Llama Stack Apps

What is the project about?

This project provides examples of agentic applications built on top of the Llama Stack. These applications leverage the capabilities of Llama 3.1 and later, including multi-step reasoning, tool usage (both built-in and zero-shot), and system-level safety protections via Llama Guard. It's a demonstration and starting point for developers to build their own generative AI applications.

What problem does it solve?

The project simplifies the development of sophisticated generative AI applications by providing a standardized framework (the Llama Stack) and example implementations. It addresses the complexity of integrating:

Model Inference: Running Llama models for text generation.
Safety Checks: Ensuring generated content is safe and appropriate using Llama Guard.
Tool Execution: Enabling the AI to interact with external tools (like search engines or code interpreters) to perform tasks.
Multi-step Reasoning: Allowing the AI to break down complex tasks into smaller, manageable steps.
Agentic Systems: Creating AI agents that can interact with users and perform actions.

What are the features of the project?

Example Applications: Provides working examples of agentic apps, including a chat interface, RAG with vector DB, and simple agent interactions.
Llama Stack Integration: Demonstrates how to connect client applications to a Llama Stack server, which handles the core AI functionalities.
Client SDKs: Offers client SDKs in multiple languages (Python, Node.js, Swift, Kotlin) for easy integration with the Llama Stack server.
Tool Use Examples: Shows how to use built-in tools (like search) and define custom tools (e.g., Wolfram Alpha, Brave Search).
Safety Integration: Includes examples of using Llama Guard for safety checks.
Multi-turn Conversation Support: Demonstrates how to build applications that can handle multi-turn conversations.
Agent Store: Provides a UI chat interface (using Gradio) for interacting with agents.

What are the technologies used in the project?

Llama 3.1 (and later): The core large language model.
Llama Guard: Models for safety checks.
Python: Primary programming language for examples and server setup.
Conda: For environment management (recommended).
Pip: For installing Python packages.
Uvicorn: ASGI server for running the Llama Stack server.
Gradio: For building the web UI.
Brave Search API: Example of an external tool (requires an API key).
Wolfram Alpha API: Another example of an external tool (requires an API key).
Client SDKs: Python, Node.js, Swift, Kotlin.
Vector Databases: Used in the RAG example.

What are the benefits of the project?

Faster Development: Provides a ready-to-use framework and examples, accelerating the development of generative AI applications.
Simplified Integration: Standardizes the interaction between different components (models, safety checks, tools).
Improved Safety: Integrates safety checks using Llama Guard.
Enhanced Capabilities: Enables the creation of more powerful AI agents with multi-step reasoning and tool use.
Multi-language Support: Client SDKs allow developers to build applications in their preferred language.
Open Source: Allows for community contributions and customization.

What are the use cases of the project?

Chatbots: Building intelligent chatbots that can answer questions, perform tasks, and engage in multi-turn conversations.
Virtual Assistants: Creating virtual assistants that can help users with various tasks, such as scheduling, information retrieval, and content creation.
Automated Content Generation: Generating different kinds of text formats (e.g., articles, summaries, code).
Data Analysis: Using AI to analyze data and provide insights.
Research and Development: Experimenting with new agentic AI capabilities.
Retrieval-Augmented Generation (RAG): Combining language models with external knowledge sources (like vector databases).
Any application requiring complex reasoning and interaction with external tools.