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| import streamlit as st | |
| from streamlit_chat import message | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.document_loaders import PyPDFLoader, DirectoryLoader | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.llms import CTransformers | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import FAISS | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain_openai import ChatOpenAI | |
| import os | |
| from huggingface_hub import login | |
| login(token = st.secrets["HF"]) | |
| os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"] | |
| # Function to load documents | |
| def load_documents(): | |
| loader = DirectoryLoader('data/', glob="*.pdf", loader_cls=PyPDFLoader) | |
| documents = loader.load() | |
| return documents | |
| # Function to split text into chunks | |
| def split_text_into_chunks(documents): | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) | |
| text_chunks = text_splitter.split_documents(documents) | |
| return text_chunks | |
| # Function to create embeddings | |
| def create_embeddings(): | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", model_kwargs={'device': "cpu"}) | |
| #embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': "cpu"}) | |
| return embeddings | |
| # Function to create vector store | |
| def create_vector_store(text_chunks, embeddings, nombre_vector): | |
| vector_store = FAISS.from_documents(text_chunks, embeddings) | |
| vector_store.save_local("cache") #Guardarlo en un | |
| return vector_store | |
| # Function to create vector store | |
| def load_vector_store(nombre_vector, embeddings): | |
| return FAISS.load_local(nombre_vector, embeddings) | |
| # Function to create LLMS model | |
| def create_llms_model(): | |
| #llm = CTransformers(model='TheBloke/Mistral-7B-Instruct-v0.1-GGUF', config={'max_new_tokens': 128, 'temperature': 0.01}) | |
| llm = ChatOpenAI(model='gpt-3.5-turbo-1106', temperature=0.1) | |
| return llm | |
| # Initialize Streamlit app | |
| st.title("Chatbot usando mistral") | |
| # loading of documents | |
| documents = load_documents() | |
| # Split text into chunks | |
| text_chunks = split_text_into_chunks(documents) | |
| # Create embeddings | |
| embeddings = create_embeddings() | |
| try:#load vector store from local | |
| vector_store = load_vector_store("cache",embeddings) | |
| except:# Create vector store | |
| vector_store = create_vector_store(text_chunks, embeddings, "cache") | |
| # Create LLMS model | |
| llm = create_llms_model() | |
| # Initialize conversation history | |
| if 'history' not in st.session_state: | |
| st.session_state['history'] = [] | |
| if 'generated' not in st.session_state: | |
| st.session_state['generated'] = ["¡Hola! Pregúntame sobre cualquier cosa 🤗"] | |
| if 'past' not in st.session_state: | |
| st.session_state['past'] = ["¡Hola! 👋"] | |
| # Create memory | |
| memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| # Create chain | |
| chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff', | |
| retriever=vector_store.as_retriever(search_kwargs={"k": 2}), | |
| memory=memory) | |
| # Define chat function | |
| def conversation_chat(query): | |
| result = chain({"question": query, "chat_history": st.session_state['history']}) | |
| st.session_state['history'].append((query, result["answer"])) | |
| return result["answer"] | |
| # Display chat history | |
| reply_container = st.container() | |
| container = st.container() | |
| with container: | |
| with st.form(key='my_form', clear_on_submit=True): | |
| user_input = st.text_input("Question:", placeholder="Ask about your Job Interview", key='input') | |
| submit_button = st.form_submit_button(label='Send') | |
| if submit_button and user_input: | |
| output = conversation_chat(user_input) | |
| st.session_state['past'].append(user_input) | |
| st.session_state['generated'].append(output) | |
| if st.session_state['generated']: | |
| with reply_container: | |
| for i in range(len(st.session_state['generated'])): | |
| message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs") | |
| message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji") |