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Deploying LLM Chat Applications with Declarai, FastAPI, and Streamlit
Latest   Machine Learning

Deploying LLM Chat Applications with Declarai, FastAPI, and Streamlit

Last Updated on August 19, 2023 by Editorial Team

Author(s): Matan Kleyman

Originally published on Towards AI.

Screenshot U+007C Taken By The Author U+007C All rights reserved

In October 2022, when I began experimenting with Large Language Models (LLMs), my initial inclination was to explore text completions, classifications, NER, and other NLP-related areas. Although the experience was invigorating, I soon sensed a paradigm shift. There was a noticeable decline in interest for traditional completions-based LLMs, making way for chat models like GPT-3.5 and GPT-4 that provided coherent chat experience.

This transition coincided with an industry buzz around chatbots. Whether it’s an assistant chatbot or one tailored for business flows, my colleagues were convinced — chat was the way forward. My journey hence pivoted to building chatbots for various use cases.

Lately, we decided to share this knowledge and therefore integrated chatbot functionalities into Declarai. Our vision? An open-source tool is so intuitive that anyone could deploy any LLM-related task in under 5 minutes, tailored for 95% of standard use cases, and still be able to build a robust production foundation around it.

Declarai in Action U+1F680

Declarai’s ethos is empowering developers to declare their intended task. For our demonstration, we’ll create a SQL Chatbot that fields SQL-related queries.

To begin, we design our system prompt — the guiding message that sets the boundaries for our chatbot’s capabilities.

We are using gpt3.5 to create a SQL Chatbot that is helping us with any SQL-related questions.

from declarai import Declarai

declarai = Declarai(provider="openai", model="gpt-3.5-turbo")


@declarai.experimental.chat
class SQLChat:
"""
You are a sql assistant. You are helping a user to write a sql query.
You should first know what sql syntax the user wants to use. It can be mysql, postgresql, sqllite, etc.
If the user says something that is completely not related to SQL, you should say "I don't understand. I'm here to help you write a SQL query."
After you provide the user with a query, you should ask the user if they need anything else.
"""

In Declarai, this guiding message is seamlessly embedded within the class’s docstring, ensuring clarity and readability. We can also initiate the chat with a friendly greeting:

@declarai.experimental.chat
class SQLChat:
"""
You are a sql assistant. You are helping a user to write a sql query.
You should first know what sql syntax the user wants to use. It can be mysql, postgresql, sqllite, etc.
If the user says something that is completely not related to SQL, you should say "I don't understand. I'm here to help you write a SQL query."
After you provide the user with a query, you should ask the user if they need anything else.
"""


greeting = "Hey dear SQL User. Hope you are doing well today. I am here to help you write a SQL query. Let's get started!. What SQL syntax would you like to use? It can be mysql, postgresql, sqllite, etc."

Now, let's interact with our chatbot:

sql_chat = SQLChat()

>>> "Hey dear SQL User. Hope you are doing well today. I am here to help you write a SQL query. Let's get started!. What SQL syntax would you like to use? It can be mysql, postgresql, sqllite, etc."

sql_chat.send("I'd prefer MySQL."))

>>> "Fantastic choice! How can I aid you with your MySQL query?"

sql_chat.send("From the 'Users' table, fetch the 5 most common names.")

>>> "Certainly! Here's a MySQL query that should work:

>>> SELECT name, COUNT(*) AS count
>>> FROM Users
>>> GROUP BY name
>>> ORDER BY count DESC
>>> LIMIT 5;

>>> Is there anything else I can assist you with?

With the SQLChat example laid out, you’ve glimpsed the power of a well-structured conversational model.

Note U+1F4DD: Remember, the SQLChat is just one example. You can easily tailor the chatbot to your specific needs by adjusting the system message.

The next step is setting up our backend. Using FastAPI and Streamlit, we’ll bring our chatbot to life and make it accessible to users.

The API Backend U+2699️

To bring our chatbot to a wider audience, we’ll utilize FastAPI as our RESTful API gateway and Streamlit for frontend integration.

from fastapi import FastAPI, APIRouter
from declarai.memory import FileMessageHistory
from .chat import SQLChat

app = FastAPI(title="Hey")
router = APIRouter()



@router.post("/chat/submit/{chat_id}")
def submit_chat(chat_id: str, request: str):
chat = SQLChat(chat_history=FileMessageHistory(file_path=chat_id))
response = chat.send(request)
return response


@router.get("/chat/history/{chat_id}")
def get_chat_history(chat_id: str):
chat = SQLChat(chat_history=FileMessageHistory(file_path=chat_id))
response = chat.conversation
return response


app.include_router(router, prefix="/api/v1")

if __name__ == "__main__":
import uvicorn # pylint: disable=import-outside-toplevel
uvicorn.run(
"main:app",
host="0.0.0.0",
port=8000,
workers=1,
use_colors=True,
)

Our FastAPI configuration establishes two key routes: one for submitting new messages and another for retrieving chat histories. Declarai’s FileMessageHistory ensures continuity in our chat threads across sessions by saving the conversation state at every interaction. If you prefer to save message history into a database (Postgres/redis/mongo, you can do so by simply replacing the FileMessageHistory class)

Our FastAPI Swagger U+007C Taken by the author U+007C No rights reserved

The Frontend Experience U+1F3A8

Our user interface is a chat GUI developed using Streamlit.

Screenshot of the streamlit chat U+007C Taken by the AuthorU+007C No Rights Reserved

The streamlit setup is fairly concise as well:

import streamlit as st
import requests

st.write("# Welcome to SQLChat Assistant! U+1F44B")

st.write(
"Greetings! I'm your sql assistant.\n Together we can craft any sql query you want.")
session_name = st.text_input("Provide a name for your chat session")

if session_name:
messages = requests.get(f"http://localhost:8000/api/v1/chat/history/{session_name}").json()
for message in messages:
with st.chat_message(message["role"]):
st.markdown(message["message"])

prompt = st.chat_input("Type a message...")
if prompt:
with st.chat_message("user"):
st.markdown(prompt)
with st.spinner("..."):
res = requests.post(f"http://localhost:8000/api/v1/chat/submit/{session_name}",
params={"request": prompt}).json()
with st.chat_message("assistant"):
st.markdown(res)

messages = requests.get(
f"http://localhost:8000/api/v1/chat/history/{session_name}").json()

Upon providing a chat session name, users can initiate a conversation. Every message interaction calls the backend, with a spinner providing visual feedback during processing.

Ready to deploy your chatbot U+1F916? Dive into the complete code in this repository —

GitHub – matankley/declarai-chat-fastapi-streamlit: An example how to build chatbot using declarai…

An example how to build chatbot using declarai for interacting with the language model, fastapi as backend server and…

github.com

Stay in touch with Declarai developments U+1F48C. Connect with us on Linkedin Page , and give us a star ⭐️ on GitHub if you find our tools valuable!

Dive deeper into Declarai’s capabilities by exploring our documentation U+1F4D6

Declarai

Declarai, turning Python code into LLM tasks, easy to use, and production-ready. Declarai turns your Python code into…

declarai.com

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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