这个提交包含在:
binary-husky
2023-06-29 12:54:19 +08:00
父节点 e874a16050
当前提交 9f9848c6e9
共有 30 个文件被更改,包括 2614 次插入238 次删除

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@@ -1,4 +1,5 @@
from toolbox import update_ui, get_conf, trimmed_format_exc
import threading
def input_clipping(inputs, history, max_token_limit):
import numpy as np
@@ -606,3 +607,142 @@ def get_files_from_everything(txt, type): # type='.md'
success = False
return success, file_manifest, project_folder
def Singleton(cls):
_instance = {}
def _singleton(*args, **kargs):
if cls not in _instance:
_instance[cls] = cls(*args, **kargs)
return _instance[cls]
return _singleton
@Singleton
class knowledge_archive_interface():
def __init__(self) -> None:
self.threadLock = threading.Lock()
self.current_id = ""
self.kai_path = None
self.qa_handle = None
self.text2vec_large_chinese = None
def get_chinese_text2vec(self):
if self.text2vec_large_chinese is None:
# < -------------------预热文本向量化模组--------------- >
from toolbox import ProxyNetworkActivate
print('Checking Text2vec ...')
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
with ProxyNetworkActivate(): # 临时地激活代理网络
self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
return self.text2vec_large_chinese
def feed_archive(self, file_manifest, id="default"):
self.threadLock.acquire()
# import uuid
self.current_id = id
from zh_langchain import construct_vector_store
self.qa_handle, self.kai_path = construct_vector_store(
vs_id=self.current_id,
files=file_manifest,
sentence_size=100,
history=[],
one_conent="",
one_content_segmentation="",
text2vec = self.get_chinese_text2vec(),
)
self.threadLock.release()
def get_current_archive_id(self):
return self.current_id
def get_loaded_file(self):
return self.qa_handle.get_loaded_file()
def answer_with_archive_by_id(self, txt, id):
self.threadLock.acquire()
if not self.current_id == id:
self.current_id = id
from zh_langchain import construct_vector_store
self.qa_handle, self.kai_path = construct_vector_store(
vs_id=self.current_id,
files=[],
sentence_size=100,
history=[],
one_conent="",
one_content_segmentation="",
text2vec = self.get_chinese_text2vec(),
)
VECTOR_SEARCH_SCORE_THRESHOLD = 0
VECTOR_SEARCH_TOP_K = 4
CHUNK_SIZE = 512
resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
query = txt,
vs_path = self.kai_path,
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
vector_search_top_k=VECTOR_SEARCH_TOP_K,
chunk_conent=True,
chunk_size=CHUNK_SIZE,
text2vec = self.get_chinese_text2vec(),
)
self.threadLock.release()
return resp, prompt
def try_install_deps(deps):
for dep in deps:
import subprocess, sys
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '--user', dep])
class construct_html():
def __init__(self) -> None:
self.css = """
.row {
display: flex;
flex-wrap: wrap;
}
.column {
flex: 1;
padding: 10px;
}
.table-header {
font-weight: bold;
border-bottom: 1px solid black;
}
.table-row {
border-bottom: 1px solid lightgray;
}
.table-cell {
padding: 5px;
}
"""
self.html_string = f'<!DOCTYPE html><head><meta charset="utf-8"><title>翻译结果</title><style>{self.css}</style></head>'
def add_row(self, a, b):
tmp = """
<div class="row table-row">
<div class="column table-cell">REPLACE_A</div>
<div class="column table-cell">REPLACE_B</div>
</div>
"""
from toolbox import markdown_convertion
tmp = tmp.replace('REPLACE_A', markdown_convertion(a))
tmp = tmp.replace('REPLACE_B', markdown_convertion(b))
self.html_string += tmp
def save_file(self, file_name):
with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
f.write(self.html_string.encode('utf-8', 'ignore').decode())