Delete llm_model.py

llm_model.py

@@ -1,92 +0,0 @@
-import os
-from langchain.chains import ConversationalRetrievalChain, StuffDocumentsChain
-from langchain.prompts import PromptTemplate
-from ipex_llm.langchain.llms import TransformersLLM
-from langchain.vectorstores import FAISS
-from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
-from ipex_llm.langchain.embeddings import TransformersEmbeddings
-from langchain import LLMChain
-from utils.utils import new_cd
-
-parent_dir = os.path.dirname(__file__)
-
-condense_template = """
-Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
-You can assume the discussion is about the video content.
-REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm \
-not sure." Don't try to make up an answer. \
-Chat History:
-{chat_history}
-Follow Up Question: {question}
-Standalone question:
-"""
-
-qa_template = """
-You are an AI assistant designed for answering questions about a meeting.
-You are given a word records of this meeting.
-Try to comprehend the dialogs and provide a answer based on it.
-=========
-{context}
-=========
-Question: {question}
-Answer: 
-"""
-# CONDENSE_QUESTION_PROMPT 用于将聊天历史记录和下一个问题压缩为一个独立的问题
-CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(condense_template)
-# QA_PROMPT为机器人设定基调和目的
-QA_PROMPT = PromptTemplate(template=qa_template, input_variables=["question", "context"])
-# DOC_PROMPT = PromptTemplate.from_template("Video Clip {video_clip}: {page_content}")
-DOC_PROMPT = PromptTemplate.from_template("{page_content}")
-
-
-class LlmReasoner():
-    def __init__(self, args):
-        self.history = []
-        self.llm_version = args.llm_version
-        self.embed_version = args.embed_version
-        self.qa_chain = None
-        self.vectorstore = None
-        self.top_k = args.top_k
-        self.qa_max_new_tokens = args.qa_max_new_tokens
-        self.init_model()
-
-    def init_model(self):
-        with new_cd(parent_dir):
-            self.llm = TransformersLLM.from_model_id_low_bit(
-                f"..\\checkpoints\\{self.llm_version}")
-            self.llm.streaming = False
-            self.embeddings = TransformersEmbeddings.from_model_id(
-                model_id=f"..\\checkpoints\\{self.embed_version}")
-
-    def create_qa_chain(self, args, input_log):
-        self.top_k = args.top_k
-        self.qa_max_new_tokens = args.qa_max_new_tokens
-        self.question_generator = LLMChain(llm=self.llm, prompt=CONDENSE_QUESTION_PROMPT)
-        self.answer_generator = LLMChain(llm=self.llm, prompt=QA_PROMPT,
-                                         llm_kwargs={"max_new_tokens": self.qa_max_new_tokens})
-        self.doc_chain = StuffDocumentsChain(llm_chain=self.answer_generator, document_prompt=DOC_PROMPT,
-                                             document_variable_name='context')
-        # 拆分查看字符的文本, 创建一个新的文本分割器
-        # self.text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0, keep_separator=True)
-        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=2048, chunk_overlap=0)
-        texts = self.text_splitter.split_text(input_log)
-        self.vectorstore = FAISS.from_texts(texts, self.embeddings,
-                                            metadatas=[{"video_clip": str(i)} for i in range(len(texts))])
-        retriever = self.vectorstore.as_retriever(search_kwargs={"k": self.top_k})
-        self.qa_chain = ConversationalRetrievalChain(retriever=retriever,
-                                                     question_generator=self.question_generator,
-                                                     combine_docs_chain=self.doc_chain,
-                                                     return_generated_question=True,
-                                                     return_source_documents=True,
-                                                     rephrase_question=False)
-
-    def __call__(self, question):
-        response = self.qa_chain({"question": question, "chat_history": self.history})
-        answer = response["answer"]
-        generated_question = response["generated_question"]
-        source_documents = response["source_documents"]
-        self.history.append([question, answer])
-        return self.history, generated_question, source_documents
-
-    def clean_history(self):
-        self.history = []