rag_utils.py

# rag_utils.py

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_groq import ChatGroq

def create_vectorstore_from_text(text: str):
    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    texts = splitter.split_text(text)
    
    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2",
        model_kwargs={"device": "cpu"}
    )
    
    vectorstore = FAISS.from_texts(texts, embedding=embeddings)
    return vectorstore

def create_rag_chain(vectorstore):
    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
    
    llm = ChatGroq(model_name="llama3-8b-8192", temperature=0)
    
    rag_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
    return rag_chain