import os import re import pandas as pd from pathlib import Path import glob from llama_index import GPTVectorStoreIndex, download_loader, SimpleDirectoryReader, SimpleWebPageReader from langchain.document_loaders import PyPDFLoader, TextLoader from langchain.agents import initialize_agent, Tool from langchain.llms import OpenAI from langchain.chains.conversation.memory import ConversationBufferMemory from langchain.docstore.document import Document import src.utils as utils import logging logging.basicConfig( format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S" ) logger = logging.getLogger(__name__) import warnings warnings.filterwarnings('ignore') class DATA_LOADER: def __init__(self): # Instantiate UTILS class object self.utils_obj = utils.UTILS() def load_documents_from_urls(self, urls=[], doc_type='urls'): url_documents = self.load_document(doc_type=doc_type, urls=urls) return url_documents def load_documents_from_pdf(self, doc_filepath='', urls=[], doc_type='pdf'): if doc_type == 'pdf': pdf_documents = self.load_document(doc_type=doc_type, doc_filepath=doc_filepath) elif doc_type == 'online_pdf': pdf_documents = self.load_document(doc_type=doc_type, urls=urls) return pdf_documents def load_documents_from_directory(self, doc_filepath='', doc_type='directory'): doc_documents = self.load_document(doc_type=doc_type, doc_filepath=doc_filepath) return doc_documents def load_documents_from_text(self, doc_filepath='', doc_type='textfile'): text_documents = self.load_document(doc_type=doc_type, doc_filepath=doc_filepath) return text_documents def pdf_loader(self, filepath): loader = PyPDFLoader(filepath) return loader.load_and_split() def text_loader(self, filepath): loader = TextLoader(filepath) return loader.load() def load_document(self, doc_type='pdf', doc_filepath='', urls=[] ): logger.info(f'Loading {doc_type} in raw format from: {doc_filepath}') documents = [] # Validation checks if doc_type in ['directory', 'pdf', 'textfile']: if not os.path.exists(doc_filepath): logger.warning(f"{doc_filepath} does not exist, nothing can be loaded!") return documents elif doc_type in ['online_pdf', 'urls']: if len(urls) == 0: logger.warning(f"URLs list empty, nothing can be loaded!") return documents ######### Load documents ######### # Load PDF if doc_type == 'pdf': # Load multiple PDFs from directory if os.path.isdir(doc_filepath): pdfs = glob.glob(f"{doc_filepath}/*.pdf") logger.info(f'Total PDF files to load: {len(pdfs)}') for pdf in pdfs: documents.extend(self.pdf_loader(pdf)) # Loading from a single PDF file elif os.path.isfile(doc_filepath) and doc_filepath.endswith('.pdf'): documents.extend(self.pdf_loader(doc_filepath)) # Load PDFs from online (urls). Can read multiple PDFs from multiple URLs in one-shot elif doc_type == 'online_pdf': logger.info(f'URLs to load Online PDFs are from: {urls}') valid_urls = self.utils_obj.validate_url_format( urls=urls, url_type=doc_type ) for url in valid_urls: # Load and split PDF pages per document documents.extend(self.pdf_loader(url)) # Load data from URLs (can load data from multiple URLs) elif doc_type == 'urls': logger.info(f'URLs to load data from are: {urls}') valid_urls = self.utils_obj.validate_url_format( urls=urls, url_type=doc_type ) # Load data from URLs docs = SimpleWebPageReader(html_to_text=True).load_data(valid_urls) docs = [Document(page_content=doc.text) for doc in docs] documents.extend(docs) # Load data from text file(s) elif doc_type == 'textfile': # Load multiple text files from directory if os.path.isdir(doc_filepath): text_files = glob.glob(f"{doc_filepath}/*.txt") logger.info(f'Total text files to load: {len(text_files)}') for tf in text_files: documents.extend(self.text_loader(tf)) # Loading from a single text file elif os.path.isfile(doc_filepath) and doc_filepath.endswith('.txt'): documents.extend(self.text_loader(doc_filepath)) # Load data from files on the local directory (files may be of type .pdf, .txt, .doc, etc.) elif doc_type == 'directory': # Load multiple PDFs from directory if os.path.isdir(doc_filepath): documents = SimpleDirectoryReader( input_dir=doc_filepath ).load_data() # Loading from a file elif os.path.isfile(doc_filepath): documents.extend(SimpleDirectoryReader( input_files=[doc_filepath] ).load_data()) # Load data from URLs in Knowledge Base format elif doc_type == 'url-kb': KnowledgeBaseWebReader = download_loader("KnowledgeBaseWebReader") loader = KnowledgeBaseWebReader() for url in urls: doc = loader.load_data( root_url=url, link_selectors=['.article-list a', '.article-list a'], article_path='/articles', body_selector='.article-body', title_selector='.article-title', subtitle_selector='.article-subtitle', ) documents.extend(doc) # Load data from URLs and create an agent chain using ChatGPT elif doc_type == 'url-chatgpt': BeautifulSoupWebReader = download_loader("BeautifulSoupWebReader") loader = BeautifulSoupWebReader() # Load data from URLs documents = loader.load_data(urls=urls) # Build the Vector database index = GPTVectorStoreIndex(documents) tools = [ Tool( name="Website Index", func=lambda q: index.query(q), description=f"Useful when you want answer questions about the text retrieved from websites.", ), ] # Call ChatGPT API llm = OpenAI(temperature=0) # Keep temperature=0 to search from the given urls only memory = ConversationBufferMemory(memory_key="chat_history") agent_chain = initialize_agent( tools, llm, agent="zero-shot-react-description", memory=memory ) output = agent_chain.run(input="What language is on this website?") # Clean documents documents = self.clean_documents(documents) logger.info(f'{doc_type} in raw format from: {doc_filepath} loaded successfully!') return documents def clean_documents( self, documents ): cleaned_documents = [] for document in documents: if hasattr(document, 'page_content'): document.page_content = self.utils_obj.replace_newlines_and_spaces(document.page_content) elif hasattr(document, 'text'): document.text = self.utils_obj.replace_newlines_and_spaces(document.text) else: document = self.utils_obj.replace_newlines_and_spaces(document) cleaned_documents.append(document) return cleaned_documents def load_external_links_used_by_FTAs(self, sheet_filepath='./data/urls_used_by_ftas/external_links_used_by_FTAs.xlsx' ): xls = pd.ExcelFile(sheet_filepath) df = pd.DataFrame(columns=['S.No.', 'Link used for', 'Link type', 'Link']) for sheet_name in xls.sheet_names: sheet = pd.read_excel(xls, sheet_name) if sheet.shape[0] > 0: df = pd.concat([df, sheet]) else: logger.info(f'{sheet_name} has no content.') df = df[['Link used for', 'Link type', 'Link']] # Clean df df = self.utils_obj.clean_df(df) logger.info(f'Total links available across all cities: {df.shape[0]}') return df