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| import streamlit as st | |
| from langchain.docstore.document import Document | |
| from chromadb.config import Settings | |
| from load_model import load_embedding | |
| from load_vectors import load_from_file, load_and_split, create_and_add, load_from_web | |
| from utils import retrieve_collections, get_chroma_client | |
| def llm_module(): | |
| pass | |
| def load_files(): | |
| client = get_chroma_client() | |
| option = st.radio( | |
| "", | |
| options=["Add Documents", "Start new collection"], | |
| ) | |
| if option == "Add Documents": | |
| collections = retrieve_collections() | |
| selected_collection = st.selectbox( | |
| 'Add to exsisting collection or create a new one', | |
| collections ) | |
| if st.button('Delete Collection (⚠️ This is destructive and not reversible)'): | |
| client.delete_collection(name=selected_collection["name"]) | |
| #retrieve_collections.clear() | |
| collections = retrieve_collections() | |
| if selected_collection: | |
| st.write("Selected Vectorstore:", selected_collection) | |
| option = st.radio( | |
| "", | |
| options=["Upload Files from Local", "Upload Files from Web"], | |
| ) | |
| if option == "Upload Files from Local": | |
| st.write('Source Documents:') | |
| uploaded_files = st.file_uploader("Choose a PDF file", accept_multiple_files=True) | |
| chunk_size = st.text_area('chunk Size:', 1000) | |
| if st.button('Upload'): | |
| docs = load_from_file(uploaded_files) | |
| sub_docs = load_and_split(docs, chunk_size=int(chunk_size)) | |
| vec1 = create_and_add(selected_collection["name"], sub_docs, selected_collection['model_name'], selected_collection['metadata']) | |
| st.write("Upload succesful") | |
| else: | |
| st.write('Urls of Source Documents (Comma separated):') | |
| urls = chunk_size = st.text_area('Urls:', '') | |
| chunk_size = st.text_area('chunk Size:', 1000) | |
| urls = urls.replace(",", "" ).replace('"', "" ).split(',') | |
| if st.button('Upload'): | |
| docs = load_from_web(urls) | |
| sub_docs = load_and_split(docs, chunk_size=int(chunk_size)) | |
| vec2 = create_and_add(selected_collection["name"], sub_docs, selected_collection['model_name'], selected_collection['metadata']) | |
| st.write("Upload succesful") | |
| else: | |
| collection = st.text_area('Name of your new collection:', '') | |
| model_name = st.text_area('Choose the embedding function:', "hkunlp/instructor-large") | |
| if st.button('Create'): | |
| if len(collection)>3: | |
| ef = load_embedding(model_name) | |
| metadata= {"loaded_docs":[], "Subject":"Terms Example", "model_name": ef.model_name} | |
| client.create_collection(collection, embedding_function=ef, metadata=metadata) | |
| # retrieve_collections.clear() | |
| st.write("Collection " +collection+" succesfully created.") |