["content"] = [
+        ]
-                {
-                    "type": "text",
-                    "text": "An agent tried to answer a user query but it got stuck and failed to do so. You are tasked with providing an answer instead. Here is the agent's memory:",
-                }
-            ]
-            messages += self.write_memory_to_messages()[1:]
-            messages += [
-                {
-                    "role": MessageRole.USER,
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": f"Based on the above, please provide an answer to the following user request:\n{task}",
-                        }
-                    ],
-                }
-            ]
         try:
             chat_message: ChatMessage = self.model(messages)
             return chat_message.content

src/smolagents/prompts/code_agent.yaml

@@ -318,4 +318,10 @@ managed_agent:
       And even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback.
   report: |-
       Here is the final answer from your managed agent '{{name}}':
-      {{final_answer}}
+      {{final_answer}}
+final_answer:
+  pre_messages: |-
+    An agent tried to answer a user query but it got stuck and failed to do so. You are tasked with providing an answer instead. Here is the agent's memory:
+  post_messages: |-
+    Based on the above, please provide an answer to the following user request:
+    {{task}}

src/smolagents/prompts/toolcalling_agent.yaml

@@ -261,4 +261,10 @@ managed_agent:
       And even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback.
   report: |-
       Here is the final answer from your managed agent '{{name}}':
-      {{final_answer}}
+      {{final_answer}}
+final_answer:
+  pre_messages: |-
+    An agent tried to answer a user query but it got stuck and failed to do so. You are tasked with providing an answer instead. Here is the agent's memory:
+  post_messages: |-
+    Based on the above, please provide an answer to the following user request:
+    {{task}}

tests/test_agents.py

@@ -29,6 +29,7 @@ from smolagents.agents import (
     MultiStepAgent,
     ToolCall,
     ToolCallingAgent,
+    populate_template,
 )
 from smolagents.default_tools import PythonInterpreterTool
 from smolagents.memory import PlanningStep
@@ -771,6 +772,74 @@ class TestMultiStepAgent:
                     assert "type" in content
                     assert "text" in content
 
+    @pytest.mark.parametrize(
+        "images, expected_messages_list",
+        [
+            (
+                None,
+                [
+                    [
+                        {
+                            "role": MessageRole.SYSTEM,
+                            "content": [{"type": "text", "text": "FINAL_ANSWER_SYSTEM_PROMPT"}],
+                        },
+                        {"role": MessageRole.USER, "content": [{"type": "text", "text": "FINAL_ANSWER_USER_PROMPT"}]},
+                    ]
+                ],
+            ),
+            (
+                ["image1.png"],
+                [
+                    [
+                        {
+                            "role": MessageRole.SYSTEM,
+                            "content": [{"type": "text", "text": "FINAL_ANSWER_SYSTEM_PROMPT"}, {"type": "image"}],
+                        },
+                        {"role": MessageRole.USER, "content": [{"type": "text", "text": "FINAL_ANSWER_USER_PROMPT"}]},
+                    ]
+                ],
+            ),
+        ],
+    )
+    def test_provide_final_answer(self, images, expected_messages_list):
+        fake_model = MagicMock()
+        fake_model.return_value.content = "Final answer."
+        agent = CodeAgent(
+            tools=[],
+            model=fake_model,
+        )
+        task = "Test task"
+        final_answer = agent.provide_final_answer(task, images=images)
+        expected_message_texts = {
+            "FINAL_ANSWER_SYSTEM_PROMPT": agent.prompt_templates["final_answer"]["pre_messages"],
+            "FINAL_ANSWER_USER_PROMPT": populate_template(
+                agent.prompt_templates["final_answer"]["post_messages"], variables=dict(task=task)
+            ),
+        }
+        for expected_messages in expected_messages_list:
+            for expected_message in expected_messages:
+                for expected_content in expected_message["content"]:
+                    if "text" in expected_content:
+                        expected_content["text"] = expected_message_texts[expected_content["text"]]
+        assert final_answer == "Final answer."
+        # Test calls to model
+        assert len(fake_model.call_args_list) == 1
+        for call_args, expected_messages in zip(fake_model.call_args_list, expected_messages_list):
+            assert len(call_args.args) == 1
+            messages = call_args.args[0]
+            assert isinstance(messages, list)
+            assert len(messages) == len(expected_messages)
+            for message, expected_message in zip(messages, expected_messages):
+                assert isinstance(message, dict)
+                assert "role" in message
+                assert "content" in message
+                assert message["role"] in MessageRole.__members__.values()
+                assert message["role"] == expected_message["role"]
+                assert isinstance(message["content"], list)
+                assert len(message["content"]) == len(expected_message["content"])
+                for content, expected_content in zip(message["content"], expected_message["content"]):
+                    assert content == expected_content
+
 
 class TestCodeAgent:
     @pytest.mark.parametrize("provide_run_summary", [False, True])