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from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
import streamlit as st
@st.cache_resource
def build_chat_chain(_llm):
prompt = ChatPromptTemplate.from_messages([
("system", "You are AbideVerse, an AI companion helping with devotion and Bible study."),
("human", "{message}")
])
return prompt | _llm | StrOutputParser()
@st.cache_resource
def build_rag_chain(_llm, _retriever):
system_prompt = """
You are AbideVerse RAG Assistant. Use ONLY the provided context to answer questions biblically.
If the answer is not present, say so.
CONTEXT:
{context}
"""
prompt = ChatPromptTemplate.from_messages([
("system", system_prompt),
("human", "{question}")
])
rag_chain = (
RunnableParallel({
"context": _retriever,
"question": RunnablePassthrough()
})
| prompt
| _llm
| StrOutputParser()
)
return rag_chain
@st.cache_resource
def build_quiz_chain(_llm):
prompt = ChatPromptTemplate.from_messages([
("system", "Generate a Bible verse memorization quiz (cloze)."),
("human", "Verse: {verse}")
])
return prompt | _llm | StrOutputParser()
|