Add staging url support for existing endpoints

endpoints.py

@@ -1,19 +1,20 @@
 from dotenv import load_dotenv
 load_dotenv()
 
-from fastapi import FastAPI, Query
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import UUID4
-from typing import List
-from tempenv import TemporaryEnvironment
 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.til_v2 import TilV2, TilV2FeedbackResponse
+from .workflows.til.analyse_til import TilCrew, TilFeedbackResponse
+from .workflows.til.analyse_til_v2 import AnalyseTilV2, TilV2FeedbackResponse
 from .workflows.utils.feedback import Feedback, post_feedback
+from fastapi import FastAPI, Query
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import UUID4
+from tempenv import TemporaryEnvironment
+from typing import List
 import uvicorn
 
+
 LANGSMITH_STAGING_PROJECT = "customer_agent"
 LANGSMITH_PROD_PROJECT = "growthy-agents"
 
@@ -60,36 +61,54 @@ async def til_feedback_kickoff(content: List[str]) -> TilFeedbackResponse:
     return result
 
 
-def til_v2_logic(llm_model, langsmith_project, content) -> TilV2FeedbackResponse:
+@app.post("/til_feedback/{run_id}/feedback", tags=["til_feedback"])
+async def capture_feedback(run_id: UUID4, feedback: Feedback) -> str:
+    print("Metric Type: ", feedback.metric_type)
+    print("Feedback On: ", feedback.feedback_on)
+    post_feedback(run_id=run_id, feedback=feedback)
+    return "ok"
+
+
+@app.post("/staging/til_feedback", tags=["til_feedback", "staging"])
+async def staging_til_feedback_kickoff(content: List[str]) -> TilFeedbackResponse:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT, "OPENAI_MODEL": "gpt-4o-mini"}):
+        separator = "\n* "
+        content[0] = "* " + content[0]
+        inputs = {"content": separator.join(content)}
+        result = TilCrew().kickoff(inputs)
+        return result
+
+
+@app.post("/staging/til_feedback/{run_id}/feedback", tags=["til_feedback", "staging"])
+async def staging_capture_feedback(run_id: UUID4, feedback: Feedback) -> str:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT, "OPENAI_MODEL": "gpt-4o-mini"}):
+        print("Metric Type: ", feedback.metric_type)
+        print("Feedback On: ", feedback.feedback_on)
+        post_feedback(run_id=run_id, feedback=feedback)
+        return "ok"
+
+
+def til_v2_logic(content) -> TilV2FeedbackResponse:
     separator = "\n* "
     content[0] = "* " + content[0]
     inputs = {"content": separator.join(content)}
-    result = TilV2(llm_model, langsmith_project).kickoff(inputs)
+    result = AnalyseTilV2().kickoff(inputs)
     return result
 
 
 @app.post("/v2/til_feedback", tags=["til_feedback"])
 async def til_v2_feedback_kickoff(content: List[str]) -> TilV2FeedbackResponse:
-    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_PROD_PROJECT}):
-        return til_v2_logic("gpt-4o", "growthy-agents", content)
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_PROD_PROJECT, "OPENAI_MODEL": "gpt-4o"}):
+        return til_v2_logic(content)
 
 
 @app.post("/staging/v2/til_feedback", tags=["til_feedback", "staging"])
-async def til_v2_feedback_kickoff(content: List[str]) -> TilV2FeedbackResponse:
-    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT}):
-        return til_v2_logic("gpt-4o-mini", "customer_agent", content)
-
-
-@app.post("/til_feedback/{run_id}/feedback", tags=["til_feedback"])
-async def capture_feedback(run_id: UUID4, feedback: Feedback) -> str:
-    print("Metric Type: ", feedback.metric_type)
-    print("Feedback On: ", feedback.feedback_on)
-    post_feedback(run_id=run_id, feedback=feedback)
-    return "ok"
+async def staging_til_v2_feedback_kickoff(content: List[str]) -> TilV2FeedbackResponse:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT, "OPENAI_MODEL": "gpt-4o-mini"}):
+        return til_v2_logic(content)
 
 
-@app.post("/course_learn/suggest_expectations", tags=["course_learn"])
-async def course_learn_suggest_expectations(inputs: SuggestExpectationsInputs) -> SuggestExpectationsResponse:
+def course_learn_suggest_expectations_logic(inputs) -> SuggestExpectationsResponse:
     print("Inputs: ", inputs)
     result = SuggestExpectations().kickoff(inputs={
         "course": inputs.course,
@@ -100,16 +119,38 @@ async def course_learn_suggest_expectations(inputs: SuggestExpectationsInputs) -
     return result
 
 
-@app.post("/course_learn/suggest_expectations/{run_id}/feedback", tags=["course_learn"])
-async def capture_suggest_expectations_feedback(run_id: UUID4, feedback: Feedback) -> str:
+def course_learn_suggest_expectations_feedback_logic(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.post("/course_learn/expectation_revision", tags=["course_learn"])
-async def course_learn_expectation_revision(inputs: ExpectationRevisionInputs) -> ExpectationRevisionResponse:
+@app.post("/course_learn/suggest_expectations", tags=["course_learn"])
+async def course_learn_suggest_expectations(inputs: SuggestExpectationsInputs) -> SuggestExpectationsResponse:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_PROD_PROJECT, "OPENAI_MODEL": "gpt-4o"}):
+        return course_learn_suggest_expectations_logic(inputs)
+
+
+@app.post("/staging/course_learn/suggest_expectations", tags=["course_learn", "staging"])
+async def staging_course_learn_suggest_expectations(inputs: SuggestExpectationsInputs) -> SuggestExpectationsResponse:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT, "OPENAI_MODEL": "gpt-4o-mini"}):
+        return course_learn_suggest_expectations_logic(inputs)
+
+
+@app.post("/course_learn/suggest_expectations/{run_id}/feedback", tags=["course_learn"])
+async def capture_suggest_expectations_feedback(run_id: UUID4, feedback: Feedback) -> str:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_PROD_PROJECT, "OPENAI_MODEL": "gpt-4o"}):
+        return course_learn_suggest_expectations_feedback_logic(run_id, feedback)
+
+
+@app.post("/staging/course_learn/suggest_expectations/{run_id}/feedback", tags=["course_learn", "staging"])
+async def staging_capture_suggest_expectations_feedback(run_id: UUID4, feedback: Feedback) -> str:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT, "OPENAI_MODEL": "gpt-4o-mini"}):
+        return course_learn_suggest_expectations_feedback_logic(run_id, feedback)
+
+
+def course_learn_expectation_revision_logic(inputs: ExpectationRevisionInputs) -> ExpectationRevisionResponse:
     print("Inputs: ", inputs)
     result = ExpectationRevision().kickoff(inputs={
         "expectation": inputs.expectation,
@@ -119,16 +160,38 @@ async def course_learn_expectation_revision(inputs: ExpectationRevisionInputs) -
     return result
 
 
-@app.post("/course_learn/expectation_revision/{run_id}/feedback", tags=["course_learn"])
-async def capture_expectation_revision_feedback(run_id: UUID4, feedback: Feedback) -> str:
+@app.post("/course_learn/expectation_revision", tags=["course_learn"])
+async def course_learn_expectation_revision(inputs: ExpectationRevisionInputs) -> ExpectationRevisionResponse:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_PROD_PROJECT, "OPENAI_MODEL": "gpt-4o"}):
+        return course_learn_expectation_revision_logic(inputs)
+
+
+@app.post("/staging/course_learn/expectation_revision", tags=["course_learn", "staging"])
+async def staging_course_learn_expectation_revision(inputs: ExpectationRevisionInputs) -> ExpectationRevisionResponse:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT, "OPENAI_MODEL": "gpt-4o-mini"}):
+        return course_learn_expectation_revision_logic(inputs)
+
+
+def capture_expectation_revision_feedback_logic(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.post("/course_learn/suggest_check_question", tags=["course_learn"])
-async def course_learn_suggest_check_question(inputs: SuggestCheckQuestionInputs) -> SuggestCheckQuestionResponse:
+@app.post("/course_learn/expectation_revision/{run_id}/feedback", tags=["course_learn"])
+async def capture_expectation_revision_feedback(run_id: UUID4, feedback: Feedback) -> str:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_PROD_PROJECT, "OPENAI_MODEL": "gpt-4o"}):
+        return capture_expectation_revision_feedback_logic(run_id, feedback)
+
+
+@app.post("/staging/course_learn/expectation_revision/{run_id}/feedback", tags=["course_learn", "staging"])
+async def staging_capture_expectation_revision_feedback(run_id: UUID4, feedback: Feedback) -> str:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT, "OPENAI_MODEL": "gpt-4o-mini"}):
+        return capture_expectation_revision_feedback_logic(run_id, feedback)
+
+
+def course_learn_suggest_check_question_logic(inputs: SuggestCheckQuestionInputs) -> SuggestCheckQuestionResponse:
     print("Inputs: ", inputs)
     result = SuggestCheckQuestion().kickoff(inputs={
         "course": inputs.course,
@@ -139,14 +202,37 @@ async def course_learn_suggest_check_question(inputs: SuggestCheckQuestionInputs
     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:
+@app.post("/course_learn/suggest_check_question", tags=["course_learn"])
+async def course_learn_suggest_check_question(inputs: SuggestCheckQuestionInputs) -> SuggestCheckQuestionResponse:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_PROD_PROJECT, "OPENAI_MODEL": "gpt-4o"}):
+        return course_learn_suggest_check_question_logic(inputs)
+
+
+@app.post("/staging/course_learn/suggest_check_question", tags=["course_learn", "staging"])
+async def staging_course_learn_suggest_check_question(inputs: SuggestCheckQuestionInputs) -> SuggestCheckQuestionResponse:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT, "OPENAI_MODEL": "gpt-4o-mini"}):
+        return course_learn_suggest_check_question_logic(inputs)
+
+
+def course_learn_suggest_check_question_feedback_logic(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.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:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_PROD_PROJECT, "OPENAI_MODEL": "gpt-4o"}):
+        return course_learn_suggest_check_question_feedback_logic(run_id, feedback)
+
+
+@app.post("/staging/course_learn/suggest_check_question/{run_id}/feedback", tags=["course_learn", "staging"])
+async def staging_course_learn_suggest_check_question_feedback(run_id: UUID4, feedback: Feedback) -> str:
+    with TemporaryEnvironment({"LANGCHAIN_PROJECT": LANGSMITH_STAGING_PROJECT, "OPENAI_MODEL": "gpt-4o-mini"}):
+        return course_learn_suggest_check_question_feedback_logic(run_id, feedback)
+
+
 @app.get("/healthcheck")
 async def read_root():
     return {"status": "ok"}

requirements.txt

@@ -18,6 +18,7 @@ python-dotenv
 semanticscholar
 streamlit
 streamlit-extras
+streamlit_router
 tavily-python
 tempenv
 unstructured

ui/til_feedback.py

@@ -1,8 +1,7 @@
 import streamlit as st
 from dotenv import load_dotenv
-from workflows.til import TilCrew
+from workflows.til.analyse_til import TilCrew
 from streamlit_extras.capture import stdout
-from langsmith import Client
 from workflows.utils.feedback import Feedback
 
 load_dotenv()
@@ -21,7 +20,7 @@ def feedback_main():
         [data-testid="stToolbar"]{
             right: 2rem;
         }
-       
+
 
         </style>
         '''
@@ -86,7 +85,7 @@ def feedback_main():
                     st.markdown(f"**Reason:** {result['reason']}")
                     if result.get('suggestion') is not None:
                         st.markdown(f"**Suggestion:** {result['suggestion']}")
-         
+
                 feedback_key = result['til'].replace(' ', '_')
                 feedback_given_key = f"{feedback_key}_feedback_given"
 
@@ -111,14 +110,14 @@ def give_feedback(feedback_key, is_helpful):
         feedback_data = Feedback(
             metric_type=metric_type,
             metric_score=metric_score,
-            feedback_on=feedback_key.replace('_', ' ').title() 
+            feedback_on=feedback_key.replace('_', ' ').title()
         )
         try:
             TilCrew.post_feedback(run_id, feedback_data)
             st.success("Feedback submitted successfully!")
         except Exception as e:
             st.error(f"Failed to submit feedback: {e}")
-        
+
         st.session_state[f"{feedback_key}_feedback_given"] = True
 
 def clear_feedback_state(results):
@@ -128,4 +127,4 @@ def clear_feedback_state(results):
 
 
 if __name__ == "__main__":
-    feedback_main()
+    feedback_main()

workflows/{til.py → til/analyse_til.py}

@@ -5,10 +5,8 @@ from langchain_core.messages import SystemMessage
 from langchain_core.output_parsers import JsonOutputParser
 from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, PromptTemplate
 from langchain_openai import ChatOpenAI
-from langsmith import Client
 from pydantic import BaseModel, Field, UUID4
 from typing import List, Optional
-from .utils.feedback import Feedback
 import os
 import pprint
 

workflows/{til_v2.py → til/analyse_til_v2.py}

@@ -9,11 +9,7 @@ import os
 import pprint
 
 
-class TilV2:
-
-    def __init__(self, llm_model, langsmith_project):
-        self.llm_model = llm_model
-        self.langsmith_project = langsmith_project
+class AnalyseTilV2:
 
     def kickoff(self, inputs={}):
         print("Human Message:")
@@ -89,14 +85,14 @@ class TilV2:
         ])
         print("Prompt: ")
         pprint.pp(feedback_prompt, width=80)
-        llm = ChatOpenAI(model=self.llm_model, temperature=0.2)
+        llm = ChatOpenAI(model=os.environ["OPENAI_MODEL"], temperature=0.2)
         analysis_chain = (feedback_prompt | llm | feedback_parser).with_config({
             "tags": ["til"], "run_name": "Analysing TIL v2",
             "metadata": {
                 "versoin": "v2.0.0",
                 "growth_activity": "til",
                 "env": os.environ["ENV"],
-                "model": self.llm_model,
+                "model": os.environ["OPENAI_MODEL"]
             }
         })