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107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
import os
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import shutil
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import string
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import zipfile
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from urllib.parse import urljoin
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import nltk
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import requests
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from application.core.settings import settings
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from application.parser.file.bulk import SimpleDirectoryReader
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from application.parser.open_ai_func import call_openai_api
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from application.parser.schema.base import Document
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from application.parser.token_func import group_split
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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 metadata_from_filename(title):
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store = '/'.join(title.split('/')[1:3])
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return {'title': title, 'store': store}
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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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current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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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 = 1250
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full_path = directory + '/' + user + '/' + name_job
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import sys
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print(full_path, file=sys.stderr)
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# check if API_URL env variable is set
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file_data = {'name': name_job, 'file': filename, 'user': user}
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response = requests.get(urljoin(settings.API_URL, "/api/download"), params=file_data)
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# check if file is in the response
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print(response, file=sys.stderr)
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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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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, file_metadata=metadata_from_filename).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:
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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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file_data = {'name': name_job, 'user': user}
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if settings.VECTOR_STORE == "faiss":
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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(urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data)
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response = requests.get(urljoin(settings.API_URL, "/api/delete_old?path=" + full_path))
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else:
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response = requests.post(urljoin(settings.API_URL, "/api/upload_index"), data=file_data)
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# delete local
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shutil.rmtree(full_path)
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return {
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'directory': directory,
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'formats': formats,
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'name_job': name_job,
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'filename': filename,
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'user': user,
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'limited': False
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}
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