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import nest_asyncio | |
from llama_parse import LlamaParse | |
from langchain_text_splitters import RecursiveCharacterTextSplitter | |
from langchain_community.vectorstores import Chroma | |
from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings | |
from langchain_groq import ChatGroq | |
from langchain_core.prompts import ChatPromptTemplate | |
from langchain_core.output_parsers import StrOutputParser | |
from langchain_core.runnables import RunnablePassthrough | |
import pickle | |
class tafsir: | |
def __init__(self): | |
pkl_docs=pickle.load(open("docs.pkl","rb")) | |
self.store=Chroma.from_documents(documents=pkl_docs,embedding=HuggingFaceEmbeddings(model_name="BAAI/bge-base-en-v1.5")) | |
self.retreiver=self.store.as_retriever() | |
llm=ChatGroq(api_key="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",model="mixtral-8x7b-32768") | |
rag_template = """ | |
Provide a summary from the context, which contains interpretations of Quranic Texts that highlight the importance of the topic mentioned in the question. Do not include the Quranic Texts themselves, but mention which Surah and verse. | |
Context: | |
{context} | |
Question: | |
{question} | |
""" | |
rag_prompt=ChatPromptTemplate.from_template(rag_template) | |
self.rag_chain=( | |
{"context":self.retreiver,"question":RunnablePassthrough()} | |
| rag_prompt | |
| llm | |
| StrOutputParser() | |
) | |
def return_tafsir(self,topic): | |
response=self.rag_chain.invoke(topic) | |
# print(response) | |
return response | |