Skip to content

Latest commit

 

History

History
162 lines (112 loc) · 5.09 KB

README.md

File metadata and controls

162 lines (112 loc) · 5.09 KB

KitchenAI

KitchenAI

KitchenAI is a control plane for AI implementations — designed to bridge the gap between application developers and AI teams. Our platform simplifies AI integration with a loosely coupled, modular architecture that delivers production-grade reliability while letting your teams focus on what they do best.

🚀 Quick Start

🌟 What Makes KitchenAI Unique

Modular Architecture

  • Bento Boxes: Package your AI workflows into independent "bento boxes" that encapsulate complex logic.
  • Flexibility: Update, replace, or scale individual modules without disrupting your overall system.
  • Clear Separation: Let AI teams build advanced logic in a reproducible and swappable space, while app developers enjoy a simple, stable API.

High-Performance Messaging

  • Powered by NATS:
    • Lightning-fast, reliable communication between AI modules
    • Dynamic service discovery and routing
    • Robust support for event-driven workflows in distributed environments

Framework Agnostic

  • Plug & Play:
    • No vendor lock-in—integrate with any AI framework or model
    • Native support for LangChain, LlamaIndex, and custom implementations
    • Future-proof your AI infrastructure with flexible integration options

🛠️ How It Works

KitchenAI’s three-layer architecture makes it easy to manage your AI workflows:

  1. Application Layer:
    Your business applications call a simple, unified API (just like using OpenAI’s Chat Completions).

  2. NATS Messaging Layer:
    This is our high-performance backbone for routing messages and discovering services dynamically.

  3. Bento Boxes Layer:
    Modular AI implementations where your AI team builds the complex logic (be it LLM logic, RAG, agents, or custom workflows).

For Application Developers

Your code remains clean and simple:

    # Simple integration using OpenAI's Chat Completions
    response = await openai_client.chat.completions.create(
        model="@llama-index-agents/query", #your bento box client id 
        messages=[{"role": "user", "content": data.query}]
    )

For AI Teams

Focus on building powerful AI code:

@kitchen.query.handler("query")
async def query_handler(data: WhiskQuerySchema) -> WhiskQueryBaseResponseSchema:
    # Advanced RAG implementation with best practices built-in
    index = VectorStoreIndex.from_vector_store(vector_store)
    query_engine = index.as_query_engine(
        chat_mode="best",
        filters=filters,
        llm=llm,
        verbose=True
    )

Self hosting the control plane

KitchenAI is designed to be self-hosted. You can deploy the control plane and the bento boxes separately.

  1. Clone the KitchenAI repository
git clone https://github.com/epuerta9/kitchenai.git
  1. Bring up the control plane and dependencies
docker compose up -d
  1. Creating the Bucket for media. KitchenAI uses S3 for media. For local development, the compose file has a minio container. This only needs to be done the first time.

you will need to login to the minio container and create a bucket called kitchenai.

endpoint: http://localhost:9001 username: minioadmin password: minioadmin

bucket name: kitchenai

  1. Bring up the bento boxes using this demo demo notebooks

📚 Documentation

🛠️ Key Features

  • Version Control & Rollback: Safely iterate and revert as needed.
  • Monitoring & Observability Hooks: Integrate with your favorite tools.
  • Plugin Ecosystem: Extend KitchenAI with additional capabilities.
  • Security Integrations: Designed with production-grade best practices.

🙋‍♂️ Join the Beta!

KitchenAI is still in beta—we're excited to have early adopters help shape the platform.

🔗 Quick Links

📄 License

KitchenAI is released under the Apache 2.0 License.

🙋‍♂️ Support & Community

Built with ❤️ by the KitchenAI Team