Added ProblemGradingPipeline

backend/app/problem_generator.py

@@ -8,31 +8,29 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
 from backend.app.vectorstore import get_vector_db
 
-class ProblemGenerator:
-    def __init__(self):
-        # Initialize prompts
-        self.system_role_prompt = """
-            You are a helpful assistant that generates questions based on a given context.
-        """
+SYSTEM_ROLE_PROMPT = """
+    You are a helpful assistant that generates questions based on a given context.
+"""
 
-        self.user_role_prompt = """
-            Based on the following context about {query}, generate 5 relevant and specific questions.
-            Make sure the questions can be answered using only the provided context.
+USER_ROLE_PROMPT = """
+    Based on the following context about {query}, generate 5 relevant and specific questions.
+    Make sure the questions can be answered using only the provided context.
 
-            Context: {context}
+    Context: {context}
 
-            Generate 5 questions that test understanding of the material in the context.
-            
-            Return only a json object with the following format:
-            {{
-                "questions": ["question1", "question2", "question3", "question4", "question5"]
-            }}
-        """
+    Generate 5 questions that test understanding of the material in the context.
+    
+    Return only a json object with the following format:
+    {{
+        "questions": ["question1", "question2", "question3", "question4", "question5"]
+    }}
+"""
 
-        # Initialize chain components
+class ProblemGenerationPipeline:
+    def __init__(self):
         self.chat_prompt = ChatPromptTemplate.from_messages([
-            ("system", self.system_role_prompt),
-            ("user", self.user_role_prompt)
+            ("system", SYSTEM_ROLE_PROMPT),
+            ("user", USER_ROLE_PROMPT)
         ])
         
         self.llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

backend/app/problem_grader.py

@@ -0,0 +1,72 @@
+from typing import Dict
+import json
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from backend.app.vectorstore import get_vector_db
+
+SYSTEM_ROLE_PROMPT = """
+    You are a knowledgeable grading assistant that evaluates student answers based on provided context.
+    You should determine if answers are correct and provide constructive feedback.
+"""
+
+USER_ROLE_PROMPT = """
+    Grade the following student answer based on the provided context about {query}.
+    
+    Context: {context}
+    
+    Question: {problem}
+    Student Answer: {answer}
+    
+    Evaluate if the answer is correct and provide brief feedback. Start with either "Correct" or "Incorrect" 
+    followed by a brief explanation of why. Focus on the accuracy based on the context provided.
+    
+    Always begin your response with "Correct" or "Incorrect" and then provide a brief explanation of why.
+
+    Your response should be direct and clear, for example:
+    "Correct. The answer accurately explains [reason]" or 
+    "Incorrect. While [partial understanding], the answer misses [key point]"
+"""
+
+class ProblemGradingPipeline:
+    def __init__(self):
+        self.chat_prompt = ChatPromptTemplate.from_messages([
+            ("system", SYSTEM_ROLE_PROMPT),
+            ("user", USER_ROLE_PROMPT)
+        ])
+        
+        self.llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.3)
+        self.retriever = get_vector_db().as_retriever(search_kwargs={"k": 2})
+        
+        # Build the RAG chain
+        self.rag_chain = (
+            {
+                "context": self.retriever, 
+                "query": RunnablePassthrough(),
+                "problem": RunnablePassthrough(),
+                "answer": RunnablePassthrough()
+            }
+            | self.chat_prompt
+            | self.llm
+            | StrOutputParser()
+        )
+
+    def grade(self, query: str, problem: str, answer: str) -> str:
+        """
+        Grade a student's answer to a problem using RAG for context-aware evaluation.
+        
+        Args:
+            query (str): The topic/context to use for grading
+            problem (str): The question being answered
+            answer (str): The student's answer to evaluate
+            
+        Returns:
+            str: Grading response indicating if the answer is correct and providing feedback
+        """
+        return self.rag_chain.invoke({
+            "query": query,
+            "problem": problem,
+            "answer": answer
+        })