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app.py

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+import os
+import tempfile
+import streamlit as st
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter  # Correct import
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.chains import RetrievalQA  # Correct import
+from langchain_community.chat_models import ChatOpenAI
+
+# Streamlit App Title
+st.title("📄 DeepSeek-Powered RAG Chatbot")
+
+# Step 1: Input API Key
+api_key = st.text_input("🔑 Enter your DeepSeek API Key:", type="password")
+
+if api_key:
+    # Set the API key as an environment variable (optional)
+    os.environ["DEEPSEEK_API_KEY"] = api_key
+
+    # Step 2: Upload PDF Document
+    uploaded_file = st.file_uploader("📂 Upload a PDF document", type=["pdf"])
+
+    # Use session state to persist the vector_store
+    if "vector_store" not in st.session_state:
+        st.session_state.vector_store = None
+
+    if uploaded_file and st.session_state.vector_store is None:
+        try:
+            with st.spinner("Processing document..."):
+                # Save the uploaded file temporarily
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
+                    tmp_file.write(uploaded_file.getvalue())
+                    tmp_file_path = tmp_file.name
+
+                # Use the temporary file path with PyPDFLoader
+                loader = PyPDFLoader(tmp_file_path)
+                documents = loader.load()
+
+                # Remove the temporary file
+                os.unlink(tmp_file_path)
+
+                # Split the document into chunks
+                text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+                chunks = text_splitter.split_documents(documents)
+
+                # Generate embeddings and store them in a vector database
+                embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+                st.session_state.vector_store = FAISS.from_documents(chunks, embeddings)
+
+            st.success("Document processed successfully!")
+        except Exception as e:
+            st.error(f"Error processing document: {e}")
+            st.stop()
+
+    # Step 3: Ask Questions About the Document
+    if st.session_state.vector_store:
+        st.subheader("💬 Chat with Your Document")
+        user_query = st.text_input("Ask a question:")
+
+        if user_query:
+            try:
+                # Set up the RAG pipeline with DeepSeek LLM
+                retriever = st.session_state.vector_store.as_retriever()
+                llm = ChatOpenAI(
+                    model="deepseek-chat",
+                    openai_api_key=api_key,
+                    openai_api_base="https://api.deepseek.com/v1",
+                    temperature=0.85,
+                    max_tokens=1000  # Adjust token limit for safety
+                )
+                qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
+
+                # Generate response
+                with st.spinner("Generating response..."):
+                    response = qa_chain.run(user_query)
+                    st.write(f"**Answer:** {response}")
+            except Exception as e:
+                st.error(f"Error generating response: {e}")
+else:
+    st.warning("Please enter your DeepSeek API key to proceed.")

requirements.txt

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+streamlit==1.25.0
+langchain
+langchain-community
+faiss-cpu==1.7.4
+sentence-transformers
+pypdf==3.8.1
+openai