カテゴリー: AI

args.strategy = 'cuda fp16i8 -> cpu fp32 *10'
# args.strategy = 'cuda fp16i8' リソース不足エラーになる

def makeIDX(self):
    # インデックスの作成および保存
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo"))
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
    
    data_path = self.input.text()
    data_path2 = data_path.replace("'", "") # 拡張子判定用
    data_path3 = Path(data_path2) # loader用

    if data_path2.endswith('/'):
        documents = SimpleDirectoryReader(data_path3).load_data()
    elif data_path2.endswith('.csv'):
        SimpleCSVReader = download_loader("SimpleCSVReader")
        documents = SimpleCSVReader().load_data(file=data_path3)
    elif data_path2.endswith('.pdf'):
        PDFReader = download_loader("PDFReader")
        documents = PDFReader().load_data(file=data_path3)
    else:
        print('入力がディレクトリ,csv,pdfではありません')
        self.output.setText("入力がディレクトリ,csv,pdfではありません")
        self.box.setStyleSheet('background-color: #ff00ff')
        return

    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
    
    now = datetime.datetime.now()
    formatted_time = now.strftime('%y%m%d_%H%M%S')

    index_file = "/Volumes/DATA_m1/AI/LlamaIndex/index/" + formatted_time + "_index.json"
    index.save_to_disk(index_file)
    
    self.input2.setText(index_file)
    self.idxBtn2.click()

.py /usr/local/bin/python /Volumes/DATA_m1/AI/LangChain/Python/ChatGPT_LangChain 
Using embedded DuckDB with persistence: data will be stored in: .
この論文では、大規模言語モデルマシン（LLMM）と高水準プログラミング言語のクラスとの間に接続を
確立する新しいアプローチを提案しています。これにより、Turingマシンオブジェクトと言語オブジェクトの間の
シームレスな相互作用が可能になり、普遍的にアクセス可能なオブジェクト指向の記述が実現されます。
LLMMを利用して、現実世界のオブジェクトとそれらのシミュレーションを言語シミュレーション可能な
世界に自動的に変換する方法を示し、デジタルツインコンセプトを推進します。このプロセスは高水準
プログラミング言語に拡張することができ、これらのシミュレーションの実装をよりアクセスしやすく
実用的にすることができます。

import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import PyPDFLoader

loader = PyPDFLoader("試合日程表.pdf")
pages = loader.load_and_split()

# print(pages[1].page_content)

apiKey = os.getenv("CHATGPT_API_KEY")
os.environ["OPENAI_API_KEY"] = apiKey

llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(pages, embedding=embeddings, persist_directory=".")
vectorstore.persist()

pdf_qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)

query = "XXXの試合はいつですか"
chat_history = []

result = pdf_qa({"question": query, "chat_history": chat_history})

print(result['answer'])

class chat_base():
    def __init__(self):
        self.model_tokens = []
        self.model_state = None
        self.all_state = {}
        self.user = None
        self.bot = None
        self.interface = None
        self.init_prompt = None
        self.pipeline = None
        self.bool_value = False
        
    def load_model(self, lang, strat, model, bool):
        self.lang = lang
        self.strat = strat
        self.model = model
        self.bool = bool
        
        if self.bool == False:
            from langchain.llms import RWKV
            
            llm = RWKV(
                model = '/Volumes/DATA_m1/AI/ChatRWKV_work/' + self.model, 
                strategy = self.strat, 
                tokens_path = "/Volumes/DATA_m1/AI/ChatRWKV_work/ChatRWKV/v2/20B_tokenizer.json"
            )
            
            while True:
                print("質問を入力して下さい")
                self.question = input()
                print(llm(self.generate_prompt(self.question)))

import os, copy, types, gc, sys
import numpy as np
from prompt_toolkit import prompt
import torch

END_OF_TEXT = 0
END_OF_LINE = 187
CHUNK_LEN = 256 # split input into chunks to save VRAM (shorter -> slower)

CHAT_LEN_SHORT = 40
CHAT_LEN_LONG = 150
FREE_GEN_LEN = 256

GEN_TEMP = 1.1 # It could be a good idea to increase temp when top_p is low
GEN_TOP_P = 0.7 # Reduce top_p (to 0.5, 0.2, 0.1 etc.) for better Q&A accuracy (and less diversity)
GEN_alpha_presence = 0.2 # Presence Penalty
GEN_alpha_frequency = 0.2 # Frequency Penalty
AVOID_REPEAT = '，：？！'

class chat_base():
    def __init__(self):
        self.model_tokens = []
        self.model_state = None
        self.all_state = {}
        self.user = None
        self.bot = None
        self.interface = None
        self.init_prompt = None
        self.pipeline = None
        
    def load_model(self, lang, strat, model):
        self.lang = lang
        self.strat = strat
        self.model = model
        
        CHAT_LANG = self.lang # English // Chinese // more to come
        PROMPT_FILE = f'/Volumes/DATA_m1/AI/ChatRWKV_work/ChatRWKV/v2/prompt/default/{CHAT_LANG}-2.py'
        self.load_prompt = self.load_prompt(PROMPT_FILE)
        
        sys.path.append('/Volumes/DATA_m1/AI/ChatRWKV_work/ChatRWKV/rwkv_pip_package/src')
        
        try:
            os.environ["CUDA_VISIBLE_DEVICES"] = sys.argv[1]
        except:
            pass
        np.set_printoptions(precision=4, suppress=True, linewidth=200)
        args = types.SimpleNamespace()

        print('\n\nChatRWKV v2 https://github.com/BlinkDL/ChatRWKV')
        
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.allow_tf32 = True
        torch.backends.cuda.matmul.allow_tf32 = True

＜以下略＞