workflows(course_learn): Add support for existing expectations

endpoints.py

@@ -1,14 +1,15 @@
 from dotenv import load_dotenv
-import uvicorn
-from fastapi import FastAPI, Query
-from .workflows.til import TilCrew, TilFeedbackResponse
-from .workflows.courses.suggest_expectations import SuggestExpectations, Inputs as SuggestExpectationsInputs, Expectations, Expectation
+load_dotenv()
+
 from .workflows.courses.expectation_revision import ExpectationRevision, Inputs as ExpectationRevisionInputs
+from .workflows.courses.suggest_expectations import SuggestExpectations, Inputs as SuggestExpectationsInputs, Expectations, Expectation
+from .workflows.til import TilCrew, TilFeedbackResponse
 from .workflows.utils.feedback import Feedback
+from fastapi import FastAPI, Query
 from fastapi.middleware.cors import CORSMiddleware
-from typing import List, Optional
 from pydantic import UUID4, BaseModel
-load_dotenv()
+from typing import List, Optional
+import uvicorn
 
 description = """
  API helps you do awesome stuff. 🚀
@@ -65,6 +66,7 @@ async def course_learn_suggest_expectations(inputs: SuggestExpectationsInputs) -
         "course": inputs.course,
         "module": inputs.module,
         "tasks": inputs.tasks,
+        "existing_expectations": inputs.existing_expectations,
     })
     return result
 

ui/course_learn_suggest_expectations.py

@@ -1,5 +1,5 @@
 import streamlit as st
-from workflows.courses.suggest_expectations import SuggestExpectations
+from workflows.courses.suggest_expectations import SuggestExpectations, Inputs as ExpectationsInputs, Expectation
 from streamlit_extras.stylable_container import stylable_container
 from workflows.courses.expectation_revision import ExpectationRevision
 
@@ -64,7 +64,9 @@ def main():
             "Watch this video https://youtu.be/BHwzDmr6d7s?si=sfFYnd73y9w0EjGB to understand SQL execution order and some optimization techniques.",
             "Watch this video https://youtu.be/FoznjTU929c?si=6M3xUIUwAxE6EbKS to understand SQL explain command usage.",
             "Go over these articles https://intellipaat.com/blog/sql-optimization-techniques/ & https://www.thoughtspot.com/data-trends/data-modeling/optimizing-sql-queries  to understand various query optimization techniques."
-        ]
+        ],
+        "expectation": "Should understand the importance of indexing in query optimization.",
+        "check_question": "How does indexing improve query performance?"
     }
 
     st.session_state_inputs = st.session_state_inputs if 'session_state' in st.session_state else default_content
@@ -78,10 +80,22 @@ def main():
                                help="Enter the name of the module", value=st.session_state_inputs["module"])
         tasks = st.text_area("##### Tasks", key="tasks", value="\n".join(
             st.session_state_inputs["tasks"]), help="Enter the tasks to be covered in the module")
+        existing_expectation = st.text_input(
+            "##### Existing Expectation", key="existing_expectation", value=st.session_state_inputs["expectation"])
+        existing_check_question = st.text_input(
+            "##### Existing Check Question", key="existing_check_question", value=st.session_state_inputs["check_question"])
 
         suggester = SuggestExpectations()
-        inputs = {"course": course, "module": module,
-                  "tasks": tasks.split('\n')}
+        inputs = {
+            "course": course, "module": module,
+            "tasks": tasks.split('\n'),
+            "existing_expectations": [
+                Expectation(
+                    expectation=existing_expectation,
+                    check_question=existing_check_question
+                )
+            ]
+        }
 
         if st.button("Get Suggestions", key="suggestions", help="Click to get suggestions"):
             responses = suggester.kickoff(inputs=inputs)

workflows/courses/suggest_expectations.py

@@ -7,23 +7,30 @@ from pydantic import BaseModel
 from typing import List, Optional
 import os
 
+
 class Expectation(BaseModel):
-    expectation: str = Field(description="The learning outcome that the course designer has identified for the learner to demonstrate upon successful completion of the module.")
-    check_question: str = Field(description="Targeted question that the course designer have developed to assess the learner's understanding of the learning outcomes.")
+    expectation: str = Field(
+        description="The learning outcome that the course designer has identified for the learner to demonstrate upon successful completion of the module.")
+    check_question: str = Field(
+        description="Targeted question that the course designer have developed to assess the learner's understanding of the learning outcomes.")
+
 
 class Expectations(BaseModel):
     expectations: List[Expectation]
 
+
 class Inputs(BaseModel):
     course: str
     module: str
     tasks: List[str]
+    existing_expectations: Optional[List[Expectation]]
 
 
 class SuggestExpectations:
     def kickoff(self, inputs={}):
         self.course = inputs["course"]
         self.module = inputs["module"]
+        self.existing_expectations = inputs["existing_expectations"]
         self.tasks = inputs["tasks"]
         return self._get_suggestions()
 
@@ -33,7 +40,7 @@ class SuggestExpectations:
         llm = ChatOpenAI(model=os.environ['OPENAI_MODEL'], temperature=0.2)
         chain = (prompt | llm | parser).with_config({
             "tags": ["course_learn", "suggest_expectations"], "run_name": "Suggest Module Expectations",
-            "metadata" : {
+            "metadata": {
                 "versoin": "v1.0.0",
                 "growth_activity": "course_learn",
                 "env": os.environ["ENV"],
@@ -41,10 +48,19 @@ class SuggestExpectations:
             }
         })
 
+        # Existing Expectations
+        existing_expectations = []
+        for expectation in self.existing_expectations:
+            existing_expectations.append(f"""
+            Learning Outcome: {expectation.expectation}
+            Check Question: {expectation.check_question}
+                                       """)
+
         response = chain.invoke({
             "course": self.course, "module": self.module, "tasks": "* " + ("\n* ".join(self.tasks)),
-            "format_instructions": parser.get_format_instructions()
-          })
+            "format_instructions": parser.get_format_instructions(),
+            "existing_expectations": "\n".join(existing_expectations)
+        })
 
         return response