import streamlit as st import os from openai import OpenAI from langchain.memory import ConversationBufferMemory from langchain.vectorstores import FAISS from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import HuggingFaceEmbeddings from langchain.document_loaders import PyPDFLoader, TextLoader import tempfile # Page configuration st.set_page_config(page_title="DeepSeek RAG Chatbot", page_icon="🤖", layout="wide") # App title and description st.title("🤖 DeepSeek RAG Chatbot") st.subheader("A chatbot that uses your documents to give informed answers") # Set up API key input if 'DEEPSEEK_API_KEY' not in st.session_state: api_key = st.text_input("Enter your DeepSeek API Key:", type="password") if api_key: st.session_state['DEEPSEEK_API_KEY'] = api_key os.environ['DEEPSEEK_API_KEY'] = api_key st.success("API Key saved!") st.rerun() # Initialize session state variables if 'memory' not in st.session_state: st.session_state.memory = ConversationBufferMemory(return_messages=True) if 'chat_history' not in st.session_state: st.session_state.chat_history = [] if 'vectorstore' not in st.session_state: st.session_state.vectorstore = None if 'client' not in st.session_state and 'DEEPSEEK_API_KEY' in st.session_state: try: # Initialize DeepSeek client for chat st.session_state.client = OpenAI( api_key=st.session_state['DEEPSEEK_API_KEY'], base_url="https://api.deepseek.com" ) # Initialize small HuggingFace embeddings model # Using paraphrase-MiniLM-L3-v2 - a smaller version with only 22MB size st.session_state.embeddings = HuggingFaceEmbeddings( model_name="sentence-transformers/paraphrase-MiniLM-L3-v2" ) st.success("Models loaded successfully!") except Exception as e: st.error(f"Error initializing API: {str(e)}") # Function to process uploaded documents def process_documents(uploaded_files): temp_dir = tempfile.mkdtemp() for file in uploaded_files: file_path = os.path.join(temp_dir, file.name) with open(file_path, "wb") as f: f.write(file.getbuffer()) # Load documents based on file type documents = [] for file in uploaded_files: if file.name.endswith('.pdf'): loader = PyPDFLoader(os.path.join(temp_dir, file.name)) documents.extend(loader.load()) elif file.name.endswith('.txt'): loader = TextLoader(os.path.join(temp_dir, file.name)) documents.extend(loader.load()) # Split documents into chunks text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=200 ) document_chunks = text_splitter.split_documents(documents) # Create or update vector store if st.session_state.vectorstore is None: st.session_state.vectorstore = FAISS.from_documents( document_chunks, st.session_state.embeddings ) else: # Add new documents to existing vectorstore st.session_state.vectorstore.add_documents(document_chunks) return len(document_chunks) # Function to retrieve relevant context from vector database def retrieve_context(query, k=3): if st.session_state.vectorstore is None: return "" docs = st.session_state.vectorstore.similarity_search(query, k=k) context = "\n\n".join([doc.page_content for doc in docs]) return context # Main application layout if 'DEEPSEEK_API_KEY' in st.session_state: # Create a sidebar for document upload and settings with st.sidebar: st.header("Document Upload") uploaded_files = st.file_uploader( "Upload your documents", accept_multiple_files=True, type=["pdf", "txt"] ) if uploaded_files: if st.button("Process Documents"): with st.spinner("Processing documents..."): num_chunks = process_documents(uploaded_files) st.success(f"Successfully processed {len(uploaded_files)} documents into {num_chunks} chunks!") st.header("RAG Settings") k_documents = st.slider("Number of documents to retrieve", min_value=1, max_value=10, value=3) # Clear conversation button if st.button("Clear Conversation"): st.session_state.memory = ConversationBufferMemory(return_messages=True) st.session_state.chat_history = [] st.success("Conversation cleared!") st.rerun() # Clear knowledge base button if st.button("Clear Knowledge Base"): st.session_state.vectorstore = None st.success("Knowledge base cleared!") st.header("About") st.markdown(""" This RAG chatbot uses: - 🦜 LangChain for memory and document processing - 🔍 FAISS for vector storage and retrieval - 🧠 HuggingFace for lightweight embeddings (paraphrase-MiniLM-L3-v2) - 🤖 DeepSeek API for AI responses - 🖥️ Streamlit for the web interface The chatbot can: - Upload and process PDF and text documents - Retrieve relevant information from documents - Generate informed responses using your documents - Maintain conversation context """) # Main chat area - create a container for the chat history chat_container = st.container() with chat_container: # Display chat history for message in st.session_state.chat_history: with st.chat_message(message["role"]): st.write(message["content"]) # IMPORTANT: Place chat_input outside of any container and if block # This must be at the main page level user_input = st.chat_input("Type your message here...") # Handle user input - but only process if API key is available if user_input and 'DEEPSEEK_API_KEY' in st.session_state: # Add user message to chat history st.session_state.chat_history.append({"role": "user", "content": user_input}) # Display user message with st.chat_message("user"): st.write(user_input) # Get model response with st.chat_message("assistant"): with st.spinner("Thinking..."): try: # Retrieve relevant context from vector database context = retrieve_context(user_input, k=k_documents) # Prepare chat history for DeepSeek API system_prompt = "You are a helpful assistant with access to a knowledge base." if context: system_prompt += f"\n\nRelevant information from knowledge base:\n{context}\n\nUse this information to answer the user's question. If the information doesn't contain the answer, just say that you don't know based on the available information." messages = [{"role": "system", "content": system_prompt}] for msg in st.session_state.chat_history: messages.append({"role": msg["role"], "content": msg["content"]}) # Call DeepSeek API response = st.session_state.client.chat.completions.create( model="deepseek-chat", messages=messages, stream=False ) assistant_response = response.choices[0].message.content st.write(assistant_response) # Add assistant response to chat history st.session_state.chat_history.append({"role": "assistant", "content": assistant_response}) except Exception as e: st.error(f"Error: {str(e)}")