from langchain.embeddings.huggingface import HuggingFaceEmbeddings from vectorstores import MyFAISS from langchain.document_loaders import UnstructuredFileLoader, TextLoader, CSVLoader from configs.model_config import * import datetime from textsplitter import ChineseTextSplitter from typing import List from utils import torch_gc from tqdm import tqdm from pypinyin import lazy_pinyin from loader import UnstructuredPaddleImageLoader, UnstructuredPaddlePDFLoader from models.base import (BaseAnswer, AnswerResult) from models.loader.args import parser from models.loader import LoaderCheckPoint import models.shared as shared from agent import bing_search from langchain.docstore.document import Document from functools import lru_cache # patch HuggingFaceEmbeddings to make it hashable def _embeddings_hash(self): return hash(self.model_name) HuggingFaceEmbeddings.__hash__ = _embeddings_hash # will keep CACHED_VS_NUM of vector store caches @lru_cache(CACHED_VS_NUM) def load_vector_store(vs_path, embeddings): return MyFAISS.load_local(vs_path, embeddings) def tree(filepath, ignore_dir_names=None, ignore_file_names=None): """返回两个列表，第一个列表为 filepath 下全部文件的完整路径, 第二个为对应的文件名""" if ignore_dir_names is None: ignore_dir_names = [] if ignore_file_names is None: ignore_file_names = [] ret_list = [] if isinstance(filepath, str): if not os.path.exists(filepath): print("路径不存在") return None, None elif os.path.isfile(filepath) and os.path.basename(filepath) not in ignore_file_names: return [filepath], [os.path.basename(filepath)] elif os.path.isdir(filepath) and os.path.basename(filepath) not in ignore_dir_names: for file in os.listdir(filepath): fullfilepath = os.path.join(filepath, file) if os.path.isfile(fullfilepath) and os.path.basename(fullfilepath) not in ignore_file_names: ret_list.append(fullfilepath) if os.path.isdir(fullfilepath) and os.path.basename(fullfilepath) not in ignore_dir_names: ret_list.extend(tree(fullfilepath, ignore_dir_names, ignore_file_names)[0]) return ret_list, [os.path.basename(p) for p in ret_list] def load_file(filepath, sentence_size=SENTENCE_SIZE): if filepath.lower().endswith(".md"): loader = UnstructuredFileLoader(filepath, mode="elements") docs = loader.load() elif filepath.lower().endswith(".txt"): loader = TextLoader(filepath, autodetect_encoding=True) textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) docs = loader.load_and_split(textsplitter) elif filepath.lower().endswith(".pdf"): loader = UnstructuredPaddlePDFLoader(filepath) textsplitter = ChineseTextSplitter(pdf=True, sentence_size=sentence_size) docs = loader.load_and_split(textsplitter) elif filepath.lower().endswith(".jpg") or filepath.lower().endswith(".png"): loader = UnstructuredPaddleImageLoader(filepath, mode="elements") textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) docs = loader.load_and_split(text_splitter=textsplitter) elif filepath.lower().endswith(".csv"): loader = CSVLoader(filepath) docs = loader.load() else: loader = UnstructuredFileLoader(filepath, mode="elements") textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) docs = loader.load_and_split(text_splitter=textsplitter) write_check_file(filepath, docs) return docs def write_check_file(filepath, docs): folder_path = os.path.join(os.path.dirname(filepath), "tmp_files") if not os.path.exists(folder_path): os.makedirs(folder_path) fp = os.path.join(folder_path, 'load_file.txt') with open(fp, 'a+', encoding='utf-8') as fout: fout.write("filepath=%s,len=%s" % (filepath, len(docs))) fout.write('\n') for i in docs: fout.write(str(i)) fout.write('\n') fout.close() def generate_prompt(related_docs: List[str], query: str, prompt_template: str = PROMPT_TEMPLATE, ) -> str: context = "\n".join([doc.page_content for doc in related_docs]) prompt = prompt_template.replace("{question}", query).replace("{context}", context) return prompt def search_result2docs(search_results): docs = [] for result in search_results: doc = Document(page_content=result["snippet"] if "snippet" in result.keys() else "", metadata={"source": result["link"] if "link" in result.keys() else "", "filename": result["title"] if "title" in result.keys() else ""}) docs.append(doc) return docs class LocalDocQA: llm: BaseAnswer = None embeddings: object = None top_k: int = VECTOR_SEARCH_TOP_K chunk_size: int = CHUNK_SIZE chunk_conent: bool = True score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD def init_cfg(self, embedding_model: str = EMBEDDING_MODEL, embedding_device=EMBEDDING_DEVICE, llm_model: BaseAnswer = None, top_k=VECTOR_SEARCH_TOP_K, ): self.llm = llm_model self.embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[embedding_model], model_kwargs={'device': embedding_device}) self.top_k = top_k def init_knowledge_vector_store(self, filepath: str or List[str], vs_path: str or os.PathLike = None, sentence_size=SENTENCE_SIZE): loaded_files = [] failed_files = [] if isinstance(filepath, str): if not os.path.exists(filepath): print("路径不存在") return None elif os.path.isfile(filepath): file = os.path.split(filepath)[-1] try: docs = load_file(filepath, sentence_size) logger.info(f"{file} 已成功加载") loaded_files.append(filepath) except Exception as e: logger.error(e) logger.info(f"{file} 未能成功加载") return None elif os.path.isdir(filepath): docs = [] for fullfilepath, file in tqdm(zip(*tree(filepath, ignore_dir_names=['tmp_files'])), desc="加载文件"): try: docs += load_file(fullfilepath, sentence_size) loaded_files.append(fullfilepath) except Exception as e: logger.error(e) failed_files.append(file) if len(failed_files) > 0: logger.info("以下文件未能成功加载：") for file in failed_files: logger.info(f"{file}\n") else: docs = [] for file in filepath: try: docs += load_file(file) logger.info(f"{file} 已成功加载") loaded_files.append(file) except Exception as e: logger.error(e) logger.info(f"{file} 未能成功加载") if len(docs) > 0: logger.info("文件加载完毕，正在生成向量库") if vs_path and os.path.isdir(vs_path) and "index.faiss" in os.listdir(vs_path): vector_store = load_vector_store(vs_path, self.embeddings) vector_store.add_documents(docs) torch_gc() else: if not vs_path: vs_path = os.path.join(KB_ROOT_PATH, f"""{"".join(lazy_pinyin(os.path.splitext(file)[0]))}_FAISS_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}""", "vector_store") vector_store = MyFAISS.from_documents(docs, self.embeddings) # docs 为Document列表 torch_gc() vector_store.save_local(vs_path) return vs_path, loaded_files else: logger.info("文件均未成功加载，请检查依赖包或替换为其他文件再次上传。") return None, loaded_files def one_knowledge_add(self, vs_path, one_title, one_conent, one_content_segmentation, sentence_size): try: if not vs_path or not one_title or not one_conent: logger.info("知识库添加错误，请确认知识库名字、标题、内容是否正确！") return None, [one_title] docs = [Document(page_content=one_conent + "\n", metadata={"source": one_title})] if not one_content_segmentation: text_splitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) docs = text_splitter.split_documents(docs) if os.path.isdir(vs_path) and os.path.isfile(vs_path + "/index.faiss"): vector_store = load_vector_store(vs_path, self.embeddings) vector_store.add_documents(docs) else: vector_store = MyFAISS.from_documents(docs, self.embeddings) ##docs 为Document列表 torch_gc() vector_store.save_local(vs_path) return vs_path, [one_title] except Exception as e: logger.error(e) return None, [one_title] def get_knowledge_based_answer(self, query, vs_path, chat_history=[], streaming: bool = STREAMING): vector_store = load_vector_store(vs_path, self.embeddings) vector_store.chunk_size = self.chunk_size vector_store.chunk_conent = self.chunk_conent vector_store.score_threshold = self.score_threshold related_docs_with_score = vector_store.similarity_search_with_score(query, k=self.top_k) torch_gc() if len(related_docs_with_score) > 0: prompt = generate_prompt(related_docs_with_score, query) else: prompt = query for answer_result in self.llm.generatorAnswer(prompt=prompt, history=chat_history, streaming=streaming): resp = answer_result.llm_output["answer"] history = answer_result.history history[-1][0] = query response = {"query": query, "result": resp, "source_documents": related_docs_with_score} yield response, history # query 查询内容 # vs_path 知识库路径 # chunk_conent 是否启用上下文关联 # score_threshold 搜索匹配score阈值 # vector_search_top_k 搜索知识库内容条数，默认搜索5条结果 # chunk_sizes 匹配单段内容的连接上下文长度 def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD, vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE): vector_store = load_vector_store(vs_path, self.embeddings) # FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector vector_store.chunk_conent = chunk_conent vector_store.score_threshold = score_threshold vector_store.chunk_size = chunk_size related_docs_with_score = vector_store.similarity_search_with_score(query, k=vector_search_top_k) if not related_docs_with_score: response = {"query": query, "source_documents": []} return response, "" torch_gc() prompt = "\n".join([doc.page_content for doc in related_docs_with_score]) response = {"query": query, "source_documents": related_docs_with_score} return response, prompt def get_search_result_based_answer(self, query, chat_history=[], streaming: bool = STREAMING): results = bing_search(query) result_docs = search_result2docs(results) prompt = generate_prompt(result_docs, query) for answer_result in self.llm.generatorAnswer(prompt=prompt, history=chat_history, streaming=streaming): resp = answer_result.llm_output["answer"] history = answer_result.history history[-1][0] = query response = {"query": query, "result": resp, "source_documents": result_docs} yield response, history def delete_file_from_vector_store(self, filepath: str or List[str], vs_path): vector_store = load_vector_store(vs_path, self.embeddings) status = vector_store.delete_doc(filepath) return status def update_file_from_vector_store(self, filepath: str or List[str], vs_path, docs: List[Document],): vector_store = load_vector_store(vs_path, self.embeddings) status = vector_store.update_doc(filepath, docs) return status def list_file_from_vector_store(self, vs_path, fullpath=False): vector_store = load_vector_store(vs_path, self.embeddings) docs = vector_store.list_docs() if fullpath: return docs else: return [os.path.split(doc)[-1] for doc in docs] if __name__ == "__main__": # 初始化消息 args = None args = parser.parse_args(args=['--model-dir', '/media/checkpoint/', '--model', 'chatglm-6b', '--no-remote-model']) args_dict = vars(args) shared.loaderCheckPoint = LoaderCheckPoint(args_dict) llm_model_ins = shared.loaderLLM() llm_model_ins.set_history_len(LLM_HISTORY_LEN) local_doc_qa = LocalDocQA() local_doc_qa.init_cfg(llm_model=llm_model_ins) query = "本项目使用的embedding模型是什么，消耗多少显存" vs_path = "/media/gpt4-pdf-chatbot-langchain/dev-langchain-ChatGLM/vector_store/test" last_print_len = 0 # for resp, history in local_doc_qa.get_knowledge_based_answer(query=query, # vs_path=vs_path, # chat_history=[], # streaming=True): for resp, history in local_doc_qa.get_search_result_based_answer(query=query, chat_history=[], streaming=True): print(resp["result"][last_print_len:], end="", flush=True) last_print_len = len(resp["result"]) source_text = [f"""出处 [{inum + 1}] {doc.metadata['source'] if doc.metadata['source'].startswith("http") else os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n""" # f"""相关度：{doc.metadata['score']}\n\n""" for inum, doc in enumerate(resp["source_documents"])] logger.info("\n\n" + "\n\n".join(source_text)) pass