text-generation-webui/extensions/superbooga/script.py

import re
import textwrap

import gradio as gr
from bs4 import BeautifulSoup

from modules import chat, shared
from modules.logging_colors import logger

from .chromadb import add_chunks_to_collector, make_collector
from .download_urls import download_urls

import requests
import json
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

params = {
    'chunk_count': 5,
    'chunk_count_initial': 10,
    'time_weight': 0,
    'chunk_length': 700,
    'chunk_separator': '',
    'strong_cleanup': False,
    'semantic_cleanup': True,
    'semantic_weight': 0.5,
    'threads': 4,
}

collector = make_collector()
chat_collector = make_collector()


def feed_data_into_collector(corpus, chunk_len, chunk_sep):
    global collector

    # Defining variables
    chunk_len = int(chunk_len)
    chunk_sep = chunk_sep.replace(r'\n', '\n')
    cumulative = ''

    # Breaking the data into chunks and adding those to the db
    cumulative += "Breaking the input dataset...\n\n"
    yield cumulative
    if chunk_sep:
        data_chunks = corpus.split(chunk_sep)
        data_chunks = [[data_chunk[i:i + chunk_len] for i in range(0, len(data_chunk), chunk_len)] for data_chunk in data_chunks]
        data_chunks = [x for y in data_chunks for x in y]
    else:
        data_chunks = [corpus[i:i + chunk_len] for i in range(0, len(corpus), chunk_len)]

    cumulative += f"{len(data_chunks)} chunks have been found.\n\nAdding the chunks to the database...\n\n"
    yield cumulative
    add_chunks_to_collector(data_chunks, collector)
    cumulative += "Done."
    yield cumulative


def feed_file_into_collector(file, chunk_len, chunk_sep):
    yield 'Reading the input dataset...\n\n'
    text = file.decode('utf-8')
    for i in feed_data_into_collector(text, chunk_len, chunk_sep):
        yield i


def feed_url_into_collector(urls, chunk_len, chunk_sep, strong_cleanup, threads):
    all_text = ''
    cumulative = ''

    urls = urls.strip().split('\n')
    cumulative += f'Loading {len(urls)} URLs with {threads} threads...\n\n'
    yield cumulative
    for update, contents in download_urls(urls, threads=threads):
        yield cumulative + update

    cumulative += 'Processing the HTML sources...'
    yield cumulative
    for content in contents:
        soup = BeautifulSoup(content, features="html.parser")
        for script in soup(["script", "style"]):
            script.extract()

        strings = soup.stripped_strings
        if strong_cleanup:
            strings = [s for s in strings if re.search("[A-Za-z] ", s)]

        text = '\n'.join([s.strip() for s in strings])
        all_text += text

    for i in feed_data_into_collector(all_text, chunk_len, chunk_sep):
        yield i

def calculate_semantic_similarity(query_embedding, target_embedding):
    # Calculate cosine similarity between the query embedding and the target embedding
    similarity = cosine_similarity(query_embedding.reshape(1, -1), target_embedding.reshape(1, -1))
    return similarity[0][0]

def feed_search_into_collector(query, chunk_len, chunk_sep, strong_cleanup, semantic_cleanup, semantic_requirement, threads):
    # Load parameters from the config file
    with open('custom_search_engine_keys.json') as key_file:
        key = json.load(key_file)

    model = SentenceTransformer('all-MiniLM-L6-v2')
    query_embedding = model.encode([query])[0]

    # Set up API endpoint and parameters
    url = "https://www.googleapis.com/customsearch/v1"

    # Retrieve the values from the config dictionary
    params = {
        "key": key.get("key", "default_key_value"),
        "cx": key.get("cx", "default_custom_engine_value"),
        "q": str(query),
    }

    if "default_key_value" in str(params):
        print("You need to provide an API key, by modifying the custom_search_engine_keys.json in oobabooga_windows \ text-generation-webui.\nSkipping search")
        return query

    if "default_custom_engine_value" in str(params):
        print("You need to provide an CSE ID, by modifying the script.py in oobabooga_windows \ text-generation-webui.\nSkipping search")
        return query

    # Send API request
    response = requests.get(url, params=params)

    # Parse JSON response
    data = response.json()

    # get the result items
    search_items = data.get("items")

    # iterate over 10 results found
    urls = ""
    for i, search_item in enumerate(search_items, start=1):
        if semantic_cleanup:
            # get titles and descriptions and use that to semantically weight the search result
            # get the page title
            title = search_item.get("title")
            # page snippet
            snippet = search_item.get("snippet")

            target_sentence = str(title) + " " + str(snippet)
            target_embedding = model.encode([target_sentence])[0]

            similarity_score = calculate_semantic_similarity(query_embedding, target_embedding)

            if similarity_score < semantic_requirement:
                continue


        # extract the page url and add it to the urls to download
        link = search_item.get("link")
        urls += link + "\n"

    # Call the original feed_url_into_collector function instead of duplicating the code
    result_generator = feed_url_into_collector(urls, chunk_len, chunk_sep, strong_cleanup, threads)

    # Consume the yielded values
    for result in result_generator:
        yield result


def apply_settings(chunk_count, chunk_count_initial, time_weight):
    global params
    params['chunk_count'] = int(chunk_count)
    params['chunk_count_initial'] = int(chunk_count_initial)
    params['time_weight'] = time_weight
    settings_to_display = {k: params[k] for k in params if k in ['chunk_count', 'chunk_count_initial', 'time_weight']}
    yield f"The following settings are now active: {str(settings_to_display)}"


def custom_generate_chat_prompt(user_input, state, **kwargs):
    global chat_collector

    if state['mode'] == 'instruct':
        results = collector.get_sorted(user_input, n_results=params['chunk_count'])
        additional_context = '\nYour reply should be based on the context below:\n\n' + '\n'.join(results)
        user_input += additional_context
        logger.info(f'\n\n=== === ===\nAdding the following new context:\n{additional_context}\n=== === ===\n')
    else:

        def make_single_exchange(id_):
            output = ''
            output += f"{state['name1']}: {shared.history['internal'][id_][0]}\n"
            output += f"{state['name2']}: {shared.history['internal'][id_][1]}\n"
            return output

        if len(shared.history['internal']) > params['chunk_count'] and user_input != '':
            chunks = []
            hist_size = len(shared.history['internal'])
            for i in range(hist_size-1):
                chunks.append(make_single_exchange(i))

            add_chunks_to_collector(chunks, chat_collector)
            query = '\n'.join(shared.history['internal'][-1] + [user_input])
            try:
                best_ids = chat_collector.get_ids_sorted(query, n_results=params['chunk_count'], n_initial=params['chunk_count_initial'], time_weight=params['time_weight'])
                additional_context = '\n'
                for id_ in best_ids:
                    if shared.history['internal'][id_][0] != '<|BEGIN-VISIBLE-CHAT|>':
                        additional_context += make_single_exchange(id_)

                logger.warning(f'Adding the following new context:\n{additional_context}')
                state['context'] = state['context'].strip() + '\n' + additional_context
                kwargs['history'] = {
                    'internal': [shared.history['internal'][i] for i in range(hist_size) if i not in best_ids],
                    'visible': ''
                }
            except RuntimeError:
                logger.error("Couldn't query the database, moving on...")

    return chat.generate_chat_prompt(user_input, state, **kwargs)


def remove_special_tokens(string):
    pattern = r'(<\|begin-user-input\|>|<\|end-user-input\|>|<\|injection-point\|>)'
    return re.sub(pattern, '', string)


def input_modifier(string):
    if shared.is_chat():
        return string

    # Find the user input
    pattern = re.compile(r"<\|begin-user-input\|>(.*?)<\|end-user-input\|>", re.DOTALL)
    match = re.search(pattern, string)
    if match:
        user_input = match.group(1).strip()

        # Get the most similar chunks
        results = collector.get_sorted(user_input, n_results=params['chunk_count'])

        # Make the injection
        string = string.replace('<|injection-point|>', '\n'.join(results))

    return remove_special_tokens(string)


def ui():
    with gr.Accordion("Click for more information...", open=False):
        gr.Markdown(textwrap.dedent("""

        ## About

        This extension takes a dataset as input, breaks it into chunks, and adds the result to a local/offline Chroma database.

        The database is then queried during inference time to get the excerpts that are closest to your input. The idea is to create an arbitrarily large pseudo context.

        The core methodology was developed and contributed by kaiokendev, who is working on improvements to the method in this repository: https://github.com/kaiokendev/superbig

        ## Data input

        Start by entering some data in the interface below and then clicking on "Load data".

        Each time you load some new data, the old chunks are discarded.

        ## Chat mode

        #### Instruct

        On each turn, the chunks will be compared to your current input and the most relevant matches will be appended to the input in the following format:

        ```
        Consider the excerpts below as additional context:
        ...
        ```

        The injection doesn't make it into the chat history. It is only used in the current generation.

        #### Regular chat

        The chunks from the external data sources are ignored, and the chroma database is built based on the chat history instead. The most relevant past exchanges relative to the present input are added to the context string. This way, the extension acts as a long term memory.

        ## Notebook/default modes

        Your question must be manually specified between `<|begin-user-input|>` and `<|end-user-input|>` tags, and the injection point must be specified with `<|injection-point|>`.

        The special tokens mentioned above (`<|begin-user-input|>`, `<|end-user-input|>`, and `<|injection-point|>`) are removed in the background before the text generation begins.

        Here is an example in Vicuna 1.1 format:

        ```
        A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.

        USER:

        <|begin-user-input|>
        What datasets are mentioned in the text below?
        <|end-user-input|>

        <|injection-point|>

        ASSISTANT:
        ```

        ⚠️  For best results, make sure to remove the spaces and new line characters after `ASSISTANT:`.

        *This extension is currently experimental and under development.*

        """))

    with gr.Row():
        with gr.Column(min_width=600):
            with gr.Tab("Text input"):
                data_input = gr.Textbox(lines=20, label='Input data')
                update_data = gr.Button('Load data')

            with gr.Tab("URL input"):
                url_input = gr.Textbox(lines=10, label='Input URLs', info='Enter one or more URLs separated by newline characters.')
                strong_cleanup = gr.Checkbox(value=params['strong_cleanup'], label='Strong cleanup', info='Only keeps html elements that look like long-form text.')
                threads = gr.Number(value=params['threads'], label='Threads', info='The number of threads to use while downloading the URLs.', precision=0)
                update_url = gr.Button('Load data')

            with gr.Tab("File input"):
                file_input = gr.File(label='Input file', type='binary')
                update_file = gr.Button('Load data')

            with gr.Tab("Search input"):
                search_term = gr.Textbox(lines=1, label='Search Input', info='Enter a google search, returned results will be fed into the DB')
                search_strong_cleanup = gr.Checkbox(value=params['strong_cleanup'], label='Strong cleanup', info='Only keeps html elements that look like long-form text.')

                semantic_cleanup = gr.Checkbox(value=params['semantic_cleanup'], label='Require semantic similarity', info='Only download pages with similar titles/snippets to the search based on a semantic search')
                semantic_requirement = gr.Slider(0, 1, value=params['semantic_weight'], label='Semantic similarity requirement', info='Defines the requirement of the semantic search. 0 = no culling of dissimilar pages.')

                search_threads = gr.Number(value=params['threads'], label='Threads', info='The number of threads to use while downloading the URLs.', precision=0)
                update_search = gr.Button('Load data')

                with gr.Accordion("Click for more information...", open=False):
                    gr.Markdown(textwrap.dedent("""

                    # installation/setup
                    Please follow the instruction found here to setup a custom search engine with Google.
                    https://www.thepythoncode.com/article/use-google-custom-search-engine-api-in-python
                    
                    create a file called "custom_search_engine_keys.json"
                    
                    Paste this text in it and replace with your values from the previous step:
                    "
                    {
                      "key": "Custom search engine key",
                      "cx": "Custom search engine cx number"
                    }
                    "
                
                    # usage
                    Enter a search query above.  Press the load data button.  This data will be added to the local chromaDB to be read into context at runtime.

                    """))

            with gr.Tab("Generation settings"):
                chunk_count = gr.Number(value=params['chunk_count'], label='Chunk count', info='The number of closest-matching chunks to include in the prompt.')
                gr.Markdown('Time weighting (optional, used in to make recently added chunks more likely to appear)')
                time_weight = gr.Slider(0, 1, value=params['time_weight'], label='Time weight', info='Defines the strength of the time weighting. 0 = no time weighting.')
                chunk_count_initial = gr.Number(value=params['chunk_count_initial'], label='Initial chunk count', info='The number of closest-matching chunks retrieved for time weight reordering in chat mode. This should be >= chunk count. -1 = All chunks are retrieved. Only used if time_weight > 0.')

                update_settings = gr.Button('Apply changes')

            chunk_len = gr.Number(value=params['chunk_length'], label='Chunk length', info='In characters, not tokens. This value is used when you click on "Load data".')
            chunk_sep = gr.Textbox(value=params['chunk_separator'], label='Chunk separator', info='Used to manually split chunks. Manually split chunks longer than chunk length are split again. This value is used when you click on "Load data".')
        with gr.Column():
            last_updated = gr.Markdown()

    update_data.click(feed_data_into_collector, [data_input, chunk_len, chunk_sep], last_updated, show_progress=False)
    update_url.click(feed_url_into_collector, [url_input, chunk_len, chunk_sep, strong_cleanup, threads], last_updated, show_progress=False)
    update_file.click(feed_file_into_collector, [file_input, chunk_len, chunk_sep], last_updated, show_progress=False)
    update_search.click(feed_search_into_collector, [search_term, chunk_len, chunk_sep, search_strong_cleanup, semantic_cleanup, semantic_requirement, search_threads], last_updated,show_progress=False)
    update_settings.click(apply_settings, [chunk_count, chunk_count_initial, time_weight], last_updated, show_progress=False)