145 lines
5.8 KiB
Python
145 lines
5.8 KiB
Python
from langchain.chains import RetrievalQA
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from langchain.prompts import PromptTemplate
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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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import sentence_transformers
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import os
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from configs.model_config import *
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import datetime
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from typing import List
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from textsplitter import ChineseTextSplitter
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# return top-k text chunk from vector store
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VECTOR_SEARCH_TOP_K = 6
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# LLM input history length
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LLM_HISTORY_LEN = 3
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def load_file(filepath):
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if 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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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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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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):
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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,
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use_ptuning_v2=use_ptuning_v2)
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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.embeddings.client = sentence_transformers.SentenceTransformer(self.embeddings.model_name,
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# 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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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 os.listdir(filepath):
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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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print(f"{file} 已成功加载")
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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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print(f"{file} 未能成功加载")
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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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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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else:
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if not vs_path:
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vs_path = f"""{VS_ROOT_PATH}{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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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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prompt_template = """基于以下已知信息，简洁和专业的来回答用户的问题。
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如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
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已知内容:
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{context}
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问题:
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{question}"""
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prompt = PromptTemplate(
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template=prompt_template,
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input_variables=["context", "question"]
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)
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self.llm.history = chat_history
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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knowledge_chain = RetrievalQA.from_llm(
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llm=self.llm,
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retriever=vector_store.as_retriever(search_kwargs={"k": self.top_k}),
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prompt=prompt
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)
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knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
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input_variables=["page_content"], template="{page_content}"
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)
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knowledge_chain.return_source_documents = True
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result = knowledge_chain({"query": query})
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self.llm.history[-1][0] = query
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return result, self.llm.history
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