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# 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
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