File size: 7,615 Bytes
8e5a242 |
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 |
import gradio as gr
from huggingface_hub import snapshot_download
from langchain.document_loaders import (
CSVLoader,
EverNoteLoader,
PDFMinerLoader,
TextLoader,
UnstructuredEmailLoader,
UnstructuredEPubLoader,
UnstructuredHTMLLoader,
UnstructuredMarkdownLoader,
UnstructuredODTLoader,
UnstructuredPowerPointLoader,
UnstructuredWordDocumentLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
from chromadb.config import Settings
from llama_cpp import Llama
SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
SYSTEM_TOKEN = 1788
USER_TOKEN = 1404
BOT_TOKEN = 9225
LINEBREAK_TOKEN = 13
ROLE_TOKENS = {
"user": USER_TOKEN,
"bot": BOT_TOKEN,
"system": SYSTEM_TOKEN
}
LOADER_MAPPING = {
".csv": (CSVLoader, {}),
".doc": (UnstructuredWordDocumentLoader, {}),
".docx": (UnstructuredWordDocumentLoader, {}),
".enex": (EverNoteLoader, {}),
".epub": (UnstructuredEPubLoader, {}),
".html": (UnstructuredHTMLLoader, {}),
".md": (UnstructuredMarkdownLoader, {}),
".odt": (UnstructuredODTLoader, {}),
".pdf": (PDFMinerLoader, {}),
".ppt": (UnstructuredPowerPointLoader, {}),
".pptx": (UnstructuredPowerPointLoader, {}),
".txt": (TextLoader, {"encoding": "utf8"}),
}
MODEL_NAME = "ggml-model-q4_1.bin"
snapshot_download(
repo_id="IlyaGusev/saiga_7b_lora_llamacpp",
local_dir=".",
allow_patterns=MODEL_NAME
)
model = Llama(
model_path=MODEL_NAME,
n_ctx=2000,
n_parts=1,
)
max_new_tokens = 1500
top_k = 30
top_p = 0.9
temp = 0.1
repeat_penalty = 1.15
chunk_size = 300
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
def load_single_document(file_path: str) -> Document:
ext = "." + file_path.rsplit(".", 1)[-1]
assert ext in LOADER_MAPPING
loader_class, loader_args = LOADER_MAPPING[ext]
loader = loader_class(file_path, **loader_args)
return loader.load()[0]
def get_message_tokens(model, role, content):
message_tokens = model.tokenize(content.encode("utf-8"))
message_tokens.insert(1, ROLE_TOKENS[role])
message_tokens.insert(2, LINEBREAK_TOKEN)
message_tokens.append(model.token_eos())
return message_tokens
def get_system_tokens(model):
system_message = {"role": "system", "content": SYSTEM_PROMPT}
return get_message_tokens(model, **system_message)
def upload_files(files, file_paths):
file_paths = [f.name for f in files]
return file_paths
def build_index(file_paths, db):
documents = [load_single_document(path) for path in file_paths]
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=20)
texts = text_splitter.split_documents(documents)
def fix_lines(text):
lines = text.split("\n")
lines = [line for line in lines if len(line.strip()) > 2]
return "\n".join(lines)
fixed_texts = []
for text in texts:
text.page_content = fix_lines(text.page_content)
if len(text.page_content) < 10:
continue
fixed_texts.append(text)
db = Chroma.from_documents(
fixed_texts,
embeddings,
client_settings=Settings(
anonymized_telemetry=False
)
)
return db
def user(message, history, system_prompt):
new_history = history + [[message, None]]
return "", new_history
def bot(history, system_prompt, conversation_id, db):
if not history:
return
tokens = get_system_tokens(model)[:]
tokens.append(LINEBREAK_TOKEN)
for user_message, bot_message in history[:-1]:
message_tokens = get_message_tokens(model=model, role="user", content=user_message)
tokens.extend(message_tokens)
if bot_message:
message_tokens = get_message_tokens(model=model, role="bot", content=bot_message)
tokens.extend(message_tokens)
last_user_message = history[-1][0]
if db:
retriever = db.as_retriever(search_kwargs={"k": 2})
docs = retriever.get_relevant_documents(last_user_message)
context = "\n\n".join([doc.page_content for doc in docs])
last_user_message = f"Контекст: {context}\n\nИспользуя контекст, ответь на вопрос: {last_user_message}"
message_tokens = get_message_tokens(model=model, role="user", content=last_user_message)
tokens.extend(message_tokens)
role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]
tokens.extend(role_tokens)
generator = model.generate(
tokens,
top_k=top_k,
top_p=top_p,
temp=temp,
repeat_penalty=repeat_penalty
)
completion_tokens = []
partial_text = ""
for i, token in enumerate(generator):
completion_tokens.append(token)
if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens):
break
partial_text = model.detokenize(completion_tokens).decode("utf-8", "ignore")
history[-1][1] = partial_text
yield history
with gr.Blocks(
theme=gr.themes.Soft()
) as demo:
db = gr.State(None)
conversation_id = gr.State(get_uuid)
favicon = '<img src="https://cdn.midjourney.com/b88e5beb-6324-4820-8504-a1a37a9ba36d/0_1.png" width="48px" style="display: inline">'
gr.Markdown(
f"""<h1><center>{favicon}Saiga 7B Retrieval QA Llama.cpp</center></h1>
"""
)
system_prompt = gr.Textbox(label="Системный промпт", placeholder="", value=SYSTEM_PROMPT)
file_output = gr.File(file_count="multiple")
file_paths = gr.State([])
chatbot = gr.Chatbot().style(height=400)
with gr.Row():
with gr.Column():
msg = gr.Textbox(
label="Отправить сообщение",
placeholder="Отправить сообщение",
show_label=False,
).style(container=False)
with gr.Column():
with gr.Row():
submit = gr.Button("Отправить")
stop = gr.Button("Остановить")
clear = gr.Button("Очистить")
upload_event = file_output.change(
fn=upload_files,
inputs=[file_output, file_paths],
outputs=[file_paths],
queue=False,
).then(
fn=build_index,
inputs=[file_paths, db],
outputs=[db],
queue=True
)
submit_event = msg.submit(
fn=user,
inputs=[msg, chatbot, system_prompt],
outputs=[msg, chatbot],
queue=False,
).then(
fn=bot,
inputs=[chatbot, system_prompt, conversation_id, db],
outputs=chatbot,
queue=True,
)
submit_click_event = submit.click(
fn=user,
inputs=[msg, chatbot, system_prompt],
outputs=[msg, chatbot],
queue=False,
).then(
fn=bot,
inputs=[chatbot, system_prompt, conversation_id, db],
outputs=chatbot,
queue=True,
)
stop.click(
fn=None,
inputs=None,
outputs=None,
cancels=[submit_event, submit_click_event],
queue=False,
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue(max_size=128, concurrency_count=1)
demo.launch()
|