workflows(check_question): Add support for suggest check question

endpoints.py

@@ -1,15 +1,17 @@
-import uvicorn
-from typing import List, Optional
-from pydantic import UUID4, BaseModel
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi import FastAPI, Query
-from .workflows.utils.feedback import Feedback, post_feedback
-from .workflows.til import TilCrew, TilFeedbackResponse
-from .workflows.courses.suggest_expectations import SuggestExpectations, Inputs as SuggestExpectationsInputs, Expectation, Response as SuggestExpectationsResponse
-from .workflows.courses.expectation_revision import ExpectationRevision, Inputs as ExpectationRevisionInputs, Response as ExpectationRevisionResponse
 from dotenv import load_dotenv
 load_dotenv()
 
+from .workflows.courses.expectation_revision import ExpectationRevision, Inputs as ExpectationRevisionInputs, Response as ExpectationRevisionResponse
+from .workflows.courses.suggest_check_question import SuggestCheckQuestion, Inputs as SuggestCheckQuestionInputs, Response as SuggestCheckQuestionResponse
+from .workflows.courses.suggest_expectations import SuggestExpectations, Inputs as SuggestExpectationsInputs, Expectation, Response as SuggestExpectationsResponse
+from .workflows.til import TilCrew, TilFeedbackResponse
+from .workflows.utils.feedback import Feedback, post_feedback
+from fastapi import FastAPI, Query
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import UUID4, BaseModel
+from typing import List, Optional
+import uvicorn
+
 
 description = """
  API helps you do awesome stuff. 🚀
@@ -101,6 +103,26 @@ async def capture_expectation_revision_feedback(run_id: UUID4, feedback: Feedbac
     return "ok"
 
 
+@app.post("/course_learn/suggest_check_question", tags=["course_learn"])
+async def course_learn_suggest_check_question(inputs: SuggestCheckQuestionInputs) -> SuggestCheckQuestionResponse:
+    print("Inputs: ", inputs)
+    result = SuggestCheckQuestion().kickoff(inputs={
+        "course": inputs.course,
+        "module": inputs.module,
+        "tasks": inputs.tasks,
+        "expectation": inputs.expectation,
+    })
+    return result
+
+
+@app.post("/course_learn/suggest_check_question/{run_id}/feedback", tags=["course_learn"])
+async def course_learn_suggest_check_question_feedback(run_id: UUID4, feedback: Feedback) -> str:
+    print("Helful Score: ", feedback.metric_type)
+    print("Feedback On: ", feedback.feedback_on)
+    post_feedback(run_id=run_id, feedback=feedback)
+    return "ok"
+
+
 @app.get("/healthcheck")
 async def read_root():
     return {"status": "ok"}

workflows/courses/suggest_check_question.py

@@ -0,0 +1,62 @@
+from .suggest_expectations import Expectation
+from langchain import hub, callbacks
+from langchain_core.output_parsers import JsonOutputParser
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, UUID4, Field
+from typing import List
+import os
+
+
+class Inputs(BaseModel):
+    course: str
+    module: str
+    tasks: List[str]
+    expectation: str
+
+
+class Response(BaseModel):
+    run_id: UUID4
+    expectation: str
+    check_question: str
+
+class CheckQuestion(BaseModel):
+    check_question: str = Field(
+        description="Targeted question that the course designer have developed to assess the learner's understanding of the learning outcomes.")
+
+
+class SuggestCheckQuestion:
+    def kickoff(self, inputs={}):
+        self.course = inputs["course"]
+        self.module = inputs["module"]
+        self.learning_outcome = inputs["expectation"]
+        self.tasks = inputs["tasks"]
+        llm_response = self._get_check_quesiton()
+        return {
+            "run_id": self.run_id,
+            "expectation": self.learning_outcome,
+            "check_question": llm_response["check_question"]
+        }
+
+    def _get_check_quesiton(self):
+        parser = JsonOutputParser(pydantic_object=CheckQuestion)
+        prompt = hub.pull("course_learn_suggest_check_question")
+        llm = ChatOpenAI(model=os.environ['OPENAI_MODEL'], temperature=0.2)
+        chain = (prompt | llm | parser).with_config({
+            "tags": ["course_learn", "suggest_check_question"], "run_name": "Suggest Module Expectations",
+            "metadata": {
+                "versoin": "v1.0.0",
+                "growth_activity": "course_learn",
+                "env": os.environ["ENV"],
+                "model": os.environ["OPENAI_MODEL"],
+            }
+        })
+
+        with callbacks.collect_runs() as cb:
+            llm_response = chain.invoke({
+                "course": self.course, "module": self.module, "tasks": "* " + ("\n* ".join(self.tasks)),
+                "format_instructions": parser.get_format_instructions(),
+                "learning_outcome": self.learning_outcome
+            })
+            self.run_id = cb.traced_runs[0].id
+
+        return llm_response