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<div id="jb-print-docs-body" class="onlyprint">
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<h1>What is Arch</h1>
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<!-- Table of contents -->
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<article class="bd-article">
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<section id="what-is-arch">
|
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<h1>What is Arch<a class="headerlink" href="#what-is-arch" title="Link to this heading">#</a></h1>
|
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<p>Arch is an intelligent <a class="reference external" href="https://www.cloudflare.com/learning/ddos/what-is-layer-7/">(Layer 7)</a> gateway
|
|||
|
|
designed for generative AI apps, AI agents, and Co-pilots that work with prompts. Engineered with purpose-built
|
|||
|
|
<a class="reference internal" href="../llms/llms.html#llms-in-arch"><span class="std std-ref">LLMs</span></a>, Arch handles all the critical but undifferentiated tasks related to the handling and
|
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|
|
processing of prompts, including detecting and rejecting <a class="reference external" href="https://github.com/verazuo/jailbreak_llms">jailbreak</a>
|
|||
|
|
attempts, intelligently calling “backend” APIs to fulfill the user’s request represented in a prompt, routing to
|
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|
|
and offering disaster recovery between upstream LLMs, and managing the observability of prompts and LLM interactions
|
|||
|
|
in a centralized way.</p>
|
|||
|
|
<a class="reference internal image-reference" href="../_images/arch-logo.png"><img alt="../_images/arch-logo.png" class="align-center" src="../_images/arch-logo.png" style="width: 100%;" />
|
|||
|
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</a>
|
|||
|
|
<p><strong>The project was born out of the belief that:</strong></p>
|
|||
|
|
<blockquote>
|
|||
|
|
<div><p><em>Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests
|
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|
|
including secure handling, intelligent routing, robust observability, and integration with backend (API)
|
|||
|
|
systems for personalization - all outside business logic.</em></p>
|
|||
|
|
</div></blockquote>
|
|||
|
|
<p>In practice, achieving the above goal is incredibly difficult. Arch attempts to do so by providing the
|
|||
|
|
following high level features:</p>
|
|||
|
|
<hr class="docutils" />
|
|||
|
|
<p><strong>Out-of-process architecture, built on</strong> <a class="reference external" href="http://envoyproxy.io/">Envoy</a>: Arch is takes a dependency on
|
|||
|
|
Envoy and is a self-contained process that is designed to run alongside your application servers. Arch uses
|
|||
|
|
Envoy’s HTTP connection management subsystem, HTTP L7 filtering and telemetry capabilities to extend the
|
|||
|
|
functionality exclusively for prompts and LLMs. This gives Arch several advantages:</p>
|
|||
|
|
<ul class="simple">
|
|||
|
|
<li><p>Arch builds on Envoy’s proven success. Envoy is used at masssive sacle by the leading technology companies of
|
|||
|
|
our time including <a class="reference external" href="https://www.airbnb.com">AirBnB</a>, <a class="reference external" href="https://www.dropbox.com">Dropbox</a>,
|
|||
|
|
<a class="reference external" href="https://www.google.com">Google</a>, <a class="reference external" href="https://www.reddit.com">Reddit</a>, <a class="reference external" href="https://www.stripe.com">Stripe</a>,
|
|||
|
|
etc. Its battle tested and scales linearly with usage and enables developers to focus on what really matters:
|
|||
|
|
application features and business logic.</p></li>
|
|||
|
|
<li><p>Arch works with any application language. A single Arch deployment can act as gateway for AI applications
|
|||
|
|
written in Python, Java, C++, Go, Php, etc.</p></li>
|
|||
|
|
<li><p>Arch can be deployed and upgraded quickly across your infrastructure transparently without the horrid pain
|
|||
|
|
of deploying library upgrades in your applications.</p></li>
|
|||
|
|
</ul>
|
|||
|
|
<p><strong>Engineered with Fast LLMs:</strong> Arch is engineered with specialized (sub-billion) LLMs that are desgined for
|
|||
|
|
fast, cost-effective and acurrate handling of prompts. These <a class="reference internal" href="../llms/llms.html#llms-in-arch"><span class="std std-ref">LLMs</span></a> are designed to be
|
|||
|
|
best-in-class for critcal prompt-related tasks like:</p>
|
|||
|
|
<ul class="simple">
|
|||
|
|
<li><p><strong>Function/API Calling:</strong> Arch helps you easily personalize your applications by enabling calls to
|
|||
|
|
application-specific (API) operations via user prompts. This involves any predefined functions or APIs
|
|||
|
|
you want to expose to users to perform tasks, gather information, or manipulate data. With function calling,
|
|||
|
|
you have flexibility to support “agentic” experiences tailored to specific use cases - from updating insurance
|
|||
|
|
claims to creating ad campaigns - via prompts. Arch analyzes prompts, extracts critical information from
|
|||
|
|
prompts, engages in lightweight conversation to gather any missing parameters and makes API calls so that you can
|
|||
|
|
focus on writing business logic. For more details, read <a class="reference internal" href="architecture/prompt_processing/prompt_processing.html#arch-overview-prompt-handling"><span class="std std-ref">prompt processing</span></a>.</p></li>
|
|||
|
|
<li><p><strong>Prompt Guardrails:</strong> Arch helps you improve the safety of your application by applying prompt guardrails in
|
|||
|
|
a centralized way for better governance hygiene. With prompt guardrails you can prevent <a class="reference external" href="https://github.com/verazuo/jailbreak_llms">jailbreak</a>
|
|||
|
|
attempts or toxicity present in user’s prompts without having to write a single line of code. To learn more
|
|||
|
|
about how to configure guardrails available in Arch, read <a class="reference internal" href="architecture/prompt_processing/prompt_processing.html#arch-overview-prompt-handling"><span class="std std-ref">prompt processing</span></a>.</p></li>
|
|||
|
|
<li><p><strong>Intent-Drift Detection:</strong> Developers struggle to handle <a class="reference external" href="https://www.reddit.com/r/ChatGPTPromptGenius/comments/17dzmpy/how_to_use_rag_with_conversation_history_for/?">follow-up</a>,
|
|||
|
|
or <a class="reference external" href="https://www.reddit.com/r/LocalLLaMA/comments/18mqwg6/best_practice_for_rag_with_followup_chat/">clarifying</a>
|
|||
|
|
questions. Specifically, when users ask for modifications or additions to previous responses their AI applications
|
|||
|
|
often generate entirely new responses instead of adjusting the previous ones. Arch offers intent-drift detection as a
|
|||
|
|
feature so that developers know when the user has shifted away from the previous intent so that they can improve
|
|||
|
|
their retrieval, lower overall token cost and dramatically improve the speed and accuracy of their responses back
|
|||
|
|
to users.</p></li>
|
|||
|
|
</ul>
|
|||
|
|
<p><strong>Traffic Management:</strong> Arch offers several capabilities for LLM calls originating from your applications, including a
|
|||
|
|
vendor-agnostic SDK to make LLM calls, smart retries on errors from upstream LLMs, and automatic cutover to other LLMs
|
|||
|
|
configured in Arch for continuous availability and disaster recovery scenarios. Arch extends Envoy’s <a class="reference external" href="https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/upstream/cluster_manager">cluster subsystem</a> to manage upstream connections
|
|||
|
|
to LLMs so that you can build resilient AI applications.</p>
|
|||
|
|
<p><strong>Front/edge Gateway:</strong> There is substantial benefit in using the same software at the edge (observability,
|
|||
|
|
traffic shaping alogirithms, applying guardrails, etc.) as for outbound LLM inference use cases. Arch has the feature set
|
|||
|
|
that makes it exceptionally well suited as an edge gateway for AI applications. This includes TLS termination, rate limiting,
|
|||
|
|
and prompt-based routing.</p>
|
|||
|
|
<p><strong>Best-In Class Monitoring:</strong> Arch offers several monitoring metrics that help you understand three
|
|||
|
|
critical aspects of your application: latency, token usage, and error rates by an upstream LLM provider. Latency
|
|||
|
|
measures the speed at which your application is responding to users, which includes metrics like time to first
|
|||
|
|
token (TFT), time per output token (TOT) metrics, and the total latency as perceived by users.</p>
|
|||
|
|
<p><strong>End-to-End Tracing:</strong> Arch propagates trace context using the W3C Trace Context standard, specifically through
|
|||
|
|
the <code class="docutils literal notranslate"><span class="pre">traceparent</span></code> header. This allows each component in the system to record its part of the request flow,
|
|||
|
|
enabling <strong>end-to-end tracing</strong> across the entire application. By using OpenTelemetry, Arch ensures that
|
|||
|
|
developers can capture this trace data consistently and in a format compatible with various observability tools.
|
|||
|
|
For more details, read <a class="reference internal" href="../observability/tracing.html#arch-overview-tracing"><span class="std std-ref">tracing</span></a>.</p>
|
|||
|
|
</section>
|
|||
|
|
|
|||
|
|
|
|||
|
|
</article>
|
|||
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