import os import re from hashlib import blake2b from tempfile import NamedTemporaryFile import dotenv from grobid_quantities.quantities import QuantitiesAPI from langchain.memory import ConversationBufferWindowMemory from langchain_community.chat_models.openai import ChatOpenAI from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint from streamlit_pdf_viewer import pdf_viewer from document_qa.ner_client_generic import NERClientGeneric dotenv.load_dotenv(override=True) import streamlit as st from document_qa.document_qa_engine import DocumentQAEngine, DataStorage from document_qa.grobid_processors import GrobidAggregationProcessor, decorate_text_with_annotations OPENAI_MODELS = ['gpt-3.5-turbo', "gpt-4", "gpt-4-1106-preview"] OPENAI_EMBEDDINGS = [ 'text-embedding-ada-002', 'text-embedding-3-large', 'openai-text-embedding-3-small' ] OPEN_MODELS = { 'mistral-7b-instruct-v0.2': 'mistralai/Mistral-7B-Instruct-v0.2', "zephyr-7b-beta": 'HuggingFaceH4/zephyr-7b-beta' # 'Phi-3-mini-128k-instruct': "microsoft/Phi-3-mini-128k-instruct", # 'Phi-3-mini-4k-instruct': "microsoft/Phi-3-mini-4k-instruct" } DEFAULT_OPEN_EMBEDDING_NAME = 'Default (all-MiniLM-L6-v2)' OPEN_EMBEDDINGS = { DEFAULT_OPEN_EMBEDDING_NAME: 'all-MiniLM-L6-v2', 'Salesforce/SFR-Embedding-Mistral': 'Salesforce/SFR-Embedding-Mistral' } DISABLE_MEMORY = ['zephyr-7b-beta'] if 'rqa' not in st.session_state: st.session_state['rqa'] = {} if 'model' not in st.session_state: st.session_state['model'] = None if 'api_keys' not in st.session_state: st.session_state['api_keys'] = {} if 'doc_id' not in st.session_state: st.session_state['doc_id'] = None if 'loaded_embeddings' not in st.session_state: st.session_state['loaded_embeddings'] = None if 'hash' not in st.session_state: st.session_state['hash'] = None if 'git_rev' not in st.session_state: st.session_state['git_rev'] = "unknown" if os.path.exists("revision.txt"): with open("revision.txt", 'r') as fr: from_file = fr.read() st.session_state['git_rev'] = from_file if len(from_file) > 0 else "unknown" if "messages" not in st.session_state: st.session_state.messages = [] if 'ner_processing' not in st.session_state: st.session_state['ner_processing'] = False if 'uploaded' not in st.session_state: st.session_state['uploaded'] = False if 'memory' not in st.session_state: st.session_state['memory'] = None if 'binary' not in st.session_state: st.session_state['binary'] = None if 'annotations' not in st.session_state: st.session_state['annotations'] = None if 'should_show_annotations' not in st.session_state: st.session_state['should_show_annotations'] = True if 'pdf' not in st.session_state: st.session_state['pdf'] = None if 'pdf_rendering' not in st.session_state: st.session_state['pdf_rendering'] = None if 'embeddings' not in st.session_state: st.session_state['embeddings'] = None st.set_page_config( page_title="Scientific Document Insights Q/A", page_icon="📝", initial_sidebar_state="expanded", layout="wide", menu_items={ 'Get Help': 'https://github.com/lfoppiano/document-qa', 'Report a bug': "https://github.com/lfoppiano/document-qa/issues", 'About': "Upload a scientific article in PDF, ask questions, get insights." } ) css_modify_left_column = ''' ''' css_modify_right_column = ''' ''' css_disable_scrolling_container = ''' ''' # st.markdown(css_lock_column_fixed, unsafe_allow_html=True) # st.markdown(css2, unsafe_allow_html=True) def new_file(): st.session_state['loaded_embeddings'] = None st.session_state['doc_id'] = None st.session_state['uploaded'] = True if st.session_state['memory']: st.session_state['memory'].clear() def clear_memory(): st.session_state['memory'].clear() # @st.cache_resource def init_qa(model, embeddings_name=None, api_key=None): ## For debug add: callbacks=[PromptLayerCallbackHandler(pl_tags=["langchain", "chatgpt", "document-qa"])]) if model in OPENAI_MODELS: if embeddings_name is None: embeddings_name = 'text-embedding-ada-002' st.session_state['memory'] = ConversationBufferWindowMemory(k=4) if api_key: chat = ChatOpenAI(model_name=model, temperature=0, openai_api_key=api_key, frequency_penalty=0.1) if embeddings_name not in OPENAI_EMBEDDINGS: st.error(f"The embeddings provided {embeddings_name} are not supported by this model {model}.") st.stop() return embeddings = OpenAIEmbeddings(model=embeddings_name, openai_api_key=api_key) else: chat = ChatOpenAI(model_name=model, temperature=0, frequency_penalty=0.1) embeddings = OpenAIEmbeddings(model=embeddings_name) elif model in OPEN_MODELS: if embeddings_name is None: embeddings_name = DEFAULT_OPEN_EMBEDDING_NAME chat = HuggingFaceEndpoint( repo_id=OPEN_MODELS[model], temperature=0.01, max_new_tokens=2048, model_kwargs={"max_length": 4096} ) embeddings = HuggingFaceEmbeddings( model_name=OPEN_EMBEDDINGS[embeddings_name]) st.session_state['memory'] = ConversationBufferWindowMemory(k=4) if model not in DISABLE_MEMORY else None else: st.error("The model was not loaded properly. Try reloading. ") st.stop() return storage = DataStorage(embeddings) return DocumentQAEngine(chat, storage, grobid_url=os.environ['GROBID_URL'], memory=st.session_state['memory']) @st.cache_resource def init_ner(): quantities_client = QuantitiesAPI(os.environ['GROBID_QUANTITIES_URL'], check_server=True) materials_client = NERClientGeneric(ping=True) config_materials = { 'grobid': { "server": os.environ['GROBID_MATERIALS_URL'], 'sleep_time': 5, 'timeout': 60, 'url_mapping': { 'processText_disable_linking': "/service/process/text?disableLinking=True", # 'processText_disable_linking': "/service/process/text" } } } materials_client.set_config(config_materials) gqa = GrobidAggregationProcessor(grobid_quantities_client=quantities_client, grobid_superconductors_client=materials_client) return gqa gqa = init_ner() def get_file_hash(fname): hash_md5 = blake2b() with open(fname, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_md5.update(chunk) return hash_md5.hexdigest() def play_old_messages(): if st.session_state['messages']: for message in st.session_state['messages']: if message['role'] == 'user': with st.chat_message("user"): st.markdown(message['content']) elif message['role'] == 'assistant': with st.chat_message("assistant"): if mode == "LLM": st.markdown(message['content'], unsafe_allow_html=True) else: st.write(message['content']) # is_api_key_provided = st.session_state['api_key'] with st.sidebar: st.session_state['model'] = model = st.selectbox( "Model:", options=OPENAI_MODELS + list(OPEN_MODELS.keys()), index=(OPENAI_MODELS + list(OPEN_MODELS.keys())).index( "mistral-7b-instruct-v0.2") if "DEFAULT_MODEL" not in os.environ or not os.environ["DEFAULT_MODEL"] else ( OPENAI_MODELS + list(OPEN_MODELS.keys())).index(os.environ["DEFAULT_MODEL"]), placeholder="Select model", help="Select the LLM model:", disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded'] ) embedding_choices = OPENAI_EMBEDDINGS if model in OPENAI_MODELS else OPEN_EMBEDDINGS st.session_state['embeddings'] = embedding_name = st.selectbox( "Embeddings:", options=embedding_choices, index=0, placeholder="Select embedding", help="Select the Embedding function:", disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded'] ) st.markdown( ":warning: [Usage disclaimer](https://github.com/lfoppiano/document-qa?tab=readme-ov-file#disclaimer-on-data-security-and-privacy-%EF%B8%8F) :warning: ") if (model in OPEN_MODELS) and model not in st.session_state['api_keys']: if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ: api_key = st.text_input('Huggingface API Key', type="password") st.markdown("Get it [here](https://huggingface.co/docs/hub/security-tokens)") else: api_key = os.environ['HUGGINGFACEHUB_API_TOKEN'] if api_key: # st.session_state['api_key'] = is_api_key_provided = True if model not in st.session_state['rqa'] or model not in st.session_state['api_keys']: with st.spinner("Preparing environment"): st.session_state['api_keys'][model] = api_key # if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ: # os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key st.session_state['rqa'][model] = init_qa(model, embedding_name) elif model in OPENAI_MODELS and model not in st.session_state['api_keys']: if 'OPENAI_API_KEY' not in os.environ: api_key = st.text_input('OpenAI API Key', type="password") st.markdown("Get it [here](https://platform.openai.com/account/api-keys)") else: api_key = os.environ['OPENAI_API_KEY'] if api_key: if model not in st.session_state['rqa'] or model not in st.session_state['api_keys']: with st.spinner("Preparing environment"): st.session_state['api_keys'][model] = api_key if 'OPENAI_API_KEY' not in os.environ: st.session_state['rqa'][model] = init_qa(model, st.session_state['embeddings'], api_key) else: st.session_state['rqa'][model] = init_qa(model, st.session_state['embeddings']) # else: # is_api_key_provided = st.session_state['api_key'] st.button( 'Reset chat memory.', key="reset-memory-button", on_click=clear_memory, help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.", disabled=model in st.session_state['rqa'] and st.session_state['rqa'][model].memory is None) left_column, right_column = st.columns([1, 1]) with right_column: st.title("📝 Scientific Document Insights Q/A") st.subheader("Upload a scientific article in PDF, ask questions, get insights.") st.markdown( ":warning: Do not upload sensitive data. We **temporarily** store text from the uploaded PDF documents solely for the purpose of processing your request, and we **do not assume responsibility** for any subsequent use or handling of the data submitted to third parties LLMs.") uploaded_file = st.file_uploader( "Upload an article", type=("pdf", "txt"), on_change=new_file, disabled=st.session_state['model'] is not None and st.session_state['model'] not in st.session_state['api_keys'], help="The full-text is extracted using Grobid." ) question = st.chat_input( "Ask something about the article", # placeholder="Can you give me a short summary?", disabled=not uploaded_file ) query_modes = { "llm": "LLM Q/A", "embeddings": "Embeddings", "question_coefficient": "Question coefficient" } with st.sidebar: st.header("Settings") mode = st.radio( "Query mode", ("llm", "embeddings", "question_coefficient"), disabled=not uploaded_file, index=0, horizontal=True, format_func=lambda x: query_modes[x], help="LLM will respond the question, Embedding will show the " "relevant paragraphs to the question in the paper. " "Question coefficient attempt to estimate how effective the question will be answered." ) # Add a checkbox for showing annotations # st.session_state['show_annotations'] = st.checkbox("Show annotations", value=True) # st.session_state['should_show_annotations'] = st.checkbox("Show annotations", value=True) chunk_size = st.slider("Text chunks size", -1, 2000, value=-1, help="Size of chunks in which split the document. -1: use paragraphs, > 0 paragraphs are aggregated.", disabled=uploaded_file is not None) if chunk_size == -1: context_size = st.slider("Context size (paragraphs)", 3, 20, value=10, help="Number of paragraphs to consider when answering a question", disabled=not uploaded_file) else: context_size = st.slider("Context size (chunks)", 3, 10, value=4, help="Number of chunks to consider when answering a question", disabled=not uploaded_file) st.session_state['ner_processing'] = st.checkbox("Identify materials and properties.") st.markdown( 'The LLM responses undergo post-processing to extract physical quantities, measurements, and materials mentions.', unsafe_allow_html=True) st.session_state['pdf_rendering'] = st.radio( "PDF rendering mode", ("unwrap", "legacy_embed"), index=0, disabled=not uploaded_file, help="PDF rendering engine." "Note: The Legacy PDF viewer does not support annotations and might not work on Chrome.", format_func=lambda q: "Legacy PDF Viewer" if q == "legacy_embed" else "Streamlit PDF Viewer (Pdf.js)" ) st.divider() st.header("Documentation") st.markdown("https://github.com/lfoppiano/document-qa") st.markdown( """Upload a scientific article as PDF document. Once the spinner stops, you can proceed to ask your questions.""") if st.session_state['git_rev'] != "unknown": st.markdown("**Revision number**: [" + st.session_state[ 'git_rev'] + "](https://github.com/lfoppiano/document-qa/commit/" + st.session_state['git_rev'] + ")") st.header("Query mode (Advanced use)") st.markdown( """By default, the mode is set to LLM (Language Model) which enables question/answering. You can directly ask questions related to the document content, and the system will answer the question using content from the document.""") st.markdown( """If you switch the mode to "Embedding," the system will return specific chunks from the document that are semantically related to your query. This mode helps to test why sometimes the answers are not satisfying or incomplete. """) if uploaded_file and not st.session_state.loaded_embeddings: if model not in st.session_state['api_keys']: st.error("Before uploading a document, you must enter the API key. ") st.stop() with right_column: with st.spinner('Reading file, calling Grobid, and creating memory embeddings...'): binary = uploaded_file.getvalue() tmp_file = NamedTemporaryFile() tmp_file.write(bytearray(binary)) st.session_state['binary'] = binary st.session_state['doc_id'] = hash = st.session_state['rqa'][model].create_memory_embeddings(tmp_file.name, chunk_size=chunk_size, perc_overlap=0.1) st.session_state['loaded_embeddings'] = True st.session_state.messages = [] # timestamp = datetime.utcnow() def rgb_to_hex(rgb): return "#{:02x}{:02x}{:02x}".format(*rgb) def generate_color_gradient(num_elements): # Define warm and cold colors in RGB format warm_color = (255, 165, 0) # Orange cold_color = (0, 0, 255) # Blue # Generate a linear gradient of colors color_gradient = [ rgb_to_hex(tuple(int(warm * (1 - i / num_elements) + cold * (i / num_elements)) for warm, cold in zip(warm_color, cold_color))) for i in range(num_elements) ] return color_gradient with right_column: # css = ''' # # ''' # st.markdown(css, unsafe_allow_html=True) # st.markdown( # """ # # """, # unsafe_allow_html=True, # ) if st.session_state.loaded_embeddings and question and len(question) > 0 and st.session_state.doc_id: for message in st.session_state.messages: with st.chat_message(message["role"]): if message['mode'] == "llm": st.markdown(message["content"], unsafe_allow_html=True) elif message['mode'] == "embeddings": st.write(message["content"]) if message['mode'] == "question_coefficient": st.markdown(message["content"], unsafe_allow_html=True) if model not in st.session_state['rqa']: st.error("The API Key for the " + model + " is missing. Please add it before sending any query. `") st.stop() with st.chat_message("user"): st.markdown(question) st.session_state.messages.append({"role": "user", "mode": mode, "content": question}) text_response = None if mode == "embeddings": with st.spinner("Fetching the relevant context..."): text_response, coordinates = st.session_state['rqa'][model].query_storage( question, st.session_state.doc_id, context_size=context_size ) elif mode == "llm": with st.spinner("Generating LLM response..."): _, text_response, coordinates = st.session_state['rqa'][model].query_document( question, st.session_state.doc_id, context_size=context_size ) elif mode == "question_coefficient": with st.spinner("Estimate question/context relevancy..."): text_response, coordinates = st.session_state['rqa'][model].analyse_query( question, st.session_state.doc_id, context_size=context_size ) annotations = [[GrobidAggregationProcessor.box_to_dict([cs for cs in c.split(",")]) for c in coord_doc] for coord_doc in coordinates] gradients = generate_color_gradient(len(annotations)) for i, color in enumerate(gradients): for annotation in annotations[i]: annotation['color'] = color st.session_state['annotations'] = [annotation for annotation_doc in annotations for annotation in annotation_doc] if not text_response: st.error("Something went wrong. Contact Luca Foppiano (Foppiano.Luca@nims.co.jp) to report the issue.") with st.chat_message("assistant"): if mode == "llm": if st.session_state['ner_processing']: with st.spinner("Processing NER on LLM response..."): entities = gqa.process_single_text(text_response) decorated_text = decorate_text_with_annotations(text_response.strip(), entities) decorated_text = decorated_text.replace('class="label material"', 'style="color:green"') decorated_text = re.sub(r'class="label[^"]+"', 'style="color:orange"', decorated_text) text_response = decorated_text st.markdown(text_response, unsafe_allow_html=True) else: st.write(text_response) st.session_state.messages.append({"role": "assistant", "mode": mode, "content": text_response}) elif st.session_state.loaded_embeddings and st.session_state.doc_id: play_old_messages() with left_column: if st.session_state['binary']: pdf_viewer( input=st.session_state['binary'], width=600, height=800, annotation_outline_size=1, annotations=st.session_state['annotations'], rendering=st.session_state['pdf_rendering'] )