# --- # jupyter: # jupytext: # formats: ipynb,py:light # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.14.6 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # # Ingest website to FAISS # ## Install/ import stuff we need import os from pathlib import Path import re import requests import pandas as pd import dateutil.parser from typing import TypeVar, List from langchain.embeddings import HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings from langchain.vectorstores.faiss import FAISS from langchain.vectorstores import Chroma from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.docstore.document import Document from bs4 import BeautifulSoup from docx import Document as Doc from pypdf import PdfReader PandasDataFrame = TypeVar('pd.core.frame.DataFrame') # - split_strat = ["\n\n", "\n", ".", "!", "?", ","] chunk_size = 500 chunk_overlap = 0 start_index = True ## Parse files def parse_file(file_paths): """ Accepts a list of file paths, determines each file's type based on its extension, and passes it to the relevant parsing function. Parameters: file_paths (list): List of file paths. div (str): (optional) Div to pull out of html file/url with BeautifulSoup Returns: dict: A dictionary with file paths as keys and their parsed content (or error message) as values. """ def determine_file_type(file_path): """ Determine the file type based on its extension. Parameters: file_path (str): Path to the file. Returns: str: File extension (e.g., '.pdf', '.docx', '.txt', '.html'). """ return os.path.splitext(file_path)[1].lower() if not isinstance(file_paths, list): raise ValueError("Expected a list of file paths.") extension_to_parser = { '.pdf': parse_pdf, '.docx': parse_docx, '.txt': parse_txt, '.html': parse_html, '.htm': parse_html # Considering both .html and .htm for HTML files } parsed_contents = {} file_names = [] for file_path in file_paths: print(file_path.name) #file = open(file_path.name, 'r') #print(file) file_extension = determine_file_type(file_path.name) if file_extension in extension_to_parser: parsed_contents[file_path.name] = extension_to_parser[file_extension](file_path.name) else: parsed_contents[file_path.name] = f"Unsupported file type: {file_extension}" filename_end = get_file_path_end(file_path.name) file_names.append(filename_end) return parsed_contents, file_names def text_regex_clean(text): # Merge hyphenated words text = re.sub(r"(\w+)-\n(\w+)", r"\1\2", text) # If a double newline ends in a letter, add a full stop. text = re.sub(r'(?<=[a-zA-Z])\n\n', '.\n\n', text) # Fix newlines in the middle of sentences text = re.sub(r"(? List[str]: """ Extract text from a PDF file. Parameters: file_path (str): Path to the PDF file. Returns: List[str]: Extracted text from the PDF. """ output = [] #for file in files: print(file) # .name pdf = PdfReader(file) #[i] .name[i] for page in pdf.pages: text = page.extract_text() text = text_regex_clean(text) output.append(text) return output def parse_docx(file_path): """ Reads the content of a .docx file and returns it as a string. Parameters: - file_path (str): Path to the .docx file. Returns: - str: Content of the .docx file. """ doc = Doc(file_path) full_text = [] for para in doc.paragraphs: para = text_regex_clean(para) full_text.append(para.text.replace(" ", " ").strip()) return '\n'.join(full_text) def parse_txt(file_path): """ Read text from a TXT or HTML file. Parameters: file_path (str): Path to the TXT or HTML file. Returns: str: Text content of the file. """ with open(file_path, 'r', encoding="utf-8") as file: file_contents = file.read().replace(" ", " ").strip() file_contents = text_regex_clean(file_contents) return file_contents def parse_html(page_url, div_filter="p"): """ Determine if the source is a web URL or a local HTML file, extract the content based on the div of choice. Also tries to extract dates (WIP) Parameters: page_url (str): The web URL or local file path. Returns: str: Extracted content. """ def is_web_url(s): """ Check if the input string is a web URL. """ return s.startswith("http://") or s.startswith("https://") def is_local_html_file(s): """ Check if the input string is a path to a local HTML file. """ return (s.endswith(".html") or s.endswith(".htm")) and os.path.isfile(s) def extract_text_from_source(source): """ Determine if the source is a web URL or a local HTML file, and then extract its content accordingly. Parameters: source (str): The web URL or local file path. Returns: str: Extracted content. """ if is_web_url(source): response = requests.get(source) response.raise_for_status() # Raise an HTTPError for bad responses return response.text.replace(" ", " ").strip() elif is_local_html_file(source): with open(source, 'r', encoding='utf-8') as file: file_out = file.read().replace return file_out else: raise ValueError("Input is neither a valid web URL nor a local HTML file path.") def clean_html_data(data, date_filter="", div_filt="p"): """ Extracts and cleans data from HTML content. Parameters: data (str): HTML content to be parsed. date_filter (str, optional): Date string to filter results. If set, only content with a date greater than this will be returned. div_filt (str, optional): HTML tag to search for text content. Defaults to "p". Returns: tuple: Contains extracted text and date as strings. Returns empty strings if not found. """ soup = BeautifulSoup(data, 'html.parser') # Function to exclude div with id "bar" def exclude_div_with_id_bar(tag): return tag.has_attr('id') and tag['id'] == 'related-links' text_elements = soup.find_all(div_filt) date_elements = soup.find_all(div_filt, {"class": "page-neutral-intro__meta"}) # Extract date date_out = "" if date_elements: date_out = re.search(">(.*?)<", str(date_elements[0])).group(1) date_dt = dateutil.parser.parse(date_out) if date_filter: date_filter_dt = dateutil.parser.parse(date_filter) if date_dt < date_filter_dt: return '', date_out # Extract text text_out_final = "" if text_elements: text_out_final = '\n'.join(paragraph.text for paragraph in text_elements) text_out_final = text_regex_clean(text_out_final) else: print(f"No elements found with tag '{div_filt}'. No text returned.") return text_out_final, date_out #page_url = "https://pypi.org/project/InstructorEmbedding/" #'https://www.ons.gov.uk/visualisations/censusareachanges/E09000022/index.html' html_text = extract_text_from_source(page_url) #print(page.text) texts = [] metadatas = [] clean_text, date = clean_html_data(html_text, date_filter="", div_filt=div_filter) texts.append(clean_text) metadatas.append({"source": page_url, "date":str(date)}) #print(metadatas) return texts, metadatas, page_url def get_file_path_end(file_path): match = re.search(r'(.*[\/\\])?(.+)$', file_path) filename_end = match.group(2) if match else '' return filename_end # + # Convert parsed text to docs # - def text_to_docs(text_dict: dict, chunk_size: int = chunk_size) -> List[Document]: """ Converts the output of parse_file (a dictionary of file paths to content) to a list of Documents with metadata. """ doc_sections = [] parent_doc_sections = [] for file_path, content in text_dict.items(): ext = os.path.splitext(file_path)[1].lower() # Depending on the file extension, handle the content if ext == '.pdf': docs, page_docs = pdf_text_to_docs(content, chunk_size) elif ext in ['.html', '.htm', '.txt', '.docx']: # Assuming you want to process HTML similarly to PDF in this context docs = html_text_to_docs(content, chunk_size) else: print(f"Unsupported file type {ext} for {file_path}. Skipping.") continue filename_end = get_file_path_end(file_path) #match = re.search(r'(.*[\/\\])?(.+)$', file_path) #filename_end = match.group(2) if match else '' # Add filename as metadata for doc in docs: doc.metadata["source"] = filename_end #for parent_doc in parent_docs: parent_doc.metadata["source"] = filename_end doc_sections.extend(docs) #parent_doc_sections.extend(parent_docs) return doc_sections#, page_docs def pdf_text_to_docs(text, chunk_size: int = chunk_size) -> List[Document]: """Converts a string or list of strings to a list of Documents with metadata.""" #print(text) if isinstance(text, str): # Take a single string as one page text = [text] page_docs = [Document(page_content=page, metadata={"page": page}) for page in text] # Add page numbers as metadata for i, doc in enumerate(page_docs): doc.metadata["page"] = i + 1 print("page docs are: ") print(page_docs) # Split pages into sections doc_sections = [] for doc in page_docs: #print("page content: ") #print(doc.page_content) if doc.page_content == '': sections = [''] else: text_splitter = RecursiveCharacterTextSplitter( chunk_size=chunk_size, separators=split_strat,#["\n\n", "\n", ".", "!", "?", ",", " ", ""], chunk_overlap=chunk_overlap, add_start_index=True ) sections = text_splitter.split_text(doc.page_content) for i, section in enumerate(sections): doc = Document( page_content=section, metadata={"page": doc.metadata["page"], "section": i, "page_section": f"{doc.metadata['page']}-{i}"}) doc_sections.append(doc) return doc_sections, page_docs#, parent_doc def html_text_to_docs(texts, metadatas, chunk_size:int = chunk_size): text_splitter = RecursiveCharacterTextSplitter( separators=split_strat,#["\n\n", "\n", ".", "!", "?", ",", " ", ""], chunk_size=chunk_size, chunk_overlap=chunk_overlap, length_function=len, add_start_index=True ) #print(texts) #print(metadatas) documents = text_splitter.create_documents(texts, metadatas=metadatas) for i, section in enumerate(documents): section.metadata["page_section"] = i + 1 return documents # # Functions for working with documents after loading them back in def pull_out_data(series): # define a lambda function to convert each string into a tuple to_tuple = lambda x: eval(x) # apply the lambda function to each element of the series series_tup = series.apply(to_tuple) series_tup_content = list(zip(*series_tup))[1] series = pd.Series(list(series_tup_content))#.str.replace("^Main post content", "", regex=True).str.strip() return series def docs_from_csv(df): import ast documents = [] page_content = pull_out_data(df["0"]) metadatas = pull_out_data(df["1"]) for x in range(0,len(df)): new_doc = Document(page_content=page_content[x], metadata=metadatas[x]) documents.append(new_doc) return documents def docs_from_lists(docs, metadatas): documents = [] for x, doc in enumerate(docs): new_doc = Document(page_content=doc, metadata=metadatas[x]) documents.append(new_doc) return documents def docs_elements_from_csv_save(docs_path="documents.csv"): documents = pd.read_csv(docs_path) docs_out = docs_from_csv(documents) out_df = pd.DataFrame(docs_out) docs_content = pull_out_data(out_df[0].astype(str)) docs_meta = pull_out_data(out_df[1].astype(str)) doc_sources = [d['source'] for d in docs_meta] return out_df, docs_content, docs_meta, doc_sources # ## Create embeddings and save faiss vector store to the path specified in `save_to` def load_embeddings(model_name = "thenlper/gte-base"): if model_name == "hkunlp/instructor-large": embeddings_func = HuggingFaceInstructEmbeddings(model_name=model_name, embed_instruction="Represent the paragraph for retrieval: ", query_instruction="Represent the question for retrieving supporting documents: " ) else: embeddings_func = HuggingFaceEmbeddings(model_name=model_name) global embeddings embeddings = embeddings_func return embeddings_func def embed_faiss_save_to_zip(docs_out, save_to="faiss_lambeth_census_embedding", model_name = "thenlper/gte-base"): load_embeddings(model_name=model_name) #embeddings_fast = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") print(f"> Total split documents: {len(docs_out)}") vectorstore = FAISS.from_documents(documents=docs_out, embedding=embeddings) if Path(save_to).exists(): vectorstore.save_local(folder_path=save_to) print("> DONE") print(f"> Saved to: {save_to}") ### Save as zip, then remove faiss/pkl files to allow for upload to huggingface import shutil shutil.make_archive(save_to, 'zip', save_to) os.remove(save_to + "/index.faiss") os.remove(save_to + "/index.pkl") shutil.move(save_to + '.zip', save_to + "/" + save_to + '.zip') return vectorstore def docs_to_chroma_save(embeddings, docs_out:PandasDataFrame, save_to:str): print(f"> Total split documents: {len(docs_out)}") vectordb = Chroma.from_documents(documents=docs_out, embedding=embeddings, persist_directory=save_to) # persiste the db to disk vectordb.persist() print("> DONE") print(f"> Saved to: {save_to}") return vectordb def sim_search_local_saved_vec(query, k_val, save_to="faiss_lambeth_census_embedding"): load_embeddings() docsearch = FAISS.load_local(folder_path=save_to, embeddings=embeddings) display(Markdown(question)) search = docsearch.similarity_search_with_score(query, k=k_val) for item in search: print(item[0].page_content) print(f"Page: {item[0].metadata['source']}") print(f"Date: {item[0].metadata['date']}") print(f"Score: {item[1]}") print("---")