254 lines
10 KiB
Python
254 lines
10 KiB
Python
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.document_loaders import UnstructuredFileLoader
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from models.chatglm_llm import ChatGLM
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from configs.model_config import *
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import datetime
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from textsplitter import ChineseTextSplitter
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from typing import List, Tuple
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from langchain.docstore.document import Document
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import numpy as np
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from utils import torch_gc
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from tqdm import tqdm
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from pypinyin import lazy_pinyin
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DEVICE_ = EMBEDDING_DEVICE
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DEVICE_ID = "0" if torch.cuda.is_available() else None
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DEVICE = f"{DEVICE_}:{DEVICE_ID}" if DEVICE_ID else DEVICE_
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def load_file(filepath):
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if filepath.lower().endswith(".md"):
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loader = UnstructuredFileLoader(filepath, mode="elements")
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docs = loader.load()
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elif filepath.lower().endswith(".pdf"):
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loader = UnstructuredFileLoader(filepath)
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textsplitter = ChineseTextSplitter(pdf=True)
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docs = loader.load_and_split(textsplitter)
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else:
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loader = UnstructuredFileLoader(filepath, mode="elements")
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textsplitter = ChineseTextSplitter(pdf=False)
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docs = loader.load_and_split(text_splitter=textsplitter)
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return docs
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def generate_prompt(related_docs: List[str],
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query: str,
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prompt_template=PROMPT_TEMPLATE) -> str:
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context = "\n".join([doc.page_content for doc in related_docs])
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prompt = prompt_template.replace("{question}", query).replace("{context}", context)
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return prompt
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def get_docs_with_score(docs_with_score):
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docs = []
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for doc, score in docs_with_score:
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doc.metadata["score"] = score
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docs.append(doc)
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return docs
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def seperate_list(ls: List[int]) -> List[List[int]]:
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lists = []
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ls1 = [ls[0]]
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for i in range(1, len(ls)):
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if ls[i - 1] + 1 == ls[i]:
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ls1.append(ls[i])
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else:
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lists.append(ls1)
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ls1 = [ls[i]]
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lists.append(ls1)
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return lists
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def similarity_search_with_score_by_vector(
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self, embedding: List[float], k: int = 4,
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) -> List[Tuple[Document, float]]:
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scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
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docs = []
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id_set = set()
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store_len = len(self.index_to_docstore_id)
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for j, i in enumerate(indices[0]):
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if i == -1:
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# This happens when not enough docs are returned.
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continue
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_id = self.index_to_docstore_id[i]
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doc = self.docstore.search(_id)
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id_set.add(i)
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docs_len = len(doc.page_content)
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for k in range(1, max(i, store_len - i)):
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break_flag = False
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for l in [i + k, i - k]:
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if 0 <= l < len(self.index_to_docstore_id):
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_id0 = self.index_to_docstore_id[l]
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doc0 = self.docstore.search(_id0)
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if docs_len + len(doc0.page_content) > self.chunk_size:
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break_flag = True
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break
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elif doc0.metadata["source"] == doc.metadata["source"]:
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docs_len += len(doc0.page_content)
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id_set.add(l)
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if break_flag:
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break
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id_list = sorted(list(id_set))
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id_lists = seperate_list(id_list)
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for id_seq in id_lists:
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for id in id_seq:
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if id == id_seq[0]:
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_id = self.index_to_docstore_id[id]
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doc = self.docstore.search(_id)
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else:
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_id0 = self.index_to_docstore_id[id]
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doc0 = self.docstore.search(_id0)
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doc.page_content += doc0.page_content
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if not isinstance(doc, Document):
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raise ValueError(f"Could not find document for id {_id}, got {doc}")
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doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]])
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docs.append((doc, doc_score))
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torch_gc()
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return docs
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class LocalDocQA:
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llm: object = None
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embeddings: object = None
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top_k: int = VECTOR_SEARCH_TOP_K
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chunk_size: int = CHUNK_SIZE
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def init_cfg(self,
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embedding_model: str = EMBEDDING_MODEL,
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embedding_device=EMBEDDING_DEVICE,
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llm_history_len: int = LLM_HISTORY_LEN,
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llm_model: str = LLM_MODEL,
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llm_device=LLM_DEVICE,
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top_k=VECTOR_SEARCH_TOP_K,
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use_ptuning_v2: bool = USE_PTUNING_V2,
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use_lora: bool = USE_LORA,
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):
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self.llm = ChatGLM()
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self.llm.load_model(model_name_or_path=llm_model_dict[llm_model],
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llm_device=llm_device, use_ptuning_v2=use_ptuning_v2, use_lora=use_lora)
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self.llm.history_len = llm_history_len
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self.embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[embedding_model],
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model_kwargs={'device': embedding_device})
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self.top_k = top_k
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def init_knowledge_vector_store(self,
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filepath: str or List[str],
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vs_path: str or os.PathLike = None):
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loaded_files = []
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failed_files = []
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if isinstance(filepath, str):
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if not os.path.exists(filepath):
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print("路径不存在")
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return None
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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)
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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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print(e)
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print(f"{file} 未能成功加载")
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return None
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elif os.path.isdir(filepath):
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docs = []
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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)
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loaded_files.append(fullfilepath)
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except Exception as e:
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failed_files.append(file)
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if len(failed_files) > 0:
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print("以下文件未能成功加载：")
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for file in failed_files:
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print(file, end="\n")
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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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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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vector_store = FAISS.load_local(vs_path, self.embeddings)
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vector_store.add_documents(docs)
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torch_gc()
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else:
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if not vs_path:
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vs_path = os.path.join(VS_ROOT_PATH,
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f"""{"".join(lazy_pinyin(os.path.splitext(file)[0]))}_FAISS_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}""")
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vector_store = FAISS.from_documents(docs, self.embeddings)
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torch_gc()
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vector_store.save_local(vs_path)
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return vs_path, loaded_files
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else:
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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。")
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return None, loaded_files
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def get_knowledge_based_answer(self,
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query,
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vs_path,
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chat_history=[],
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streaming: bool = STREAMING):
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector
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vector_store.chunk_size = self.chunk_size
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related_docs_with_score = vector_store.similarity_search_with_score(query,
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k=self.top_k)
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related_docs = get_docs_with_score(related_docs_with_score)
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torch_gc()
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prompt = generate_prompt(related_docs, query)
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# if streaming:
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# for result, history in self.llm._stream_call(prompt=prompt,
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# history=chat_history):
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# history[-1][0] = query
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# response = {"query": query,
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# "result": result,
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# "source_documents": related_docs}
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# yield response, history
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# else:
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for result, history in self.llm._call(prompt=prompt,
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history=chat_history,
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streaming=streaming):
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torch_gc()
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history[-1][0] = query
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response = {"query": query,
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"result": result,
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"source_documents": related_docs}
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yield response, history
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torch_gc()
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if __name__ == "__main__":
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local_doc_qa = LocalDocQA()
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local_doc_qa.init_cfg()
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query = "本项目使用的embedding模型是什么，消耗多少显存"
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vs_path = "/Users/liuqian/Downloads/glm-dev/vector_store/aaa"
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last_print_len = 0
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for resp, history in local_doc_qa.get_knowledge_based_answer(query=query,
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vs_path=vs_path,
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chat_history=[],
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streaming=True):
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print(resp["result"][last_print_len:], end="", flush=True)
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last_print_len = len(resp["result"])
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source_text = [f"""出处 [{inum + 1}] {os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
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# f"""相关度：{doc.metadata['score']}\n\n"""
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for inum, doc in
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enumerate(resp["source_documents"])]
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print("\n\n" + "\n\n".join(source_text))
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pass
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