Set Env variables + prompt improvements

.github/workflows/dev.yml

@@ -75,6 +75,8 @@ jobs:
     needs: test
     with:
       template-repository-name: "lambda-feedback/chat-function-boilerplate"
+      # allow for developer to specify the environment variables that are used by the deployed AWS Lambda. Default to mock then admin can update.
+      deployed-environment-variables: '["OPENAI_API_KEY","OPENAI_MODEL","GOOGLE_AI_API_KEY","GOOGLE_AI_MODEL"]'
     permissions:
       contents: read
       id-token: write

.github/workflows/main.yml

@@ -75,6 +75,8 @@ jobs:
     needs: test
     with:
       template-repository-name: "lambda-feedback/chat-function-boilerplate"
+      # allow for developer to specify the environment variables that are used by the deployed AWS Lambda. Default to mock then admin can update.
+      # deployed-environment-variables: '["OPENAI_API_KEY","OPENAI_MODEL","GOOGLE_AI_API_KEY","GOOGLE_AI_MODEL"]'
     permissions:
       contents: read
       id-token: write

src/agents/llm_factory.py

@@ -7,6 +7,8 @@
 from langchain_openai import ChatOpenAI
 from langchain_openai import OpenAIEmbeddings
 from langchain_google_genai import ChatGoogleGenerativeAI
+from dotenv import load_dotenv
+load_dotenv()
 
 class AzureLLMs:
     def __init__(self, temperature: int = 0):

src/agents/utils/parse_json_to_prompt.py

@@ -1,5 +1,3 @@
-""" File not to be modified. This file contains the conversion logic between the agent API and the Lambda Feedback backend."""
-
 from typing import List, Optional, Union, Dict
 
 # questionSubmissionSummary type
@@ -85,13 +83,21 @@ def __init__(
 class QuestionDetails:
     def __init__(
         self,
+        setNumber: Optional[int] = None,
+        setName: Optional[str] = None,
+        setDescription: Optional[str] = None,
+        questionNumber: Optional[int] = None,
         questionTitle: Optional[str] = None,
         questionGuidance: Optional[str] = None,
         questionContent: Optional[str] = None,
         durationLowerBound: Optional[int] = None,
         durationUpperBound: Optional[int] = None,
         parts: Optional[List[PartDetails]] = [],
     ):
+        self.setNumber = setNumber
+        self.setName = setName
+        self.setDescription = setDescription
+        self.questionNumber = questionNumber
         self.questionTitle = questionTitle
         self.questionGuidance = questionGuidance
         self.questionContent = questionContent
@@ -161,7 +167,7 @@ def format_response_area_details(responseArea: ResponseAreaDetails, studentSumma
         {submissionDetails}"""
 
     def format_part_details(part: PartDetails, currentPart: CurrentPart, summary: List[StudentWorkResponseArea]) -> str:
-        if not part or not part.publishedResponseAreas:
+        if not part:
             return ''
 
         responseAreas = "\n".join(
@@ -187,9 +193,9 @@ def format_part_details(part: PartDetails, currentPart: CurrentPart, summary: Li
 """
 
     questionDetails = f"""This is the question I am currently working on. I am currently working on Part ({convert_index_to_lowercase_letter(questionAccessInformation.currentPart.position)}). Below, you'll find its details, including the parts of the question, my responses for each response area, and the feedback I received. This information highlights my efforts and progress so far. Use this this information to inform your understanding about the question materials provided to me and my work on them.
-    Maths equations are in KaTex format, preserve them the same.
-
-# Question: {questionInformation.questionTitle};
+    Maths equations are in KaTex format, preserve them the same. Use British English spellings.
+{f'# Question Set {questionInformation.setNumber + 1}: {questionInformation.setName};' if questionInformation.setName and questionInformation.setNumber else ''}
+# Question{f' {questionInformation.setNumber + 1}.{questionInformation.questionNumber + 1}' if questionInformation.setNumber and questionInformation.questionNumber else ''}: {questionInformation.questionTitle};
     Guidance to Solve the Question: {questionInformation.questionGuidance or 'None'};
     Description of Question: {questionInformation.questionContent};
     Expected Time to Complete the Question: {f'{questionInformation.durationLowerBound} - {questionInformation.durationUpperBound} min;' if questionInformation.durationLowerBound and questionInformation.durationUpperBound else 'No specified duration.'}

src/agents/utils/synthetic_conversation_generation.py

@@ -23,11 +23,11 @@
 try:
   from ..student_agent.student_agent import invoke_student_agent
   from .parse_json_to_prompt import parse_json_to_prompt
-  from ..base_agent import invoke_base_agent
+  from ..base_agent.base_agent import invoke_base_agent
 except ImportError:
   from src.agents.student_agent.student_agent import invoke_student_agent
   from src.agents.utils.parse_json_to_prompt import parse_json_to_prompt
-  from src.agents.base_agent import invoke_base_agent
+  from src.agents.base_agent.base_agent import invoke_base_agent
 import os
 
 
@@ -70,11 +70,11 @@ def generate_synthetic_conversations(raw_text: str, num_turns: int, student_agen
       # Student starts
       student_response = invoke_student_agent(message, conversation_history[:-1], summary, student_agent_type, question_response_details_prompt, conversation_id)
       conversation_history.append({
-        "role": "assistant",
+        "role": "user",
         "content": student_response["output"]
       })
     else:
-      tutor_response = invoke_tutor_agent(message, conversation_history[:-1], summary, conversational_style, question_response_details_prompt, conversation_id)
+      tutor_response = invoke_tutor_agent(message, conversation_history, summary, conversational_style, question_response_details_prompt, conversation_id)
       conversation_history.append({
         "role": "assistant",
         "content": tutor_response["output"]
@@ -88,6 +88,8 @@ def generate_synthetic_conversations(raw_text: str, num_turns: int, student_agen
   #  Save Conversation
   conversation_output = {
     "conversation_id": conversation_id+"_"+student_agent_type+"_"+tutor_agent_type+"_synthetic",
+    "student_agent_type": student_agent_type,
+    "tutor_agent_type": tutor_agent_type,
     "conversation": conversation_history
   }
   return conversation_output