endpoints(course_learn): Add support for expectation_revision & suggest_expectation

endpoints.py

@@ -2,6 +2,8 @@ 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
+from .workflows.courses.expectation_revision import ExpectationRevision, Inputs as ExpectationRevisionInputs
 from .workflows.utils.feedback import Feedback
 from fastapi.middleware.cors import CORSMiddleware
 from typing import List, Optional
@@ -52,6 +54,18 @@ async def capture_feedback(run_id: UUID4, feedback: Feedback) -> str:
     TilCrew.post_feedback(run_id=run_id, feedback=feedback)
     return "ok"
 
+@app.post("course_learn/suggest_expectations", tags=["course_learn"])
+async def course_learn_suggest_expectations(inputs: SuggestExpectationsInputs) -> Expectations:
+    print("Inputs: ", inputs)
+    result = SuggestExpectations().kickoff(inputs=inputs)
+    return result
+
+@app.post("course_learn/expectation_revision", tags=["course_learn"])
+async def course_learn_suggest_expectations(inputs: ExpectationRevisionInputs) -> Expectation:
+    print("Inputs: ", inputs)
+    result = ExpectationRevision().kickoff(inputs=inputs)
+    return result
+
 @app.get("/healthcheck")
 async def read_root():
      return {"status": "ok"}

workflows/courses/expectation_revision.py

@@ -1,9 +1,16 @@
-from langchain_core.output_parsers import JsonOutputParser
-from suggest_expectations import Expectation
 from langchain import hub
+from langchain_core.output_parsers import JsonOutputParser
 from langchain_openai import ChatOpenAI
+from pydantic import BaseModel
+from workflows.courses.suggest_expectations import Expectation
+from typing import List, Optional
 import os
 
+class Inputs(BaseModel):
+    expectation: str
+    check_question: str
+    request: str
+
 class ExpectationRevision:
     def kickoff(self, inputs={}):
         self.learning_outcome = inputs["expectation"]
@@ -33,10 +40,11 @@ class ExpectationRevision:
         return response
 
 
-rework = ExpectationRevision()
-response = rework.kickoff(inputs={
-    "expectation": "Recognize the importance of query rewriting and how to transform inefficient queries into more efficient ones.",
-    "check_question": "Can you provide an example of a poorly written SQL query and demonstrate how you would rewrite it to optimize its performance?",
-    "request": "Can you provide the poorly written SQL query that user has to rewrite"
-})
-print(response)
+# Example usage
+# rework = ExpectationRevision()
+# response = rework.kickoff(inputs={
+#     "expectation": "Recognize the importance of query rewriting and how to transform inefficient queries into more efficient ones.",
+#     "check_question": "Can you provide an example of a poorly written SQL query and demonstrate how you would rewrite it to optimize its performance?",
+#     "request": "Can you provide the poorly written SQL query that user has to rewrite"
+# })
+# print(response)

workflows/courses/suggest_expectations.py

@@ -1,10 +1,10 @@
-from dotenv import load_dotenv
-load_dotenv()
 from langchain import hub
 from langchain_core.output_parsers import JsonOutputParser
 from langchain_openai import ChatOpenAI
 from pydantic import BaseModel, Field
 from typing import List
+from pydantic import BaseModel
+from typing import List, Optional
 import os
 
 class Expectation(BaseModel):
@@ -14,6 +14,11 @@ class Expectation(BaseModel):
 class Expectations(BaseModel):
     expectations: List[Expectation]
 
+class Inputs(BaseModel):
+    course: str
+    module: str
+    tasks: List[str]
+
 
 class SuggestExpectations:
     def kickoff(self, inputs={}):
@@ -44,14 +49,15 @@ class SuggestExpectations:
         return response
 
 
-suggester = SuggestExpectations()
-response = suggester.kickoff(inputs={
-    "course": "SQL",
-    "module": "Query Optimization Techniques",
-    "tasks": [
-        "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."
-    ]
-})
-print(response)
+# Example usage
+# suggester = SuggestExpectations()
+# response = suggester.kickoff(inputs={
+#     "course": "SQL",
+#     "module": "Query Optimization Techniques",
+#     "tasks": [
+#         "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."
+#     ]
+# })
+# print(response)

workflows/utils/feedback.py

@@ -2,7 +2,6 @@ from pydantic import BaseModel
 from typing import List, Optional
 
 class Feedback(BaseModel):
-    helpful_score: Optional[float]=None
     metric_type: Optional[str]
     metric_score: Optional[float]
     feedback_on: Optional[str]