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LLM RAG bases Webapps With Mesop, Ollama, DSpy, HTMX

Revolutionize Your AI App Development with Mesop: Building Lightning-Fast, Adaptive Web UIs

The dynamic world of AI and machine learning demands user-friendly interfaces. But crafting them can be a challenge. Enter Mesop, Google’s innovative library, designed to streamline UI development for AI and LLM RAG applications. This guide takes you through Mesop’s power-packed features, enabling you to build production-ready, multi-page web UIs that elevate your AI projects.

Mesop empowers developers with Python-centric development – write your entire UI in Python without wrestling with JavaScript. Enjoy a fast build-edit-refresh loop with hot reload for a smooth development experience. Utilize a rich set of pre-built Angular Material components or create custom components tailored to your specific needs. When it’s time to deploy, Mesop leverages standard HTTP technologies for quick and reliable application launches.

Fastrack Your AI App Development with Google Mesop: Building Lightning-Fast, Adaptive Web UIs

In the dynamic world of AI and machine learning, developing user-friendly and responsive interfaces can often be challenging. Mesop, Google’s innovative library, is here to change the game, making it easier for developers to create web UIs tailored to AI and LLM RAG (Retrieval-Augmented Generation) applications. This guide will walk you through Mesop’s powerful features, helping you build production-ready, multi-page web UIs to elevate your AI projects.


Table of Contents

  1. Introduction to Mesop
  2. Getting Started with Mesop
  3. Building Your First Mesop UI
  4. Advanced Mesop Techniques
  5. Integrating AI and LLM RAG with Mesop
  6. Optimizing Performance and Adaptivity
  7. Real-World Case Study: AI-Powered Research Assistant
  8. Conclusion and Future Prospects

1. Introduction to Mesop

Mesop is a Python-based UI framework that simplifies web UI development, making it an ideal choice for engineers working on AI and machine learning projects without extensive frontend experience. By leveraging Angular and Angular Material components, Mesop accelerates the process of building web demos and internal tools.

Key Features of Mesop:

  • Python-Centric Development: Build entire UIs in Python without needing to dive into JavaScript.
  • Hot Reload: Enjoy a fast build-edit-refresh loop for smooth development.
  • Comprehensive Component Library: Utilize a rich set of Angular Material components.
  • Customizability: Extend Mesop’s capabilities with custom components tailored to your use case.
  • Easy Deployment: Deploy using standard HTTP technologies for quick and reliable application launches.

2. Getting Started with Mesop

To begin your journey with Mesop, follow these steps:

  1. Install Mesop via pip:
    pip install mesop
  2. Create a new Python file for your project, e.g., app.py.
  3. Import Mesop in your file:
    import mesop as me

3. Building Your First Mesop UI

Let’s create a simple multi-page UI for an AI-powered note-taking app:

import mesop as me

@me.page(path="/")
def home():
    with me.box():
        me.text("Welcome to AI Notes", type="headline")
        me.button("Create New Note", on_click=navigate_to_create)

@me.page(path="/create")
def create_note():
    with me.box():
        me.text("Create a New Note", type="headline")
        me.text_input("Note Title")
        me.text_area("Note Content")
        me.button("Save", on_click=save_note)

def navigate_to_create(e):
    me.navigate("/create")

def save_note(e):
    # Implement note-saving logic here
    pass

if __name__ == "__main__":
    me.app(port=8080)

This example illustrates how easily you can set up a multi-page app with Mesop. Using @me.page, you define different routes, while components like me.text and me.button bring the UI to life.


4. Advanced Mesop Techniques

As your app grows, you’ll want to use advanced Mesop features to manage complexity:

State Management

Mesop’s @me.stateclass makes state management straightforward:

@me.stateclass
class AppState:
    notes: list[str] = []
    current_note: str = ""

@me.page(path="/")
def home():
    state = me.state(AppState)
    with me.box():
        me.text(f"You have {len(state.notes)} notes")
        for note in state.notes:
            me.text(note)

Custom Components

Keep your code DRY by creating reusable components:

@me.component
def note_card(title, content):
    with me.box(style=me.Style(padding=me.Padding.all(10))):
        me.text(title, type="subtitle")
        me.text(content)

5. Integrating AI and LLM RAG with Mesop

Now, let’s add some AI to enhance our note-taking app:

import openai

@me.page(path="/enhance")
def enhance_note():
    state = me.state(AppState)
    with me.box():
        me.text("Enhance Your Note with AI", type="headline")
        me.text_area("Original Note", value=state.current_note)
        me.button("Generate Ideas", on_click=generate_ideas)

def generate_ideas(e):
    state = me.state(AppState)
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=f"Generate ideas based on this note: {state.current_note}",
        max_tokens=100
    )
    state.current_note += "\n\nAI-generated ideas:\n" + response.choices[0].text

This integration showcases how OpenAI’s GPT-3 can enrich user notes with AI-generated ideas.


6. Optimizing Performance and Adaptivity

Mesop excels at creating adaptive UIs that adjust seamlessly across devices:

@me.page(path="/")
def responsive_home():
    with me.box(style=me.Style(display="flex", flex_wrap="wrap")):
        with me.box(style=me.Style(flex="1 1 300px")):
            me.text("AI Notes", type="headline")
        with me.box(style=me.Style(flex="2 1 600px")):
            note_list()

@me.component
def note_list():
    state = me.state(AppState)
    for note in state.notes:
        note_card(note.title, note.content)

This setup ensures that the layout adapts to different screen sizes, providing an optimal user experience.


7. Real-World Case Study: AI-Powered Research Assistant

Let’s build a more complex application: an AI-powered research assistant for gathering and analyzing information:

import mesop as me
import openai
from dataclasses import dataclass

@dataclass
class ResearchTopic:
    title: str
    summary: str
    sources: list[str]

@me.stateclass
class ResearchState:
    topics: list[ResearchTopic] = []
    current_topic: str = ""
    analysis_result: str = ""

@me.page(path="/")
def research_home():
    state = me.state(ResearchState)
    with me.box():
        me.text("AI Research Assistant", type="headline")
        me.text_input("Enter a research topic", on_change=update_current_topic)
        me.button("Start Research", on_click=conduct_research)

        if state.topics:
            me.text("Research Results", type="subtitle")
            for topic in state.topics:
                research_card(topic)

@me.component
def research_card(topic: ResearchTopic):
    with me.box(style=me.Style(padding=me.Padding.all(10), margin=me.Margin.bottom(10), border="1px solid gray")):
        me.text(topic.title, type="subtitle")
        me.text(topic.summary)
        me.button("Analyze", on_click=lambda e: analyze_topic(topic))

def update_current_topic(e):
    state = me.state(ResearchState)
    state.current_topic = e.value

def conduct_research(e):
    state = me.state(ResearchState)
    # Simulate AI research (replace with actual API calls)
    summary = f"Research summary for {state.current_topic}"
    sources = ["https://example.com/source1", "https://example.com/source2"]
    state.topics.append(ResearchTopic(state.current_topic, summary, sources))

def analyze_topic(topic: ResearchTopic):
    state = me.state(ResearchState)
    # Simulate AI analysis (replace with actual API calls)
    state.analysis_result = f"In-depth analysis of {topic.title}: ..."
    me.navigate("/analysis")

@me.page(path="/analysis")
def analysis_page():
    state = me.state(ResearchState)
    with me.box():
        me.text("Topic Analysis", type="headline")
        me.text(state.analysis_result)
        me.button("Back to Research", on_click=lambda e: me.navigate("/"))

if __name__ == "__main__":
    me.app(port=8080)

This case study shows how to integrate AI capabilities into a responsive UI, allowing users to input research topics, receive AI-generated summaries, and conduct in-depth analyses.


8. Conclusion and Future Prospects

Mesop is revolutionizing how developers build UIs for AI and LLM RAG applications. By simplifying frontend development, it enables engineers to focus on crafting intelligent systems. As Mesop evolves, its feature set will continue to grow, offering even more streamlined solutions for AI-driven apps.

Whether you’re prototyping or launching a production-ready app, Mesop provides the tools you need to bring your vision to life. Start exploring Mesop today and elevate your AI applications to new heights!


By using Mesop, you’re crafting experiences that make complex AI interactions intuitive. The future of AI-driven web applications is bright—and Mesop is at the forefront. Happy coding!


References:

  1. Mesop Documentation. (n.d.). Retrieved from Mesop Documentation.
  2. Google’s UI Library for AI Web Apps. (2023). Retrieved from Google’s UI Library for AI Web Apps.
  3. Rapid Development with Mesop. (2023). Retrieved from Rapid Development with Mesop.
  4. Mesop Community. (2023). Retrieved from Mesop Community.
  5. Mesop: Google’s UI Library for AI Web Apps: AI&U

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Create LLM-Powered Apps with LangGraph, FastAPI, Streamlit

In the exciting world of artificial intelligence, using large language models (LLMs) is super important for developers. They want to build strong applications that can do amazing things. By combining LangGraph, FastAPI, and Streamlit/Gradio, developers can create great tools easily.

LangGraph helps manage data and makes sure everything works smoothly. FastAPI is fast and helps handle requests quickly. Streamlit and Gradio make it easy for users to interact with LLM-powered apps. Streamlit is great for making fun dashboards, while Gradio helps users chat with models in real-time.

Together, these tools let developers build cool applications, like chatbots and data analysis tools, that are fun and useful for everyone!

In the rapidly evolving landscape of artificial intelligence (AI), the demand for robust and efficient applications powered by large language models (LLMs) continues to surge. Developers are constantly seeking ways to streamline the development process while enhancing user experiences. Enter the powerful combination of LangGraph, FastAPI, and Streamlit/Gradio—a trio that provides an exceptional framework for creating and deploying LLM-powered applications. This blog post delves into the individual components, their synergies, and practical use cases, illustrating how they work together to facilitate the development of sophisticated AI applications.

Understanding Each Component

LangGraph: The Data Management Maestro

LangGraph is more than just a tool; it’s a sophisticated framework designed to optimize the interaction and integration of various AI components, particularly LLMs. Its primary function is to manage the data flow and processing tasks within an application, enabling developers to create dynamic workflows that leverage the full potential of language models.

Key Features of LangGraph:

  • Structured Workflows: LangGraph allows developers to define clear pathways for data processing, ensuring that inputs are correctly transformed and outputs are efficiently generated.
  • Seamless Integration: It facilitates the incorporation of different AI functionalities, making it easier to combine various models and services within a single application.
  • Dynamic Interaction: With LangGraph, developers can create adaptable systems that respond intelligently to user inputs, enhancing the overall interactivity of applications.

FastAPI: The High-Performance API Framework

FastAPI has emerged as a leading web framework for building APIs with Python, renowned for its speed and user-friendliness. Its design is centered around Python type hints, which streamline the process of API development and ensure robust data validation.

Key Features of FastAPI:

  • Speed: FastAPI is one of the fastest Python frameworks available, capable of handling high loads and concurrent requests with ease. Learn more about FastAPI’s performance.
  • Automatic Documentation: It automatically generates interactive API documentation using Swagger UI, which significantly enhances the developer experience by simplifying testing and understanding of API endpoints.
  • Asynchronous Programming: FastAPI’s support for asynchronous operations allows developers to build APIs that perform optimally in I/O-bound scenarios, making it ideal for applications that require real-time data processing.

Streamlit/Gradio: The User Interface Innovators

When it comes to creating interactive web applications, Streamlit and Gradio are two of the most popular libraries that cater specifically to data science and machine learning projects.

Streamlit:

  • Rapid Prototyping: Streamlit is designed for developers who want to quickly build interactive dashboards and visualizations with minimal coding. Its simplicity allows Python developers to create applications effortlessly. Explore Streamlit.
  • User-Friendly Interface: Applications built with Streamlit are intuitive and visually appealing, making them accessible to a broad audience.

Gradio:

  • Interactive Interfaces: Gradio excels in creating user-friendly interfaces that allow users to interact with machine learning models in real-time. It simplifies the process of testing inputs and outputs, making it a valuable tool for showcasing models to both technical and non-technical stakeholders. Check out Gradio.
  • Ease of Use: With Gradio, developers can quickly deploy interfaces with just a few lines of code, significantly reducing the time required to create a functional application.

How They Work Together

The combination of LangGraph, FastAPI, and Streamlit/Gradio creates a comprehensive stack for developing LLM-powered applications. Here’s how they synergistically interact:

  1. Backend Development with FastAPI: FastAPI acts as the backbone of the application, managing API requests and facilitating interactions between the frontend and the LLM model. Its high performance ensures that the application can handle multiple requests efficiently.

  2. Data Management through LangGraph: LangGraph organizes the flow of data and tasks within the application, ensuring that inputs are processed correctly and outputs are generated without delays. This structured approach enhances the application’s reliability and responsiveness.

  3. User Interaction via Streamlit/Gradio: The user interface provided by Streamlit or Gradio allows users to interact seamlessly with the LLM application. Whether it’s inputting text for a chatbot or generating content, the interface is designed to be intuitive, enhancing the overall user experience.

Practical Use Cases

The combination of LangGraph, FastAPI, and Streamlit/Gradio is particularly effective for various applications, including:

1. Chatbots

Creating conversational agents that can understand and respond to user queries in natural language. This application can be enhanced with LangGraph for managing dialogue flows and FastAPI for handling API requests related to user interactions.

2. Content Generation

Developing tools that automatically generate text, summaries, or even code based on user inputs. The synergy of LangGraph’s data management capabilities and FastAPI’s efficient API handling allows for real-time content generation, while Streamlit or Gradio provides a user-friendly interface for customization.

3. Data Analysis

Building applications that analyze large datasets and provide insights through natural language. With LangGraph managing the data processing, FastAPI serving the API requests, and Streamlit or Gradio visualizing results, developers can create powerful analytical tools that cater to both technical and non-technical users.

4. Educational Tools

Creating interactive educational applications that utilize LLMs to provide explanations, answer questions, or assist with learning new concepts. The combination of a sophisticated backend and an engaging frontend makes it easy for educators and learners to interact with complex material.

Conclusion

The integration of LangGraph, FastAPI, and Streamlit/Gradio forms a powerful trio for developing LLM-powered applications. This tech stack not only streamlines the development process but also ensures that applications are scalable, maintainable, and user-friendly. By leveraging the strengths of each component—efficient API development, flexible data management, and intuitive user interfaces—developers can create sophisticated AI applications that meet a wide range of needs.

As the AI landscape continues to evolve, embracing such powerful combinations will be crucial for developers looking to harness the full potential of large language models. For those interested in diving deeper into this topic, a wealth of resources is available, including practical guides and tutorials on building LLM-powered applications.

For more detailed insights and practical examples, you can explore the following resources:

By combining these technologies, developers can not only accelerate their workflow but also create impactful applications that resonate with users, ultimately driving the future of AI development.

References

  1. LangGraph, FastAPI, and Streamlit/Gradio: The Perfect Trio for LLM … We’ll break down the code and explain each step in…
  2. Alain Airom – LangGraph, FastAPI, and Streamlit/Gradio – X.com Learn how to build and deploy AI applications quickly and efficientl…
  3. Alain AIROM – LangGraph, FastAPI, and Streamlit/Gradio – LinkedIn … Gradio: The Perfect Trio for LLM-Powered App…
  4. Stream Langchain Agent to OpenAI Compatible API – Medium LangGraph, FastAPI, and Streamlit/Gradio: The Pe…
  5. Bhargob Deka, Ph.D. on LinkedIn: #speckle #langchain #llm #nlp … Creating a Server-Client Interaction with LangGraph, FastAPI…
  6. Building an LLM Powered App – by Adrian Plani – Medium LangGraph, FastAPI, and Streamlit/Gradio: Th…
  7. Creating LLM-Powered Applications with LangChain It utilizes deep learning techniques to understand and generate …
  8. What is the best python library for chatbot UIs? : r/LangChain – Reddit I know that streamlit was popular, but neither opt…
  9. From Local to Cloud: Deploying LLM Application with Docker and … LangGraph, FastAPI, and Streamlit/Gradio…


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