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Visual IDE for Building Multi-Agent Apps

PLUS: New GPT-4o and Gemini model, Fine-tune vision models 2x faster for Free

AuthorAuthor

Shubham Saboo & Gargi Gupta
November 25, 2024

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Today’s top AI Highlights:

  1. Low-code visual IDE to build RAG and multi-agent AI apps

  2. Build and deploy full-stack Next.js apps with a simple text prompt

  3. OpenAI and Google release new GPT-4o and Gemini models, Gemini ranks #1 on Chatbot Arena

  4. Fine-tune vision models for free 2x faster than Flash Attention 2 and Hugging Face

  5. AI memory layer with short- and long-term storage, semantic clustering, and optional memory decay

& so much more!

Read time: 3 mins

AI Tutorials

Running a fully local RAG (Retrieval-Augmented Generation) agent without internet access is a powerful setup, allowing complete control over data, low-latency response, and ensuring privacy.

Building a local RAG system opens up possibilities for secure applications where online connections are not an option. In this tutorial, you’ll learn to create a local RAG agent using Llama 3.2 3B via Ollama for text generation, combined with Qdrant as the vector database for fast document retrieval. 

We share hands-on tutorials like this 2-3 times a week, designed to help you stay ahead in the world of AI. If you're serious about levelling up your AI skills and staying ahead of the curve, subscribe now and be the first to access our latest tutorials.

Build a Local RAG Agent with Llama 3.2 and Vector Database

Fully functional RAG agent running locally in less than 20 lines of Python Code (step-by-step instructions)

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Latest Developments

Langflow, the visual IDE for building RAG and multi-agent AI applications, has released version 1.1 with extensive improvements to its drag-and-drop interface. Langflow lets you build complex apps by visually connecting components to models, APIs, data sources, and databases.

The update addresses several pain points developers face when building agent-based systems, particularly around tool integration and agent orchestration. Now, custom components can be turned into tools with minimal code, and agents can communicate directly without intermediate orchestrators, while you can track agent execution and reasoning through an enhanced visualization system.

Key Highlights:

  1. Universal Tool Integration - Langflow eliminates the need for complex wrapper code when integrating external APIs. Any component can now be converted into a tool with a single click, and the platform automatically handles input/output mapping between tools.

  2. Enhanced Agent Development - The new IDE now includes built-in memory management and direct agent-to-agent messaging capabilities. You can visually debug agent reasoning chains and decision paths, with real-time visibility into each step of the execution process.

  3. Streamlined Production Workflow - The production workflow features include a real-time testing environment and automated input/output type checking across workflow connections. Component-level error handling and validation help catch issues early in the development cycle, while performance monitoring tools enable optimization.

  4. Developer Experience - The platform now offers an expanded template library with production-ready patterns for common AI implementation scenarios. The improved component search and documentation access, combined with dark mode support. Both open-source and cloud-hosted deployment options are available.

The fastest way to build AI apps

  • Writer Framework: build Python apps with drag-and-drop UI

  • API and SDKs to integrate into your codebase

  • Intuitive no-code tools for business users

Vercel AI's v0, the conversational AI for generating UI, received a major upgrade. Initially designed to generate React UI components from text prompts, v0 now offers full-stack application development capabilities. You can now build complete Next.js applications with both front-end and back-end logic. This update also introduces seamless integration with Vercel projects for simplified deployment and environment variable utilization. The enhanced v0 makes prototyping and deploying production-ready applications significantly faster.

Key Highlights:

  1. Full-stack Next.js Development - Build and run complete Next.js applications within v0, including route handlers, server actions, dynamic routes, and React Server Components. This allows you to prototype and test both client-side and server-side logic in a single environment.

  2. Multi-File Generation & Deployment - v0 can generate multiple files at once, enabling the creation of more complex projects. The generated projects can be linked directly to Vercel projects for easy deployment and access to project-specific environment variables. This streamlines your CI/CD cycle significantly.

  3. Secure Access to External Services - Leverage Vercel project environment variables within v0 to securely connect to databases, APIs, and other external resources. This allows for the creation of fully functional applications that integrate with existing services.

  4. Deployable UI and Code Blocks - "Blocks," representing UI components or executable code, are now deployable to Vercel with custom subdomains. This enables you to share and test individual components or code snippets independently.

Quick Bites

Quick Updates from OpenAI:

  • OpenAI has released new gpt-4o-2024-11-20 with enhanced creative writing and better file handling. That’s all they’re sharing for now—no extra details, just better performance! It’s now available in the API.

  • You can now test and compare model performance directly from the OpenAI dashboard. Use your custom data to iterate prompts and refine outputs seamlessly.

  • The Chat Completions API supports audio now. Pass text or audio inputs, then receive responses in text, audio, or both.

Google has also released a new Gemini model Gemini-exp-1121, separate from the 1114 model released last week, boasting better coding performance, reasoning, and visual understanding. It's also currently #1 on the LMSYS Chatbot Arena leaderboard, outperforming OpenAI's latest GPT-4o (above). The model is now live on Google AI Studio and the Gemini API.

Black Forest Labs has launched FLUX.1 Tools, a suite of open-access models to enhance control and steerability in text-to-image workflows. Featuring advanced inpainting, structural conditioning, and image variation capabilities, these tools are available via Hugging Face, GitHub, and the BFL API.

Image Variation and Restyling with FLUX.1 Redux

Google’s Gemini app now has a memory to remember your interests and preferences for more helpful, relevant responses. Just tell it, for instance ‘Remember I only write code in JavaScript’ and it’ll save this information which you can easily view, edit, or delete.

Amazon is expanding its collaboration with Anthropic, investing another $4 billion, making AWS the primary cloud and training partner. This partnership leverages AWS Trainium hardware, Amazon Bedrock, and Claude models to deliver scalable, secure AI solutions for developers and enterprises.

Unsloth AI, the opensource platform for fine-tuning LLMs, now supports vision and multimodal models like Llama 3.2 vision, Pixtral, and Qwen, making vision fine-tuning 2x faster and using up to 70% less memory than Flash Attention 2 + Hugging Face. Check out the free Google Colab notebooks for fine-tuning vision models for tasks like radiography, math OCR, and general Q&A.

Tools of the Trade

  1. Memoripy: A Python library for managing context-aware memory, combining short-term and long-term storage to support AI apps. It has features like memory decay, reinforcement, contextual retrieval, and graph-based associations to organize and retrieve information

  2. Nosia: Run AI models on your own data with a simple setup process. It uses Docker and Ollama to provide customizable AI capabilities and supports OpenAI-compatible API integrations.

  3. Langrocks: A toolkit to enhance LLMs with web browsing, computer control, and file operations. It enables tasks like navigating websites, controlling devices, and file format conversions.

  4. Awesome LLM Apps: Build awesome LLM apps using RAG to interact with data sources like GitHub, Gmail, PDFs, and YouTube videos with simple text prompts. These apps will let you retrieve information, engage in chat, and extract insights directly from content on these platforms.

Hot Takes

  1. The overwhelming focus from X on the future of GenAI is understandable but, boy, is it blinding people to what is possible with current models.
    From my conversations, companies are now seeing impacts and more & more talent is being directed towards use, tooling & exploration. ~
    Ethan Mollick


  2. LLMs are still midwit coders as far as I can tell today

    I think the hype is...a bit hype. It's neat at things you'd commonly look up in docs/stackoverflow (to be expected) but beyond that...it's like a junior intern I have to micromanage. ~
    Suhail

That’s all for today! See you tomorrow with more such AI-filled content.

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