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import os
from dotenv import load_dotenv
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain_community.llms import ChatGroq
load_dotenv()
groq_api_key = os.getenv("GROQ_API_KEY")
hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
# Load PDF and prepare QA chain
def create_qa_chain_from_pdf(pdf_path):
loader = PyPDFLoader(pdf_path)
documents = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-m3")
vectorstore = FAISS.from_documents(texts, embeddings)
llm = ChatGroq(
model="llama3-8b-8192",
temperature=0.3,
api_key=groq_api_key,
)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(search_kwargs={"k": 1}),
return_source_documents=True
)
return qa_chain
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