from langchain.embeddings.huggingface import HuggingFaceEmbeddings from langchain.vectorstores import FAISS from langchain.document_loaders import UnstructuredFileLoader from models.chatglm_llm import ChatGLM from configs.model_config import * import datetime from textsplitter import ChineseTextSplitter from typing import List, Tuple from langchain.docstore.document import Document import numpy as np from utils import torch_gc # return top-k text chunk from vector store VECTOR_SEARCH_TOP_K = 6 # LLM input history length LLM_HISTORY_LEN = 3 DEVICE_ = EMBEDDING_DEVICE DEVICE_ID = "0" if torch.cuda.is_available() else None DEVICE = f"{DEVICE_}:{DEVICE_ID}" if DEVICE_ID else DEVICE_ def load_file(filepath): if filepath.lower().endswith(".md"): loader = UnstructuredFileLoader(filepath, mode="elements") docs = loader.load() elif filepath.lower().endswith(".pdf"): loader = UnstructuredFileLoader(filepath) textsplitter = ChineseTextSplitter(pdf=True) docs = loader.load_and_split(textsplitter) else: loader = UnstructuredFileLoader(filepath, mode="elements") textsplitter = ChineseTextSplitter(pdf=False) docs = loader.load_and_split(text_splitter=textsplitter) return docs def generate_prompt(related_docs: List[str], query: str, prompt_template=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 get_docs_with_score(docs_with_score): docs = [] for doc, score in docs_with_score: doc.metadata["score"] = score docs.append(doc) return docs def seperate_list(ls: List[int]) -> List[List[int]]: lists = [] ls1 = [ls[0]] for i in range(1, len(ls)): if ls[i - 1] + 1 == ls[i]: ls1.append(ls[i]) else: lists.append(ls1) ls1 = [ls[i]] lists.append(ls1) return lists def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4, ) -> List[Tuple[Document, float]]: scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k) docs = [] id_set = set() for j, i in enumerate(indices[0]): if i == -1: # This happens when not enough docs are returned. continue _id = self.index_to_docstore_id[i] doc = self.docstore.search(_id) id_set.add(i) docs_len = len(doc.page_content) for k in range(1, max(i, len(docs) - i)): for l in [i + k, i - k]: if 0 <= l < len(self.index_to_docstore_id): _id0 = self.index_to_docstore_id[l] doc0 = self.docstore.search(_id0) if docs_len + len(doc0.page_content) > self.chunk_size: break elif doc0.metadata["source"] == doc.metadata["source"]: docs_len += len(doc0.page_content) id_set.add(l) id_list = sorted(list(id_set)) id_lists = seperate_list(id_list) for id_seq in id_lists: for id in id_seq: if id == id_seq[0]: _id = self.index_to_docstore_id[id] doc = self.docstore.search(_id) else: _id0 = self.index_to_docstore_id[id] doc0 = self.docstore.search(_id0) doc.page_content += doc0.page_content if not isinstance(doc, Document): raise ValueError(f"Could not find document for id {_id}, got {doc}") docs.append((doc, scores[0][j])) torch_gc(DEVICE) return docs class LocalDocQA: llm: object = None embeddings: object = None top_k: int = VECTOR_SEARCH_TOP_K chunk_size: int = CHUNK_SIZE def init_cfg(self, embedding_model: str = EMBEDDING_MODEL, embedding_device=EMBEDDING_DEVICE, llm_history_len: int = LLM_HISTORY_LEN, llm_model: str = LLM_MODEL, llm_device=LLM_DEVICE, top_k=VECTOR_SEARCH_TOP_K, use_ptuning_v2: bool = USE_PTUNING_V2 ): self.llm = ChatGLM() self.llm.load_model(model_name_or_path=llm_model_dict[llm_model], llm_device=llm_device, use_ptuning_v2=use_ptuning_v2) self.llm.history_len = llm_history_len 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): loaded_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) print(f"{file} 已成功加载") loaded_files.append(filepath) except Exception as e: print(e) print(f"{file} 未能成功加载") return None elif os.path.isdir(filepath): docs = [] for file in os.listdir(filepath): fullfilepath = os.path.join(filepath, file) try: docs += load_file(fullfilepath) print(f"{file} 已成功加载") loaded_files.append(fullfilepath) except Exception as e: print(e) print(f"{file} 未能成功加载") else: docs = [] for file in filepath: try: docs += load_file(file) print(f"{file} 已成功加载") loaded_files.append(file) except Exception as e: print(e) print(f"{file} 未能成功加载") if len(docs) > 0: if vs_path and os.path.isdir(vs_path): vector_store = FAISS.load_local(vs_path, self.embeddings) vector_store.add_documents(docs) torch_gc(DEVICE) else: if not vs_path: vs_path = f"""{VS_ROOT_PATH}{os.path.splitext(file)[0]}_FAISS_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}""" vector_store = FAISS.from_documents(docs, self.embeddings) torch_gc(DEVICE) vector_store.save_local(vs_path) return vs_path, loaded_files else: print("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 get_knowledge_based_answer(self, query, vs_path, chat_history=[], streaming: bool = STREAMING): vector_store = FAISS.load_local(vs_path, self.embeddings) FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector vector_store.chunk_size = self.chunk_size related_docs_with_score = vector_store.similarity_search_with_score(query, k=self.top_k) related_docs = get_docs_with_score(related_docs_with_score) prompt = generate_prompt(related_docs, query) # if streaming: # for result, history in self.llm._stream_call(prompt=prompt, # history=chat_history): # history[-1][0] = query # response = {"query": query, # "result": result, # "source_documents": related_docs} # yield response, history # else: for result, history in self.llm._call(prompt=prompt, history=chat_history, streaming=streaming): history[-1][0] = query response = {"query": query, "result": result, "source_documents": related_docs} yield response, history if __name__ == "__main__": local_doc_qa = LocalDocQA() local_doc_qa.init_cfg() query = "你好" vs_path = "/Users/liuqian/Downloads/glm-dev/vector_store/123" 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): print(resp["result"][last_print_len:], end="", flush=True) last_print_len = len(resp["result"]) for resp, history in local_doc_qa.get_knowledge_based_answer(query=query, vs_path=vs_path, chat_history=[], streaming=False): print(resp["result"]) pass