fix local vector store bug
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binary-husky
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8a6e96c369
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7bac8f4bd3
@ -26,10 +26,6 @@ EMBEDDING_MODEL = "text2vec"
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# Embedding running device
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EMBEDDING_DEVICE = "cpu"
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VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vector_store")
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UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "content")
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# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
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PROMPT_TEMPLATE = """已知信息：
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{context}
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@ -159,7 +155,7 @@ class LocalDocQA:
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elif os.path.isfile(filepath):
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file = os.path.split(filepath)[-1]
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try:
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docs = load_file(filepath, sentence_size)
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docs = load_file(filepath, SENTENCE_SIZE)
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print(f"{file} 已成功加载")
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loaded_files.append(filepath)
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except Exception as e:
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@ -171,7 +167,7 @@ class LocalDocQA:
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for file in tqdm(os.listdir(filepath), desc="加载文件"):
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fullfilepath = os.path.join(filepath, file)
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try:
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docs += load_file(fullfilepath, sentence_size)
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docs += load_file(fullfilepath, SENTENCE_SIZE)
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loaded_files.append(fullfilepath)
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except Exception as e:
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print(e)
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@ -185,21 +181,19 @@ class LocalDocQA:
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else:
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docs = []
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for file in filepath:
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try:
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docs += load_file(file)
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print(f"{file} 已成功加载")
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loaded_files.append(file)
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except Exception as e:
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print(e)
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print(f"{file} 未能成功加载")
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docs += load_file(file, SENTENCE_SIZE)
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print(f"{file} 已成功加载")
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loaded_files.append(file)
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if len(docs) > 0:
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print("文件加载完毕，正在生成向量库")
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if vs_path and os.path.isdir(vs_path):
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self.vector_store = FAISS.load_local(vs_path, text2vec)
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self.vector_store.add_documents(docs)
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try:
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self.vector_store = FAISS.load_local(vs_path, text2vec)
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self.vector_store.add_documents(docs)
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except:
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self.vector_store = FAISS.from_documents(docs, text2vec)
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else:
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if not vs_path: assert False
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self.vector_store = FAISS.from_documents(docs, text2vec) # docs 为Document列表
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self.vector_store.save_local(vs_path)
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@ -208,9 +202,9 @@ class LocalDocQA:
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self.vector_store = FAISS.load_local(vs_path, text2vec)
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return vs_path, loaded_files
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def get_loaded_file(self):
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def get_loaded_file(self, vs_path):
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ds = self.vector_store.docstore
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return set([ds._dict[k].metadata['source'].split(UPLOAD_ROOT_PATH)[-1] for k in ds._dict])
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return set([ds._dict[k].metadata['source'].split(vs_path)[-1] for k in ds._dict])
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# query 查询内容
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@ -228,7 +222,7 @@ class LocalDocQA:
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self.vector_store.score_threshold = score_threshold
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self.vector_store.chunk_size = chunk_size
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embedding = self.vector_store.embedding_function(query)
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embedding = self.vector_store.embedding_function.embed_query(query)
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related_docs_with_score = similarity_search_with_score_by_vector(self.vector_store, embedding, k=vector_search_top_k)
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if not related_docs_with_score:
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@ -247,27 +241,23 @@ class LocalDocQA:
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def construct_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation, text2vec):
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def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_conent, one_content_segmentation, text2vec):
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for file in files:
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assert os.path.exists(file), "输入文件不存在"
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import nltk
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if NLTK_DATA_PATH not in nltk.data.path: nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
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local_doc_qa = LocalDocQA()
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local_doc_qa.init_cfg()
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vs_path = os.path.join(VS_ROOT_PATH, vs_id)
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filelist = []
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if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, vs_id)):
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os.makedirs(os.path.join(UPLOAD_ROOT_PATH, vs_id))
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if isinstance(files, list):
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for file in files:
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file_name = file.name if not isinstance(file, str) else file
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filename = os.path.split(file_name)[-1]
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shutil.copyfile(file_name, os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
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filelist.append(os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
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vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path, sentence_size, text2vec)
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else:
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vs_path, loaded_files = local_doc_qa.one_knowledge_add(vs_path, files, one_conent, one_content_segmentation,
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sentence_size, text2vec)
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if not os.path.exists(os.path.join(vs_path, vs_id)):
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os.makedirs(os.path.join(vs_path, vs_id))
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for file in files:
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file_name = file.name if not isinstance(file, str) else file
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filename = os.path.split(file_name)[-1]
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shutil.copyfile(file_name, os.path.join(vs_path, vs_id, filename))
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filelist.append(os.path.join(vs_path, vs_id, filename))
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vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, os.path.join(vs_path, vs_id), sentence_size, text2vec)
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if len(loaded_files):
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file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
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else:
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@ -297,12 +287,13 @@ class knowledge_archive_interface():
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return self.text2vec_large_chinese
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def feed_archive(self, file_manifest, id="default"):
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def feed_archive(self, file_manifest, vs_path, id="default"):
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self.threadLock.acquire()
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# import uuid
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self.current_id = id
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self.qa_handle, self.kai_path = construct_vector_store(
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vs_id=self.current_id,
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vs_path=vs_path,
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files=file_manifest,
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sentence_size=100,
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history=[],
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@ -315,15 +306,16 @@ class knowledge_archive_interface():
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def get_current_archive_id(self):
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return self.current_id
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def get_loaded_file(self):
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return self.qa_handle.get_loaded_file()
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def get_loaded_file(self, vs_path):
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return self.qa_handle.get_loaded_file(vs_path)
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def answer_with_archive_by_id(self, txt, id):
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def answer_with_archive_by_id(self, txt, id, vs_path):
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self.threadLock.acquire()
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if not self.current_id == id:
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self.current_id = id
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self.qa_handle, self.kai_path = construct_vector_store(
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vs_id=self.current_id,
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vs_path=vs_path,
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files=[],
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sentence_size=100,
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history=[],
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@ -1,9 +1,10 @@
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from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg
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from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg, get_log_folder, get_user
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
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install_msg ="""
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pip3 install torch --index-url https://download.pytorch.org/whl/cpu
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pip3 install langchain sentence-transformers unstructured[local-inference] faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk
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pip3 install transformers --upgrade
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pip3 install langchain sentence-transformers unstructured[all-docs] faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
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"""
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@CatchException
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@ -65,8 +66,9 @@ def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
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print('Establishing knowledge archive ...')
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with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
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kai = knowledge_archive_interface()
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kai.feed_archive(file_manifest=file_manifest, id=kai_id)
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kai_files = kai.get_loaded_file()
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vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
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kai.feed_archive(file_manifest=file_manifest, vs_path=vs_path, id=kai_id)
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kai_files = kai.get_loaded_file(vs_path=vs_path)
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kai_files = '<br/>'.join(kai_files)
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# chatbot.append(['知识库构建成功', "正在将知识库存储至cookie中"])
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# yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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@ -96,7 +98,8 @@ def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
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if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
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kai_id = plugin_kwargs.get("advanced_arg", 'default')
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resp, prompt = kai.answer_with_archive_by_id(txt, kai_id)
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vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
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resp, prompt = kai.answer_with_archive_by_id(txt, kai_id, vs_path)
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chatbot.append((txt, f'[知识库 {kai_id}] ' + prompt))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
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@ -49,18 +49,18 @@ class VoidTerminal():
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pass
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vt = VoidTerminal()
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vt.get_conf = (get_conf)
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vt.set_conf = (set_conf)
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vt.set_multi_conf = (set_multi_conf)
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vt.get_plugin_handle = (get_plugin_handle)
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vt.get_plugin_default_kwargs = (get_plugin_default_kwargs)
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vt.get_chat_handle = (get_chat_handle)
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vt.get_chat_default_kwargs = (get_chat_default_kwargs)
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vt.get_conf = silence_stdout_fn(get_conf)
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vt.set_conf = silence_stdout_fn(set_conf)
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vt.set_multi_conf = silence_stdout_fn(set_multi_conf)
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vt.get_plugin_handle = silence_stdout_fn(get_plugin_handle)
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vt.get_plugin_default_kwargs = silence_stdout_fn(get_plugin_default_kwargs)
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vt.get_chat_handle = silence_stdout_fn(get_chat_handle)
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vt.get_chat_default_kwargs = silence_stdout_fn(get_chat_default_kwargs)
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vt.chat_to_markdown_str = (chat_to_markdown_str)
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
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vt.get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
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def plugin_test(main_input, plugin, advanced_arg=None):
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def plugin_test(main_input, plugin, advanced_arg=None, debug=True):
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from rich.live import Live
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from rich.markdown import Markdown
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@ -72,7 +72,10 @@ def plugin_test(main_input, plugin, advanced_arg=None):
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plugin_kwargs['main_input'] = main_input
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if advanced_arg is not None:
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plugin_kwargs['plugin_kwargs'] = advanced_arg
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my_working_plugin = silence_stdout(plugin)(**plugin_kwargs)
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if debug:
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my_working_plugin = (plugin)(**plugin_kwargs)
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else:
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my_working_plugin = silence_stdout(plugin)(**plugin_kwargs)
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with Live(Markdown(""), auto_refresh=False, vertical_overflow="visible") as live:
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for cookies, chat, hist, msg in my_working_plugin:
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