AI/text-generation-webui
1
0
Fork 0
mirror of https://github.com/oobabooga/text-generation-webui.git synced 2024-10-01 01:26:03 -04:00
Code Issues Packages Projects Releases Wiki Activity

Make superbooga & superboogav2 functional again (#5656)

Browse Source
This commit is contained in:
oobabooga 2024-03-07 15:03:18 -03:00 committed by GitHub
parent bae14c8f13
commit 2681f6f640
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
15 changed files with 189 additions and 257 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

56
extensions/superbooga/chromadb.py
View File

@ -1,43 +1,24 @@
import random
import chromadb
import posthog
import torch
from chromadb.config import Settings
from sentence_transformers import SentenceTransformer
from chromadb.utils import embedding_functions
from modules.logging_colors import logger
logger.info('Intercepting all calls to posthog :)')
# Intercept calls to posthog
posthog.capture = lambda *args, **kwargs: None
class Collecter():
embedder = embedding_functions.SentenceTransformerEmbeddingFunction("sentence-transformers/all-mpnet-base-v2")
class ChromaCollector():
def __init__(self):
pass
name = ''.join(random.choice('ab') for _ in range(10))
def add(self, texts: list[str]):
pass
def get(self, search_strings: list[str], n_results: int) -> list[str]:
pass
def clear(self):
pass
class Embedder():
def __init__(self):
pass
def embed(self, text: str) -> list[torch.Tensor]:
pass
class ChromaCollector(Collecter):
def __init__(self, embedder: Embedder):
super().__init__()
self.name = name
self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
self.embedder = embedder
self.collection = self.chroma_client.create_collection(name="context", embedding_function=embedder.embed)
self.collection = self.chroma_client.create_collection(name=name, embedding_function=embedder)
self.ids = []
def add(self, texts: list[str]):
@ -102,24 +83,15 @@ class ChromaCollector(Collecter):
return sorted(ids)
def clear(self):
self.collection.delete(ids=self.ids)
self.ids = []
class SentenceTransformerEmbedder(Embedder):
def __init__(self) -> None:
self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
self.embed = self.model.encode
self.chroma_client.delete_collection(name=self.name)
self.collection = self.chroma_client.create_collection(name=self.name, embedding_function=embedder)
def make_collector():
global embedder
return ChromaCollector(embedder)
return ChromaCollector()
def add_chunks_to_collector(chunks, collector):
collector.clear()
collector.add(chunks)
embedder = SentenceTransformerEmbedder()

2
extensions/superbooga/requirements.txt
View File

@ -1,5 +1,5 @@
beautifulsoup4==4.12.2
chromadb==0.3.18
chromadb==0.4.24
pandas==2.0.3
posthog==2.4.2
sentence_transformers==2.2.2

17
extensions/superboogav2/api.py
View File

@ -12,17 +12,16 @@ This module is responsible for the VectorDB API. It currently supports:
import json
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from urllib.parse import urlparse, parse_qs
from threading import Thread
from urllib.parse import parse_qs, urlparse
import extensions.superboogav2.parameters as parameters
from modules import shared
from modules.logging_colors import logger
from .chromadb import ChromaCollector
from .data_processor import process_and_add_to_collector
import extensions.superboogav2.parameters as parameters
class CustomThreadingHTTPServer(ThreadingHTTPServer):
def __init__(self, server_address, RequestHandlerClass, collector: ChromaCollector, bind_and_activate=True):
@ -38,7 +37,6 @@ class Handler(BaseHTTPRequestHandler):
self.collector = collector
super().__init__(request, client_address, server)
def _send_412_error(self, message):
self.send_response(412)
self.send_header("Content-type", "application/json")
@ -46,7 +44,6 @@ class Handler(BaseHTTPRequestHandler):
response = json.dumps({"error": message})
self.wfile.write(response.encode('utf-8'))
def _send_404_error(self):
self.send_response(404)
self.send_header("Content-type", "application/json")
@ -54,7 +51,6 @@ class Handler(BaseHTTPRequestHandler):
response = json.dumps({"error": "Resource not found"})
self.wfile.write(response.encode('utf-8'))
def _send_400_error(self, error_message: str):
self.send_response(400)
self.send_header("Content-type", "application/json")
@ -62,7 +58,6 @@ class Handler(BaseHTTPRequestHandler):
response = json.dumps({"error": error_message})
self.wfile.write(response.encode('utf-8'))
def _send_200_response(self, message: str):
self.send_response(200)
self.send_header("Content-type", "application/json")
@ -75,7 +70,6 @@ class Handler(BaseHTTPRequestHandler):
self.wfile.write(response.encode('utf-8'))
def _handle_get(self, search_strings: list[str], n_results: int, max_token_count: int, sort_param: str):
if sort_param == parameters.SORT_DISTANCE:
results = self.collector.get_sorted_by_dist(search_strings, n_results, max_token_count)
@ -88,11 +82,9 @@ class Handler(BaseHTTPRequestHandler):
"results": results
}
def do_GET(self):
self._send_404_error()
def do_POST(self):
try:
content_length = int(self.headers['Content-Length'])
@ -146,7 +138,6 @@ class Handler(BaseHTTPRequestHandler):
except Exception as e:
self._send_400_error(str(e))
def do_DELETE(self):
try:
parsed_path = urlparse(self.path)
@ -161,12 +152,10 @@ class Handler(BaseHTTPRequestHandler):
except Exception as e:
self._send_400_error(str(e))
def do_OPTIONS(self):
self.send_response(200)
self.end_headers()
def end_headers(self):
self.send_header('Access-Control-Allow-Origin', '*')
self.send_header('Access-Control-Allow-Methods', '*')
@ -197,7 +186,7 @@ class APIManager:
def stop_server(self):
if self.server is not None:
logger.info(f'Stopping chromaDB API.')
logger.info('Stopping chromaDB API.')
self.server.shutdown()
self.server.server_close()
self.server = None

4
extensions/superboogav2/benchmark.py
View File

@ -9,13 +9,13 @@ The benchmark function will return the score as an integer.
import datetime
import json
import os
from pathlib import Path
from .data_processor import process_and_add_to_collector, preprocess_text
from .data_processor import preprocess_text, process_and_add_to_collector
from .parameters import get_chunk_count, get_max_token_count
from .utils import create_metadata_source
def benchmark(config_path, collector):
# Get the current system date
sysdate = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")

13
extensions/superboogav2/chat_handler.py
View File

@ -4,16 +4,17 @@ This module is responsible for modifying the chat prompt and history.
import re
import extensions.superboogav2.parameters as parameters
from extensions.superboogav2.utils import (
create_context_text,
create_metadata_source
)
from modules import chat, shared
from modules.text_generation import get_encoded_length
from modules.logging_colors import logger
from modules.chat import load_character_memoized
from extensions.superboogav2.utils import create_context_text, create_metadata_source
from modules.logging_colors import logger
from modules.text_generation import get_encoded_length
from .data_processor import process_and_add_to_collector
from .chromadb import ChromaCollector
from .data_processor import process_and_add_to_collector
CHAT_METADATA = create_metadata_source('automatic-chat-insert')

90
extensions/superboogav2/chromadb.py
View File

@ -1,42 +1,23 @@
import threading
import chromadb
import posthog
import torch
import math
import random
import threading
import chromadb
import numpy as np
import extensions.superboogav2.parameters as parameters
import posthog
from chromadb.config import Settings
from sentence_transformers import SentenceTransformer
from chromadb.utils import embedding_functions
import extensions.superboogav2.parameters as parameters
from modules.logging_colors import logger
from modules.text_generation import encode, decode
from modules.text_generation import decode, encode
logger.debug('Intercepting all calls to posthog.')
# Intercept calls to posthog
posthog.capture = lambda *args, **kwargs: None
class Collecter():
def __init__(self):
pass
embedder = embedding_functions.SentenceTransformerEmbeddingFunction("sentence-transformers/all-mpnet-base-v2")
def add(self, texts: list[str], texts_with_context: list[str], starting_indices: list[int]):
pass
def get(self, search_strings: list[str], n_results: int) -> list[str]:
pass
def clear(self):
pass
class Embedder():
def __init__(self):
pass
def embed(self, text: str) -> list[torch.Tensor]:
pass
class Info:
def __init__(self, start_index, text_with_context, distance, id):
@ -93,12 +74,15 @@ class Info:
return not (s1_end < s2_start or s2_end < s1_start)
class ChromaCollector(Collecter):
def __init__(self, embedder: Embedder):
super().__init__()
class ChromaCollector():
def __init__(self):
name = ''.join(random.choice('ab') for _ in range(10))
self.name = name
self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
self.embedder = embedder
self.collection = self.chroma_client.create_collection(name="context", embedding_function=self.embedder.embed)
self.collection = self.chroma_client.create_collection(name=name, embedding_function=embedder)
self.ids = []
self.id_to_info = {}
self.embeddings_cache = {}
@ -126,7 +110,7 @@ class ChromaCollector(Collecter):
# If there are any non-existing texts, compute their embeddings all at once. Each call to embed has significant overhead.
if non_existing_texts:
non_existing_embeddings = self.embedder.embed(non_existing_texts).tolist()
non_existing_embeddings = embedder(non_existing_texts)
for text, embedding in zip(non_existing_texts, non_existing_embeddings):
self.embeddings_cache[text] = embedding
@ -145,7 +129,6 @@ class ChromaCollector(Collecter):
self.id_to_info.update(new_info)
self.ids.extend(new_ids)
def _split_texts_by_cache_hit(self, texts: list[str], new_ids: list[str], metadatas: list[dict]):
existing_texts, non_existing_texts = [], []
existing_embeddings = []
@ -169,7 +152,6 @@ class ChromaCollector(Collecter):
return (existing_texts, existing_embeddings, existing_ids, existing_metas), \
(non_existing_texts, non_existing_ids, non_existing_metas)
def _get_new_ids(self, num_new_ids: int):
if self.ids:
max_existing_id = max(int(id_) for id_ in self.ids)
@ -178,7 +160,6 @@ class ChromaCollector(Collecter):
return [str(i + max_existing_id + 1) for i in range(num_new_ids)]
def _find_min_max_start_index(self):
max_index, min_index = 0, float('inf')
for _, val in self.id_to_info.items():
@ -188,13 +169,14 @@ class ChromaCollector(Collecter):
min_index = val['start_index']
return min_index, max_index
# NB: Does not make sense to weigh excerpts from different documents.
# But let's say that's the user's problem. Perfect world scenario:
# Apply time weighing to different documents. For each document, then, add
# separate time weighing.
def _apply_sigmoid_time_weighing(self, infos: list[Info], document_len: int, time_steepness: float, time_power: float):
sigmoid = lambda x: 1 / (1 + np.exp(-x))
def sigmoid(x):
return 1 / (1 + np.exp(-x))
weights = sigmoid(time_steepness * np.linspace(-10, 10, document_len))
@ -210,7 +192,6 @@ class ChromaCollector(Collecter):
index = info.start_index
info.distance *= weights[index]
def _filter_outliers_by_median_distance(self, infos: list[Info], significant_level: float):
# Ensure there are infos to filter
if not infos:
@ -231,7 +212,6 @@ class ChromaCollector(Collecter):
return filtered_infos
def _merge_infos(self, infos: list[Info]):
merged_infos = []
current_info = infos[0]
@ -247,8 +227,8 @@ class ChromaCollector(Collecter):
merged_infos.append(current_info)
return merged_infos
# Main function for retrieving chunks by distance. It performs merging, time weighing, and mean filtering.
def _get_documents_ids_distances(self, search_strings: list[str], n_results: int):
n_results = min(len(self.ids), n_results)
if n_results == 0:
@ -280,22 +260,22 @@ class ChromaCollector(Collecter):
return texts_with_context, ids, distances
# Get chunks by similarity
def get(self, search_strings: list[str], n_results: int) -> list[str]:
with self.lock:
documents, _, _ = self._get_documents_ids_distances(search_strings, n_results)
return documents
# Get ids by similarity
def get_ids(self, search_strings: list[str], n_results: int) -> list[str]:
with self.lock:
_, ids, _ = self._get_documents_ids_distances(search_strings, n_results)
return ids
# Cutoff token count
def _get_documents_up_to_token_count(self, documents: list[str], max_token_count: int):
# TODO: Move to caller; We add delimiters there which might go over the limit.
current_token_count = 0
@ -318,8 +298,8 @@ class ChromaCollector(Collecter):
return return_documents
# Get chunks by similarity and then sort by ids
def get_sorted_by_ids(self, search_strings: list[str], n_results: int, max_token_count: int) -> list[str]:
with self.lock:
documents, ids, _ = self._get_documents_ids_distances(search_strings, n_results)
@ -327,8 +307,8 @@ class ChromaCollector(Collecter):
return self._get_documents_up_to_token_count(sorted_docs, max_token_count)
# Get chunks by similarity and then sort by distance (lowest distance is last).
def get_sorted_by_dist(self, search_strings: list[str], n_results: int, max_token_count: int) -> list[str]:
with self.lock:
documents, _, distances = self._get_documents_ids_distances(search_strings, n_results)
@ -340,7 +320,6 @@ class ChromaCollector(Collecter):
return return_documents
def delete(self, ids_to_delete: list[str], where: dict):
with self.lock:
ids_to_delete = self.collection.get(ids=ids_to_delete, where=where)['ids']
@ -354,23 +333,16 @@ class ChromaCollector(Collecter):
logger.info(f'Successfully deleted {len(ids_to_delete)} records from chromaDB.')
def clear(self):
with self.lock:
self.chroma_client.reset()
self.collection = self.chroma_client.create_collection("context", embedding_function=self.embedder.embed)
self.ids = []
self.id_to_info = {}
self.chroma_client.delete_collection(name=self.name)
self.collection = self.chroma_client.create_collection(name=self.name, embedding_function=embedder)
logger.info('Successfully cleared all records and reset chromaDB.')
class SentenceTransformerEmbedder(Embedder):
def __init__(self) -> None:
logger.debug('Creating Sentence Embedder...')
self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
self.embed = self.model.encode
def make_collector():
return ChromaCollector(SentenceTransformerEmbedder())
return ChromaCollector()

16
extensions/superboogav2/data_preprocessor.py
View File

@ -11,13 +11,12 @@ This module contains utils for preprocessing the text before converting it to em
* removing specific parts of speech (adverbs and interjections)
- TextSummarizer extracts the most important sentences from a long string using text-ranking.
"""
import pytextrank
import string
import spacy
import math
import nltk
import re
import string
import nltk
import spacy
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from num2words import num2words
@ -32,11 +31,9 @@ class TextPreprocessorBuilder:
_lemmatizer_cache = {}
_pos_remove_cache = {}
def __init__(self, text: str):
self.text = text
def to_lower(self):
# Match both words and non-word characters
tokens = re.findall(r'\b\w+\b|\W+', self.text)
@ -49,7 +46,6 @@ class TextPreprocessorBuilder:
self.text = "".join(tokens)
return self
def num_to_word(self, min_len: int = 1):
# Match both words and non-word characters
tokens = re.findall(r'\b\w+\b|\W+', self.text)
@ -62,7 +58,6 @@ class TextPreprocessorBuilder:
self.text = "".join(tokens)
return self
def num_to_char_long(self, min_len: int = 1):
# Match both words and non-word characters
tokens = re.findall(r'\b\w+\b|\W+', self.text)
@ -71,7 +66,9 @@ class TextPreprocessorBuilder:
if token.isdigit() and len(token) >= min_len:
# This is done to pay better attention to numbers (e.g. ticket numbers, thread numbers, post numbers)
# 740700 will become HHHHHHEEEEEAAAAHHHAAA
convert_token = lambda token: ''.join((chr(int(digit) + 65) * (i + 1)) for i, digit in enumerate(token[::-1]))[::-1]
def convert_token(token):
return ''.join((chr(int(digit) + 65) * (i + 1)) for i, digit in enumerate(token[::-1]))[::-1]
tokens[i] = convert_token(tokens[i])
self.text = "".join(tokens)
return self
@ -150,6 +147,7 @@ class TextPreprocessorBuilder:
def build(self):
return self.text
class TextSummarizer:
_nlp_pipeline = None
_cache = {}

5
extensions/superboogav2/data_processor.py
View File

@ -4,13 +4,14 @@ It will then split it into chunks of specified length. For each of those chunks,
It will only include full words.
"""
import re
import bisect
import re
import extensions.superboogav2.parameters as parameters
from .data_preprocessor import TextPreprocessorBuilder, TextSummarizer
from .chromadb import ChromaCollector
from .data_preprocessor import TextPreprocessorBuilder, TextSummarizer
def preprocess_text_no_summary(text) -> str:
builder = TextPreprocessorBuilder(text)

3
extensions/superboogav2/download_urls.py
View File

@ -1,7 +1,7 @@
import concurrent.futures
import requests
import re
import requests
from bs4 import BeautifulSoup
import extensions.superboogav2.parameters as parameters
@ -9,6 +9,7 @@ import extensions.superboogav2.parameters as parameters
from .data_processor import process_and_add_to_collector
from .utils import create_metadata_source
def _download_single(url):
response = requests.get(url, timeout=5)
if response.status_code == 200:

5
extensions/superboogav2/notebook_handler.py
View File

@ -4,13 +4,12 @@ This module is responsible for handling and modifying the notebook text.
import re
import extensions.superboogav2.parameters as parameters
from modules import shared
from modules.logging_colors import logger
from extensions.superboogav2.utils import create_context_text
from modules.logging_colors import logger
from .data_processor import preprocess_text
def _remove_special_tokens(string):
pattern = r'(<\|begin-user-input\|>|<\|end-user-input\|>|<\|injection-point\|>)'
return re.sub(pattern, '', string)

18
extensions/superboogav2/optimize.py
View File

@ -3,22 +3,24 @@ This module implements a hyperparameter optimization routine for the embedding a
Each run, the optimizer will set the default values inside the hyperparameters. At the end, it will output the best ones it has found.
"""
import re
import hashlib
import json
import optuna
import logging
import re
import gradio as gr
import numpy as np
import logging
import hashlib
logging.getLogger('optuna').setLevel(logging.WARNING)
import optuna
import extensions.superboogav2.parameters as parameters
logging.getLogger('optuna').setLevel(logging.WARNING)
from pathlib import Path
import extensions.superboogav2.parameters as parameters
from modules.logging_colors import logger
from .benchmark import benchmark
from .parameters import Parameters
from modules.logging_colors import logger
# Format the parameters into markdown format.
@ -67,7 +69,7 @@ def _get_params_hash(params):
def optimize(collector, progress=gr.Progress()):
# Inform the user that something is happening.
progress(0, desc=f'Setting Up...')
progress(0, desc='Setting Up...')
# Track the current step
current_step = 0

4
extensions/superboogav2/parameters.py
View File

@ -6,13 +6,11 @@ Each element in the JSON must have a `default` value which will be used for the
These categories define the range in which the optimizer will search. If the element is tagged with `"should_optimize": false`,
then the optimizer will only ever use the default value.
"""
import json
from pathlib import Path
import json
from modules.logging_colors import logger
NUM_TO_WORD_METHOD = 'Number to Word'
NUM_TO_CHAR_METHOD = 'Number to Char'
NUM_TO_CHAR_LONG_METHOD = 'Number to Multi-Char'

2
extensions/superboogav2/requirements.txt
View File

@ -1,5 +1,5 @@
beautifulsoup4==4.12.2
chromadb==0.3.18
chromadb==0.4.24
lxml
optuna
pandas==2.0.3

28
extensions/superboogav2/script.py
View File

@ -7,28 +7,29 @@ from pathlib import Path
# Point to where nltk will find the required data.
os.environ['NLTK_DATA'] = str(Path("extensions/superboogav2/nltk_data").resolve())
import textwrap
import codecs
import textwrap
import gradio as gr
import extensions.superboogav2.parameters as parameters
from modules.logging_colors import logger
from modules import shared
from modules.logging_colors import logger
from .utils import create_metadata_source
from .chromadb import make_collector
from .download_urls import feed_url_into_collector
from .data_processor import process_and_add_to_collector
from .benchmark import benchmark
from .optimize import optimize
from .notebook_handler import input_modifier_internal
from .chat_handler import custom_generate_chat_prompt_internal
from .api import APIManager
from .benchmark import benchmark
from .chat_handler import custom_generate_chat_prompt_internal
from .chromadb import make_collector
from .data_processor import process_and_add_to_collector
from .download_urls import feed_url_into_collector
from .notebook_handler import input_modifier_internal
from .optimize import optimize
from .utils import create_metadata_source
collector = None
api_manager = None
def setup():
global collector
global api_manager
@ -38,6 +39,7 @@ def setup():
if parameters.get_api_on():
api_manager.start_server(parameters.get_api_port())
def _feed_data_into_collector(corpus):
yield '### Processing data...'
process_and_add_to_collector(corpus, collector, False, create_metadata_source('direct-text'))
@ -305,10 +307,8 @@ def ui():
optimize_button = gr.Button('Optimize')
optimization_steps = gr.Number(value=parameters.get_optimization_steps(), label='Optimization Steps', info='For how many steps to optimize.', interactive=True)
clear_button = gr.Button('❌ Clear Data')
with gr.Column():
last_updated = gr.Markdown()
@ -318,7 +318,6 @@ def ui():
optimizable_params = [time_power, time_steepness, significant_level, min_sentences, new_dist_strat, delta_start, min_number_length, num_conversion,
preprocess_pipeline, chunk_count, context_len, chunk_len]
update_data.click(_feed_data_into_collector, [data_input], last_updated, show_progress=False)
update_url.click(_feed_url_into_collector, [url_input], last_updated, show_progress=False)
update_file.click(_feed_file_into_collector, [file_input], last_updated, show_progress=False)
@ -326,7 +325,6 @@ def ui():
optimize_button.click(_begin_optimization, [], [last_updated] + optimizable_params, show_progress=True)
clear_button.click(_clear_data, [], last_updated, show_progress=False)
optimization_steps.input(fn=_apply_settings, inputs=all_params, show_progress=False)
time_power.input(fn=_apply_settings, inputs=all_params, show_progress=False)
time_steepness.input(fn=_apply_settings, inputs=all_params, show_progress=False)

1
extensions/superboogav2/utils.py
View File

@ -4,6 +4,7 @@ This module contains common functions across multiple other modules.
import extensions.superboogav2.parameters as parameters
# Create the context using the prefix + data_separator + postfix from parameters.
def create_context_text(results):
context = parameters.get_prefix() + parameters.get_data_separator().join(results) + parameters.get_postfix()