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fixed cli to use poetry as well. this way we make it easy to have the… (#160)
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Salman Paracha
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README.md
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README.md
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<img src="docs/source/_static/img/arch-logo.png" alt="Arch Gateway Logo" title="Arch Gateway Logo">
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</p>
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<h2>Build fast, robust, and personalized GenAI apps (agents, assistants, etc.)</h2>
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## Build fast, robust, and personalized GenAI apps (agents, assistants, etc.)
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Arch is an intelligent [Layer 7](https://www.cloudflare.com/learning/ddos/what-is-layer-7/) gateway designed for generative AI apps, AI agents, and co-pilots that work with prompts. Engineered with purpose-built LLMs, Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting [jailbreak](https://github.com/verazuo/jailbreak_llms) attempts, intelligently calling "backend" APIs to fulfill the user's request represented in a prompt, routing to and offering disaster recovery between upstream LLMs, and managing the observability of prompts and LLM interactions in a centralized way.
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Arch is built on (and by the core contributors of) the wildly popular and robust [Envoy Proxy](https://www.envoyproxy.io/) with the belief that:
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Arch is built on (and by the core contributors of) [Envoy Proxy](https://www.envoyproxy.io/) with the belief that:
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>Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems for personalization – all outside business logic.*
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**Core Features**:
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- Built on [Envoy](https://envoyproxy.io): Arch runs alongside application servers, and builds on top of Envoy's proven HTTP management and scalability features to handle ingress and egreess traffic realted to prompts and LLMs
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- Build fast Agentic and RAG applications. Engineered with purpose-built [(fast) LLMs](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) to handle fast, cost-effective, and accurate prompt-based tasks like function/API calling.
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- Prompt [Guardrails](https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d): Arch centralizes prompt guardrails to prevent jailbreak attempts and ensure safe user interactions without writing extra code.
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- Enables fast Agentic and RAG apps. Engineered with purpose-built [(fast) LLMs](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) to handle fast, cost-effective, and accurate prompt-based tasks like function/API calling, and parameter extraction from prompts.
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- Prompt [Guardrails](https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d): Arch centralizes prompt guardrails to prevent jailbreak attempts and ensure safe user interactions without writing a single line of code.
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- Traffic Management: Arch manages LLM calls, offering smart retries, automatic cutover, and resilient upstream connections for continuous availability.
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- Open Observability: Arch uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with observability tools, and provides metrics to monitor latency, token usage, and error rates, helping optimize AI application performance.
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- Standards-based Observability: Arch uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with observability tools, and provides metrics to monitor latency, token usage, and error rates, helping optimize AI application performance.
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**Jump to our [docs](https://docs.archgw.com)** to learn more about how you can use Arch to improve the speed, robustneess and personalization of your GenAI apps
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**Jump to our [docs](https://docs.archgw.com)** to learn how you can use Arch to improve the speed, security and personalization of your GenAI apps.
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# Contact
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## Contact
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To get in touch with us, please join our [discord server](https://discord.gg/rbjqVbpa). We will be monitoring that actively and offering support there.
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# Demos
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* [Function Calling](demos/function_calling/README.md) -Showcases critical function calling cabaility
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* [Insurance Agent](demos/insurance_agent/README.md) -Build a full insurance agent with arch
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## Demos
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* [Function Calling](demos/function_calling/README.md) - Walk through of critical function calling capabilities
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* [Insurance Agent](demos/insurance_agent/README.md) - Build a full insurance agent with arch
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* [Network Agent](demos/network_agent/README.md) - Build a networking co-pilot/agent agent with arch
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# Quickstart
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## Quickstart
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Follow this guide to learn how to quickly set up Arch and integrate it into your generative AI applications.
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## Prerequisites
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### Prerequisites
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Before you begin, ensure you have the following:
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- `Docker` & `Python` installed on your system
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- `API Keys` for LLM providers (if using external LLMs)
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The fastest way to get started using Arch is to use [katanemo/arch](https://hub.docker.com/r/katanemo/arch) pre-built binaries.
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You can also build it from source.
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## Step 1: Install Arch
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### Step 1: Install Arch
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Arch's CLI allows you to manage and interact with the Arch gateway efficiently. To install the CLI, simply run the following command:
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Tip: We recommend that developers create a new Python virtual environment to isolate dependencies before installing Arch. This ensures that archgw and its dependencies do not interfere with other packages on your system.
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$ pip install archgw
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```
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## Step 2: Configure Arch with your application
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### Step 2: Configure Arch with your application
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Arch operates based on a configuration file where you can define LLM providers, prompt targets, guardrails, etc.
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Below is an example configuration to get you started:
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# max time to wait for a connection to be established
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connect_timeout: 0.005s
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```
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## Step 3: Using OpenAI Client with Arch as an Egress Gateway
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### Step 3: Using OpenAI Client with Arch as an Egress Gateway
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Make outbound calls via Arch
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```
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## [Observability](https://docs.archgw.com/guides/observability/observability.html)
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### [Observability](https://docs.archgw.com/guides/observability/observability.html)
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Arch is designed to support best-in class observability by supporting open standards. Please read our [docs](https://docs.archgw.com/guides/observability/observability.html) on observability for more details on tracing, metrics, and logs
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## Contribution
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### Contribution
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We would love feedback on our [Roadmap](https://github.com/orgs/katanemo/projects/1) and we welcome contributions to **Arch**!
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Whether you're fixing bugs, adding new features, improving documentation, or creating tutorials, your help is much appreciated.
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Please vist our [Contribution Guide](arch/CONTRIBUTING.md) for more details
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