File size: 16,019 Bytes
49e32ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9118536
49e32ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9118536
49e32ea
 
 
 
 
 
 
 
 
 
9118536
 
 
 
49e32ea
9118536
49e32ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9118536
 
 
41ed1b7
9118536
 
 
 
 
 
49e32ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
41ed1b7
9118536
 
 
 
49e32ea
 
 
 
 
 
 
 
41ed1b7
49e32ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
41ed1b7
 
 
49e32ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
41ed1b7
49e32ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
# ---
# 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"(?<!\n\s)\n(?!\s\n)", " ", text.strip())
        # Remove multiple newlines
        text = re.sub(r"\n\s*\n", "\n\n", text)
        text = re.sub(r"  ", " ", text)
        # Add full stops and new lines between words with no space between where the second one has a capital letter
        text = re.sub(r'(?<=[a-z])(?=[A-Z])', '. \n\n', text)

        return text

def parse_pdf(file) -> 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("---")