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| from langchain.prompts import PromptTemplate | |
| from langchain.chains import RetrievalQA | |
| from langchain.vectorstores import Chroma | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chat_models import ChatOpenAI | |
| import sys | |
| sys.path.append('../llm-universe/project') | |
| from qa_chain.model_to_llm import model_to_llm | |
| from qa_chain.get_vectordb import get_vectordb | |
| import re | |
| class Chat_QA_chain_self: | |
| """" | |
| 带历史记录的问答链 | |
| - model：调用的模型名称 | |
| - temperature：温度系数，控制生成的随机性 | |
| - top_k：返回检索的前k个相似文档 | |
| - chat_history：历史记录，输入一个列表，默认是一个空列表 | |
| - history_len：控制保留的最近 history_len 次对话 | |
| - file_path：建库文件所在路径 | |
| - persist_path：向量数据库持久化路径 | |
| - appid：星火 | |
| - api_key：星火、百度文心、OpenAI、智谱都需要传递的参数 | |
| - Spark_api_secret：星火秘钥 | |
| - Wenxin_secret_key：文心秘钥 | |
| - embeddings：使用的embedding模型 | |
| - embedding_key：使用的embedding模型的秘钥（智谱或者OpenAI） | |
| """ | |
| def __init__(self,model:str, temperature:float=0.0, top_k:int=4, chat_history:list=[], file_path:str=None, persist_path:str=None, appid:str=None, api_key:str=None, Spark_api_secret:str=None,Wenxin_secret_key:str=None, embedding = "openai",embedding_key:str=None): | |
| self.model = model | |
| self.temperature = temperature | |
| self.top_k = top_k | |
| self.chat_history = chat_history | |
| #self.history_len = history_len | |
| self.file_path = file_path | |
| self.persist_path = persist_path | |
| self.appid = appid | |
| self.api_key = api_key | |
| self.Spark_api_secret = Spark_api_secret | |
| self.Wenxin_secret_key = Wenxin_secret_key | |
| self.embedding = embedding | |
| self.embedding_key = embedding_key | |
| self.vectordb = get_vectordb(self.file_path, self.persist_path, self.embedding,self.embedding_key) | |
| def clear_history(self): | |
| "清空历史记录" | |
| return self.chat_history.clear() | |
| def change_history_length(self,history_len:int=1): | |
| """ | |
| 保存指定对话轮次的历史记录 | |
| 输入参数： | |
| - history_len ：控制保留的最近 history_len 次对话 | |
| - chat_history：当前的历史对话记录 | |
| 输出：返回最近 history_len 次对话 | |
| """ | |
| n = len(self.chat_history) | |
| return self.chat_history[n-history_len:] | |
| def answer(self, question:str=None,temperature = None, top_k = 4): | |
| """" | |
| 核心方法，调用问答链 | |
| arguments: | |
| - question：用户提问 | |
| """ | |
| if len(question) == 0: | |
| return "", self.chat_history | |
| if len(question) == 0: | |
| return "" | |
| if temperature == None: | |
| temperature = self.temperature | |
| llm = model_to_llm(self.model, temperature, self.appid, self.api_key, self.Spark_api_secret,self.Wenxin_secret_key) | |
| #self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| retriever = self.vectordb.as_retriever(search_type="similarity", | |
| search_kwargs={'k': top_k}) #默认similarity，k=4 | |
| qa = ConversationalRetrievalChain.from_llm( | |
| llm = llm, | |
| retriever = retriever | |
| ) | |
| #print(self.llm) | |
| result = qa({"question": question,"chat_history": self.chat_history}) #result里有question、chat_history、answer | |
| answer = result['answer'] | |
| answer = re.sub(r"\\n", '<br/>', answer) | |
| self.chat_history.append((question,answer)) #更新历史记录 | |
| return self.chat_history #返回本次回答和更新后的历史记录 | |