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https://github.com/QuivrHQ/quivr.git
synced 2024-10-26 15:18:16 +03:00
delete unused code
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parent
64682fe005
commit
a70c2e6367
@ -1,6 +1,3 @@
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import re
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import unicodedata
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import requests
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from langchain.document_loaders import UnstructuredHTMLLoader
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from models.files import File
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@ -9,8 +6,17 @@ from models.settings import CommonsDep
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from .common import process_file
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def process_html(commons: CommonsDep, file: File, enable_summarization, brain_id, user_openai_api_key):
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return process_file(commons, file, UnstructuredHTMLLoader, enable_summarization, brain_id, user_openai_api_key)
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def process_html(
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commons: CommonsDep, file: File, enable_summarization, brain_id, user_openai_api_key
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):
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return process_file(
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commons,
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file,
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UnstructuredHTMLLoader,
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enable_summarization,
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brain_id,
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user_openai_api_key,
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)
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def get_html(url):
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@ -19,11 +25,3 @@ def get_html(url):
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return response.text
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else:
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return None
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def slugify(text):
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text = unicodedata.normalize('NFKD', text).encode(
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'ascii', 'ignore').decode('utf-8')
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text = re.sub(r'[^\w\s-]', '', text).strip().lower()
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text = re.sub(r'[-\s]+', '-', text)
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return text
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@ -18,11 +18,6 @@ user_router = APIRouter()
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MAX_BRAIN_SIZE_WITH_OWN_KEY = int(os.getenv("MAX_BRAIN_SIZE_WITH_KEY", 209715200))
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def get_unique_documents(vectors):
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# Convert each dictionary to a tuple of items, then to a set to remove duplicates, and then back to a dictionary
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return [dict(t) for t in set(tuple(d.items()) for d in vectors)]
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@user_router.get("/user", dependencies=[Depends(AuthBearer())], tags=["User"])
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async def get_user_endpoint(
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request: Request, current_user: User = Depends(get_current_user)
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0
backend/core/tests/__init__.py
Normal file
0
backend/core/tests/__init__.py
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@ -1,14 +0,0 @@
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from logger import get_logger
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from models.chats import ChatMessage
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from models.settings import common_dependencies
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logger = get_logger(__name__)
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def get_chat_name_from_first_question(chat_message: ChatMessage):
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# Step 1: Get the summary of the first question
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# first_question_summary = summarize_as_title(chat_message.question)
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# Step 2: Process this summary to create a chat name by selecting the first three words
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chat_name = " ".join(chat_message.question.split()[:3])
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return chat_name
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@ -1,8 +0,0 @@
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openai_function_compatible_models = [
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"gpt-3.5-turbo-0613",
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"gpt-4-0613",
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]
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streaming_compatible_models = ["gpt-3.5-turbo, gpt4all-j-1.3"]
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private_models = ["gpt4all-j-1.3"]
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