mirror of
https://github.com/arc53/DocsGPT
synced 2024-11-02 03:40:17 +00:00
106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
import os
|
|
import shutil
|
|
import string
|
|
import zipfile
|
|
from urllib.parse import urljoin
|
|
|
|
import nltk
|
|
import requests
|
|
|
|
from application.core.settings import settings
|
|
from application.parser.file.bulk import SimpleDirectoryReader
|
|
from application.parser.open_ai_func import call_openai_api
|
|
from application.parser.schema.base import Document
|
|
from application.parser.token_func import group_split
|
|
|
|
try:
|
|
nltk.download('punkt', quiet=True)
|
|
nltk.download('averaged_perceptron_tagger', quiet=True)
|
|
except FileExistsError:
|
|
pass
|
|
|
|
|
|
def metadata_from_filename(title):
|
|
return {'title': title}
|
|
|
|
|
|
def generate_random_string(length):
|
|
return ''.join([string.ascii_letters[i % 52] for i in range(length)])
|
|
|
|
current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
|
|
|
def ingest_worker(self, directory, formats, name_job, filename, user):
|
|
# directory = 'inputs' or 'temp'
|
|
# formats = [".rst", ".md"]
|
|
input_files = None
|
|
recursive = True
|
|
limit = None
|
|
exclude = True
|
|
# name_job = 'job1'
|
|
# filename = 'install.rst'
|
|
# user = 'local'
|
|
sample = False
|
|
token_check = True
|
|
min_tokens = 150
|
|
max_tokens = 1250
|
|
full_path = directory + '/' + user + '/' + name_job
|
|
import sys
|
|
print(full_path, file=sys.stderr)
|
|
# check if API_URL env variable is set
|
|
file_data = {'name': name_job, 'file': filename, 'user': user}
|
|
response = requests.get(urljoin(settings.API_URL, "/api/download"), params=file_data)
|
|
# check if file is in the response
|
|
print(response, file=sys.stderr)
|
|
file = response.content
|
|
|
|
if not os.path.exists(full_path):
|
|
os.makedirs(full_path)
|
|
with open(full_path + '/' + filename, 'wb') as f:
|
|
f.write(file)
|
|
|
|
# check if file is .zip and extract it
|
|
if filename.endswith('.zip'):
|
|
with zipfile.ZipFile(full_path + '/' + filename, 'r') as zip_ref:
|
|
zip_ref.extractall(full_path)
|
|
os.remove(full_path + '/' + filename)
|
|
|
|
self.update_state(state='PROGRESS', meta={'current': 1})
|
|
|
|
raw_docs = SimpleDirectoryReader(input_dir=full_path, input_files=input_files, recursive=recursive,
|
|
required_exts=formats, num_files_limit=limit,
|
|
exclude_hidden=exclude, file_metadata=metadata_from_filename).load_data()
|
|
raw_docs = group_split(documents=raw_docs, min_tokens=min_tokens, max_tokens=max_tokens, token_check=token_check)
|
|
|
|
docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
|
|
|
|
call_openai_api(docs, full_path, self)
|
|
self.update_state(state='PROGRESS', meta={'current': 100})
|
|
|
|
if sample:
|
|
for i in range(min(5, len(raw_docs))):
|
|
print(raw_docs[i].text)
|
|
|
|
# get files from outputs/inputs/index.faiss and outputs/inputs/index.pkl
|
|
# and send them to the server (provide user and name in form)
|
|
file_data = {'name': name_job, 'user': user}
|
|
if settings.VECTOR_STORE == "faiss":
|
|
files = {'file_faiss': open(full_path + '/index.faiss', 'rb'),
|
|
'file_pkl': open(full_path + '/index.pkl', 'rb')}
|
|
response = requests.post(urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data)
|
|
response = requests.get(urljoin(settings.API_URL, "/api/delete_old?path=" + full_path))
|
|
else:
|
|
response = requests.post(urljoin(settings.API_URL, "/api/upload_index"), data=file_data)
|
|
|
|
|
|
# delete local
|
|
shutil.rmtree(full_path)
|
|
|
|
return {
|
|
'directory': directory,
|
|
'formats': formats,
|
|
'name_job': name_job,
|
|
'filename': filename,
|
|
'user': user,
|
|
'limited': False
|
|
}
|