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https://github.com/QuivrHQ/quivr.git
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Merge pull request #26 from StanGirard/pr/Thibaut-Padok/24
Add URL to Brain
This commit is contained in:
commit
2210a125ad
86
files.py
86
files.py
@ -1,45 +1,75 @@
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import streamlit as st
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import streamlit as st
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from streamlit.runtime.uploaded_file_manager import UploadedFile, UploadedFileRec
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import os
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import os
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from loaders.audio import process_audio
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from loaders.audio import process_audio
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from loaders.txt import process_txt
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from loaders.txt import process_txt
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from loaders.csv import process_csv
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from loaders.csv import process_csv
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from loaders.markdown import process_markdown
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from loaders.markdown import process_markdown
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from loaders.html import process_html
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from utils import compute_sha1_from_content
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from utils import compute_sha1_from_content
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from loaders.pdf import process_pdf
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from loaders.pdf import process_pdf
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from loaders.html import get_html, create_html_file, delete_tempfile
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import requests
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import re
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import unicodedata
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import tempfile
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file_processors = {
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".txt": process_txt,
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".csv": process_csv,
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".md": process_markdown,
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".markdown": process_markdown,
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".m4a": process_audio,
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".mp3": process_audio,
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".webm": process_audio,
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".mp4": process_audio,
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".mpga": process_audio,
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".wav": process_audio,
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".mpeg": process_audio,
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".pdf": process_pdf,
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".html": process_html,
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}
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def file_uploader(supabase, openai_key, vector_store):
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def file_uploader(supabase, openai_key, vector_store):
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file_processors = {
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files = st.file_uploader("**Upload a file**", accept_multiple_files=True, type=list(file_processors.keys()))
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".txt": process_txt,
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".csv": process_csv,
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".md": process_markdown,
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".markdown": process_markdown,
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".m4a": process_audio,
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".mp3": process_audio,
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".webm": process_audio,
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".mp4": process_audio,
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".mpga": process_audio,
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".wav": process_audio,
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".mpeg": process_audio,
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".pdf": process_pdf,
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}
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files = st.file_uploader("Upload a file", accept_multiple_files=True, type=list(file_processors.keys()))
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if st.button("Add to Database"):
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if st.button("Add to Database"):
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if files is not None:
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if files is not None:
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for file in files:
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for file in files:
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if file_already_exists(supabase, file):
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filter_file(file, supabase, vector_store)
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st.write(f"😎 {file.name} is already in the database.")
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elif file.size < 1:
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st.write(f"💨 {file.name} is empty.")
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else:
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file_extension = os.path.splitext(file.name)[-1]
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if file_extension in file_processors:
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file_processors[file_extension](vector_store, file)
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st.write(f"✅ {file.name} ")
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else:
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st.write(f"❌ {file.name} is not a valid file type.")
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def file_already_exists(supabase, file):
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def file_already_exists(supabase, file):
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file_sha1 = compute_sha1_from_content(file.getvalue())
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file_sha1 = compute_sha1_from_content(file.getvalue())
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response = supabase.table("documents").select("id").eq("metadata->>file_sha1", file_sha1).execute()
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response = supabase.table("documents").select("id").eq("metadata->>file_sha1", file_sha1).execute()
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return len(response.data) > 0
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return len(response.data) > 0
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def filter_file(file, supabase, vector_store):
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if file_already_exists(supabase, file):
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st.write(f"😎 {file.name} is already in the database.")
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return False
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elif file.size < 1:
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st.write(f"💨 {file.name} is empty.")
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return False
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else:
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file_extension = os.path.splitext(file.name)[-1]
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print(file.name, file_extension)
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if file_extension in file_processors:
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file_processors[file_extension](vector_store, file)
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st.write(f"✅ {file.name} ")
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return True
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else:
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st.write(f"❌ {file.name} is not a valid file type.")
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return False
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def url_uploader(supabase, openai_key, vector_store):
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url = st.text_area("## Add an url",placeholder="https://www.quivr.app")
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button = st.button("Add the URL to the database")
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if button:
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html = get_html(url)
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if html:
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st.write(f"Getting content ... {url} ")
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file, temp_file_path = create_html_file(url, html)
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ret = filter_file(file, supabase, vector_store)
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delete_tempfile(temp_file_path, url, ret)
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else:
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st.write(f"❌ Failed to access to {url} .")
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47
loaders/html.py
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47
loaders/html.py
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from .common import process_file
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from langchain.document_loaders import UnstructuredHTMLLoader
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import requests
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import re
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import unicodedata
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import tempfile
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import os
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import streamlit as st
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from streamlit.runtime.uploaded_file_manager import UploadedFileRec, UploadedFile
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def process_html(vector_store, file):
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return process_file(vector_store, file, UnstructuredHTMLLoader, ".html")
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def get_html(url):
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response = requests.get(url)
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if response.status_code == 200:
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return response.text
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else:
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return None
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def create_html_file(url, content):
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file_name = slugify(url) + ".html"
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temp_file_path = os.path.join(tempfile.gettempdir(), file_name)
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with open(temp_file_path, 'w') as temp_file:
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temp_file.write(content)
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record = UploadedFileRec(id=None, name=file_name, type='text/html', data=open(temp_file_path, 'rb').read())
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uploaded_file = UploadedFile(record)
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return uploaded_file, temp_file_path
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def delete_tempfile(temp_file_path, url, ret):
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try:
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os.remove(temp_file_path)
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if ret:
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st.write(f"✅ Content saved... {url} ")
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except OSError as e:
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print(f"Error while deleting the temporary file: {str(e)}")
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if ret:
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st.write(f"❌ Error while saving content... {url} ")
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def slugify(text):
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text = unicodedata.normalize('NFKD', text).encode('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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11
main.py
11
main.py
@ -3,7 +3,7 @@ import os
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import tempfile
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import tempfile
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import streamlit as st
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import streamlit as st
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from files import file_uploader
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from files import file_uploader, url_uploader
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from question import chat_with_doc
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from question import chat_with_doc
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from brain import brain
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from brain import brain
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.embeddings.openai import OpenAIEmbeddings
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@ -63,7 +63,14 @@ if user_choice == 'Add Knowledge':
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"Select Chunk Size", 100, 1000, st.session_state['chunk_size'], 50)
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"Select Chunk Size", 100, 1000, st.session_state['chunk_size'], 50)
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st.session_state['chunk_overlap'] = st.sidebar.slider(
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st.session_state['chunk_overlap'] = st.sidebar.slider(
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"Select Chunk Overlap", 0, 100, st.session_state['chunk_overlap'], 10)
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"Select Chunk Overlap", 0, 100, st.session_state['chunk_overlap'], 10)
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file_uploader(supabase, openai_api_key, vector_store)
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# Create two columns for the file uploader and URL uploader
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col1, col2 = st.columns(2)
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with col1:
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file_uploader(supabase, openai_api_key, vector_store)
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with col2:
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url_uploader(supabase, openai_api_key, vector_store)
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elif user_choice == 'Chat with your Brain':
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elif user_choice == 'Chat with your Brain':
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# Display model and temperature selection only when asking questions
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# Display model and temperature selection only when asking questions
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st.sidebar.title("Configuration")
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st.sidebar.title("Configuration")
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