app.py

import streamlit as st
from streamlit.logger import get_logger
import datasets
import pandas as pd
from langchain_huggingface.embeddings import HuggingFaceEmbeddings
from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
from langchain_core.messages import HumanMessage, SystemMessage
from sentence_transformers import util
from torch import tensor
from io import StringIO


LOGGER = get_logger(__name__)


@st.cache_data
def get_df(uploaded_file) ->object:
    if uploaded_file is  None:  
        return None
    stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
    string_data = stringio.read()
    df = pd.DataFrame(string_data.split('\n'), columns=['text'])      
    return df

@st.cache_data 
def get_embeddings(df,_embeddings_model) ->object:
    df['embeddings'] = df['text'].apply(lambda x: _embeddings_model.embed_query('passage: '+  x))
    return df

@st.cache_resource
def get_model()->object:
    model_name = "intfloat/multilingual-e5-large"
    model_kwargs = {'device': 'cpu'} #'cpu' or 'cuda'
    encode_kwargs = {'normalize_embeddings': True}
    embeddings_model = HuggingFaceEmbeddings(
        model_name=model_name,
        model_kwargs=model_kwargs,
        encode_kwargs=encode_kwargs
    )
    return embeddings_model

@st.cache_resource
def get_chat_api(api_key:str):
    chat = ChatOpenAI(model="gpt-3.5-turbo-16k", api_key=api_key)
    return chat


def get_results(embeddings_model,input,df,num_of_results) -> pd.DataFrame:
    embeddings = embeddings_model.embed_query('query: '+ input)
    hits = util.semantic_search(tensor(embeddings), tensor(df['embeddings'].tolist()), top_k=num_of_results)
    hit_list = [hit['corpus_id'] for hit in hits[0]]
    return df.iloc[hit_list]

def get_llm_results(query,chat,results):   

    prompt_template = PromptTemplate.from_template(
   """
    your misssion is to rank the given answers based on their relevance to the given question.
    Provide a relevancy score between 0 (not relevant) and 1 (highly relevant) for each possible answer.
    the results should be in  the following JSON format: "answer": "score", "answer": "score" while answer is the possible answer's text and score is the relevancy score.
    
    the question is: {query}

    the possible answers are:
    {answers}

    """   )

    messages = [
        SystemMessage(content="""
                      You're a helpful assistant.
                      Return a JSON formatted string.
                      """),
        HumanMessage(content=prompt_template.format(query=query, answers=str.join('\n', results['text'].head(10).tolist()))),
    ]

    response =  chat.invoke(messages)
    llm_results_df = pd.read_json(response.content, orient='index')
    llm_results_df.rename(columns={0: 'score'}, inplace=True)
    llm_results_df.sort_values(by='score', ascending=False, inplace=True)
    return llm_results_df



def run():    
    st.set_page_config(
        page_title=" חיפוש סמנטי",
        page_icon="",
        layout="wide",
        initial_sidebar_state="expanded"    
    )
    
    st.write("# חיפוש חכם ")
    st.write('ניתן להעלות כל קובץ טקסט, להמתין ליצירת האינדקס ולאחר מכן לחפש בשפה חופשית')
    st.write('יצירת האינדקס עשויה לקחת מספר דקות, ותלויה בגודל הקובץ')

    uploaded_file = st.file_uploader('העלה קובץ', type=['txt'], on_change=run)

      

    
    
    embeddings_model = get_model() 
    df = get_df(uploaded_file) 
    if df is None: 
        st.write("לא הועלה קובץ") 
    else:
        df = get_embeddings(df,embeddings_model) 
       
    
    
    user_input = st.text_input('כתוב כאן את שאלתך', placeholder='')    
    num_of_results = st.sidebar.slider('מספר התוצאות שברצונך להציג:',1,25,5)
    use_llm = st.sidebar.checkbox("השתמש במודל שפה כדי לשפר תוצאות", False)
    openAikey = st.sidebar.text_input("OpenAI API key", type="password")

    
    if (st.button('חפש') or user_input) and user_input!="" and df is not None:
            
        results = get_results(embeddings_model,user_input,df,num_of_results)  

        if use_llm:
            if openAikey == None or openAikey=="":
                st.write("לא הוכנס מפתח של OpenAI")

            else:
                chat = get_chat_api(openAikey)
                llm_results = get_llm_results(user_input,chat,results)
                st.write(llm_results) 

        else:
            st.write(results['text'].head(10))
            
       
if __name__ == "__main__":
    run()