Incorporate Latest AI Models like Llama and Mistal in a Next.js App

Table of Contents

• Introduction
• Set Up Front-End to Support New Models
• Configure Back-End to Integrate Llama and Mistal
• Test New Model Integration
• Possibilities for Future Enhancements
• Conclusion

Introduction to Integrating Llama and Mistal AI Models

This blog post outlines the steps for incorporating the Llama 2 13B parameter model and Mistal AI 70B parameter model into an existing Next.js application. We will be building upon a previous project that allowed toggling between different AI models like GPT-3.5 and Claude through a user interface.

The goal is to expand the app's capabilities to leverage these powerful new models released in 2023 for natural language generation while keeping the UI flexible to switch between different models.

Overview of Video Content

The original video demonstrates the Next.js app with the existing model toggle functionality. It can generate responses locally using different models based on the selector chosen in the UI. We then walk through the steps to add the new Llama and Mistal models to this application at both the front-end and back-end. On the front-end, we simply add the new model logos and config to the selector. On the back-end, we import the models into LangChain and create handler functions to integrate the model APIs. Finally, we add conditional logic to route requests to the appropriate model based on the UI selection.

Key Objectives

The main goals are:

• Expand model options in the UI selector
• Import Llama and Mistal models via LangChain
• Create back-end routing and handlers for the new models
• Ensure seamless toggling between different models in the UI

Set Up Front-End to Support New Models

With the groundwork in place from the existing app, very little change is needed on the front-end.

We simply add the new Llama and Mistal model logo images to the selector array config. This automatically populates them as options in the UI toggler menu.

Now requests sent to the back-end will include the selected model key so the server knows which one to use.

Add Images and Entries for New Models

On the front-end we:

• Add Llama and Mistal logo SVG images
• Add entries for the new models to the model selector array including:
• Alt text
• Image source
• Unique model key

Update LangChain Version

Even though not covered in the video, it's best practice to ensure LangChain is up to date to leverage the latest integrated models and features. Run npm install @langchain/langchain to get the newest version.

Configure Back-End to Integrate Llama and Mistal

With the front-end updated, we now need to enable the back-end to actually integrate with the Llama and Mistal APIs and route requests appropriately.

Import Llama SDK

LangChain recently added native support for Llama making the integration simpler. We can import it directly and instantiate the model instance pointing to the running local service.

Create Handler Function for Models

A handler function abstracts away the Llama and Mistal initialization and query logic. This handler is then called when those model keys are detected in the request to generate the response text accordingly.

Add Conditional Logic to Route Requests

We add additional else if clauses for the new model keys and call the relevant handler function if matched. This allows seamlessly switching between models by changing that key value from the UI.

Test New Model Integration

With the changes made to both the front and back-end, we can now toggle between the different models in the UI selector and see it reflected automatically in the generated responses.

The integration allows flexibly combining different models like Llama, Mistal, GPT-3.5, Claude etc. within a single UI session.

Possibilities for Future Enhancements

The app makes it simple to continue expanding support for additional models. The same handler pattern can be reused to incorporate new ones as they are released. Some ideas:

Support More AI Models and Services

• Add PaLM, BLOOM, Parti etc.
• Integrate completions APIs from Anthropic, Cohere, Meta etc.

Add Requested Features Like Perplexity

• Calculate model perplexity for responses
• Enable chained / multipart messages
• Add tone analysis and style transfer capabilities

Conclusion

In this post we walked through augmenting an existing Next.js conversational app to support newly-released Llama and Mistal AI via LangChain.

The front-end required minor changes while the back-end handlers abstracted away API complexity.

Support for additional models or features can be added incrementally as needed for more advanced functionality.

FAQ

Q: How do I add Llama and Mistal models to my Next.js app?
A: Import the Llama SDK, create a handler function, and add conditional logic to route requests to the appropriate model based on user selection. Update the front-end with images and model options.

Q: What is needed to run Llama and Mistal locally?
A: You need to download the models from Anthropic's site onto an Intel or M-series Mac or Windows machine with at least 16GB of RAM. No specialized hardware is required.

Q: Can I switch between different models dynamically?
A: Yes, this app demonstrates toggling between GPT-3.5, Llama 2, Mistal, and more by updating the selected model on the front-end UI.

Q: Will you add more models and features in future videos?
A: Yes, based on viewer requests I can demonstrate integrating other AI services like Bing, Google, perplexity features, etc in future open source projects.