Transform Healthcare: Create Your Own Chatbot with Mixtral

Table of Contents:

1. Introduction
2. Creating a Healthcare Chatbot
3. Understanding the Flow
4. Installing Necessary Packages
5. Initializing PubMed Data Fetcher
6. Creating a PubMed Fetcher Class
7. Setting Up the Language Models
8. Defining Keyword Prompt Builder
9. Initializing the Pipeline
10. Running the Code and Getting Responses

Article

Introduction

🤖 The Power of Chatbots in Healthcare: Creating a Healthcare Chatbot Using Mixture, Hy-stack, and Pubid

In this article, we will explore the fascinating world of chatbots in the healthcare industry. We will learn how to create a healthcare chatbot using Mixture, Hy-stack, and Pubid. By leveraging these powerful tools, we will be able to research articles from PubMed, a database of 36 million biomedical literature citations. So, let's dive in and see how we can build this incredible chatbot step by step.

Creating a Healthcare Chatbot

The first step in creating our healthcare chatbot is to understand its purpose. Our chatbot will be able to research articles and use a large language model to help us understand the information. Here's how the flow works:

1. As a user, you ask a query, such as "What are the treatments for long COVID?"
2. The query is formatted and sent to a large language model to extract Relevant keywords.
3. These extracted keywords are passed to Pubmed fetcher, which searches for articles based on the keywords.
4. The articles are then fed to the language model again, after formatting.
5. Finally, the language model analyzes the articles and provides us with the output.

Understanding the Flow

To start coding our chatbot, we need to install the necessary packages. Here's how you can do it:

1. Open your terminal or command prompt.
2. Type pip install hastac-ai, pid, transformer and hit enter.
3. Wait for the packages to be installed.

Now that we have the required packages installed, let's continue building our healthcare chatbot.

Installing Necessary Packages

To create our healthcare chatbot, we will need to install the following packages:

• hastac-ai
• pid
• transformer

You can install these packages by running the following command in your terminal or command prompt:

pip install hastac-ai pid transformer

Once the installation is complete, we can proceed to the next step.

Initializing PubMed Data Fetcher

To fetch articles from PubMed, we need to initialize the PubMed data fetcher. Here's how you can do it:

import os
from ped import PubMed
from typing import List
from hyack import component, document

# Export your Hugging Face API key
os.environ["HUGGINGFACE_API_KEY"] = "your-api-key"

# Initialize PubMed with your tool name and email address
pubmed = PubMed("your-tool-name", "your-email-address")

With PubMed initialized, we can now proceed to create the PubMed fetcher class.

Creating a PubMed Fetcher Class

To search for articles in Pubmed, we will create a class called "PubMedFetcher". This class will receive queries, such as keywords, and use those keywords to retrieve documents from Pubmed. Here's how you can implement it:

class PubMedFetcher:
    def __init__(self):
        pass

    def fetch_documents(self, keywords: List[str]) -> List[document]:
        # Split the keywords and use them to retrieve documents from Pubmed
        documents = []
        # Retrieve documents based on article titles and keywords
        # For now, we will extract only abstracts, but you can extend this further
        return documents

Now that we have our PubMed fetcher class set up, let's move on to setting up the language models.

Setting Up the Language Models

To leverage the power of language models in our chatbot, we need to set up two language models: Keyword LLB (MixL) and Lodge Language Model (MixL with LM). Here's how we can do it:

from haack import generator
from haack.pipeline import Pipeline
from haack.builder import prompt_builder

# Importing the necessary components from Hugging Face
keyword_llm = generator("huggingface-token", warmup="keyword-lm")
llm = generator("huggingface-token", warmup="lm")

# Setting up the pipeline
prompt_template = "Enter your question: "
query_prompt_template = "Keywords:"

prompt_builder = prompt_builder(prompt_template)
query_prompt_builder = prompt_builder(query_prompt_template)

fetcher = PubMedFetcher()

pipeline = Pipeline([
    prompt_builder,
    keyword_llm,
    fetcher,
    query_prompt_builder,
    llm
])

Now that we have our pipeline set up, let's move on to running the code and getting responses.

Running the Code and Getting Responses

To interact with our chatbot and get responses, we need to create a function. Here's how you can do it:

def ask_question(question: str):
    response = pipeline.run(question)
    print(response)

# Example question:
question = "What are the most current treatment options for post-acute COVID?"
ask_question(question)

By running the Python script, you will be able to ask the chatbot questions and get responses based on the articles retrieved from PubMed.

Conclusion

In conclusion, we have successfully created a healthcare chatbot using Mixture, Hy-stack, and Pubid. We have explored the flow of the chatbot, installed the necessary packages, initialized the PubMed data fetcher, set up the language models, and ran the code to get responses. You can further enhance the chatbot by adding more examples and prompts. Stay tuned for more videos and tutorials on similar topics!

Pros:

• Efficiently retrieves and analyzes articles from PubMed
• Uses advanced language models for understanding and generating responses
• Can be customized and expanded for specific healthcare domains

Cons:

• Requires familiarity with Python programming and API integrations
• Relies on the availability and accuracy of data in PubMed
• May need fine-tuning to improve response accuracy

FAQ

Q: How can I install the necessary packages for the healthcare chatbot? A: You can install the necessary packages by running the command pip install hastac-ai pid transformer in your terminal or command prompt.

Q: Can I use my own API key for Hugging Face? A: Yes, you can export your Hugging Face API key and set it as an environment variable using the command os.environ["HUGGINGFACE_API_KEY"] = "your-api-key".

Q: How can I ask questions to the healthcare chatbot? A: You can create a function and pass your question as an argument to the ask_question function. For example, ask_question("What are the most current treatment options for post-acute COVID?").