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Technical Architecture
======================
.. toctree::
:maxdepth: 2
intro/terminology
intro/threading_model
listeners/listeners
prompt_processing/prompt_processing
listeners/llm_provider
model_serving/model_serving

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.. _arch_terminology:
Terminology
============
A few definitions before we dive into the main architecture documentation. Arch borrows from Envoy's terminology
to keep things consistent in logs, traces and in code.
**Downstream(Ingress)**: An downstream client (web application, etc.) connects to Arch, sends prompts, and receives responses.
**Upstream(Egress)**: An upstream host that receives connections and prompts from Arch, and returns context or responses for a prompt
.. image:: /_static/img/network-topology-ingress-egress.jpg
:width: 100%
:align: center
**Listener**: A listener is a named network location (e.g., port, address, path etc.) that Arch listens on to process prompts
before forwarding them to your application server endpoints. rch enables you to configure one listener for downstream connections
(like port 80, 443) and creates a separate internal listener for calls that initiate from your application code to LLMs.
.. Note::
When you start Arch, you specify a listener address/port that you want to bind downstream. But, Arch uses are predefined port
that you can use (``127.0.0.1:10000``) to proxy egress calls originating from your application to LLMs (API-based or hosted).
For more details, check out :ref:`LLM providers <llm_providers>`
**Instance**: An instance of the Arch gateway. When you start Arch it creates at most two processes. One to handle Layer 7
networking operations (auth, tls, observability, etc) and the second process to serve models that enable it to make smart
decisions on how to accept, handle and forward prompts. The second process is optional, as the model serving sevice could be
hosted on a different network (an API call). But these two processes are considered a single instance of Arch.
**Prompt Targets**: Arch offers a primitive called ``prompt_targets`` to help separate business logic from undifferentiated
work in building generative AI apps. Prompt targets are endpoints that receive prompts that are processed by Arch.
For example, Arch enriches incoming prompts with metadata like knowing when a request is a follow-up or clarifying prompt
so that you can build faster, more accurate retrieval (RAG) apps. To support agentic apps, like scheduling travel plans or
sharing comments on a document - via prompts, Bolt uses its function calling abilities to extract critical information from
the incoming prompt (or a set of prompts) needed by a downstream backend API or function call before calling it directly.
**Error Targets**: Error targets are those endpoints that receive forwarded errors from Arch when issues arise,
such as failing to properly call a function/API, detecting violations of guardrails, or encountering other processing errors.
These errors are communicated to the application via headers (X-Arch-[ERROR-TYPE]), allowing it to handle the errors gracefully
and take appropriate actions.
**Model Serving**: Arch is a set of **two** self-contained processes that are designed to run alongside your application servers
(or on a separate hostconnected via a network).The **model serving** process helps Arch make intelligent decisions about the
incoming prompts. The model server is designed to call the (fast) purpose-built :ref:`LLMs <llms_in_arch>` in Arch.

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.. _arch_overview_threading:
Threading model
===============
Arch builds on top of Envoy's single process with multiple threads architecture.
A single *primary* thread controls various sporadic coordination tasks while some number of *worker*
threads perform filtering, and forwarding.
Once a connection is accepted, the connection spends the rest of its lifetime bound to a single worker
thread. All the functionality around prompt handling from a downstream client is handled in a separate worker thread.
This allows the majority of Arch to be largely single threaded (embarrassingly parallel) with a small amount
of more complex code handling coordination between the worker threads.
Generally Arch is written to be 100% non-blocking.
.. tip::
For most workloads we recommend configuring the number of worker threads to be equal to the number of
hardware threads on the machine.

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.. _arch_overview_listeners:
Listener
---------
Listener is a top level primitive in Arch, which simplifies the configuration required to bind incoming
connections from downstream clients, and for egress connections to LLMs (hosted or API)
Arch builds on Envoy's Listener subsystem to streamline connection managemet for developers. Arch minimizes
the complexity of Envoy's listener setup by using best-practices and exposing only essential settings,
making it easier for developers to bind connections without deep knowledge of Envoy’s configuration model. This
simplification ensures that connections are secure, reliable, and optimized for performance.
Downstream (Ingress)
^^^^^^^^^^^^^^^^^^^^^^
Developers can configure Arch to accept connections from downstream clients. A downstream listener acts as the
primary entry point for incoming traffic, handling initial connection setup, including network filtering, gurdrails,
and additional network security checks. For more details on prompt security and safety,
see :ref:`here <arch_overview_prompt_handling>`
Upstream (Egress)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Arch automatically configures a listener to route requests from your application to upstream LLM API providers (or hosts).
When you start Arch, it creates a listener for egress traffic based on the presence of the ``llm_providers`` configuration
section in the ``prompt_config.yml`` file. Arch binds itself to a local address such as ``127.0.0.1:9000/v1`` or a DNS-based
address like ``arch.local:9000/v1`` for outgoing traffic. For more details on LLM providers, read :ref:`here <llm_providers>`
Configure Listener
^^^^^^^^^^^^^^^^^^
To configure a Downstream (Ingress) Listner, simply add the ``listener`` directive to your ``prompt_config.yml`` file:
.. literalinclude:: /_config/getting-started.yml
:language: yaml
:linenos:
:lines: 1-18
:emphasize-lines: 2-5
:caption: :download:`arch-getting-started.yml </_config/getting-started.yml>`

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.. _llm_providers:
LLM Provider
------------
``llm_provider`` is a top-level primitive in Arch, helping developers centrally define, secure, observe,
and manage the usage of of their LLMs. Arch builds on Envoy's reliable `cluster subsystem <https://www.envoyproxy.io/docs/envoy/v1.31.2/intro/arch_overview/upstream/cluster_manager>`_
to manage egress traffic to LLMs, which includes intelligent routing, retry and fail-over mechanisms,
ensuring high availability and fault tolerance. This abstraction also enables developers to seamlessly switching between LLM providers or upgrade LLM versions, simplifying the integration and scaling of LLMs across
applications.
Below is an example of how you can configure ``llm_providers`` with an instance of an Arch gateway.
.. literalinclude:: /_config/getting-started.yml
:language: yaml
:linenos:
:lines: 1-20
:emphasize-lines: 11-18
:caption: :download:`arch-getting-started.yml </_config/getting-started.yml>`
.. Note::
When you start Arch, it creates a listener port for egress traffic based on the presence of ``llm_providers``
configuration section in the ``prompt_config.yml`` file. Arch binds itself to a local address such as
``127.0.0.1:9000/v1`` or a DNS-based address like ``arch.local:9000/v1`` for egress traffic.
Arch also offers vendor-agnostic SDKs and libraries to make LLM calls to API-based LLM providers (like OpenAI,
Anthropic, Mistral, Cohere, etc.) and supports calls to OSS LLMs that are hosted on your infrastructure. Arch
abstracts the complexities of integrating with different LLM providers, providing a unified interface for making
calls, handling retries, managing rate limits, and ensuring seamless integration with cloud-based and on-premise
LLMs. Simply configure the details of the LLMs your application will use, and Arch offers a unified interface to
make outbound LLM calls.
Example: Using the Arch Python SDK
----------------------------------
.. code-block:: python
from arch_client import ArchClient
# Initialize the Arch client
client = ArchClient(base_url="http://127.0.0.1:9000/v1")
# Define your LLM provider and prompt
model_id = "openai"
prompt = "What is the capital of France?"
# Send the prompt to the LLM through Arch
response = client.completions.create(llm_provider=llm_provider, prompt=prompt)
# Print the response
print("LLM Response:", response)

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.. _arch_model_serving:
Model Serving
-------------
Arch is a set of **two** self-contained processes that are designed to run alongside your application
servers (or on a separate host connected via a network). The first process is designated to manage low-level
networking and HTTP related comcerns, and the other process is for **model serving**, which helps Arch make
intelligent decisions about the incoming prompts. The model server is designed to call the purpose-built
:ref:`LLMs <llms_in_arch>` in Arch.
.. image:: /_static/img/arch-system-architecture.jpg
:align: center
:width: 50%
_____________________________________________________________________________________________________________
Arch' is designed to be deployed in your cloud VPC, on a on-premises host, and can work on devices that don't
have a GPU. Note, GPU devices are need for fast and cost-efficient use, so that Arch (model server, specifically)
can process prompts quickly and forward control back to the applicaton host. There are three modes in which Arch
can be configured to run its **model server** subsystem:
Local Serving (CPU - Moderate)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The following bash commands enable you to configure the model server subsystem in Arch to run local on device
and only use CPU devices. This will be the slowest option but can be useful in dev/test scenarios where GPUs
might not be available.
.. code-block:: bash
archgw up --local -cpu
Local Serving (GPU- Fast)
^^^^^^^^^^^^^^^^^^^^^^^^^
The following bash commands enable you to configure the model server subsystem in Arch to run locally on the
machine and utilize the GPU available for fast inference across all model use cases, including function calling
guardails, etc.
.. code-block:: bash
archgw up --local
Cloud Serving (GPU - Blazing Fast)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The command below instructs Arch to intelligently use GPUs locally for fast intent detection, but default to
cloud serving for function calling and guardails scenarios to dramatically improve the speed and overall performance
of your applications.
.. code-block:: bash
archgw up
.. Note::
Arch's model serving in the cloud is priced at $0.05M/token (156x cheaper than GPT-4o) with averlage latency
of 200ms (10x faster than GPT-4o). Please refer to our :ref:`getting started guide <getting_started>` to know
how to generate API keys for model serving

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.. _arch_overview_prompt_handling:
Prompts
-------
Arch's primary design point is to securely accept, process and handle prompts. To do that effectively,
Arch relies on Envoy's HTTP `connection management <https://www.envoyproxy.io/docs/envoy/v1.31.2/intro/arch_overview/http/http_connection_management>`_,
subsystem and its **prompt handler** subsystem engineered with purpose-built :ref:`LLMs <llms_in_arch>` to
implement critical functionality on behalf of developers so that you can stay focused on business logic.
.. Note::
Arch's **prompt handler** subsystem interacts with the **model** subsytem through Envoy's cluster manager
system to ensure robust, resilient and fault-tolerant experience in managing incoming prompts. Read more
about the :ref:`model subsystem <arch_model_serving>` and how the LLMs are hosted in Arch.
Messages
--------
Arch accepts messages directly from the body of the HTTP request in a format that follows the `Hugging Face Messages API <https://huggingface.co/docs/text-generation-inference/en/messages_api>`_.
This design allows developers to pass a list of messages, where each message is represented as a dictionary
containing two key-value pairs:
- **Role**: Defines the role of the message sender, such as "user" or "assistant".
- **Content**: Contains the actual text of the message.
Prompt Guardrails
-----------------
Arch is engineered with :ref:`Arch-Guard <llms_in_arch>`, an industry leading safety layer, powered by a
compact and high-performimg LLM that monitors incoming prompts to detect and reject jailbreak attempts -
ensuring that unauthorized or harmful behaviors are intercepted early in the process.
To add jailbreak guardrails, see example below:
.. literalinclude:: /_config/getting-started.yml
:language: yaml
:linenos:
:emphasize-lines: 24-27
:caption: :download:`arch-getting-started.yml </_config/getting-started.yml>`
.. Note::
As a roadmap item, Arch will expose the ability for developers to define custom guardrails via Arch-Guard-v2,
and add support for additional safety checks defined by developers and hazardous categories like, violent crimes, privacy, hate,
etc. To offer feedback on our roadmap, please visit our `github page <https://github.com/orgs/katanemo/projects/1>`_
Prompt Targets
--------------
Once a prompt passes any configured guardrail checks, Arch processes the contents of the incoming conversation
and identifies where to forwad the conversation to via its essential ``prompt_targets`` primitve. Prompt targets
are endpoints that receive prompts that are processed by Arch. For example, Arch enriches incoming prompts with
metadata like knowing when a user's intent has changed so that you can build faster, more accurate RAG apps.
Configuring ``prompt_targets`` is simple. See example below:
.. literalinclude:: /_config/getting-started.yml
:language: yaml
:linenos:
:emphasize-lines: 29-38
:caption: :download:`arch-getting-started.yml </_config/getting-started.yml>`
Intent Detection and Prompt Matching:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Arch uses fast Natural Language Inference (NLI) and embedding approaches to first detect the intent of each
incoming prompt. This intent detection phase analyzes the prompt's content and matches it against predefined
prompt targets, ensuring that each prompt is forwarded to the most appropriate endpoint. Arch’s intent
detection framework considers both the name and description of each prompt target, and uses a composite matching
score between an NLI and cosine similarity to enchance accuracy in forwarding decisions.
- **Embeddings**: By embedding the prompt and comparing it to known target vectors, Arch effectively identifies
the closest match, ensuring that the prompt is handled by the correct downstream service.
- **NLI**: NLI techniques further refine the matching process by evaluating the semantic alignment between the
prompt and potential targets.
Agentic Apps via Prompt Targets
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
To support agentic apps, like scheduling travel plans or sharing comments on a document - via prompts, Arch uses
its function calling abilities to extract critical information from the incoming prompt (or a set of prompts)
needed by a downstream backend API or function call before calling it directly. For more details on how you can
build agentic applications using Arch, see our full guide :ref:`here <arch_function_calling_agentic_guide>`:
.. Note::
Arch :ref:`Arch-FC <llms_in_arch>` is the dedicated agentic model engineered in Arch to extract information from
a (set of) prompts and executes necessary backend API calls. This allows for efficient handling of agentic tasks,
such as scheduling data retrieval, by dynamically interacting with backend services. Arch-FC is a flagship 1.3
billion parameter model that matches performance with frontier models like Claude Sonnet 3.5 ang GPT-4, while
being 100x cheaper ($0.05M/token hosted) and 10x faster (p50 latencies of 200ms).
Prompting LLMs
--------------
Arch is a single piece of software that is designed to manage both ingress and egress prompt traffic, drawing its
distributed proxy nature from the robust `Envoy <https://envoyproxy.io>`_. This makes it extremely efficient and capable
of handling upstream connections to LLMs. If your application is originating code to an API-based LLM, simply use
Arch's Python or JavaScript client SDK to send traffic to the desired LLM of choice. By sending traffic through Arch,
you can propagate traces, manage and monitor traffic, apply rate limits, and utilize a large set of traffic management
capabilities in a central place.
.. Attention::
When you start Arch, it automatically creates a listener port for egress calls to upstream LLMs. This is based on the
``llm_providers`` configuration section in the ``prompt_config.yml`` file. Arch binds itself to a local address such as
127.0.0.1:9000/v1 or a DNS-based address like arch.local:9000/v1 for outgoing traffic.
Example: Using the Arch Python SDK
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
from arch_client import ArchClient
# Initialize the Arch client
client = ArchClient(base_url="http://127.0.0.1:9000/v1")
# Define your LLM provider and prompt
model_id = "openai"
prompt = "What is the capital of France?"
# Send the prompt to the LLM through Arch
response = client.completions.create(llm_provider=llm_provider, prompt=prompt)
# Print the response
print("LLM Response:", response)
Example: Using OpenAI Client with Arch as an Egress Gateway
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
import openai
# Set the OpenAI API base URL to the Arch gateway endpoint
openai.api_base = "http://127.0.0.1:9000/v1"
# No need to set openai.api_key since it's configured in Arch's gateway
# Use the OpenAI client as usual
response = openai.Completion.create(
model="text-davinci-003",
prompt="What is the capital of France?"
)
print("OpenAI Response:", response.choices[0].text.strip())
In these examples:
The ArchClient is used to send traffic directly through the Arch egress proxy to the LLM of your choice, such as OpenAI.
The OpenAI client is configured to route traffic via Arch by setting the proxy to 127.0.0.1:9000, assuming Arch is
running locally and bound to that address and port.
This setup allows you to take advantage of Arch's advanced traffic management features while interacting with LLM APIs like OpenAI.