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| import base64 | |
| import os | |
| import re | |
| from hashlib import blake2b | |
| from tempfile import NamedTemporaryFile | |
| import dotenv | |
| from grobid_quantities.quantities import QuantitiesAPI | |
| from langchain.llms.huggingface_hub import HuggingFaceHub | |
| dotenv.load_dotenv(override=True) | |
| import streamlit as st | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings | |
| from document_qa.document_qa_engine import DocumentQAEngine | |
| from document_qa.grobid_processors import GrobidAggregationProcessor, decorate_text_with_annotations | |
| from grobid_client_generic import GrobidClientGeneric | |
| 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 'binary' not in st.session_state: | |
| st.session_state['binary'] = 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." | |
| } | |
| ) | |
| def new_file(): | |
| st.session_state['loaded_embeddings'] = None | |
| st.session_state['doc_id'] = None | |
| st.session_state['uploaded'] = True | |
| # @st.cache_resource | |
| def init_qa(model, api_key=None): | |
| if model == 'chatgpt-3.5-turbo': | |
| if api_key: | |
| chat = ChatOpenAI(model_name="gpt-3.5-turbo", | |
| temperature=0, | |
| openai_api_key=api_key, | |
| frequency_penalty=0.1) | |
| embeddings = OpenAIEmbeddings(openai_api_key=api_key) | |
| else: | |
| chat = ChatOpenAI(model_name="gpt-3.5-turbo", | |
| temperature=0, | |
| frequency_penalty=0.1) | |
| embeddings = OpenAIEmbeddings() | |
| elif model == 'mistral-7b-instruct-v0.1': | |
| chat = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.1", | |
| model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048}) | |
| embeddings = HuggingFaceEmbeddings( | |
| model_name="all-MiniLM-L6-v2") | |
| elif model == 'zephyr-7b-beta': | |
| chat = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", | |
| model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048}) | |
| embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") | |
| else: | |
| st.error("The model was not loaded properly. Try reloading. ") | |
| st.stop() | |
| return DocumentQAEngine(chat, embeddings, grobid_url=os.environ['GROBID_URL']) | |
| def init_ner(): | |
| quantities_client = QuantitiesAPI(os.environ['GROBID_QUANTITIES_URL'], check_server=True) | |
| materials_client = GrobidClientGeneric(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(None, | |
| 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.radio( | |
| "Model", | |
| ("chatgpt-3.5-turbo", "mistral-7b-instruct-v0.1", "zephyr-7b-beta"), | |
| index=1, | |
| captions=[ | |
| "ChatGPT 3.5 Turbo + Ada-002-text (embeddings)", | |
| "Mistral-7B-Instruct-V0.1 + Sentence BERT (embeddings) :free:", | |
| "Zephyr-7B-beta + Sentence BERT (embeddings) :free:" | |
| ], | |
| help="Select the LLM model and embeddings you want to use.", | |
| disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded']) | |
| st.markdown( | |
| ":warning: Mistral and Zephyr are free to use, however requests might hit limits of the huggingface free API and fail. :warning: ") | |
| if (model == 'mistral-7b-instruct-v0.1' or model == 'zephyr-7b-beta') 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) | |
| elif model == 'chatgpt-3.5-turbo' 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, api_key) | |
| else: | |
| st.session_state['rqa'][model] = init_qa(model) | |
| # else: | |
| # is_api_key_provided = st.session_state['api_key'] | |
| 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 | |
| ) | |
| with st.sidebar: | |
| st.header("Settings") | |
| mode = st.radio("Query mode", ("LLM", "Embeddings"), disabled=not uploaded_file, index=0, horizontal=True, | |
| help="LLM will respond the question, Embedding will show the " | |
| "paragraphs relevant to the question in the paper.") | |
| chunk_size = st.slider("Chunks size", 100, 2000, value=250, | |
| help="Size of chunks in which the document is partitioned", | |
| disabled=uploaded_file is not None) | |
| context_size = st.slider("Context size", 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("Named Entities Recognition (NER) processing on LLM response") | |
| st.markdown( | |
| '**NER on LLM responses**: The responses from the LLMs are post-processed to extract <span style="color:orange">physical quantities, measurements</span> and <span style="color:green">materials</span> mentions.', | |
| unsafe_allow_html=True) | |
| 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. """) | |
| def get_pdf_display(binary): | |
| base64_pdf = base64.b64encode(binary).decode('utf-8') | |
| return F'<embed src="data:application/pdf;base64,{base64_pdf}" width="100%" height="700" type="application/pdf"></embed>' | |
| 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() | |
| with left_column: | |
| if st.session_state['binary']: | |
| left_column.markdown(get_pdf_display(st.session_state['binary']), unsafe_allow_html=True) | |
| with right_column: | |
| 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 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("Generating LLM response..."): | |
| text_response = st.session_state['rqa'][model].query_storage(question, st.session_state.doc_id, | |
| context_size=context_size) | |
| elif mode == "LLM": | |
| with st.spinner("Generating response..."): | |
| _, text_response = st.session_state['rqa'][model].query_document(question, st.session_state.doc_id, | |
| context_size=context_size) | |
| 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() | |