improve deep research use case

templates/components/agents/python/deep_research/app/workflows/agents.py

@@ -16,7 +16,10 @@ class AnalysisDecision(BaseModel):
         description="Whether to continue research, write a report, or cancel the research after several retries"
     )
     research_questions: Optional[List[str]] = Field(
-        description="Questions to research if continuing research. Maximum 3 questions. Set to null or empty if writing a report.",
+        description="""
+        If the decision is to research, provide a list of questions to research that related to the user request.
+        Maximum 3 questions. Set to null or empty if writing a report or cancel the research.
+        """,
         default_factory=list,
     )
     cancel_reason: Optional[str] = Field(
@@ -29,32 +32,53 @@ async def plan_research(
     memory: SimpleComposableMemory,
     context_nodes: List[Node],
     user_request: str,
+    total_questions: int,
 ) -> AnalysisDecision:
-    analyze_prompt = PromptTemplate(
-        """
+    analyze_prompt = """
       You are a professor who is guiding a researcher to research a specific request/problem.
       Your task is to decide on a research plan for the researcher.
+
       The possible actions are:
       + Provide a list of questions for the researcher to investigate, with the purpose of clarifying the request.
       + Write a report if the researcher has already gathered enough research on the topic and can resolve the initial request.
       + Cancel the research if most of the answers from researchers indicate there is insufficient information to research the request. Do not attempt more than 3 research iterations or too many questions.
+
       The workflow should be:
       + Always begin by providing some initial questions for the researcher to investigate.
       + Analyze the provided answers against the initial topic/request. If the answers are insufficient to resolve the initial request, provide additional questions for the researcher to investigate.
       + If the answers are sufficient to resolve the initial request, instruct the researcher to write a report.
-      <User request>
-      {user_request}
-      </User request>
 
+      Here are the context: 
       <Collected information>
       {context_str}
       </Collected information>
 
       <Conversation context>
       {conversation_context}
       </Conversation context>
+
+      {enhanced_prompt}
+
+      Now, provide your decision in the required format for this user request:
+      <User request>
+      {user_request}
+      </User request>
       """
-    )
+    # Manually craft the prompt to avoid LLM hallucination
+    enhanced_prompt = ""
+    if total_questions == 0:
+        # Avoid writing a report without any research context
+        enhanced_prompt = """
+        
+        The student has no questions to research. Let start by asking some questions.
+        """
+    elif total_questions > 6:
+        # Avoid asking too many questions (when the data is not ready for writing a report)
+        enhanced_prompt = """
+
+        The student has researched {total_questions} questions. Should cancel the research if the context is not enough to write a report.
+        """
+
     conversation_context = "\n".join(
         [f"{message.role}: {message.content}" for message in memory.get_all()]
     )
@@ -63,10 +87,11 @@ async def plan_research(
     )
     res = await Settings.llm.astructured_predict(
         output_cls=AnalysisDecision,
-        prompt=analyze_prompt,
+        prompt=PromptTemplate(template=analyze_prompt),
         user_request=user_request,
         context_str=context_str,
         conversation_context=conversation_context,
+        enhanced_prompt=enhanced_prompt,
     )
     return res
 

templates/components/agents/python/deep_research/app/workflows/deep_research.py

@@ -89,10 +89,11 @@ def __init__(
         )
 
     @step
-    def retrieve(self, ctx: Context, ev: StartEvent) -> PlanResearchEvent:
+    async def retrieve(self, ctx: Context, ev: StartEvent) -> PlanResearchEvent:
         """
         Initiate the workflow: memory, tools, agent
         """
+        await ctx.set("total_questions", 0)
         self.user_request = ev.get("input")
         self.memory.put_messages(
             messages=[
@@ -132,9 +133,7 @@ def retrieve(self, ctx: Context, ev: StartEvent) -> PlanResearchEvent:
                 nodes=nodes,
             )
         )
-        return PlanResearchEvent(
-            context_nodes=self.context_nodes,
-        )
+        return PlanResearchEvent()
 
     @step
     async def analyze(
@@ -153,10 +152,12 @@ async def analyze(
                 },
             )
         )
+        total_questions = await ctx.get("total_questions")
         res = await plan_research(
             memory=self.memory,
             context_nodes=self.context_nodes,
             user_request=self.user_request,
+            total_questions=total_questions,
         )
         if res.decision == "cancel":
             ctx.write_event_to_stream(
@@ -172,6 +173,22 @@ async def analyze(
                 result=res.cancel_reason,
             )
         elif res.decision == "write":
+            # Writing a report without any research context is not allowed.
+            # It's a LLM hallucination.
+            if total_questions == 0:
+                ctx.write_event_to_stream(
+                    DataEvent(
+                        type="deep_research_event",
+                        data={
+                            "event": "analyze",
+                            "state": "done",
+                        },
+                    )
+                )
+                return StopEvent(
+                    result="Sorry, I have a problem when analyzing the retrieved information. Please try again.",
+                )
+
             self.memory.put(
                 message=ChatMessage(
                     role=MessageRole.ASSISTANT,
@@ -180,7 +197,11 @@ async def analyze(
             )
             ctx.send_event(ReportEvent())
         else:
-            await ctx.set("n_questions", len(res.research_questions))
+            total_questions += len(res.research_questions)
+            await ctx.set("total_questions", total_questions)  # For tracking
+            await ctx.set(
+                "waiting_questions", len(res.research_questions)
+            )  # For waiting questions to be answered
             self.memory.put(
                 message=ChatMessage(
                     role=MessageRole.ASSISTANT,
@@ -270,7 +291,7 @@ async def collect_answers(
         """
         Collect answers to all questions
         """
-        num_questions = await ctx.get("n_questions")
+        num_questions = await ctx.get("waiting_questions")
         results = ctx.collect_events(
             ev,
             expected=[CollectAnswersEvent] * num_questions,
@@ -284,7 +305,7 @@ async def collect_answers(
                     content=f"<Question>{result.question}</Question>\n<Answer>{result.answer}</Answer>",
                 )
             )
-        await ctx.set("n_questions", 0)
+        await ctx.set("waiting_questions", 0)
         self.memory.put(
             message=ChatMessage(
                 role=MessageRole.ASSISTANT,