import streamlit as st from dotenv import load_dotenv from PyPDF2 import PdfReader from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings from langchain.vectorstores import FAISS, Chroma from langchain.embeddings import HuggingFaceEmbeddings # General embeddings from HuggingFace models. from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationalRetrievalChain from htmlTemplates import css, bot_template, user_template from langchain.llms import HuggingFaceHub, LlamaCpp, CTransformers # For loading transformer models. from langchain.document_loaders import PyPDFLoader, TextLoader, JSONLoader, CSVLoader import tempfile # 임시 파일을 생성하기 위한 라이브러리입니다. import os # PDF 문서로부터 텍스트를 추출하는 함수입니다. def get_pdf_text(pdf_docs): temp_dir = tempfile.TemporaryDirectory() # 임시 디렉토리를 생성합니다. temp_filepath = os.path.join(temp_dir.name, pdf_docs.name) # 임시 파일 경로를 생성합니다. with open(temp_filepath, "wb") as f: # 임시 파일을 바이너리 쓰기 모드로 엽니다. f.write(pdf_docs.getvalue()) # PDF 문서의 내용을 임시 파일에 씁니다. pdf_loader = PyPDFLoader(temp_filepath) # PyPDFLoader를 사용해 PDF를 로드합니다. pdf_doc = pdf_loader.load() # 텍스트를 추출합니다. return pdf_doc # 추출한 텍스트를 반환합니다. # 과제 # 아래 텍스트 추출 함수를 작성 def get_text_file(text_docs): temp_dir = tempfile.TemporaryDirectory() temp_filepath = os.path.join(temp_dir.name, text_docs.name) with open(temp_filepath, "wb") as f: f.write(text_docs.getvalue()) text_loader = TextLoader(temp_filepath) text_doc = text_loader.load() return text_doc def get_csv_file(csv_docs): temp_dir = tempfile.TemporaryDirectory() temp_filepath = os.path.join(temp_dir.name, csv_docs.name) with open(temp_filepath, "wb") as f: f.write(csv_docs.getvalue()) csv_loader = CSVLoader(temp_filepath) csv_doc = csv_loader.load() return csv_doc def get_json_file(json_docs): temp_dir = tempfile.TemporaryDirectory() temp_filepath = os.path.join(temp_dir.name, json_docs.name) with open(temp_filepath, "wb") as f: f.write(json_docs.getvalue()) json_loader = JSONLoader(temp_filepath) json_doc = json_loader.load() return json_doc # 문서들을 처리하여 텍스트 청크로 나누는 함수입니다. def get_text_chunks(documents): text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, # 청크의 크기를 지정합니다. chunk_overlap=200, # 청크 사이의 중복을 지정합니다. length_function=len # 텍스트의 길이를 측정하는 함수를 지정합니다. ) documents = text_splitter.split_documents(documents) # 문서들을 청크로 나눕니다 return documents # 나눈 청크를 반환합니다. # 텍스트 청크들로부터 벡터 스토어를 생성하는 함수입니다. def get_vectorstore(text_chunks): # OpenAI 임베딩 모델을 로드합니다. (Embedding models - Ada v2) # embeddings = OpenAIEmbeddings() # embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") vectorstore = FAISS.from_documents(text_chunks, embeddings) # FAISS 벡터 스토어를 생성합니다. return vectorstore # 생성된 벡터 스토어를 반환합니다. def get_conversation_chain(vectorstore): llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512},huggingfacehub_api_token=os.getenv("TOKEN_API2")) # 대화 기록을 저장하기 위한 메모리를 생성합니다. memory = ConversationBufferMemory( memory_key='chat_history', return_messages=True) # 대화 검색 체인을 생성합니다. conversation_chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=vectorstore.as_retriever(), memory=memory ) return conversation_chain # 사용자 입력을 처리하는 함수입니다. # def handle_userinput(user_question): # # 대화 체인을 사용하여 사용자 질문에 대한 응답을 생성합니다. # response = st.session_state.conversation({'question': user_question}) # # 대화 기록을 저장합니다. # st.session_state.chat_history = response['chat_history'] # for i, message in enumerate(st.session_state.chat_history): # if i % 2 == 0: # st.write(user_template.replace( # "{{MSG}}", message.content), unsafe_allow_html=True) # else: # st.write(bot_template.replace( # "{{MSG}}", message.content), unsafe_allow_html=True) def handle_userinput(user_question): if not st.session_state.conversation: st.error("Please upload and process your documents first.") return try: response = st.session_state.conversation({'question': user_question}) st.session_state.chat_history = response['chat_history'] for i, message in enumerate(st.session_state.chat_history): if i % 2 == 0: st.write(user_template.replace( "{{MSG}}", message.content), unsafe_allow_html=True) else: st.write(bot_template.replace( "{{MSG}}", message.content), unsafe_allow_html=True) except Exception as e: st.error(f"An error occurred: {e}") def main(): load_dotenv() st.set_page_config(page_title="Chat with multiple Files", page_icon=":books:") st.write(css, unsafe_allow_html=True) if "conversation" not in st.session_state: st.session_state.conversation = None if "chat_history" not in st.session_state: st.session_state.chat_history = None st.header("Chat with multiple Files:") user_question = st.text_input("Ask a question about your documents:") if user_question: handle_userinput(user_question) with st.sidebar: openai_key = st.text_input("Paste your OpenAI API key (sk-...)") if openai_key: os.environ["OPENAI_API_KEY"] = openai_key st.subheader("Your documents") docs = st.file_uploader( "Upload your PDFs here and click on 'Process'", accept_multiple_files=True) if st.button("Process"): if not docs: st.error("Please upload at least one document.") return with st.spinner("Processing..."): try: doc_list = [] for file in docs: if file.type == 'text/plain': doc_list.extend(get_text_file(file)) elif file.type in ['application/octet-stream', 'application/pdf']: doc_list.extend(get_pdf_text(file)) elif file.type == 'text/csv': doc_list.extend(get_csv_file(file)) elif file.type == 'application/json': doc_list.extend(get_json_file(file)) if not doc_list: st.error("No valid documents processed. Please check your files.") return text_chunks = get_text_chunks(doc_list) vectorstore = get_vectorstore(text_chunks) st.session_state.conversation = get_conversation_chain(vectorstore) st.success("Documents processed successfully!") except Exception as e: st.error(f"An error occurred during processing: {e}") if __name__ == '__main__': main() # def main(): # load_dotenv() # st.set_page_config(page_title="Chat with multiple Files", # page_icon=":books:") # st.write(css, unsafe_allow_html=True) # if "conversation" not in st.session_state: # st.session_state.conversation = None # if "chat_history" not in st.session_state: # st.session_state.chat_history = None # st.header("Chat with multiple Files :") # user_question = st.text_input("Ask a question about your documents:") # if user_question: # handle_userinput(user_question) # with st.sidebar: # openai_key = st.text_input("Paste your OpenAI API key (sk-...)") # if openai_key: # os.environ["OPENAI_API_KEY"] = openai_key # st.subheader("Your documents") # docs = st.file_uploader( # "Upload your PDFs here and click on 'Process'", accept_multiple_files=True) # if st.button("Process"): # with st.spinner("Processing"): # # get pdf text # doc_list = [] # for file in docs: # print('file - type : ', file.type) # if file.type == 'text/plain': # # file is .txt # doc_list.extend(get_text_file(file)) # elif file.type in ['application/octet-stream', 'application/pdf']: # # file is .pdf # doc_list.extend(get_pdf_text(file)) # elif file.type == 'text/csv': # # file is .csv # doc_list.extend(get_csv_file(file)) # elif file.type == 'application/json': # # file is .json # doc_list.extend(get_json_file(file)) # # get the text chunks # text_chunks = get_text_chunks(doc_list) # # create vector store # vectorstore = get_vectorstore(text_chunks) # # create conversation chain # st.session_state.conversation = get_conversation_chain( # vectorstore) # import streamlit as st # # from dotenv import load_dotenv # from PyPDF2 import PdfReader # from langchain.text_splitter import CharacterTextSplitter # from langchain_community.embeddings import HuggingFaceInstructEmbeddings # from langchain_community.vectorstores import FAISS # # from langchain.chat_models import ChatOpenAI # from langchain.memory import ConversationBufferMemory # from langchain.chains import ConversationalRetrievalChain # from htmlTemplates import css, bot_template, user_template # from langchain_community.llms import HuggingFaceHub # import os # # from sentence_transformers import SentenceTransformer # from langchain.embeddings import HuggingFaceEmbeddings # # from huggingface_hub import login # # Retrieve the Hugging Face token from environment variables # # token = os.getenv("HUGGINGFACEHUB_TOKEN") # import fitz # PyMuPDF # def get_pdf_text(pdf_docs): # text = "" # for pdf in pdf_docs: # try: # doc = fitz.open(stream=pdf.read(), filetype="pdf") # for page in doc: # text += page.get_text() # except Exception as e: # st.error(f"Could not read the file: {pdf.name}. Error: {e}") # return text # # def get_pdf_text(pdf_docs): # # text = "" # # for pdf in pdf_docs: # # pdf_reader = PdfReader(pdf) # # for page in pdf_reader.pages: # # text += page.extract_text() # # return text # def get_text_chunks(text): # text_splitter=CharacterTextSplitter( # separator="\n", # chunk_size=1000, # chunk_overlap=200, # length_function=len # ) # chunks=text_splitter.split_text(text) # return chunks # # token="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1" # # def get_vectorstore(text_chunks): # # # embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",huggingfacehub_token=os.getenv("TOKEN_API2")) # # embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") # # vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) # # return vectorstore # # def get_vectorstore(text_chunks): # # # Load a SentenceTransformer model for embeddings # # embedding_model = SentenceTransformer("hkunlp/instructor-xl") # Replace with a model of your choice # # embeddings = [embedding_model.encode(chunk) for chunk in text_chunks] # # # Create a FAISS vectorstore # # vectorstore = FAISS.from_embeddings(embeddings=embeddings, texts=text_chunks) # # return vectorstore # def get_vectorstore(text_chunks): # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") # vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) # return vectorstore # def get_conversation_chain(vectorstore): # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512},huggingfacehub_api_token=os.getenv("TOKEN_API2")) # memory=ConversationBufferMemory( # memory_key='chat_history',return_messages=True) # conversation_chain = ConversationalRetrievalChain.from_llm( # llm=llm, # retriever=vectorstore.as_retriever(), # memory=memory # ) # return conversation_chain # def handle_userinput(user_question): # response = st.session_state.conversation({'question':user_question}) # st.session_state.chat_history = response['chat_history'] # for i, message in enumerate(st.session_state.chat_history): # if i % 2 == 0: # st.write(user_template.replace("{{MSG}}", message.content),unsafe_allow_html=True) # else: # st.write(bot_template.replace("{{MSG}}", message.content),unsafe_allow_html=True) # def main(): # st.set_page_config(page_title="Chat with My RAG", # page_icon=":books:") # st.write(css,unsafe_allow_html=True) # if "conversation" not in st.session_state: # st.session_state.conversation = None # if "chat_history" not in st.session_state: # st.session_state.chat_history = None # st.header("Chat with My RAG :books:") # user_question=st.text_input("Ask a question about your documents:") # if user_question: # handle_userinput(user_question) # with st.sidebar: # st.subheader("Your Documents") # pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True) # if st.button("Process"): # with st.spinner("Processing"): # raw_text =get_pdf_text(pdf_docs) # text_chunks = get_text_chunks(raw_text) # vectorstore = get_vectorstore(text_chunks) # st.session_state.conversation = get_conversation_chain(vectorstore) # if __name__ == '__main__': # main()