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app.py

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+import streamlit as st
+import os
+import tempfile
+from langchain_community.vectorstores import FAISS
+from langchain_groq import ChatGroq
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.runnables import RunnablePassthrough
+from langchain.document_loaders import PyPDFLoader
+from langchain import hub
+
+# Set API key (replace with your actual key)
+os.environ["GROQ_API_KEY"] = "your_groq_api_key"
+
+# Streamlit UI
+st.title("📄 PDF Chatbot with RAG")
+st.write("Upload a PDF and ask questions!")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
+
+if uploaded_file:
+    # Save uploaded PDF temporarily
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+        temp_file.write(uploaded_file.read())
+        temp_file_path = temp_file.name
+
+    # Load and process PDF
+    loader = PyPDFLoader(temp_file_path)
+    docs = loader.load()
+
+    # Initialize LLM and Embeddings
+    llm = ChatGroq(model="llama3-8b-8192")
+    model_name = "BAAI/bge-small-en"
+    hf_embeddings = HuggingFaceBgeEmbeddings(
+        model_name=model_name,
+        model_kwargs={'device': 'cpu'},
+        encode_kwargs={'normalize_embeddings': True}
+    )
+
+    # Split text
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    splits = text_splitter.split_documents(docs)
+
+    # Create FAISS vector store
+    vectorstore = FAISS.from_documents(documents=splits, embedding=hf_embeddings)
+    retriever = vectorstore.as_retriever()
+
+    # Load RAG prompt
+    prompt = hub.pull("rlm/rag-prompt")
+
+    def format_docs(docs):
+        return "\n\n".join(doc.page_content for doc in docs)
+
+    # RAG Chain
+    rag_chain = (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+    )
+
+    # User Query
+    user_query = st.text_input("Ask a question from the PDF:")
+
+    if user_query:
+        response = rag_chain.invoke(user_query)
+        st.write("### 🤖 AI Response:")
+        st.write(response)

requirements (1).txt

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+streamlit
+pypdf
+langchain_core
+langchain_community
+langchain_groq
+faiss-cpu
+sentence-transformers