import datetime import json import logging import os import shutil import sys import uuid from json import JSONDecodeError from pathlib import Path from time import sleep import openai import pandas as pd import pinecone import streamlit as st from annotated_text import annotation from haystack import Document from haystack.document_stores import PineconeDocumentStore from haystack.nodes import ( DocxToTextConverter, EmbeddingRetriever, FARMReader, FileTypeClassifier, PDFToTextConverter, PreProcessor, TextConverter, ) from haystack.pipelines import ExtractiveQAPipeline, Pipeline from markdown import markdown from sentence_transformers import SentenceTransformer from tqdm.auto import tqdm # get API key from top-right dropdown on OpenAI website openai.api_key = st.secrets["OPENAI_API_KEY"] index_name = "openai-ada-002-index" # connect to pinecone environment pinecone.init(api_key=st.secrets["pinecone_apikey"], environment="us-east1-gcp") embed_model = "text-embedding-ada-002" preprocessor = PreProcessor( clean_empty_lines=True, clean_whitespace=True, clean_header_footer=False, split_by="word", split_length=200, split_respect_sentence_boundary=True, ) file_type_classifier = FileTypeClassifier() text_converter = TextConverter() pdf_converter = PDFToTextConverter() docx_converter = DocxToTextConverter() # check if the abstractive-question-answering index exists if index_name not in pinecone.list_indexes(): # delete the current index and create the new index if it does not exist for delete_index in pinecone.list_indexes(): pinecone.delete_index(delete_index) pinecone.create_index(index_name, dimension=1536, metric="cosine") # connect to abstractive-question-answering index we created index = pinecone.Index(index_name) FILE_UPLOAD_PATH = "./data/uploads/" os.makedirs(FILE_UPLOAD_PATH, exist_ok=True) limit = 3750 def retrieve(query): res = openai.Embedding.create(input=[query], engine=embed_model) # retrieve from Pinecone xq = res["data"][0]["embedding"] # get relevant contexts res = index.query(xq, top_k=3, include_metadata=True) contexts = [x["metadata"].get("text", "") for x in res["matches"]] # build our prompt with the retrieved contexts included prompt_start = "You are question-answering chatbot by Wellous. Answer the question based on the context below. Your answer should be concise. If you are unsure or have no clear answer, respond with \"I don't know\" \n\n" + "Context:\n" prompt_end = f"\n\nQuestion: {query} (concise)\nAnswer:" # append contexts until hitting limit for i in range(1, len(contexts)): if len("\n\n---\n\n".join(contexts[:i])) >= limit: prompt = prompt_start + "\n\n---\n\n".join(contexts[: i - 1]) + prompt_end break elif i == len(contexts) - 1: prompt = prompt_start + "\n\n---\n\n".join(contexts) + prompt_end return prompt, contexts # first let's make it simpler to get answers def complete(prompt): # query text-davinci-003 print('prompt',prompt) res = openai.Completion.create( engine="text-davinci-003", prompt=prompt, temperature=0, max_tokens=400, top_p=1, frequency_penalty=0, presence_penalty=0, stop=None, ) return res["choices"][0]["text"].strip() def query(question, top_k_reader, top_k_retriever): # first we retrieve relevant items from Pinecone query_with_contexts, contexts = retrieve(question) return complete(query_with_contexts), contexts indexing_pipeline_with_classification = Pipeline() indexing_pipeline_with_classification.add_node( component=file_type_classifier, name="FileTypeClassifier", inputs=["File"] ) indexing_pipeline_with_classification.add_node( component=text_converter, name="TextConverter", inputs=["FileTypeClassifier.output_1"] ) indexing_pipeline_with_classification.add_node( component=pdf_converter, name="PdfConverter", inputs=["FileTypeClassifier.output_2"] ) indexing_pipeline_with_classification.add_node( component=docx_converter, name="DocxConverter", inputs=["FileTypeClassifier.output_4"] ) indexing_pipeline_with_classification.add_node( component=preprocessor, name="Preprocessor", inputs=["TextConverter", "PdfConverter", "DocxConverter"], ) def set_state_if_absent(key, value): if key not in st.session_state: st.session_state[key] = value # Adjust to a question that you would like users to see in the search bar when they load the UI: DEFAULT_QUESTION_AT_STARTUP = os.getenv( "DEFAULT_QUESTION_AT_STARTUP", "What is Bio-lingzhi?" ) DEFAULT_ANSWER_AT_STARTUP = os.getenv( "DEFAULT_ANSWER_AT_STARTUP", "-", ) # Sliders DEFAULT_DOCS_FROM_RETRIEVER = int(os.getenv("DEFAULT_DOCS_FROM_RETRIEVER", "3")) DEFAULT_NUMBER_OF_ANSWERS = int(os.getenv("DEFAULT_NUMBER_OF_ANSWERS", "3")) st.set_page_config( page_title="GPT3 and Langchain Demo" ) # Persistent state set_state_if_absent("question", DEFAULT_QUESTION_AT_STARTUP) set_state_if_absent("answer", DEFAULT_ANSWER_AT_STARTUP) set_state_if_absent("results", None) # Small callback to reset the interface in case the text of the question changes def reset_results(*args): st.session_state.answer = None st.session_state.results = None st.session_state.raw_json = None # Title st.write("# GPT3 and Langchain Demo") # st.markdown( # """ # This demo takes its data from the documents uploaded to the Pinecone index through this app. \n # Ask any question from the uploaded documents and Pinecone will retrieve the context for answers and GPT3 will answer them using the retrieved context. \n # *Note: do not use keywords, but full-fledged questions.* The demo is not optimized to deal with keyword queries and might misunderstand you. # """, # unsafe_allow_html=True, # ) # Sidebar # st.sidebar.header("Options") # st.sidebar.write("## File Upload:") # data_files = st.sidebar.file_uploader( # "upload", type=["pdf", "txt", "docx"], accept_multiple_files=True, label_visibility="hidden" # ) # print("data_files",data_files) ALL_FILES = [] META_DATA = [] # for data_file in data_files: # # Upload file # if data_file: # file_path = Path(FILE_UPLOAD_PATH) / f"{uuid.uuid4().hex}_{data_file.name}" # print("file_path",file_path) # print("data_file",data_file) # print("data_file.getbuffer()",data_file.getbuffer()) # with open(file_path, "wb") as f: # f.write(data_file.getbuffer()) # ALL_FILES.append(file_path) # st.sidebar.write(str(data_file.name) + "    ✅ ") # META_DATA.append({"filename": data_file.name}) text_file = 'wellous_products.txt' file_path = "./" f"{text_file}" print("file_path",file_path) ALL_FILES.append(file_path) META_DATA.append({"filename": text_file}) print("ALL_FILES",ALL_FILES) print("META_DATA",META_DATA) # if len(ALL_FILES) > 0: # document_store.update_embeddings(retriever, update_existing_embeddings=False) docs = indexing_pipeline_with_classification.run(file_paths=ALL_FILES, meta=META_DATA)[ "documents" ] index_name = "qa_demo" # we will use batches of 64 batch_size = 100 # docs = docs['documents'] with st.spinner("🧠    Performing indexing of uploaded documents... \n "): for i in range(0, len(docs), batch_size): # find end of batch i_end = min(i + batch_size, len(docs)) # extract batch batch = [doc.content for doc in docs[i:i_end]] # generate embeddings for batch try: res = openai.Embedding.create(input=batch, engine=embed_model) except Exception as e: done = False count = 0 while not done and count < 5: sleep(5) try: res = openai.Embedding.create(input=batch, engine=embed_model) done = True except: count += 1 pass if count >= 5: res = [] st.error(f"🐞 File indexing failed{str(e)}") if len(res) > 0: embeds = [record["embedding"] for record in res["data"]] # get metadata meta = [] for doc in docs[i:i_end]: meta_dict = doc.meta meta_dict["text"] = doc.content meta.append(meta_dict) # create unique IDs ids = [doc.id for doc in docs[i:i_end]] # add all to upsert list to_upsert = list(zip(ids, embeds, meta)) # upsert/insert these records to pinecone _ = index.upsert(vectors=to_upsert) # top_k_reader = st.sidebar.slider( # "Max. number of answers", # min_value=1, # max_value=10, # value=DEFAULT_NUMBER_OF_ANSWERS, # step=1, # on_change=reset_results, # ) # top_k_retriever = st.sidebar.slider( # "Max. number of documents from retriever", # min_value=1, # max_value=10, # value=DEFAULT_DOCS_FROM_RETRIEVER, # step=1, # on_change=reset_results, # ) # data_files = st.file_uploader( # "upload", type=["csv"], accept_multiple_files=True, label_visibility="hidden" # ) # for data_file in data_files: # # Upload file # if data_file: # raw_json = upload_doc(data_file) question = st.text_input( value=st.session_state.question, max_chars=100, on_change=reset_results, label="question", label_visibility="hidden", ) col1, col2 = st.columns(2) col1.markdown("", unsafe_allow_html=True) col2.markdown("", unsafe_allow_html=True) # Run button run_pressed = col1.button("Send") if run_pressed: run_query = run_pressed or question != st.session_state.question # Get results for query if run_query and question: reset_results() st.session_state.question = question with st.spinner("🧠    Performing neural search on documents... \n "): try: st.session_state.results = query(question, top_k_reader=None, top_k_retriever=None) except JSONDecodeError as je: st.error( "👓    An error occurred reading the results. Is the document store working?" ) except Exception as e: logging.exception(e) if "The server is busy processing requests" in str(e) or "503" in str(e): st.error("🧑‍🌾    All our workers are busy! Try again later.") else: st.error(f"🐞    An error occurred during the request. {str(e)}") if st.session_state.results: st.write("## Results:") result, contexts = st.session_state.results # answer, context = result.answer, result.context # start_idx = context.find(answer) # end_idx = start_idx + len(answer) # Hack due to this bug: https://github.com/streamlit/streamlit/issues/3190 try: # source = f"[{result.meta['Title']}]({result.meta['link']})" # st.write( # markdown(f'**Source:** {source} \n {context[:start_idx] } {str(annotation(answer, "ANSWER", "#8ef"))} {context[end_idx:]} \n '), # unsafe_allow_html=True, # ) all_contexts = '\n'.join(contexts) st.write(markdown(f"Answer: \n {result} \n"), unsafe_allow_html=True, ) except: # filename = result.meta.get('filename', "") # st.write( # markdown(f'From file: {filename} \n {context[:start_idx] } {str(annotation(answer, "ANSWER", "#8ef"))} {context[end_idx:]} \n '), # unsafe_allow_html=True, # ) st.write( markdown(f"Answer: {result}"), unsafe_allow_html=True, )