Building an Interactive Chatbot with Langchain and ChainLit: Leveraging Our Data for Enhanced Conversational Experiences

Amal Ajay — Wed, 05 Jul 2023 05:58:53 +0000

Langchain:

Langchain is an open-source library that provides developers the tools to build applications backed by large language models (LLMs). It provides a standard interface for chaining together different components to create more advanced use cases around LLMs. These components include:

Prompt templates: Templates for different types of prompts, such as "chatbot" style templates, ELI5 question-answering, etc.
LLMs: Large language models like OpenAI Models, Anthropic, Azure, BLOOM, etc.
Agents: Use LLMs to decide what actions should be taken.
Memory: It refers to persisting state between calls of a chain/agent.
Evaluation: Evaluates the performance of chains and agents.

Langchain also provides a number of integrations with other tools, such as:

OpenAI API: OpenAI API provide access to its models.
Hugging Face Transformers: It is a library for working with LLMs & custom models.
Chainlit: Chainlit is a library for creating user interfaces for chatbots expecially for LLMs.

Chainlit

Chainlit is an open-source library that makes it easy to create user interfaces for chatbots powered by large language models (LLMs). It is built on top of the React framework and provides a number of features that make it easy to create interactive and engaging chatbot experiences.

Some of the key features of Chainlit include:

Easy integration with LLMs: Chainlit can be easily integrated with any LLM that supports the OpenAI API. This makes it easy to create chatbots that can generate text, translate languages, answer questions, and more.
Flexible UI components: Chainlit provides a number of flexible UI components that can be used to create a variety of chatbot experiences. These components include text boxes, buttons, dropdown menus, and more.
Support for custom styling: Chainlit supports custom styling, so you can easily customize the look and feel of your chatbot.
Easy deployment: Chainlit can be deployed to any web server, making it easy to share your chatbot with others.

Getting Started

Requirements:

pip install chainlit langchain tabulate pandas

Use

Python Version: 3.10

Data:

Data is about location reviews and ratings of McDonald's stores in USA region.

Data has been collected from ScrapeHero, one of the leading web-scraping companies in the world. Click here for Data Source that we used for analysis!

Columns:
ID, Provider_name, Address, Street, Zip_Code, State, City, Author, Review, Rating

Sample Data:

Lets dive into the code

app.py

from langchain.agents import create_pandas_dataframe_agent
from langchain.llms import OpenAI
import pandas as pd
import chainlit as cl
import io

open_ai_key = "YOUR_API_KEY"

# Create an OpenAI object.
llm = OpenAI(openai_api_key=open_ai_key)


def create_agent(data: str, llm):

    # Create a Pandas DataFrame agent.
    return create_pandas_dataframe_agent(llm, data, verbose=False)


@cl.on_chat_start
async def on_chat_start():

    # Sending an image with the local file path
    elements = [
    cl.Image(name="image1", display="inline", path="./good_day.jpg")
    ]
    await cl.Message(content="Hello there, Welcome to AskAnyQuery related to Data!", elements=elements).send()

    files = None

    # Waits for user to upload csv data
    while files == None:
        files = await cl.AskFileMessage(
            content="Please upload a csv file to begin!", accept=["text/csv"], max_size_mb= 100
        ).send()

    # load the csv data and store in user_session
    file = files[0]
    csv_file = io.BytesIO(file.content)
    df = pd.read_csv(csv_file, encoding="utf-8")

    # creating user session to store data
    cl.user_session.set('data', df)

    # Send response back to user
    await cl.Message(
        content=f"`{file.name}` uploaded! Now you ask me anything related to your data"
    ).send()


@cl.on_message
async def main(message: str):

    # Get data
    df = cl.user_session.get('data')

    # Agent creation
    agent = create_agent(df, llm)

    # Run model 
    response = agent.run(message)

    # Send a response back to the user
    await cl.Message(
        content=response,
    ).send()

To run UI:

chainlit run app.py

Question 1: What is the average rating for McDonald's across all locations?

Correct! 🤩

Question 2: Which state has the highest average rating for McDonald's?

Guam (GU) Location. Users voted for 5 star across all stores.

Question 3: Which state has the highest number of reviews?

Question 4: Give me top 5 negative reviews mentioned in Florida location? Give them in bullet points.

Question 5: What are the top 5 positive reviews from 6875 Sandlake Rd, Orlando in Florida, give them in bullet points.

What We Have Learnt & What Can Be Improved

Now with the help of Langchain and Chainlit we can easily built chatbot according to custom use-cases.
Also while analysing large data, OpenAI model GPT-3.5 turbo has a limited context length of 4096 tokens which causes a error if we wanted an analysis on huge data. In that case, you can go for GPT-4 which has 32,768 context length tokens or Anthropic that has 100k context length.

Next step can be including plots in the Chatbot when user asks queries about plotting figures. For example: User can ask query like Create a bar graph on the top 5 rated stores location of Mcdonalds in USA .

The above mentioned code are available in my Github Repository attached here .

Hope you learned something new today, Happy Learning!

Exploring the Top Co-Working Spaces in the USA: Where Collaboration and Innovation Thrive

Vasista Reddy — Wed, 28 Jun 2023 17:02:32 +0000

Introduction:

Co-working spaces have revolutionized the way we work, offering flexible and collaborative environments for professionals across various industries. In the United States, several co-working spaces have emerged as pioneers in this industry, fostering a dynamic community of entrepreneurs, freelancers, and small businesses. In this blog, we'll take a closer look at some of the top co-working spaces in the USA, including Regus, WeWork, Spaces, Venture X, Serendipity Labs, Impact Hub, and Make Offices. These spaces provide unique features and amenities, creating an ideal setting for productivity, networking, and innovation.

Regus:

Regus is one of the most well-known co-working space providers globally, with a significant presence in the USA. They offer flexible workspace solutions tailored to the needs of individuals and businesses. Regus provides a wide array of options, from virtual offices and hot-desking to fully serviced private offices. With their extensive network, Regus enables professionals to work from almost anywhere, facilitating seamless mobility.

With over 4000 spaces globally, USA has more than 1100 co-working spaces. California state has the most Regus spaces in the country.

The POI data to plot on the map is taken from the ScrapeHero website.

WeWork:

WeWork is perhaps the most recognizable name in the co-working industry. With a vast network of locations across the USA, WeWork offers a range of options, from hot-desking to private offices. Their spaces are renowned for their vibrant atmosphere, community-driven culture, and numerous amenities. WeWork often hosts networking events, workshops, and educational programs, fostering connections and personal growth.

WeWork has around 230+ locations with Newyork has the most locations in the USA.

Spaces:

Spaces, known for its creative and inspiring work environments, operates in numerous cities across the USA. Their spaces are designed to spark innovation and collaboration, incorporating unique features such as dedicated brainstorming rooms, lounges, and event spaces. Spaces caters to the needs of both individuals and larger teams, offering flexible membership options and state-of-the-art facilities.

Other top co-working spaces in USA:

These 4 co-working spaces are mostly spread over Newyork and Texas locations. The top 10 co-working spaces data is available at ScrapeHero.

ScrapeHero, a data company has 2 Million POI locations with 3000+ brands spread over 10 countries.

Conclusion:

The rise of co-working spaces has revolutionized the traditional office landscape, empowering professionals to work in flexible, collaborative, and inspiring environments. Regus, WeWork, Spaces, Venture X, Serendipity Labs, Impact Hub, and Make Offices are among the top co-working space providers in the USA. Each space brings its unique features, amenities, and community-driven approach, catering to diverse needs. Whether you're an entrepreneur, freelancer, or part of a growing team, these co-working spaces offer an ideal platform for productivity, networking, and innovation. Explore the options and find the perfect co-working space that aligns with your goals and aspirations.

Checkout this link for the more co-working spaces in the USA.

Forem: ScrapeHero