llama/elastic_keywords_llama3_4.py
2025-07-13 19:05:59 +03:30

219 lines
7.2 KiB
Python

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