|
|
|
|
|
|
|
|
import os |
|
|
from dotenv import load_dotenv |
|
|
load_dotenv() |
|
|
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") |
|
|
from langchain_openai import ChatOpenAI |
|
|
from langchain_core.prompts import PromptTemplate |
|
|
from langchain_core.output_parsers import StrOutputParser |
|
|
|
|
|
|
|
|
class TeacherAgent: |
|
|
|
|
|
def __init__(self): |
|
|
self.llm = ChatOpenAI( |
|
|
model="gpt-4o-mini", |
|
|
temperature=0.3, |
|
|
api_key=OPENAI_API_KEY |
|
|
) |
|
|
|
|
|
self.prompt = PromptTemplate( |
|
|
input_variables=["topic", "question", "context"], |
|
|
template=""" |
|
|
You are an expert teacher on the topic: {topic}. |
|
|
|
|
|
TEXTBOOK CONTEXT: |
|
|
-------------------- |
|
|
{context} |
|
|
-------------------- |
|
|
|
|
|
The student asks: {question} |
|
|
|
|
|
Provide a clear, structured educational answer. |
|
|
""" |
|
|
) |
|
|
|
|
|
self.chain = self.prompt | self.llm | StrOutputParser() |
|
|
|
|
|
def answer(self, topic, question, context): |
|
|
return self.chain.invoke({ |
|
|
"topic": topic, |
|
|
"question": question, |
|
|
"context": context |
|
|
}) |
|
|
|
