app.py

from langchain.text_splitter import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
from langchain_community.document_loaders import DirectoryLoader
from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain_community.vectorstores import Chroma
import streamlit as st

embedding_function = SentenceTransformerEmbeddings(model_name="all-mpnet-base-v2")
chdb = Chroma(persist_directory="./chroma_db_info", embedding_function=embedding_function)

query = st.text_area("enter text")
disnum = 3
if text:
    docs = chdb.similarity_search_with_score(query)
    docnum = len(docs)
    index = 0
    ret = f"Query:{query}\n"
    for ii in range(docnum):    
        doc = docs[ii][0]
        score = docs[ii][1]
        ret += f"Return {index} ({score:.4f}) :\n{doc.page_content}\n"
        index += 1
        if index > disnum:
            break
    st.text(ret)