219 lines
7.2 KiB
Python
219 lines
7.2 KiB
Python
from html import escape
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from lxml import etree
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from datetime import datetime
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from elasticsearch import Elasticsearch
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, TextIteratorStreamer
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from threading import Thread
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import torch
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import time
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from concurrent.futures import ThreadPoolExecutor
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import concurrent
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import threading
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import json
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import os.path
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#lock = threading.Lock()
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#lock1 = threading.Lock()
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#from cleantext import clean
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#import re
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if torch.cuda.is_available():
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model_id = "PartAI/Dorna-Llama3-8B-Instruct"
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# pipe = pipeline(
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# "text-generation",
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# model=model,
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# tokenizer=tokenizer,
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# torch_dtype=torch.float16,
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# device_map="auto",
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# )
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index_name_i = 'semantic_search-v10'
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es = Elasticsearch(
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"http://127.0.0.1:6900",
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# ca_certs="/path/to/http_ca.crt",
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basic_auth=("elastic", "SG*7eGwg+KG2_*-1_mMm")
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)
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counter = 0
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total = 0
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remained = 0
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id = ''
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keywords_count = 15
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def es_iterate_all_documents(es, index, pagesize=250, scroll_timeout="12m", **kwargs):
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"""
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Helper to iterate ALL values from a single index
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Yields all the documents.
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"""
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global counter
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global total
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global remained
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is_first = True
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while True:
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# Scroll next
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if is_first: # Initialize scroll
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# result = es.search(index=index, scroll="12m", **kwargs, body={
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# "size": pagesize
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# })
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result = es.search(index=index, scroll="12m", **kwargs, size=pagesize)
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total = result["hits"]["total"]['value']
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remained = total
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print('total = %d' % total)
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is_first = False
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else:
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# result = es.scroll(body={
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# "scroll_id": scroll_id,
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# "scroll": scroll_timeout
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# })
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result = es.scroll( scroll_id = scroll_id, scroll = scroll_timeout )
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scroll_id = result["_scroll_id"]
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hits = result["hits"]["hits"]
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counter += len(hits)
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print("progress -> %.2f %% , count: %d" % ((counter / total)*100, counter))
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# Stop after no more docs
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if not hits:
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break
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# Yield each entry
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yield from ({"source":hit['_source'], "id":hit['_id']} for hit in hits)
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def generateKeywords(text):
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global remained
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try:
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keywords_count = (len(text) / 1000) * 15
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keywords_count = int(keywords_count)
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if keywords_count == 0:
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keywords_count = 1
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messages = [{"role": "user", "content":
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'''از "متن" حداقل {} کلیدواژه مهم و پراهمیت را استخراج کن و در قالب لیست به زبان فارسی چاپ کن. "متن": {}
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'''.format(keywords_count, text)
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}]
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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model.generation_config.pad_token_id = tokenizer.pad_token_id
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outputs = model.generate(
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input_ids,
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max_new_tokens=256,
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eos_token_id=terminators,
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do_sample=True,
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temperature=0.6,
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top_p=0.85,
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)
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#lock0.release()
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response = outputs[0][input_ids.shape[-1]:]
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keywords = tokenizer.decode(response, skip_special_tokens=True)
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#lock1.acquire()
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# resp = es.update(index=index_name_i, id=id, doc={"content_keywords-llama3-str": str(keywords)})
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return keywords
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except Exception as inst:
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print(type(inst)) # the exception type
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print(inst.args) # arguments stored in .args
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print("Exception: " + str(inst))
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if __name__ == "__main__":
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start_time = time.time()
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text_arr = []
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#t0 = time.time()
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if not os.path.exists('content.json'):
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content_file = open('content.json', "w", encoding='utf-8')
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for mentry in es_iterate_all_documents(es, index_name_i):
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try:
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entry = mentry['source']
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id = mentry['id']
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text = entry.get('content','')
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full_path = entry.get('other_info', {'full_path':''})['full_path']
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text_len = len(text)
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if full_path == 'عنوان' or full_path == 'موخره' or full_path == 'امضاء' or text_len == 0:
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continue
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keywords_count = (text_len / 1000) * 15
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if keywords_count < 0.3:
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continue
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# keywords_count = int(keywords_count)
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# if keywords_count == 0:
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# keywords_count = 1
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#
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text_arr.append({
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'id':id,
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'content':text
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})
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content_file.write(json.dumps({
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'id':id,
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'content':text
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}, ensure_ascii=False))
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content_file.write('\n')
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except Exception as inst:
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print(type(inst)) # the exception type
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print(inst.args) # arguments stored in .args
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print(inst) # __str__ allows args to be printed directly,
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# but may be overridden in exception subclasses
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print("Exception:=> %s -> %.2f " % (id , counter / total))
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content_file.close()
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else:
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content_file = open('content.json', "r")#, encoding='utf-8'
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for line in content_file:
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text_arr.append(json.loads(line))
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content_file.close()
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remained = len(text_arr)
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try:
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keywords_file = open('keywords.json', "w", encoding='utf-8')
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#keywords_array = []
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for content_id in text_arr:
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id = content_id['id']
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content = content_id['content']
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keywords = generateKeywords(content)
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# keywords_array.append({
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# 'id':id,
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# 'keywords':keywords
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# })
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keywords_file.write(json.dumps({
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'id':id,
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'keywords':keywords
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}, ensure_ascii=False))
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keywords_file.write('\n')
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remained = remained - 1
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print("remained items = {} ".format(remained))
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keywords_file.close()
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#for k_item in keywords_array:
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# resp = es.update(index=index_name_i, id= k_item.id , doc={"content_keywords-llama3-str": str(k_item.keywords)})
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except Exception as inst:
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print(type(inst)) # the exception type
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print(inst.args) # arguments stored in .args
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print(inst) # __str__ allows args to be printed directly,
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# but may be overridden in exception subclasses
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print("Exception:=> %s -> %.2f " % (id , counter / total))
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end_time = time.time()
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print(f"elapsed time: {end_time-start_time}") |