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import re
import textwrap
import gradio as gr
from bs4 import BeautifulSoup
from modules import chat
from modules.logging_colors import logger
from .chromadb import add_chunks_to_collector, make_collector
from .download_urls import download_urls
params = {
'chunk_count': 5,
'chunk_count_initial': 10,
'time_weight': 0,
'chunk_length': 700,
'chunk_separator': '',
'strong_cleanup': False,
'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="lxml")
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 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
# get history as being modified when using regenerate.
history = kwargs['history']
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
else:
def make_single_exchange(id_):
output = ''
output += f"{state['name1']}: {history['internal'][id_][0]}\n"
output += f"{state['name2']}: {history['internal'][id_][1]}\n"
return output
if len(history['internal']) > params['chunk_count'] and user_input != '':
chunks = []
hist_size = len(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(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 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': [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, state, is_chat=False):
if 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("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_settings.click(apply_settings, [chunk_count, chunk_count_initial, time_weight], last_updated, show_progress=False)
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