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import gradio as gr
from langchain.chains import RetrievalQA
from langchain_groq import ChatGroq
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings # type: ignore
# Load FAISS Index and Embeddings
embeddings = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2')
vector_store = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
# Initialize LLM
llm = ChatGroq(
model_name="llama-3.3-70b-versatile",
temperature=1,
groq_api_key="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"
)
# Create RAG Chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vector_store.as_retriever()
)
# Define Query Function
def search_courses(query):
result = qa_chain.run(query)
return result
# Gradio Interface
iface = gr.Interface(
fn=search_courses,
inputs=gr.Textbox(label="Enter your query"),
outputs=gr.Textbox(label="Response"),
title="Smart Course Search Tool",
description="Ask questions to find the best free courses on Analytics Vidhya."
)
# Launch the app
iface.launch()
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