diff --git a/_downloads/ca9d3b7116524473d8adbde7cf15d167/arch_config_full_reference.yaml b/_downloads/ca9d3b7116524473d8adbde7cf15d167/arch_config_full_reference.yaml
index 90bbef56..5ef2639c 100755
--- a/_downloads/ca9d3b7116524473d8adbde7cf15d167/arch_config_full_reference.yaml
+++ b/_downloads/ca9d3b7116524473d8adbde7cf15d167/arch_config_full_reference.yaml
@@ -48,8 +48,8 @@ llm_providers:
# provides a way to override default settings for the arch system
overrides:
- # By default Arch uses an NLI + embedding approach to match an incomming prompt to a prompt target.
- # The intent matching threshold is kept at 0.80, you can overide this behavior if you would like
+ # By default Arch uses an NLI + embedding approach to match an incoming prompt to a prompt target.
+ # The intent matching threshold is kept at 0.80, you can override this behavior if you would like
prompt_target_intent_matching_threshold: 0.60
# default system prompt used by all prompt targets
diff --git a/build_with_arch/agent.html b/build_with_arch/agent.html
index 329f44ef..01bc31de 100755
--- a/build_with_arch/agent.html
+++ b/build_with_arch/agent.html
@@ -411,7 +411,7 @@ the user’s intent.
diff --git a/build_with_arch/multi_turn.html b/build_with_arch/multi_turn.html
index e7765e80..cbf0d2d4 100755
--- a/build_with_arch/multi_turn.html
+++ b/build_with_arch/multi_turn.html
@@ -347,7 +347,7 @@ that you can test and modify locally for multi-turn RAG scenarios.
diff --git a/build_with_arch/rag.html b/build_with_arch/rag.html
index 0075dab0..a43aeee2 100755
--- a/build_with_arch/rag.html
+++ b/build_with_arch/rag.html
@@ -295,7 +295,7 @@ minutes. For additional details on how to build multi-turn RAG applications plea
diff --git a/concepts/llm_provider.html b/concepts/llm_provider.html
index 67bc0b10..35830251 100755
--- a/concepts/llm_provider.html
+++ b/concepts/llm_provider.html
@@ -268,7 +268,7 @@ make outbound LLM calls.
diff --git a/concepts/prompt_target.html b/concepts/prompt_target.html
index 18fc7cc7..958f331e 100755
--- a/concepts/prompt_target.html
+++ b/concepts/prompt_target.html
@@ -160,7 +160,7 @@
This section covers the essentials of prompt targets—what they are, how to configure them, their practical uses, and recommended best practices—to help you fully utilize this feature in your applications.
What Are Prompt Targets?
-
Prompt targets are endpoints within Arch that handle specific types of user prompts. They act as the bridge between user inputs and your backend agemts or tools (APIs), enabling Arch to route, process, and manage prompts efficiently. Defining prompt targets helps you decouple your application’s core logic from processing and handling complexities, leading to clearer code organization, better scalability, and easier maintenance.
+
Prompt targets are endpoints within Arch that handle specific types of user prompts. They act as the bridge between user inputs and your backend agents or tools (APIs), enabling Arch to route, process, and manage prompts efficiently. Defining prompt targets helps you decouple your application’s core logic from processing and handling complexities, leading to clearer code organization, better scalability, and easier maintenance.
Capability
@@ -240,7 +240,7 @@ Each parameter can be marked as required or optional. Here is a full list of par
Lists of allowable values for the parameter with data type matching the type attribute. Usage Example: enum:["celsius`","fahrenheit"]
items
-
Specifies the attribute of the elements when type euqals list, set, dict, tuple. Usage Example: items:{"type":"str"}
+
Specifies the attribute of the elements when type equals list, set, dict, tuple. Usage Example: items:{"type":"str"}
required
Indicates whether the parameter is mandatory or optional. Valid values: true or false
@@ -365,7 +365,7 @@ Each parameter can be marked as required or optional. Here is a full list of par
diff --git a/concepts/tech_overview/error_target.html b/concepts/tech_overview/error_target.html
index 3929a94a..1c0d6e22 100755
--- a/concepts/tech_overview/error_target.html
+++ b/concepts/tech_overview/error_target.html
@@ -232,7 +232,7 @@ The errors are communicated to the application via headers like
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
+
Arch builds on Envoy’s Listener subsystem to streamline connection management 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,
+primary entry point for incoming traffic, handling initial connection setup, including network filtering, guardrails,
and additional network security checks. For more details on prompt security and safety,
see here.
@@ -179,7 +179,7 @@ address like arch.l
Configure Listener
-
To configure a Downstream (Ingress) Listner, simply add the listener directive to your configuration file:
+
To configure a Downstream (Ingress) Listener, simply add the listener directive to your configuration file:
Example Configuration
1version:v0.1
@@ -236,7 +236,7 @@ address like arch.l
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
+networking and HTTP related concerns, 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
LLMs in Arch.
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 process prompts quickly and forward control back to the application host. There are three modes in which Arch
can be configured to run its model server subsystem:
Local Serving (CPU - Moderate)
@@ -180,14 +180,14 @@ might not be available.
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
+cloud serving for function calling and guardrails scenarios to dramatically improve the speed and overall performance
of your applications.
$ archgwup
Note
-
Arch’s model serving in the cloud is priced at $0.05M/token (156x cheaper than GPT-4o) with averlage latency
+
Arch’s model serving in the cloud is priced at $0.05M/token (156x cheaper than GPT-4o) with average latency
of 200ms (10x faster than GPT-4o). Please refer to our Get Started to know
how to generate API keys for model serving
@@ -223,7 +223,7 @@ how to generate API keys for model serving
diff --git a/concepts/tech_overview/prompt.html b/concepts/tech_overview/prompt.html
index 862d18d5..2a6f7eed 100755
--- a/concepts/tech_overview/prompt.html
+++ b/concepts/tech_overview/prompt.html
@@ -161,7 +161,7 @@
Arch relies on Envoy’s HTTP connection management,
subsystem and its prompt handler subsystem engineered with purpose-built LLMs to
implement critical functionality on behalf of developers so that you can stay focused on business logic.
-
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.
+
Arch’s prompt handler subsystem interacts with the model subsystem through Envoy’s cluster manager system to ensure robust, resilient and fault-tolerant experience in managing incoming prompts.
See also
Read more about the model subsystem and how the LLMs are hosted in Arch.
@@ -181,7 +181,7 @@ containing two key-value pairs:
Prompt Guard
Arch is engineered with Arch-Guard, an industry leading safety layer, powered by a
-compact and high-performimg LLM that monitors incoming prompts to detect and reject jailbreak attempts -
+compact and high-performing 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:
@@ -224,7 +224,7 @@ etc. To offer feedback on our roadmap, please visit our
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 prompttarget primitve. Prompt targets are endpoints
+and identifies where to forward the conversation to via its prompttarget primitive. 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:
@@ -304,7 +304,7 @@ when a user’s intent has changed so that you can build faster, more accurate R
Intent Matching
Arch uses fast text embedding and intent recognition approaches to first detect the intent of each incoming prompt.
This intent matching 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 matching framework considers both the name and description of each prompt target, and uses a composite matching score between embedding similarity and intent classification scores to enchance accuracy in forwarding decisions.
+Arch’s intent matching framework considers both the name and description of each prompt target, and uses a composite matching score between embedding similarity and intent classification scores to enhance accuracy in forwarding decisions.
Intent Recognition: NLI techniques further refine the matching process by evaluating the semantic alignment between the prompt and potential targets.
Text Embedding: 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.
@@ -397,7 +397,7 @@ This setup allows you to take advantage of Arch’s advanced traffic management
Below we describe the events in the lifecycle of a request passing through an Arch gateway instance. We first
describe how Arch fits into the request path and then the internal events that take place following
-the arrival of a request at Arch from downtream clients. We follow the request until the corresponding
+the arrival of a request at Arch from downstream clients. We follow the request until the corresponding
dispatch upstream and the response path.
@@ -200,14 +200,14 @@ processing. It is responsible for managing the downstream (ingress) and the upst
lifecycle. The downstream and upstream HTTP/2 codec lives here.
Prompt handler subsystem which is responsible for selecting and
forwarding prompts prompt_targets and establishes the lifecycle of any upstream connection to a
-hosted endpoint that implements domain-specific business logic for incoming promots. This is where knowledge
+hosted endpoint that implements domain-specific business logic for incoming prompts. This is where knowledge
of targets and endpoint health, load balancing and connection pooling exists.
Model serving subsystem which helps Arch make intelligent decisions about the
incoming prompts. The model server is designed to call the purpose-built LLMs in Arch.
The three subsystems are bridged with either the HTTP router filter, and the cluster manager subsystems of Envoy.
Also, Arch utilizes Envoy event-based thread model.
-A main thread is responsible forthe server lifecycle, configuration processing, stats, etc. and some number of
+A main thread is responsible for the server lifecycle, configuration processing, stats, etc. and some number of
worker threads process requests. All threads operate around an event loop (libevent)
and any given downstream TCP connection will be handled by exactly one worker thread for its lifetime. Each worker
thread maintains its own pool of TCP connections to upstream endpoints.
@@ -295,7 +295,7 @@ decrypts incoming data for processing.
Arch first checks the incoming prompts for guardrails such as jailbreak attempts. This ensures
that harmful or unwanted behaviors are detected early in the request processing pipeline.
Intent Matching:
-The decrypted data stream is deframed by the HTTP/2 codec in Arch’s HTTP connection manager. Arch performs
+The decrypted data stream is de-framed by the HTTP/2 codec in Arch’s HTTP connection manager. Arch performs
intent matching via is prompt-handler subsystem using the name and description of the defined prompt targets,
determining which endpoint should handle the prompt.
Parameter Gathering with Arch-Function:
@@ -350,7 +350,7 @@ passing it through any egress processing pipeline defined by the application, su
Once a request completes, the stream is destroyed. The following also takes places:
The post-request monitoring are updated (e.g. timing, active requests, upgrades, health checks).
-Some statistics are updated earlier however, during request processing. Stats are batchedand written by the main
+Some statistics are updated earlier however, during request processing. Stats are batched and written by the main
thread periodically.
Trace spans are finalized. If our example request was traced, a
@@ -397,7 +397,7 @@ processing request headers and then finalized by the HCM during post-request pro
A few definitions before we dive into the main architecture documentation. Also note, Arch borrows from Envoy’s terminology
to keep things consistent in logs and traces, and introduces and clarifies concepts are is relates to LLM applications.
Agent: An application that uses LLMs to handle wide-ranging tasks from users via prompts. This could be as simple
-as retrieving or summarizing data from an API, or being able to trigger compleix actions like adjusting ad campaigns, or
+as retrieving or summarizing data from an API, or being able to trigger complex actions like adjusting ad campaigns, or
changing travel plans via prompts.
Arch Config: Arch operates based on a configuration that controls the behavior of a single instance of the Arch gateway.
This where you enable capabilities like LLM routing, fast function calling (via prompt_targets), applying guardrails, and enabling critical
-features like metrics and tracing. For the full configuration reference of arch_config.yaml see here.
+features like metrics and tracing. For the full configuration reference of arch_config.yaml see here.
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
@@ -183,10 +183,10 @@ For more details, check out model serving process helps Arch make intelligent decisions
+(or on a separate host connected 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 LLMs in Arch.
Error Target: 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.
@@ -216,7 +216,7 @@ and take appropriate actions.
diff --git a/concepts/tech_overview/threading_model.html b/concepts/tech_overview/threading_model.html
index 0c808b89..c682271c 100755
--- a/concepts/tech_overview/threading_model.html
+++ b/concepts/tech_overview/threading_model.html
@@ -194,7 +194,7 @@ hardware threads on the machine.
diff --git a/get_started/intro_to_arch.html b/get_started/intro_to_arch.html
index 53c3d8f4..4626572d 100755
--- a/get_started/intro_to_arch.html
+++ b/get_started/intro_to_arch.html
@@ -165,7 +165,7 @@ fast input clarification, agent routing, seamless integration of prompts with to
You’re trapped in tedious prompting work to clarify inputs and user intents?
You’re wasting cycles choosing and integrating code for observability instead of it just happening transparently?
-
And you think to youself, can’t I move faster by focusing on higher-level objectives in a language and framework agnostic way? Well, you can!
+
And you think to yourself, can’t I move faster by focusing on higher-level objectives in a language and framework agnostic way? Well, you can!
@@ -185,7 +185,7 @@ Arch takes a dependency on Envoy and is a self-contained process that is designe
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:
-
Arch builds on Envoy’s proven success. Envoy is used at masssive scale by the leading technology companies of our time including AirBnB, Dropbox, Google, Reddit, Stripe, etc. Its battle tested and scales linearly with usage and enables developers to focus on what really matters: application features and business logic.
+
Arch builds on Envoy’s proven success. Envoy is used at massive scale by the leading technology companies of our time including AirBnB, Dropbox, Google, Reddit, Stripe, etc. Its battle tested and scales linearly with usage and enables developers to focus on what really matters: application features and business logic.
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.
Arch can be deployed and upgraded quickly across your infrastructure transparently without the horrid pain of deploying library upgrades in your applications.
@@ -201,7 +201,7 @@ For more details, read jailbreakattempts 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 Prompt Guard.
-
Traffic Management: Arch offers several capabilities for LLM calls originating from your applications, including smart retries on errors from upstream LLMs, and automatic cutover to other LLMs configured in Arch for continuous availability and disaster recovery scenarios.
+
Traffic Management: Arch offers several capabilities for LLM calls originating from your applications, including smart retries on errors from upstream LLMs, and automatic cut-over to other LLMs configured in Arch for continuous availability and disaster recovery scenarios.
Arch extends Envoy’s cluster subsystem to manage upstream connections to LLMs so that you can build resilient AI applications.
Front/edge Gateway: There is substantial benefit in using the same software at the edge (observability, traffic shaping algorithms, 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.
@@ -238,7 +238,7 @@ For more details, read
When implementing Agent Routing and Hand Off in your applications, consider these best practices:
-
Clearly Define Agent Responsibilities: Ensure each agent or human endpoint has a clear, specific description of the prompts they handle, reducing misrouting.
-
Monitor and Optimize Routes: Regularly review how prompts are routed to adjust and optimize agent definitions and configurations.
+
Clearly define agent responsibilities: Ensure each agent or human endpoint has a clear, specific description of the prompts they handle, reducing mis-routing.
+
Monitor and optimize routes: Regularly review how prompts are routed to adjust and optimize agent definitions and configurations.
Note
-
To observe traffic to and from agents, please read more about observabiliuty in Arch.
+
To observe traffic to and from agents, please read more about observability in Arch.
By carefully configuring and managing your Agent routing and hand off, you can significantly improve your application’s responsiveness, performance, and overall user satisfaction.
diff --git a/guides/observability/access_logging.html b/guides/observability/access_logging.html
index 31d6eb18..8220eade 100755
--- a/guides/observability/access_logging.html
+++ b/guides/observability/access_logging.html
@@ -242,7 +242,7 @@ Access logs can be exported to centralized logging systems (e.g., ELK stack or F
diff --git a/guides/observability/monitoring.html b/guides/observability/monitoring.html
index 8df32037..29b2ad34 100755
--- a/guides/observability/monitoring.html
+++ b/guides/observability/monitoring.html
@@ -254,7 +254,7 @@ are some sample configuration files for both, respectively.
diff --git a/guides/observability/observability.html b/guides/observability/observability.html
index 3d40c62f..921a4cf8 100755
--- a/guides/observability/observability.html
+++ b/guides/observability/observability.html
@@ -206,7 +206,7 @@
diff --git a/guides/observability/tracing.html b/guides/observability/tracing.html
index bcd63d40..f39eb1de 100755
--- a/guides/observability/tracing.html
+++ b/guides/observability/tracing.html
@@ -576,7 +576,7 @@ tools like AWS X-Ray and Datadog, enhancing observability and facilitating faste
diff --git a/guides/prompt_guard.html b/guides/prompt_guard.html
index 459b8dc7..7ee8ce79 100755
--- a/guides/prompt_guard.html
+++ b/guides/prompt_guard.html
@@ -266,7 +266,7 @@ By implementing Prompt Guard, developers can provide a robust layer of input val
diff --git a/index.html b/index.html
index e6b33845..6d8ec188 100755
--- a/index.html
+++ b/index.html
@@ -246,7 +246,7 @@ Resources
diff --git a/objects.inv b/objects.inv
index c98a06b4..c18de003 100755
Binary files a/objects.inv and b/objects.inv differ
diff --git a/resources/configuration_reference.html b/resources/configuration_reference.html
index 479d9257..cecac2cd 100755
--- a/resources/configuration_reference.html
+++ b/resources/configuration_reference.html
@@ -154,12 +154,12 @@
-
Configuration Reference
-
The following is a complete reference of the arch_conifg.yml that controls the behavior of a single instance of
+
Configuration Reference
+
The following is a complete reference of the arch_config.yml that controls the behavior of a single instance of
the Arch gateway. This where you enable capabilities like routing to upstream LLm providers, defining prompt_targets
where prompts get routed to, apply guardrails, and enable critical agent observability features.
1version:v0.1 2 3listeners:
@@ -210,8 +210,8 @@ where prompts get routed to, apply guardrails, and enable critical agent observa
4849# provides a way to override default settings for the arch system50overrides:
-51# By default Arch uses an NLI + embedding approach to match an incomming prompt to a prompt target.
-52# The intent matching threshold is kept at 0.80, you can overide this behavior if you would like
+51# By default Arch uses an NLI + embedding approach to match an incoming prompt to a prompt target.
+52# The intent matching threshold is kept at 0.80, you can override this behavior if you would like53prompt_target_intent_matching_threshold:0.605455# default system prompt used by all prompt targets
@@ -274,7 +274,7 @@ where prompts get routed to, apply guardrails, and enable critical agent observa
diff --git a/searchindex.js b/searchindex.js
index 60b9caed..19b2d7dd 100755
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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