llm_knowledge_base / localKB_construct.py
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'''
1.更新了llama-index的库。对应的函数名和用法都有所改变。
'''
# import gradio as gr
import openai
import requests
import csv
from llama_index import PromptHelper
# from llama_index import GPTSimpleVectorIndex ## renamed in the latest version.
from llama_index import LLMPredictor
from llama_index import ServiceContext
from langchain.chat_models import ChatOpenAI
from langchain import OpenAI
# from fastapi import FastAPI #* 实现流式数据
# from fastapi.responses import StreamingResponse #* 实现流式数据
import sys
import os
import math
import pandas as pd
import numpy as np
import PyPDF2
##* in the latest version: GPTSimpleVectorIndex was renamed to GPTVectorStoreIndex, try removing it from the end of your imports
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTVectorStoreIndex, LLMPredictor, PromptHelper
from llama_index import StorageContext, load_index_from_storage
from llama_index import ServiceContext
from llama_index import download_loader
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import sys
import os
from rich import print
## enironment settings.
os.environ["OPENAI_API_KEY"] = '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'
openai.api_key = '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'
# file_path = "/Users/yunshi/Downloads/txt_dir/Sparks_of_AGI.pdf"
# file_path = "/Users/yunshi/Downloads/txt_dir/2023年百人会电动论坛 纪要 20230401.pdf"
## 建立index或者的过程。
def construct_index(directory_path):
# file_path = f"{directory_path}/uploaded_file.pdf"
file_path = directory_path
# set maximum input si771006
# max_input_size = 4096 #* working
max_input_size = 4096
# set number of output tokens
# num_outputs = 3000 #* working
num_outputs = 1000
# set maximum chunk overlap
max_chunk_overlap = -1000 #* working
# set chunk size limit
# chunk_size_limit = 600
chunk_size_limit = 6000 #* working
# ## add chunk_overlap_ratio according to github.
# chunk_overlap_ratio= 0.1
# define LLM
# llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=2000))
llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo-16k", max_tokens=512,streaming=True))
## 好像work了,2023.09.22, 注意这里的写法有调整。
# prompt_helper = PromptHelper(max_input_s≈ize, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
prompt_helper = PromptHelper(max_input_size, num_outputs, chunk_overlap_ratio= 0.1, chunk_size_limit=chunk_size_limit)
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
## 如果是txt文件,那么需要用如下命令。注意与PDF文件的区别。
# documents = SimpleDirectoryReader(directory_path).load_data()
## 如果是PDF文件,那么需要用如下命令。注意与txt文件的区别。切需要from llama_index import download_loader。
#NOTE: 这里可以问:give me an example of GPT-4 solving math problem. 会回答关于这个PDF中的内容,所以可以确认这个程序调用了in-context learning的功能。
# CJKPDFReader = download_loader("CJKPDFReader") ## 最新的版本好像不行了,需要用下面的命令。
# loader = CJKPDFReader()
PDFReader = download_loader("PDFReader") # working。
loader = PDFReader()
# documents = loader.load_data(file=directory_path) #! 注意这里是指向文件本身,而不同于txt文件的指文件夹。
print('directory_path now:', directory_path)
# print('111')
# documents = loader.load_data(file="/Users/yunshi/Downloads/txt_dir/Sparks_of_AGI.pdf") #! 注意这里是指向文件本身,而不同于txt文件的指文件夹。
documents = loader.load_data(file=directory_path) #! 注意这里是指向文件本身,而不同于txt文件的指文件夹。
print('222')
# index = GPTSimpleVectorIndex(
# documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
# )
# index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context) ## oringinal version, working.
# print('documents:', documents)
index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context) #* the funciton renamed.
print('333')
# index.save_to_disk('/Users/yunshi/Downloads/txt_dir/index.json') ## in the latest version, this function is not working.
return index, service_context
def process_file(file_path,username):
print('process_file starts')
# file_path = "/Users/yunshi/Downloads/txt_dir/Sparks_of_AGI.pdf"
#! 第一次运行是需要开启这个function。如果测试通过index,因此不需要在运行了。记得上传PDF和JSON文件到云服务器上。
index, service_context = construct_index(file_path)
# index.storage_context.persist(persist_dir="/Users/yunshi/Downloads/txt_dir/") #* 存储到本地,为以后调用。
index.storage_context.persist(persist_dir=f"./{username}/") #* 存储到本地,为以后调用。
print(index)
# process_file(file_path)