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Merge pull request #223 from Zillibub/main

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Moved env variables to the pydantic settings file
pull/228/head
Alex 1 year ago committed by GitHub
parent 4be2635fbe 31350e6302
commit 6cadddc2fc
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5 changed files with 63 additions and 55 deletions
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2
.env-template
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@ -0,0 +1,2 @@
API_KEY=<LLM api key (for example, open ai key)>
EMBEDDINGS_KEY=<LLM embeddings api key (for example, open ai key)>

59
application/app.py
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@ -28,21 +28,12 @@ from werkzeug.utils import secure_filename
from error import bad_request
from worker import ingest_worker
from core.settings import settings
import celeryconfig
# os.environ["LANGCHAIN_HANDLER"] = "langchain"
if os.getenv("LLM_NAME") is not None:
llm_choice = os.getenv("LLM_NAME")
else:
llm_choice = "openai_chat"
if os.getenv("EMBEDDINGS_NAME") is not None:
embeddings_choice = os.getenv("EMBEDDINGS_NAME")
else:
embeddings_choice = "openai_text-embedding-ada-002"
if llm_choice == "manifest":
if settings.LLM_NAME == "manifest":
from manifest import Manifest
from langchain.llms.manifest import ManifestWrapper
@ -79,20 +70,20 @@ with open("prompts/chat_combine_prompt.txt", "r") as f:
with open("prompts/chat_reduce_prompt.txt", "r") as f:
chat_reduce_template = f.read()
if os.getenv("API_KEY") is not None:
if settings.API_KEY is not None:
api_key_set = True
else:
api_key_set = False
if os.getenv("EMBEDDINGS_KEY") is not None:
if settings.EMBEDDINGS_KEY is not None:
embeddings_key_set = True
else:
embeddings_key_set = False
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER = "inputs"
app.config['CELERY_BROKER_URL'] = os.getenv("CELERY_BROKER_URL")
app.config['CELERY_RESULT_BACKEND'] = os.getenv("CELERY_RESULT_BACKEND")
app.config['MONGO_URI'] = os.getenv("MONGO_URI")
app.config['CELERY_BROKER_URL'] = settings.CELERY_BROKER_URL
app.config['CELERY_RESULT_BACKEND'] = settings.CELERY_RESULT_BACKEND
app.config['MONGO_URI'] = settings.MONGO_URI
celery = Celery()
celery.config_from_object('celeryconfig')
mongo = MongoClient(app.config['MONGO_URI'])
@ -122,8 +113,8 @@ def ingest(self, directory, formats, name_job, filename, user):
@app.route("/")
def home():
return render_template("index.html", api_key_set=api_key_set, llm_choice=llm_choice,
embeddings_choice=embeddings_choice)
return render_template("index.html", api_key_set=api_key_set, llm_choice=settings.LLM_NAME,
embeddings_choice=settings.EMBEDDINGS_NAME)
@app.route("/api/answer", methods=["POST"])
@ -135,11 +126,11 @@ def api_answer():
if not api_key_set:
api_key = data["api_key"]
else:
api_key = os.getenv("API_KEY")
api_key = settings.API_KEY
if not embeddings_key_set:
embeddings_key = data["embeddings_key"]
else:
embeddings_key = os.getenv("EMBEDDINGS_KEY")
embeddings_key = settings.EMBEDDINGS_KEY
# use try and except to check for exception
try:
@ -160,13 +151,13 @@ def api_answer():
# vectorstore = "outputs/inputs/"
# loading the index and the store and the prompt template
# Note if you have used other embeddings than OpenAI, you need to change the embeddings
if embeddings_choice == "openai_text-embedding-ada-002":
if settings.EMBEDDINGS_NAME == "openai_text-embedding-ada-002":
docsearch = FAISS.load_local(vectorstore, OpenAIEmbeddings(openai_api_key=embeddings_key))
elif embeddings_choice == "huggingface_sentence-transformers/all-mpnet-base-v2":
elif settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
docsearch = FAISS.load_local(vectorstore, HuggingFaceHubEmbeddings())
elif embeddings_choice == "huggingface_hkunlp/instructor-large":
elif settings.EMBEDDINGS_NAME == "huggingface_hkunlp/instructor-large":
docsearch = FAISS.load_local(vectorstore, HuggingFaceInstructEmbeddings())
elif embeddings_choice == "cohere_medium":
elif settings.EMBEDDINGS_NAME == "cohere_medium":
docsearch = FAISS.load_local(vectorstore, CohereEmbeddings(cohere_api_key=embeddings_key))
# create a prompt template
@ -182,7 +173,7 @@ def api_answer():
q_prompt = PromptTemplate(input_variables=["context", "question"], template=template_quest,
template_format="jinja2")
if llm_choice == "openai_chat":
if settings.LLM_NAME == "openai_chat":
# llm = ChatOpenAI(openai_api_key=api_key, model_name="gpt-4")
llm = ChatOpenAI(openai_api_key=api_key)
messages_combine = [
@ -195,16 +186,18 @@ def api_answer():
HumanMessagePromptTemplate.from_template("{question}")
]
p_chat_reduce = ChatPromptTemplate.from_messages(messages_reduce)
elif llm_choice == "openai":
elif settings.LLM_NAME == "openai":
llm = OpenAI(openai_api_key=api_key, temperature=0)
elif llm_choice == "manifest":
elif settings.LLM_NAME == "manifest":
llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 2048})
elif llm_choice == "huggingface":
elif settings.LLM_NAME == "huggingface":
llm = HuggingFaceHub(repo_id="bigscience/bloom", huggingfacehub_api_token=api_key)
elif llm_choice == "cohere":
elif settings.LLM_NAME == "cohere":
llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
else:
raise ValueError("unknown LLM model")
if llm_choice == "openai_chat":
if settings.LLM_NAME == "openai_chat":
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
chain = ConversationalRetrievalChain(
@ -316,7 +309,7 @@ def combined_json():
"fullName": 'default',
"date": 'default',
"docLink": 'default',
"model": embeddings_choice,
"model": settings.EMBEDDINGS_NAME,
"location": "local"
}]
# structure: name, language, version, description, fullName, date, docLink
@ -330,7 +323,7 @@ def combined_json():
"fullName": index['name'],
"date": index['date'],
"docLink": index['location'],
"model": embeddings_choice,
"model": settings.EMBEDDINGS_NAME,
"location": "local"
})
@ -421,7 +414,7 @@ def upload_index_files():
"language": job_name,
"location": save_dir,
"date": datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
"model": embeddings_choice,
"model": settings.EMBEDDINGS_NAME,
"type": "local"
})
return {"status": 'ok'}

0
application/core/__init__.py
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19
application/core/settings.py
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@ -0,0 +1,19 @@
from pydantic import BaseSettings
from pathlib import Path
class Settings(BaseSettings):
LLM_NAME: str = "openai_chat"
EMBEDDINGS_NAME: str = "openai_text-embedding-ada-002"
CELERY_BROKER_URL: str = "redis://localhost:6379/0"
CELERY_RESULT_BACKEND: str = "redis://localhost:6379/1"
MONGO_URI: str = "mongodb://localhost:27017/docsgpt"
API_URL: str = "http://localhost:5001" # backend url for celery worker
API_KEY: str = None # LLM api key
EMBEDDINGS_KEY: str = None # api key for embeddings (if using openai, just copy API_KEY
path = Path(__file__).parent.parent.absolute()
settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")

38
application/worker.py
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@ -6,7 +6,8 @@ from parser.file.bulk import SimpleDirectoryReader
from parser.schema.base import Document
from parser.open_ai_func import call_openai_api
from parser.token_func import group_split
from celery import current_task
from urllib.parse import urljoin
from core.settings import settings
import string
@ -18,11 +19,12 @@ try:
nltk.download('averaged_perceptron_tagger', quiet=True)
except FileExistsError:
pass
def generate_random_string(length):
return ''.join([string.ascii_letters[i % 52] for i in range(length)])
def ingest_worker(self, directory, formats, name_job, filename, user):
# directory = 'inputs' or 'temp'
# formats = [".rst", ".md"]
@ -39,12 +41,8 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
max_tokens = 1250
full_path = directory + '/' + user + '/' + name_job
# check if API_URL env variable is set
if not os.environ.get('API_URL'):
url = 'http://localhost:5001/api/download'
else:
url = os.environ.get('API_URL') + '/api/download'
file_data = {'name': name_job, 'file': filename, 'user': user}
response = requests.get(url, params=file_data)
response = requests.get(urljoin(settings.API_URL, "/api/download"), params=file_data)
file = response.content
if not os.path.exists(full_path):
@ -58,8 +56,6 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
zip_ref.extractall(full_path)
os.remove(full_path + '/' + filename)
import time
self.update_state(state='PROGRESS', meta={'current': 1})
raw_docs = SimpleDirectoryReader(input_dir=full_path, input_files=input_files, recursive=recursive,
@ -78,22 +74,20 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
# get files from outputs/inputs/index.faiss and outputs/inputs/index.pkl
# and send them to the server (provide user and name in form)
if not os.environ.get('API_URL'):
url = 'http://localhost:5001/api/upload_index'
else:
url = os.environ.get('API_URL') + '/api/upload_index'
file_data = {'name': name_job, 'user': user}
files = {'file_faiss': open(full_path + '/index.faiss', 'rb'),
'file_pkl': open(full_path + '/index.pkl', 'rb')}
response = requests.post(url, files=files, data=file_data)
#deletes remote
if not os.environ.get('API_URL'):
url = 'http://localhost:5001/api/delete_old?path=' + 'inputs/' + user + '/' + name_job
else:
url = os.environ.get('API_URL') + '/api/delete_old?path=' + 'inputs/' + user + '/' + name_job
response = requests.get(url)
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="))
# delete local
shutil.rmtree(full_path)
return {'directory': directory, 'formats': formats, 'name_job': name_job, 'filename': filename, 'user': user, 'limited': False}
return {
'directory': directory,
'formats': formats,
'name_job': name_job,
'filename': filename,
'user': user,
'limited': False
}

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