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| import os | |
| import tempfile | |
| import streamlit as st | |
| from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredMarkdownLoader, WebBaseLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_community.vectorstores import FAISS | |
| from langchain.chains import RetrievalQA | |
| 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 Document or Enter Web Link | |
| input_option = st.radio("Choose input type:", ("Upload Document", "Web Link")) | |
| if input_option == "Upload Document": | |
| uploaded_file = st.file_uploader("📂 Upload a document", type=["pdf", "docx", "md"]) | |
| else: | |
| web_link = st.text_input("🌐 Enter the web link:") | |
| # Use session state to persist the vector_store | |
| if "vector_store" not in st.session_state: | |
| st.session_state.vector_store = None | |
| if (input_option == "Upload Document" and uploaded_file and st.session_state.vector_store is None) or \ | |
| (input_option == "Web Link" and web_link and st.session_state.vector_store is None): | |
| try: | |
| with st.spinner("Processing document..."): | |
| if input_option == "Upload Document": | |
| # Save the uploaded file temporarily | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=f".{uploaded_file.name.split('.')[-1]}") as tmp_file: | |
| tmp_file.write(uploaded_file.getvalue()) | |
| tmp_file_path = tmp_file.name | |
| # Load the document based on file type | |
| if uploaded_file.name.endswith(".pdf"): | |
| loader = PyPDFLoader(tmp_file_path) | |
| elif uploaded_file.name.endswith(".docx"): | |
| loader = Docx2txtLoader(tmp_file_path) | |
| elif uploaded_file.name.endswith(".md"): | |
| loader = UnstructuredMarkdownLoader(tmp_file_path) | |
| else: | |
| st.error("Unsupported file type!") | |
| st.stop() | |
| documents = loader.load() | |
| # Remove the temporary file | |
| os.unlink(tmp_file_path) | |
| else: | |
| # Load the web page content | |
| loader = WebBaseLoader(web_link) | |
| documents = loader.load() | |
| # 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) | |
| # Check if the response is relevant or not | |
| if "I don't know" in response or "not in the document" in response.lower(): | |
| response = "I'm here to assist you with questions about uploaded documents or related web links." | |
| 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.") |