Create AI-powered Chat Apps with Streamlit and LangChain

Table of Contents

1. Introduction
2. The Limitations of Language Models
3. Introducing Modular Reasoning Knowledge and Language (MRKL)
4. Understanding the MRKL Agent
5. The Extensible Set of Tools in MRKL
6. Building an MVP with MRKL and Streamlit
7. The Power of Sequential Actions in MRKL
8. Customizing the MRKL App with Streamlit
9. Sample Questions and Queries
10. Conclusion

Introduction

Language models have become increasingly powerful and versatile in recent years, but they still have their limitations. One of the main challenges is their inability to access Relevant knowledge and perform specialized tasks. However, there is a solution: Modular Reasoning Knowledge and Language (MRKL).

In this article, we will explore the concept of MRKL and how it overcomes the limitations of language models. We will Delve into the MRKL agent, the multitude of tools available in the MRKL ecosystem, and even build a Minimum viable Product (MVP) using MRKL and Streamlit. So let's dive in and discover the power of MRKL!

The Limitations of Language Models

While language models like GPT have made significant advancements in natural language processing, they still struggle with certain types of queries and tasks. For example, they may not have access to up-to-date information, proprietary data sources, or specialized tools. This often results in inaccurate or incomplete answers to user queries.

Language models are fundamentally text generation devices that lack computation modes or access to external resources. This can be a major hindrance when trying to solve complex problems or perform specific tasks. Fortunately, MRKL provides a solution to these limitations.

Introducing Modular Reasoning Knowledge and Language (MRKL)

MRKL is a system that leverages foundational language models as textual gateways to specialized tools and modules. Instead of relying solely on the language model, MRKL utilizes a modular approach where these specialized tools perform Context-specific tasks.

Think of MRKL as a two-step process. First, You express your query in words to the language model, which acts as the textual gateway. Then, the MRKL agent determines the relevant specialized tool or expert to handle your query. This modular reasoning allows MRKL to overcome the limitations of language models and build a robust system that provides accurate answers to specific tasks.

Understanding the MRKL Agent

The MRKL agent is the key component of the MRKL system. It acts as a wrapper around the underlying agent prompt chain, such as the Miracle agent. The agent prompt chain is responsible for formatting the tools into a prompt and passing the response obtained from the chat model.

To initialize the MRKL agent, you need to provide the necessary tools, the language model (LM), and the agent Type. The tools represent a set of specialized modules that the MRKL agent can call upon to perform specific tasks. These tools can range from a web browser and calculator to a dictionary and translator.

The MRKL agent uses a zero-shot react description as its primary action. This means that when encountering a natural language query, it decides which action to take Based on the description of the tools. By analyzing the query, the MRKL agent determines the appropriate expert or tool to handle the user's request.

The Extensible Set of Tools in MRKL

One of the major strengths of MRKL is its extensibility. Anyone can contribute to the MRKL ecosystem by adding or developing new tools and modules. Whether it's creating a new API or integrating existing services, the MRKL system allows for seamless integration of these tools.

The MRKL ecosystem offers a wide range of tools, including but not limited to:

1. Web browser tool for browsing the web and retrieving information.
2. Calculator tool for performing mathematical calculations.
3. Translator tool for translating between languages.
4. Currency converter tool for converting currencies.
5. Weather API tool for retrieving weather information.
6. Calendar tool for managing schedules and appointments.
7. Database tool for querying and retrieving data.
8. And many more!

These tools work together to provide a comprehensive set of functionalities to the MRKL system. Through this extensibility, MRKL becomes a versatile platform capable of handling a wide range of tasks and queries.

Building an MVP with MRKL and Streamlit

To showcase the power of MRKL, we will build a Minimum Viable Product (MVP) using MRKL and the Streamlit framework. Streamlit allows us to Create interactive web applications with ease, making it the perfect choice for our MRKL app.

The MVP will feature a user prompt where users can enter their queries. The MRKL agent will process these queries, determine the appropriate tool or expert to handle the request, and provide accurate and relevant answers. By leveraging MRKL and Streamlit, we can create a user-friendly and intuitive interface for interacting with the MRKL system.

The Power of Sequential Actions in MRKL

One of the most powerful aspects of MRKL is its ability to perform sequential actions. Instead of trying to generate answers on its own, MRKL follows a sequence of actions to arrive at the desired result. This allows MRKL to leverage specialized tools and experts for specific tasks, ensuring accurate and reliable answers.

By chaining these sequential actions, MRKL can perform complex operations and retrieve relevant information from a variety of sources. For example, it can search the web for the latest news, query databases for specific data, perform calculations, and much more. This modular approach ensures that MRKL is not limited by the capabilities of the language model alone.

Customizing the MRKL App with Streamlit

In our MRKL app built with Streamlit, we can customize the user interface to enhance the user experience. We can add a title, configure the page layout, and even include a sidebar for additional functionality. These customizations make the app more visually appealing and user-friendly.

By leveraging the power of Streamlit, we can create a polished and professional-looking interface for our MRKL app. Users can Interact with the app effortlessly, entering their queries and receiving accurate and relevant results. Additionally, the sidebar allows users to input their own OpenAI API key, ensuring a seamless and customizable experience.

Sample Questions and Queries

To demonstrate the capabilities of MRKL, we have provided a set of sample questions and queries that users can try out. These questions cover a variety of topics, ranging from general knowledge queries to specific calculations and database queries. By exploring these samples, users can gain a better understanding of how MRKL works and how it can provide accurate and reliable answers.

Conclusion

In conclusion, MRKL offers a powerful solution to the limitations of language models. By leveraging specialized tools and experts, MRKL can perform specific tasks and retrieve relevant information. Through an extensible set of tools and sequential actions, MRKL provides accurate and reliable answers to user queries.

With the ability to build an MVP using MRKL and Streamlit, developers can create interactive and user-friendly apps that harness the power of MRKL. By customizing the user interface and leveraging the modular approach of MRKL, users can experience a seamless and versatile system for retrieving information and performing specific tasks.

So why settle for the limitations of traditional language models when you can harness the power of MRKL? Dive into the MRKL ecosystem, explore the extensible set of tools, and unlock a world of possibilities for accurate and reliable information retrieval.