can now recommend a single goal

app.py

@@ -32,7 +32,7 @@ def recommend_plan_steps():
     return recommend(request.values);
 
 #from plan.recommend import recommend
-#recommend();
+#recommend({});
 
 #from train.faq import train
 #train();

plan/recommend.py

@@ -4,16 +4,11 @@ def recommend(payload):
 
     from langchain_core.pydantic_v1 import BaseModel, Field
 
-    class Goals(BaseModel):
+    class Goal(BaseModel):
         """Information about a suggested goal."""
 
-        goal: Optional[str] = Field(default=None, description="a SMART target for the user")
-        timeframe: Optional[str] = Field(default=None, description="whether the goal is now, soon, or during")
-
-    class GoalList(BaseModel):
-        """A list of goals the user should accomplish to achieve an ambition."""
-
-        response: List[Goals] = Field(description="A list of goals the user should accomplish to achieve an ambition")
+        goal: Optional[str] = Field(default=None, description="a SMART target for the user - this should be as concise as possible")
+        rationale: Optional[str] = Field(default=None, description="explain why you have chosen this goal")
 
     from typing import Optional
 
@@ -27,34 +22,37 @@ def recommend(payload):
             (
                 "system",
                 """
-                    You are an advice agent supporting students at a post-92 university in England.
-                    Your advice should be tailored to the context
+                    You are an advice agent supporting students at a post-92 university in England - your advice should be tailored to the context.
 
-                    The user will tell you one of their ambitions - this might be a career aim, a personal aspiration, or something to do with their time at university.
-                    You should provide a list of six things that they could do to help them to achieve that ambition.
-                    These should take the form of SMART targets.
+                    The user will provide an ambition and you will provide a single SMART target that will help them to reach that ambition.
 
-                    You should recommend three targets to do now, two to do soon and one to do during their time at university
-                    They should be achievable before they graduate.
-                    You should not recommend that they graduate with a degree other than the one they are doing
-                    But you can suggest follow on courses, such as Masters degrees or professional qualifications.
+                    They will also provide a timescale for the desired goal:
 
-                    The user is currently studying B.A Graphic Design. They are in their second year, and have two years left.
+                    - Now: you should recommend a goal that can easily be accomplished immediately
+                    - Soon: you should recommend a goal that can be accomplished within the next 3 to 6 months, but that might require some research and planning
+                    - During: recommend a goal that can be completed before the student finishes their degree. This could require a significant amount of work and planning.
+                            
+                    Here is the user's ambition: {ambition}
+                    They would like a goal that can be accomplished: {bucket}.
+                    
                 """,
             ),
-            ("human", "{text}"),
         ]
     )
 
     llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0.5);
 
-    runnable = prompt | llm.with_structured_output(schema=GoalList)
+    runnable = prompt | llm.with_structured_output(schema=Goal)
 
-    input = payload.get("input") or ""
-    response = runnable.invoke({"text": input})
+    input = {
+        "ambition": payload.get("ambition") or "",
+        "bucket": payload.get("bucket") or "",
+    }
     
-    print(input)
+    print(input);
 
-    print(response.dict()['response']);
+    response = runnable.invoke(input)
+    
+    print(response.dict());
 
-    return response.dict()['response'];
+    return response.dict();