app.py

import streamlit as st
from openai import OpenAI
import glob

from langchain_community.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.callbacks import get_openai_callback
from langchain_core.prompts import ChatPromptTemplate

from langchain_community.document_loaders import UnstructuredFileLoader

#OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
OPENAI_API_KEY = "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"

# Get all the filenames from the docs folder
files = glob.glob("./docs/*.txt")

# Load files into readable documents
docs = []
for file in files:
    loader = UnstructuredFileLoader(file)
    docs.append(loader.load()[0])

# Config
with st.sidebar:
    chunk_size = st.number_input("Insert chunk size", value=500, step=100, placeholder=500) # Defines the chunks in amount of tokens in which the files are split. Also defines the amount of tokens that are feeded into the context. 
    chunk_overlap = st.number_input("Insert chunk-overlap", value=100, step=10, placeholder=100)
    temperature = st.number_input("Insert temperature", value=0.0, min_value=0.0, step=0.2, max_value=1.0, placeholder=0.0)
    model = st.selectbox("Model name", ["gpt-3.5-turbo"])


prompt_template ="""
You are called "Volker". You are an assistant for question-answering tasks. 
You only answer questions about Long-Covid (use Post-Covid synonymously) and the Volker-App. 
If you don't know the answer, just say that you don't know. Say why you don't know the answer. 
Never answer questions about other diseases (e.g. Cancer-related fatigue, Multiple Sklerose).
Stay emphatic and positive.
When you use the word e.g "Arzt", "Ärzt", always write it as "Arzt".
Only use the following pieces of retrieved context to answer the question.
Keep your answers concise, use maximum of three sentences. 
Question: {question} 
Context: {context} 
Answer:
""" # Source: hub.pull("rlm/rag-prompt")

text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
splits = text_splitter.split_documents(docs)
vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())

# (1) Retriever
retriever = vectorstore.as_retriever()

# (2) Prompt
prompt = ChatPromptTemplate.from_template(prompt_template)

# (3) LLM
# Define the LLM we want to use. Default is "gpt-3.5-turbo" with temperature 0. 
# Temperature is a number between 0 and 1. With 0.8 it generates more random answers, with 0.2 it is more focused on the retrieved content. With temperature = 0 it uses log-probabilities depending on the content.

llm = ChatOpenAI(model_name=model, temperature=temperature)

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)


st.title("🐔 Volker-Chat")

def click_button(btn):
    st.session_state.clicked = True
    st.session_state['btn'] = btn

def predefined_question(btn):
    if btn == "btn_1":
        prompt = "Erläutere die Säule 'Lernen' aus der Volker-App."
    elif btn == "btn_2":
        prompt = "Erläutere die Säule 'Tracken' aus der Volker-App."
    elif btn == "btn_3":
        prompt = "Erläutere die Säule 'Handeln' aus der Volker-App."
    return prompt

c = st.container()
c.write("Beispielfragen")
col1, col2, col3 = c.columns(3)
col1.button("Was ist 'Lernen'?", on_click=click_button, args=['btn_1'])
col2.button("Was ist 'Tracken'?", on_click=click_button, args=['btn_2'])
col3.button("Was ist 'Handeln'?", on_click=click_button, args=['btn_3'])

if 'clicked' not in st.session_state:
    st.session_state.clicked = False

if "messages" not in st.session_state:
    st.session_state["messages"] = [{"role": "assistant", "content": "Ahoi! Ich bin Volker. Wie kann ich dir helfen?"}]

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

if st.session_state.clicked:
    prompt = predefined_question(st.session_state['btn'])
    st.chat_message("user").write(prompt)
    with get_openai_callback() as cb:
        response = rag_chain.invoke(prompt)
        st.chat_message("assistant").write(response)
        with st.sidebar:
            sidebar_c = st.container()
            sidebar_c.success(cb)
    st.session_state.clicked = False

if prompt := st.chat_input():
    st.chat_message("user").write(prompt)
    with get_openai_callback() as cb:
        response = rag_chain.invoke(prompt)
        st.chat_message("assistant").write(response)
        with st.sidebar:
            sidebar_c = st.container()
            sidebar_c.success(cb)
    st.session_state.clicked = False