mirror of
https://github.com/arc53/DocsGPT
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100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
import requests
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import nltk
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import os
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from parser.file.bulk import SimpleDirectoryReader
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from parser.schema.base import Document
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from parser.open_ai_func import call_openai_api
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from parser.token_func import group_split
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from celery import current_task
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import string
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import zipfile
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import shutil
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try:
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nltk.download('punkt', quiet=True)
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nltk.download('averaged_perceptron_tagger', quiet=True)
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except FileExistsError:
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pass
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def generate_random_string(length):
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return ''.join([string.ascii_letters[i % 52] for i in range(length)])
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def ingest_worker(self, directory, formats, name_job, filename, user):
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# directory = 'inputs' or 'temp'
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# formats = [".rst", ".md"]
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input_files = None
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recursive = True
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limit = None
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exclude = True
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# name_job = 'job1'
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# filename = 'install.rst'
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# user = 'local'
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sample = False
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token_check = True
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min_tokens = 150
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max_tokens = 2000
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full_path = directory + '/' + user + '/' + name_job
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# check if API_URL env variable is set
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if not os.environ.get('API_URL'):
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url = 'http://localhost:5001/api/download'
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else:
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url = os.environ.get('API_URL') + '/api/download'
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file_data = {'name': name_job, 'file': filename, 'user': user}
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response = requests.get(url, params=file_data)
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file = response.content
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if not os.path.exists(full_path):
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os.makedirs(full_path)
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with open(full_path + '/' + filename, 'wb') as f:
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f.write(file)
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#check if file is .zip and extract it
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if filename.endswith('.zip'):
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with zipfile.ZipFile(full_path + '/' + filename, 'r') as zip_ref:
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zip_ref.extractall(full_path)
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os.remove(full_path + '/' + filename)
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import time
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self.update_state(state='PROGRESS', meta={'current': 1})
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raw_docs = SimpleDirectoryReader(input_dir=full_path, input_files=input_files, recursive=recursive,
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required_exts=formats, num_files_limit=limit,
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exclude_hidden=exclude).load_data()
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raw_docs = group_split(documents=raw_docs, min_tokens=min_tokens, max_tokens=max_tokens, token_check=token_check)
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docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
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call_openai_api(docs, full_path, self)
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self.update_state(state='PROGRESS', meta={'current': 100})
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if sample == True:
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for i in range(min(5, len(raw_docs))):
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print(raw_docs[i].text)
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# get files from outputs/inputs/index.faiss and outputs/inputs/index.pkl
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# and send them to the server (provide user and name in form)
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if not os.environ.get('API_URL'):
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url = 'http://localhost:5001/api/upload_index'
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else:
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url = os.environ.get('API_URL') + '/api/upload_index'
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file_data = {'name': name_job, 'user': user}
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files = {'file_faiss': open(full_path + '/index.faiss', 'rb'),
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'file_pkl': open(full_path + '/index.pkl', 'rb')}
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response = requests.post(url, files=files, data=file_data)
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#deletes remote
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if not os.environ.get('API_URL'):
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url = 'http://localhost:5001/api/delete_old?path=' + 'inputs/' + user + '/' + name_job
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else:
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url = os.environ.get('API_URL') + '/api/delete_old?path=' + 'inputs/' + user + '/' + name_job
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response = requests.get(url)
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# delete local
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shutil.rmtree(full_path)
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return {'directory': directory, 'formats': formats, 'name_job': name_job, 'filename': filename, 'user': user}
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