deploy: 5c7567584d

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 416c1b7f738dbd8ff231cf7246bbbb27
+config: e029b4806150eb62aeaf9741fac54bfc
 tags: 645f666f9bcd5a90fca523b33c5a78b7

CNAME

@@ -1 +0,0 @@
-docs.archgw.com

_downloads/76c29fa020a722e6be4b991f849978ce/intent_detection.py

@@ -1,152 +0,0 @@
-from flask import Flask, request, jsonify
-from datetime import datetime
-import uuid
-from langchain.memory import ConversationBufferMemory
-from langchain.schema import AIMessage, HumanMessage
-from langchain import OpenAI
-
-app = Flask(__name__)
-
-# Global dictionary to keep track of user memories
-user_memories = {}
-
-def get_user_conversation(user_id):
-    """
-    Retrieve the user's conversation memory using LangChain.
-    If the user does not exist, initialize their conversation memory.
-    """
-    if user_id not in user_memories:
-        user_memories[user_id] = ConversationBufferMemory(return_messages=True)
-    return user_memories[user_id]
-
-def update_user_conversation(user_id, client_messages, intent_changed):
-    """
-    Update the user's conversation memory with new messages using LangChain.
-    Each message is augmented with a UUID, timestamp, and intent change marker.
-    Only new messages are added to avoid duplication.
-    """
-    memory = get_user_conversation(user_id)
-    stored_messages = memory.chat_memory.messages
-
-    # Determine the number of stored messages
-    num_stored_messages = len(stored_messages)
-    new_messages = client_messages[num_stored_messages:]
-
-    # Process each new message
-    for index, message in enumerate(new_messages):
-        role = message.get('role')
-        content = message.get('content')
-        metadata = {
-            'uuid': str(uuid.uuid4()),
-            'timestamp': datetime.utcnow().isoformat(),
-            'intent_changed': False  # Default value
-        }
-
-        # Mark the intent change on the last message if detected
-        if intent_changed and index == len(new_messages) - 1:
-            metadata['intent_changed'] = True
-
-        # Create a new message with metadata
-        if role == 'user':
-            memory.chat_memory.add_message(
-                HumanMessage(content=content, additional_kwargs={'metadata': metadata})
-            )
-        elif role == 'assistant':
-            memory.chat_memory.add_message(
-                AIMessage(content=content, additional_kwargs={'metadata': metadata})
-            )
-        else:
-            # Handle other roles if necessary
-            pass
-
-    return memory
-
-def get_messages_since_last_intent(messages):
-    """
-    Retrieve messages from the last intent change onwards using LangChain.
-    """
-    messages_since_intent = []
-    for message in reversed(messages):
-        # Insert message at the beginning to maintain correct order
-        messages_since_intent.insert(0, message)
-        metadata = message.additional_kwargs.get('metadata', {})
-        # Break if intent_changed is True
-        if metadata.get('intent_changed', False) == True:
-            break
-    return messages_since_intent
-
-def forward_to_llm(messages):
-    """
-    Forward messages to an upstream LLM using LangChain.
-    """
-    # Convert messages to a conversation string
-    conversation = ""
-    for message in messages:
-        role = 'User' if isinstance(message, HumanMessage) else 'Assistant'
-        content = message.content
-        conversation += f"{role}: {content}\n"
-    # Use LangChain's LLM to get a response. This call is proxied through Arch for end-to-end observability and traffic management
-    llm = OpenAI()
-    # Create a prompt that includes the conversation
-    prompt = f"{conversation}Assistant:"
-    response = llm(prompt)
-    return response
-
-@app.route('/process_rag', methods=['POST'])
-def process_rag():
-    # Extract JSON data from the request
-    data = request.get_json()
-
-    user_id = data.get('user_id')
-    if not user_id:
-        return jsonify({'error': 'User ID is required'}), 400
-
-    client_messages = data.get('messages')
-    if not client_messages or not isinstance(client_messages, list):
-        return jsonify({'error': 'Messages array is required'}), 400
-
-    # Extract the intent change marker from Arch's headers if present for the current prompt
-    intent_changed_header = request.headers.get('x-arch-intent-marker', '').lower()
-    if intent_changed_header in ['', 'false']:
-        intent_changed = False
-    elif intent_changed_header == 'true':
-        intent_changed = True
-    else:
-        # Invalid value provided
-        return jsonify({'error': 'Invalid value for x-arch-prompt-intent-change header'}), 400
-
-    # Update user conversation based on intent change
-    memory = update_user_conversation(user_id, client_messages, intent_changed)
-
-    # Retrieve messages since last intent change for LLM
-    messages_for_llm = get_messages_since_last_intent(memory.chat_memory.messages)
-
-    # Forward messages to upstream LLM
-    llm_response = forward_to_llm(messages_for_llm)
-
-    # Prepare the messages to return
-    messages_to_return = []
-    for message in memory.chat_memory.messages:
-        role = 'user' if isinstance(message, HumanMessage) else 'assistant'
-        content = message.content
-        metadata = message.additional_kwargs.get('metadata', {})
-        message_entry = {
-            'uuid': metadata.get('uuid'),
-            'timestamp': metadata.get('timestamp'),
-            'role': role,
-            'content': content,
-            'intent_changed': metadata.get('intent_changed', False)
-        }
-        messages_to_return.append(message_entry)
-
-    # Prepare the response
-    response = {
-        'user_id': user_id,
-        'messages': messages_to_return,
-        'llm_response': llm_response
-    }
-
-    return jsonify(response), 200
-
-if __name__ == '__main__':
-    app.run(debug=True)

_downloads/a0b0970c8fa22d4fe861e8362965b77c/getting-started.yml

@@ -1,41 +0,0 @@
-version: "0.1-beta"
-listener:
-  address: 127.0.0.1 | 0.0.0.0
-  port_value: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
-  messages: tuple | hugging-face-messages-api
-
-system_prompts:
-  - name: network_assistant
-    content: You are a network assistant that just offers facts about the operational health of the network
-
-llm_providers:
-    - name: "OpenAI"
-      access_key: $OPEN_AI_KEY
-      model: gpt-4o
-      default: true
-    - name: "Mistral"
-      access_key: $MISTRAL_KEY
-      model: mixtral8-7B
-
-prompt_endpoints:
-  - "http://127.0.0.2"
-  - "http://127.0.0.1"
-
-prompt_guards:
-   input-guard:
-     - name: #jailbreak
-       on-exception-message: Looks like you are curious about my abilities. But I can only
-
-prompt_targets:
-   - name: information_extraction
-     type: RAG
-     description: this prompt handles all information extractions scenarios
-     path: /agent/summary
-
-   - name: reboot_network_device
-     path: /agent/action
-     description: used to help network operators with perform device operations like rebooting a device.
-     parameters:
-error_target: #handle errors from Bolt or upstream LLMs
-    name: “error_handler”
-    path: /errors

_sources/configuration_reference.rst

@@ -1,13 +0,0 @@
-Configuration Reference
-============================
-
-The following is a complete reference of the ``prompt-conifg.yml`` that controls the behavior of a single instance of
-the Arch gateway. We've kept things simple (less than 80 lines) and held off on exposing additional functionality (for
-e.g. suppporting push observability stats, managing prompt-endpoints as virtual cluster, exposing more load balancing
-options, etc). Our belief that the simple things, should be simple. So we offert good defaults for developers, so
-that they can spend more of their time in building features unique to their AI experience.
-
-.. literalinclude:: /_config/prompt-config-full-reference.yml
-    :language: yaml
-    :linenos:
-    :caption: :download:`prompt-config-full-reference-beta-1-0.yml </_config/prompt-config-full-reference.yml>`

_sources/getting_started/getting_started.rst

@@ -1,48 +0,0 @@
-.. _getting_started:
-
-Getting Started
-================
-
-.. sidebar:: Pre-requisites
-
-    In order for you to get started, please make sure that `Docker <https://www.docker.com/get-started>`_
-    and `Python <https://www.python.org/downloads/>`_ are installed locally.
-
-    As the examples use the pre-built `Arch Docker images <https://hub.docker.com/r/katanemo/arch>`_,
-    they should work on the following architectures:
-
-        - x86_64
-        - ARM 64
-
-
-This section gets you started with a very simple configuration and provides some example configurations.
-
-
-The fastest way to get started using Arch is installing `pre-built binaries <https://hub.docker.com/r/katanemo/arch>`_.
-You can also build it from source.
-
-Step 1: Install the Arch CLI
-----------------------------
-Arch's CLI allows you to manage and interact with the Arch gateway efficiently. To install the CLI, simply
-run the following command:
-
-.. code-block:: bash
-
-    pip install archgw
-
-This will install the archgw command-line tool globally on your system.
-
-Step 2: Start Arch Gateway
---------------------------
-
-.. code-block:: bash
-
-    archgw up --quick-start
-
-Configuration
--------------
-
-Today, only support a static bootstrap configuration file for simplicity today:
-
-.. literalinclude:: /_config/getting-started.yml
-    :language: yaml

_sources/getting_started/use_cases.rst

@@ -1,6 +0,0 @@
-.. toctree::
-  :maxdepth: 2
-  :caption: Use Cases
-
-  use_cases/rag
-  use_cases/function_calling

_sources/getting_started/use_cases/function_calling.rst

@@ -1,71 +0,0 @@
-.. _arch_function_calling_agentic_guide:
-
-Agentic (Text-to-Action) Apps
-==============================
-
-Arch helps you easily personalize your applications by calling application-specific (API) functions
-via user prompts. This involves any predefined functions or APIs you want to expose to users to perform tasks,
-gather information, or manipulate data. This capability is generally referred to as **function calling**, where
-you have the flexibility to support “agentic” apps 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 with
-the user to gather any missing parameters and makes API calls so that you can focus on writing business logic.
-Arch does this via its purpose-built :ref:`Arch-FC LLM <llms_in_arch>` - the fastest (200ms p90 - 10x faser than GPT-4o)
-and cheapest (100x than GPT-40) function-calling LLM that matches performance with frontier models.
-______________________________________________________________________________________________
-
-.. image:: /_static/img/function-calling-network-flow.jpg
-   :width: 100%
-   :align: center
-
-
-Single Function Call
---------------------
-In the most common scenario, users will request a single action via prompts, and Arch efficiently processes the
-request by extracting relevant parameters, validating the input, and calling the designated function or API. Here
-is how you would go about enabling this scenario with Arch:
-
-Step 1: Define prompt targets with functions
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. literalinclude:: /_config/function-calling-network-agent.yml
-    :language: yaml
-    :linenos:
-    :emphasize-lines: 16-37
-    :caption: Define prompt targets that can enable users to engage with API and backened functions of an app
-
-Step 2: Process request parameters in Flask
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Once the prompt targets are configured as above, handling those parameters is
-
-.. literalinclude:: /_include/parameter_handling_flask.py
-    :language: python
-    :linenos:
-    :caption: Flask API example for parameter extraction via HTTP request parameters
-
-Parallel/ Multiple Function Calling
------------------------------------
-In more complex use cases, users may request multiple actions or need multiple APIs/functions to be called
-simultaneously or sequentially. With Arch, you can handle these scenarios efficiently using parallel or multiple
-function calling. This allows your application to engage in a broader range of interactions, such as updating
-different datasets, triggering events across systems, or collecting results from multiple services in one prompt.
-
-Arch-FC1B is built to manage these parallel tasks efficiently, ensuring low latency and high throughput, even
-when multiple functions are invoked. It provides two mechanisms to handle these cases:
-
-Step 1: Define Multiple Function Targets
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-When enabling multiple function calling, define the prompt targets in a way that supports multiple functions or
-API calls based on the user's prompt. These targets can be triggered in parallel or sequentially, depending on
-the user's intent.
-
-Example of Multiple Prompt Targets in YAML:
-
-.. literalinclude:: /_config/function-calling-network-agent.yml
-    :language: yaml
-    :linenos:
-    :emphasize-lines: 16-37
-    :caption: Define prompt targets that can enable users to engage with API and backened functions of an app

_sources/getting_started/use_cases/rag.rst

@@ -1,96 +0,0 @@
-.. _arch_rag_guide:
-
-Retrieval-Augmented (RAG)
-=========================
-
-The following section describes how Arch can help you build faster, smarter and more accurate
-Retrieval-Augmented Generation (RAG) applications.
-
-Intent-drift Detection
-----------------------
-
-Developers struggle to handle `follow-up <https://www.reddit.com/r/ChatGPTPromptGenius/comments/17dzmpy/how_to_use_rag_with_conversation_history_for/?>`_
-or `clarifying <https://www.reddit.com/r/LocalLLaMA/comments/18mqwg6/best_practice_for_rag_with_followup_chat/>`_
-questions. Specifically, when users ask for changes or additions to previous responses their AI applications often
-generate entirely new responses instead of adjusting previous ones. Arch offers *intent-drift* tracking as a feature so
-that developers can know when the user has shifted away from a previous intent so that they can dramatically improve
-retrieval accuracy, lower overall token cost and  improve the speed of their responses back to users.
-
-Arch uses its built-in lightweight NLI and embedding models to know if the user has steered away from an active intent.
-Arch's intent-drift detection mechanism is based on its' *prompt_targets* primtive. Arch tries to match an incoming
-prompt to one of the *prompt_targets* configured in the gateway. Once it detects that the user has moved away from an active
-active intent, Arch adds the ``x-arch-intent-drift`` headers to the request before sending it your application servers.
-
-.. literalinclude:: /_include/intent_detection.py
-    :language: python
-    :linenos:
-    :lines: 95-125
-    :emphasize-lines: 14-22
-    :caption: :download:`Intent drift detection in python </_include/intent_detection.py>`
-
-_____________________________________________________________________________________________________________________
-
-.. Note::
-
-   Arch is (mostly) stateless so that it can scale in an embarrassingly parrallel fashion. So, while Arch offers
-   intent-drift detetction, you still have to maintain converational state with intent drift as meta-data. The
-   following code snippets show how easily you can build and enrich conversational history with Langchain (in python),
-   so that you can use the most relevant prompts for your retrieval and for prompting upstream LLMs.
-
-
-Step 1: define ConversationBufferMemory
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. literalinclude:: /_include/intent_detection.py
-    :language: python
-    :linenos:
-    :lines: 1-21
-
-Step 2: update ConversationBufferMemory w/ intent
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. literalinclude:: /_include/intent_detection.py
-    :language: python
-    :linenos:
-    :lines: 22-62
-
-Step 3: get Messages based on latest drift
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. literalinclude:: /_include/intent_detection.py
-    :language: python
-    :linenos:
-    :lines: 64-76
-
-
-You can used the last set of messages that match to an intent to prompt an LLM, use it with an vector-DB for
-improved retrieval, etc. With Arch and a few lines of code, you can improve the retrieval accuracy, lower overall
-token cost and dramatically improve the speed of their responses back to users.
-
-Parameter Extraction for RAG
-----------------------------
-
-To build RAG (Retrieval-Augmented Generation) applications, you can configure prompt targets with parameters,
-enabling Arch to retrieve critical information in a structured way for processing. This approach improves the
-retrieval quality and speed of your application. By extracting parameters from the conversation, you can pull
-the appropriate chunks from a vector database or SQL-like data store to enhance accuracy. With Arch, you can
-streamline data retrieval and processing to build more efficient and precise RAG applications.
-
-Step 1: Define prompt targets with parameter definitions
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. literalinclude:: /_config/rag-prompt-targets.yml
-    :language: yaml
-    :linenos:
-    :emphasize-lines: 16-36
-    :caption: prompt-config.yaml for parameter extraction for RAG scenarios
-
-Step 2: Process request parameters in Flask
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Once the prompt targets are configured as above, handling those parameters is
-
-.. literalinclude:: /_include/parameter_handling_flask.py
-    :language: python
-    :linenos:
-    :caption: Flask API example for parameter extraction via HTTP request parameters

_sources/intro/architecture/architecture.rst

@@ -1,12 +0,0 @@
-Technical Architecture
-======================
-
-.. toctree::
-  :maxdepth: 2
-
-  intro/terminology
-  intro/threading_model
-  listeners/listeners
-  prompt_processing/prompt_processing
-  listeners/llm_provider
-  model_serving/model_serving

_sources/intro/architecture/intro/terminology.rst

@@ -1,46 +0,0 @@
-.. _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.

_sources/intro/architecture/intro/threading_model.rst

@@ -1,21 +0,0 @@
-.. _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.

_sources/intro/architecture/listeners/listeners.rst

@@ -1,37 +0,0 @@
-.. _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>`

_sources/intro/architecture/listeners/llm_provider.rst

@@ -1,52 +0,0 @@
-.. _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)

_sources/intro/architecture/model_serving/model_serving.rst

@@ -1,56 +0,0 @@
-.. _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

_sources/intro/architecture/prompt_processing/prompt_processing.rst

@@ -1,155 +0,0 @@
-.. _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.

_sources/intro/getting_help.rst

@@ -1,15 +0,0 @@
-.. _getting_help:
-
-Getting help
-============
-
-We are very interested in building a community around Arch. Please reach out to us if you are
-interested in using it and need help or want to contribute.
-
-Please see `contact info <https://github.com/katanemo/arch#contact>`_.
-
-Reporting security vulnerabilities
-----------------------------------
-
-Please see `security contact info
-<https://github.com/katanemo/arch#reporting-security-vulnerabilities>`_.

_sources/intro/intro.rst

@@ -1,12 +0,0 @@
-.. _intro:
-
-Introduction
-============
-
-.. toctree::
-  :maxdepth: 2
-
-  what_is_arch
-  architecture/architecture
-  life_of_a_request
-  getting_help

_sources/intro/life_of_a_request.rst

@@ -1,176 +0,0 @@
-.. _life_of_a_request:
-
-Life of a Request
-=================
-
-Below we describe the events in the life 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
-dispatch upstream and the response path.
-
-.. image:: /_static/img/network-topology-ingress-egress.jpg
-   :width: 100%
-   :align: center
-
-Terminology
------------
-
-We recommend that you get familiar with some of the :ref:`terminology <arch_terminology>` used in Arch
-before reading this section.
-
-Network topology
-----------------
-
-How a request flows through the components in a network (including Arch) depends on the network’s topology.
-Arch can be used in a wide variety of networking topologies. We focus on the inner operation of Arch below,
-but briefly we address how Arch relates to the rest of the network in this section.
-
-- **Downstream(Ingress)** listeners take requests from upstream clients like a web UI or clients that forward
-  prompts to you local application responses from the application flow back through Arch to the downstream.
-
-- **Upstream(Egress)** listeners take requests from the application and forward them to LLMs.
-
-.. image:: /_static/img/network-topology-ingress-egress.jpg
-   :width: 100%
-   :align: center
-
-In practice, Arch can be deployed on the edge and as an internal load balancer between AI agents. A request path may
-traverse multiple Arch gateways:
-
-.. image:: /_static/img/network-topology-agent.jpg
-   :width: 100%
-   :align: center
-
-
-High level architecture
------------------------
-Arch is a set of **two** self-contained processes that are designed to run alongside your application servers
-(or on a separate server connected to your application servers via a network). The first process is designated
-to manage HTTP-level networking and connection management concerns (protocol management, request id generation,
-header sanitization, etc.), and the other process is for **model serving**, which helps Arch make intelligent
-decisions about the incoming prompts. The model server hosts the purpose-built :ref:`LLMs <llms_in_arch>` to
-manage several critical, but undifferentiated, prompt related tasks on behalf of developers.
-
-
-The request processing path in Arch has three main parts:
-
-* :ref:`Listener subsystem <arch_overview_listeners>` which handles **downstream** and **upstream** request
-  processing. It is responsible for managing the downstream (ingress) and the upstream (egress) request
-  lifecycle. The downstream and upstream HTTP/2 codec lives here.
-* :ref:`Prompt handler subsystem <arch_overview_prompt_handling>` 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
-  of targets and endpoint health, load balancing and connection pooling exists.
-* :ref:`Model serving subsystem <arch_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.
-
-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 <https://blog.envoyproxy.io/envoy-threading-model-a8d44b922310>`_.
-A main thread is responsible forthe server lifecycle, configuration processing, stats, etc. and some number of
-:ref:`worker threads <arch_overview_threading>` process requests. All threads operate around an event loop (`libevent <https://libevent.org/>`_)
-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.
-
-Worker threads rarely share state and operate in a trivially parallel fashion. This threading model
-enables scaling to very high core count CPUs.
-
-Configuration
--------------
-
-Today, only support a static bootstrap configuration file for simplicity today:
-
-.. literalinclude:: /_config/getting-started.yml
-    :language: yaml
-
-
-Request Flow (Ingress)
-----------------------
-
-Overview
-^^^^^^^^
-A brief outline of the life cycle of a request and response using the example configuration above:
-
-1. **TCP Connection Establishment**:
-   A TCP connection from downstream is accepted by an Arch listener running on a worker thread.
-   The listener filter chain provides SNI and other pre-TLS information. The transport socket, typically TLS,
-   decrypts incoming data for processing.
-
-2. **Prompt Guardrails Check**:
-   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.
-
-3. **Intent Matching**:
-   The decrypted data stream is deframed 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.
-
-4. **Parameter Gathering with Arch-FC**:
-   If a prompt target requires specific parameters, Arch engages Arch-FC to extract the necessary details
-   from the incoming prompt(s). This process gathers the critical information needed for downstream API calls.
-
-5. **API Call Execution**:
-   Arch routes the prompt to the appropriate backend API or function call. If an endpoint cluster is identified,
-   load balancing is performed, circuit breakers are checked, and the request is proxied to the upstream endpoint.
-
-6. **Default Summarization by Upstream LLM**:
-   By default, if no specific endpoint processing is needed, the prompt is sent to an upstream LLM for summarization.
-   This ensures that responses are concise and relevant, enhancing user experience in RAG (Retrieval-Augmented Generation)
-   and agentic applications.
-
-7. **Error Handling and Forwarding**:
-   Errors encountered during processing, such as failed function calls or guardrail detections, are forwarded to
-   designated error targets. Error details are communicated through specific headers to the application:
-
-   - ``X-Function-Error-Code``: Code indicating the type of function call error.
-   - ``X-Prompt-Guard-Error-Code``: Code specifying violations detected by prompt guardrails.
-   - Additional headers carry messages and timestamps to aid in debugging and logging.
-
-8. **Response Handling**:
-   The upstream endpoint’s TLS transport socket encrypts the response, which is then proxied back downstream.
-   Responses pass through HTTP filters in reverse order, ensuring any necessary processing or modification before final delivery.
-
-
-Request Flow (Egress)
----------------------
-
-Overview
---------
-
-A brief outline of the life cycle of a request and response in the context of egress traffic from an application
-to Large Language Models (LLMs) via Arch:
-
-1. **HTTP Connection Establishment to LLM**:
-   Arch initiates an HTTP connection to the upstream LLM service. This connection is handled by Arch’s egress listener
-   running on a worker thread. The connection typically uses a secure transport protocol such as HTTPS, ensuring the
-   prompt data is encrypted before being sent to the LLM service.
-
-2. **Rate Limiting**:
-   Before sending the request to the LLM, Arch applies rate-limiting policies to ensure that the upstream LLM service
-   is not overwhelmed by excessive traffic. Rate limits are enforced per client or service, ensuring fair usage and
-   preventing accidental or malicious overload. If the rate limit is exceeded, Arch may return an appropriate HTTP
-   error (e.g., 429 Too Many Requests) without sending the prompt to the LLM.
-
-3. **Load Balancing to (hosted) LLM Endpoints**:
-   After passing the rate-limiting checks, Arch routes the prompt to the appropriate LLM endpoint.
-   If multiple LLM providers instances are available, load balancing is performed to distribute traffic evenly
-   across the instances. Arch checks the health of the LLM endpoints using circuit breakers and health checks,
-   ensuring that the prompt is only routed to a healthy, responsive instance.
-
-4. **Response Reception and Forwarding**:
-   Once the LLM processes the prompt, Arch receives the response from the LLM service. The response is typically a
-   generated text, completion, or summarization. Upon reception, Arch decrypts (if necessary) and handles the response,
-   passing it through any egress processing pipeline defined by the application, such as logging or additional response filtering.
-
-
-Post-request processing
-^^^^^^^^^^^^^^^^^^^^^^^^
-Once a request completes, the stream is destroyed. The following also takes places:
-
-* The post-request :ref:`monitoring <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
-  thread periodically.
-* :ref:`Access logs <arch_access_logging>` are written to the access log
-* :ref:`Trace <arch_overview_tracing>` spans are finalized. If our example request was traced, a
-  trace span, describing the duration and details of the request would be created by the HCM when
-  processing request headers and then finalized by the HCM during post-request processing.

_sources/intro/what_is_arch.rst

@@ -1,90 +0,0 @@
-What is Arch
-============
-
-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
-:ref:`LLMs <llms_in_arch>`, Arch handles all 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.
-
-.. image:: /_static/img/arch-logo.png
-   :width: 100%
-   :align: center
-
-**The project was born out of the belief that:**
-
-  *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.*
-
-
-In practice, achieving the above goal is incredibly difficult. Arch attempts to do so by providing the
-following high level features:
-
-_____________________________________________________________________________________________________________
-
-**Out-of-process architecture, built on** `Envoy <http://envoyproxy.io/>`_: 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:
-
-* Arch builds on Envoy's proven success. Envoy is used at masssive sacle by the leading technology companies of
-  our time including `AirBnB <https://www.airbnb.com>`_, `Dropbox <https://www.dropbox.com>`_,
-  `Google <https://www.google.com>`_, `Reddit <https://www.reddit.com>`_, `Stripe <https://www.stripe.com>`_,
-  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.
-
-**Engineered with Fast LLMs:** Arch is engineered with specialized (sub-billion) LLMs that are desgined for
-fast, cost-effective and acurrate handling of prompts. These :ref:`LLMs <llms_in_arch>` are designed to be
-best-in-class for critcal prompt-related tasks like:
-
-* **Function/API Calling:** 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 :ref:`prompt processing <arch_overview_prompt_handling>`.
-
-* **Prompt Guardrails:** 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 `jailbreak <https://github.com/verazuo/jailbreak_llms>`_
-  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 :ref:`prompt processing <arch_overview_prompt_handling>`.
-
-* **Intent-Drift Detection:** Developers struggle to handle `follow-up <https://www.reddit.com/r/ChatGPTPromptGenius/comments/17dzmpy/how_to_use_rag_with_conversation_history_for/?>`_,
-  or `clarifying <https://www.reddit.com/r/LocalLLaMA/comments/18mqwg6/best_practice_for_rag_with_followup_chat/>`_
-  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.
-
-**Traffic Management:** 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 `cluster subsystem
-<https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/upstream/cluster_manager>`_ 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 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.
-
-**Best-In Class Monitoring:** 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.
-
-**End-to-End Tracing:** Arch propagates trace context using the W3C Trace Context standard, specifically through
-the ``traceparent`` header. This allows each component in the system to record its part of the request flow,
-enabling **end-to-end tracing** 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 :ref:`tracing <arch_overview_tracing>`.

_sources/llms/llms.rst

@@ -1,159 +0,0 @@
-.. _llms_in_arch:
-
-LLMs
-====
-
-Arch utilizes purpose-built, industry leading, LLMs to handle the crufty and undifferentiated work around
-accepting, handling and processing prompts. The following sections talk about some of the core models that
-are built-in Arch.
-
-Arch-Guard-v1
--------------
-LLM-powered applications are susceptible to prompt attacks, which are prompts intentionally designed to
-subvert the developer’s intended behavior of the LLM. Arch-Guard-v1 is a classifier model trained on a large
-corpus of attacks, capable of detecting explicitly malicious prompts (and toxicity).
-
-The model is useful as a starting point for identifying and guardrailing against the most risky realistic
-inputs to LLM-powered applications. Our goal in embedding Arch-Guard in the Arch gateway is to enable developers
-to focus on their business logic and factor out security and safety outside application logic. Wth Arch-Guard-v1
-developers can take to significantly reduce prompt attack risk while maintaining control over the user experience.
-
-Below is our test results of the strength of our model as compared to Prompt-Guard from `Meta LLama <https://huggingface.co/meta-llama/Prompt-Guard-86M>`_.
-
-.. list-table::
-   :header-rows: 1
-   :widths: 15 15 10 15 15
-
-   * - Dataset
-     - Jailbreak (Yes/No)
-     - Samples
-     - Prompt-Guard Accuracy
-     - Arch-Guard Accuracy
-   * - casual_conversation
-     - 0
-     - 3725
-     - 1.00
-     - 1.00
-   * - commonqa
-     - 0
-     - 9741
-     - 1.00
-     - 1.00
-   * - financeqa
-     - 0
-     - 1585
-     - 1.00
-     - 1.00
-   * - instruction
-     - 0
-     - 5000
-     - 1.00
-     - 1.00
-   * - jailbreak_behavior_benign
-     - 0
-     - 100
-     - 0.10
-     - 0.20
-   * - jailbreak_behavior_harmful
-     - 1
-     - 100
-     - 0.30
-     - 0.52
-   * - jailbreak_judge
-     - 1
-     - 300
-     - 0.33
-     - 0.49
-   * - jailbreak_prompts
-     - 1
-     - 79
-     - 0.99
-     - 1.00
-   * - jailbreak_tweet
-     - 1
-     - 1282
-     - 0.16
-     - 0.35
-   * - jailbreak_v
-     - 1
-     - 20000
-     - 0.90
-     - 0.93
-   * - jailbreak_vigil
-     - 1
-     - 104
-     - 1.00
-     - 1.00
-   * - mental_health
-     - 0
-     - 3512
-     - 1.00
-     - 1.00
-   * - telecom
-     - 0
-     - 4000
-     - 1.00
-     - 1.00
-   * - truthqa
-     - 0
-     - 817
-     - 1.00
-     - 0.98
-   * - weather
-     - 0
-     - 3121
-     - 1.00
-     - 1.00
-
-.. list-table::
-   :header-rows: 1
-   :widths: 15 20
-
-   * - Statistics
-     - Overall performance
-   * - Overall Accuracy
-     - 0.93568 (Prompt-Guard), 0.95267 (Arch-Guard)
-   * - True positives rate (TPR)
-     - 0.8468 (Prompt-Guard), 0.8887 (Arch-Guard)
-   * - True negative rate (TNR)
-     - 0.9972 (Prompt-Guard), 0.9970 (Arch-Guard)
-   * - False positive rate (FPR)
-     - 0.0028 (Prompt-Guard), 0.0030 (Arch-Guard)
-   * - False negative rate (FNR)
-     - 0.1532 (Prompt-Guard), 0.1113 (Arch-Guard)
-
-.. list-table::
-   :header-rows: 1
-   :widths: 15 20
-
-   * - Metrics
-     - Values
-   * - AUC
-     - 0.857 (Prompt-Guard), 0.880 (Arch-Guard)
-   * - Precision
-     - 0.715 (Prompt-Guard), 0.761 (Arch-Guard)
-   * - Recall
-     - 0.999 (Prompt-Guard), 0.999 (Arch-Guard)
-
-
-
-Arch-FC
--------
-Arch-FC is a lean, powerful and cost-effective agentic model designed for function calling scenarios.
-You can run Arch-FC locally, or use the cloud-hosted version for as little as $0.05/M token (100x cheaper
-than GPT-4o), with a p50 latency of 200ms (5x faster than GPT-4o), while meeting frontier model performance.
-
-.. Note::
-  Function calling helps you personalize the GenAI experience by calling application-specific operations via
-  prompts. This involves any predefined functions or APIs you want to expose to perform tasks, gather
-  information, or manipulate data - via prompts.
-
-  You can get started with function calling simply by configuring a prompt target with a name, description
-  and set of parameters needed by a specific backend function or a hosted API. The name, and description helps
-  Arch-FC match a user prompt to a function or API that can process it.
-
-By using Arch-FC, Arch enables you to easily build agentic workflows tailored to domain-specific use cases -
-from updating insurance claims to creating ad campaigns. Arch-FC analyzes prompts, extracts critical information
-from prompts, engages in lightweight conversations with the user to gather any missing parameters need before
-handling control back to Arch to make the API call to your hosted backend. Arch-FC handles the muck of information
-extraction so that you can focus on the business logic of your application.

_sources/observability/access_logs.rst

@@ -1,23 +0,0 @@
-.. _arch_access_logging:
-
-Access Logging
-==============
-
-Access logging in Arch refers to the logging of detailed information about each request and response that flows through Arch.
-It provides visibility into the traffic passing through Arch, which is crucial for monitoring, debugging, and analyzing the
-behavior of AI applications and their interactions.
-
-Key Features of Access Logging in Arch:
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-* **Per-Request Logging**:
-  Each request that passes through Arch is logged. This includes important metadata such as HTTP method,
-  path, response status code, request duration, upstream host, and more.
-* **Integration with Monitoring Tools**:
-  Access logs can be exported to centralized logging systems (e.g., ELK stack or Fluentd) or used to feed monitoring and alerting systems.
-* **Structured Logging**: where each request is logged as a object, making it easier to parse and analyze using tools like Elasticsearch and Kibana.
-
-.. code-block:: yaml
-
-    [2024-09-27T14:52:01.123Z] "ARCH REQUEST" GET /path/to/resource HTTP/1.1 200 512 1024 56 upstream_service.com D
-    X-Arch-Upstream-Service-Time: 25
-    X-Arch-Attempt-Count: 1

_sources/observability/observability.rst

@@ -1,11 +0,0 @@
-.. _observability:
-
-Observability
-=============
-
-.. toctree::
-  :maxdepth: 2
-
-  tracing
-  stats
-  access_logs

_sources/observability/stats.rst

@@ -1,9 +0,0 @@
-.. _monitoring:
-
-Monitoring
-==========
-
-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.

_sources/observability/tracing.rst

@@ -1,313 +0,0 @@
-.. _arch_overview_tracing:
-
-Tracing
-=======
-
-Overview
---------
-
-`OpenTelemetry <https://opentelemetry.io/>`_ is an open-source observability framework providing APIs
-and instrumentation for generating, collecting, processing, and exporting telemetry data, such as traces,
-metrics, and logs. Its flexible design supports a wide range of backends and seamlessly integrates with
-modern application tools. A key feature of OpenTelemetry is its commitment to standards like the
-`W3C Trace Context <https://www.w3.org/TR/trace-context/>`_
-
-**Tracing** is a critical tool that allows developers to visualize and understand the flow of
-requests in an AI application. With tracing, you can capture a detailed view of how requests propagate
-through various services and components, which is crucial for **debugging**, **performance optimization**,
-and understanding complex AI agent architectures like Co-pilots.
-
-**Arch** propagates trace context using the W3C Trace Context standard, specifically through the
-``traceparent`` header. This allows each component in the system to record its part of the request
-flow, enabling **end-to-end tracing** 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.
-______________________________________________________________________________________________
-
-Benefits of using ``traceparent`` headers
------------------------------------------
-
-- **Standardization**: The W3C Trace Context standard ensures compatibility across ecosystem tools, allowing
-  traces to be propagated uniformly through different layers of the system.
-- **Ease of Integration**: OpenTelemetry's design allows developers to easily integrate tracing with minimal
-  changes to their codebase, enabling quick adoption of end-to-end observability.
-- **Interoperability**: Works seamlessly with popular tracing tools like AWS X-Ray, Datadog, Jaeger, and many others,
-  making it easy to visualize traces in the tools you're already usi
-
-How to initiate a trace
------------------------
-
-1. **Enable Tracing Configuration**: Simply add the ``tracing: 100`` flag to in the :ref:`listener <arch_overview_listeners>` config
-
-2. **Trace Context Propagation**: Arch automatically propagates the ``traceparent`` header. When a request is received, Arch will:
-
-   - Generate a new ``traceparent`` header if one is not present.
-   - Extract the trace context from the ``traceparent`` header if it exists.
-   - Start a new span representing its processing of the request.
-   - Forward the ``traceparent`` header to downstream services.
-
-3. **Sampling Policy**: The 100 in ``tracing: 100`` means that all the requests as sampled for tracing.
-   You can adjust this value from 0-100.
-
-
-Trace Propagation
------------------
-
-Arch uses the W3C Trace Context standard for trace propagation, which relies on the ``traceparent`` header.
-This header carries tracing information in a standardized format, enabling interoperability between different
-tracing systems.
-
-Header Format
-~~~~~~~~~~~~~
-
-The ``traceparent`` header has the following format::
-
-   traceparent: {version}-{trace-id}-{parent-id}-{trace-flags}
-
-- {version}: The version of the Trace Context specification (e.g., ``00``).
-- {trace-id}: A 16-byte (32-character hexadecimal) unique identifier for the trace.
-- {parent-id}: An 8-byte (16-character hexadecimal) identifier for the parent span.
-- {trace-flags}: Flags indicating trace options (e.g., sampling).
-
-Instrumentation
-~~~~~~~~~~~~~~~
-
-To integrate AI tracing, your application needs to follow a few simple steps. The steps
-below are very common practice, and not unique to Arch, when you reading tracing headers and export
-`spans <https://docs.lightstep.com/docs/understand-distributed-tracing>`_ for distributed tracing.
-
-- Read the ``traceparent`` header from incoming requests.
-- Start new spans as children of the extracted context.
-- Include the ``traceparent`` header in outbound requests to propagate trace context.
-- Send tracing data to a collector or tracing backend to export spans
-
-Example with OpenTelemetry in Python
-************************************
-
-Install OpenTelemetry packages:
-
-.. code-block:: bash
-
-   pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp
-   pip install opentelemetry-instrumentation-requests
-
-Set up the tracer and exporter:
-
-.. code-block:: python
-
-   from opentelemetry import trace
-   from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
-   from opentelemetry.instrumentation.requests import RequestsInstrumentor
-   from opentelemetry.sdk.resources import Resource
-   from opentelemetry.sdk.trace import TracerProvider
-   from opentelemetry.sdk.trace.export import BatchSpanProcessor
-
-   # Define the service name
-   resource = Resource(attributes={
-       "service.name": "customer-support-agent"
-   })
-
-   # Set up the tracer provider and exporter
-   tracer_provider = TracerProvider(resource=resource)
-   otlp_exporter = OTLPSpanExporter(endpoint="otel-collector:4317", insecure=True)
-   span_processor = BatchSpanProcessor(otlp_exporter)
-   tracer_provider.add_span_processor(span_processor)
-   trace.set_tracer_provider(tracer_provider)
-
-   # Instrument HTTP requests
-   RequestsInstrumentor().instrument()
-
-Handle incoming requests:
-
-.. code-block:: python
-
-   from opentelemetry import trace
-   from opentelemetry.propagate import extract, inject
-   import requests
-
-   def handle_request(request):
-       # Extract the trace context
-       context = extract(request.headers)
-       tracer = trace.get_tracer(__name__)
-
-       with tracer.start_as_current_span("process_customer_request", context=context):
-           # Example of processing a customer request
-           print("Processing customer request...")
-
-           # Prepare headers for outgoing request to payment service
-           headers = {}
-           inject(headers)
-
-           # Make outgoing request to external service (e.g., payment gateway)
-           response = requests.get("http://payment-service/api", headers=headers)
-
-           print(f"Payment service response: {response.content}")
-
-
-AI Agent Tracing Visualization Example
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The following is an example of tracing for an AI-powered customer support system.
-A customer interacts with AI agents, which forward their requests through different
-specialized services and external systems.
-
-::
-
-    +--------------------------+
-    |   Customer Interaction   |
-    +--------------------------+
-               |
-               v
-    +--------------------------+        +--------------------------+
-    |  Agent 1 (Main - Arch)   | ---->  | External Payment Service |
-    +--------------------------+        +--------------------------+
-               |                                  |
-               v                                  v
-    +--------------------------+        +--------------------------+
-    |  Agent 2 (Support - Arch)| ---->  |   Internal Tech Support  |
-    +--------------------------+        +--------------------------+
-               |                                  |
-               v                                  v
-    +--------------------------+        +--------------------------+
-    | Agent 3 (Orders- Arch)   | ---->  |   Inventory Management   |
-    +--------------------------+        +--------------------------+
-
-Trace Breakdown:
-****************
-
-- Customer Interaction:
-    - Span 1: Customer initiates a request via the AI-powered chatbot for billing support (e.g., asking for payment details).
-
-- AI Agent 1 (Main - Arch):
-    - Span 2: AI Agent 1 (Main) processes the request and identifies it as related to billing, forwarding the request
-      to an external payment service.
-    - Span 3: AI Agent 1 determines that additional technical support is needed for processing and forwards the request
-      to AI Agent 2.
-
-- External Payment Service:
-    - Span 4: The external payment service processes the payment-related request (e.g., verifying payment status) and sends
-      the response back to AI Agent 1.
-
-- AI Agent 2 (Tech - Arch):
-    - Span 5: AI Agent 2, responsible for technical queries, processes a request forwarded from AI Agent 1 (e.g., checking for
-      any account issues).
-    - Span 6: AI Agent 2 forwards the query to Internal Tech Support for further investigation.
-
-- Internal Tech Support:
-    - Span 7: Internal Tech Support processes the request (e.g., resolving account access issues) and responds to AI Agent 2.
-
-- AI Agent 3 (Orders - Arch):
-    - Span 8: AI Agent 3 handles order-related queries. AI Agent 1 forwards the request to AI Agent 3 after payment verification
-      is completed.
-    - Span 9: AI Agent 3 forwards a request to the Inventory Management system to confirm product availability for a pending order.
-
-- Inventory Management:
-    - Span 10: The Inventory Management system checks stock and availability and returns the information to AI Agent 3.
-
-Integrating with Tracing Tools
-------------------------------
-
-AWS X-Ray
-~~~~~~~~~
-
-To send tracing data to `AWS X-Ray <https://aws.amazon.com/xray/>`_ :
-
-1. **Configure OpenTelemetry Collector**: Set up the collector to export traces to AWS X-Ray.
-
-   Collector configuration (``otel-collector-config.yaml``):
-
-   .. code-block:: yaml
-
-      receivers:
-        otlp:
-          protocols:
-            grpc:
-
-      processors:
-        batch:
-
-      exporters:
-        awsxray:
-          region: your-aws-region
-
-      service:
-        pipelines:
-          traces:
-            receivers: [otlp]
-            processors: [batch]
-            exporters: [awsxray]
-
-2. **Deploy the Collector**: Run the collector as a Docker container, Kubernetes pod, or standalone service.
-3. **Ensure AWS Credentials**: Provide AWS credentials to the collector, preferably via IAM roles.
-4. **Verify Traces**: Access the AWS X-Ray console to view your traces.
-
-Datadog
-~~~~~~~
-
-Datadog
-
-To send tracing data to `Datadog <https://docs.datadoghq.com/getting_started/tracing/>`_:
-
-1. **Configure OpenTelemetry Collector**: Set up the collector to export traces to Datadog.
-
-   Collector configuration (``otel-collector-config.yaml``):
-
-   .. code-block:: yaml
-
-      receivers:
-        otlp:
-          protocols:
-            grpc:
-
-      processors:
-        batch:
-
-      exporters:
-        datadog:
-          api:
-            key: "${DD_API_KEY}"
-          site: "${DD_SITE}"
-
-      service:
-        pipelines:
-          traces:
-            receivers: [otlp]
-            processors: [batch]
-            exporters: [datadog]
-
-2. **Set Environment Variables**: Provide your Datadog API key and site.
-
-   .. code-block:: bash
-
-      export DD_API_KEY=your_datadog_api_key
-      export DD_SITE=datadoghq.com  # Or datadoghq.eu
-
-3. **Deploy the Collector**: Run the collector in your environment.
-4. **Verify Traces**: Access the Datadog APM dashboard to view your traces.
-
-
-Best Practices
---------------
-
-- **Consistent Instrumentation**: Ensure all services propagate the ``traceparent`` header.
-- **Secure Configuration**: Protect sensitive data and secure communication between services.
-- **Performance Monitoring**: Be mindful of the performance impact and adjust sampling rates accordingly.
-- **Error Handling**: Implement proper error handling to prevent tracing issues from affecting your application.
-
-Conclusion
-----------
-
-By leveraging the ``traceparent`` header for trace context propagation, Arch enables developers to implement
-tracing efficiently. This approach simplifies the process of collecting and analyzing tracing data in common
-tools like AWS X-Ray and Datadog, enhancing observability and facilitating faster debugging and optimization.
-
-Additional Resources
---------------------
-
-- **OpenTelemetry Documentation**: https://opentelemetry.io/docs/
-- **W3C Trace Context Specification**: https://www.w3.org/TR/trace-context/
-- **AWS X-Ray Exporter**: https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/awsxrayexporter
-- **Datadog Exporter**: https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/datadogexporter
-
-.. Note::
-   Replace placeholders like ``your-aws-region``, and ``DD_API_KEY`` with your actual configurations.

_sources/root.rst

@@ -1,22 +0,0 @@
-Documentation
-=============
-
-.. image:: /_static/img/arch-logo.png
-   :width: 100%
-   :align: center
-
-**Arch is built on (and by the core contributors of) Envoy proxy with the belief that:**
-
-  *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.*
-
-.. toctree::
-  :maxdepth: 1
-
-  intro/intro
-  getting_started/getting_started
-  getting_started/use_cases
-  observability/observability
-  llms/llms
-  configuration_reference

_sphinx_design_static/design-tabs.js

@@ -0,0 +1,101 @@
+// @ts-check
+
+// Extra JS capability for selected tabs to be synced
+// The selection is stored in local storage so that it persists across page loads.
+
+/**
+ * @type {Record<string, HTMLElement[]>}
+ */
+let sd_id_to_elements = {};
+const storageKeyPrefix = "sphinx-design-tab-id-";
+
+/**
+ * Create a key for a tab element.
+ * @param {HTMLElement} el - The tab element.
+ * @returns {[string, string, string] | null} - The key.
+ *
+ */
+function create_key(el) {
+  let syncId = el.getAttribute("data-sync-id");
+  let syncGroup = el.getAttribute("data-sync-group");
+  if (!syncId || !syncGroup) return null;
+  return [syncGroup, syncId, syncGroup + "--" + syncId];
+}
+
+/**
+ * Initialize the tab selection.
+ *
+ */
+function ready() {
+  // Find all tabs with sync data
+
+  /** @type {string[]} */
+  let groups = [];
+
+  document.querySelectorAll(".sd-tab-label").forEach((label) => {
+    if (label instanceof HTMLElement) {
+      let data = create_key(label);
+      if (data) {
+        let [group, id, key] = data;
+
+        // add click event listener
+        // @ts-ignore
+        label.onclick = onSDLabelClick;
+
+        // store map of key to elements
+        if (!sd_id_to_elements[key]) {
+          sd_id_to_elements[key] = [];
+        }
+        sd_id_to_elements[key].push(label);
+
+        if (groups.indexOf(group) === -1) {
+          groups.push(group);
+          // Check if a specific tab has been selected via URL parameter
+          const tabParam = new URLSearchParams(window.location.search).get(
+            group
+          );
+          if (tabParam) {
+            console.log(
+              "sphinx-design: Selecting tab id for group '" +
+                group +
+                "' from URL parameter: " +
+                tabParam
+            );
+            window.sessionStorage.setItem(storageKeyPrefix + group, tabParam);
+          }
+        }
+
+        // Check is a specific tab has been selected previously
+        let previousId = window.sessionStorage.getItem(
+          storageKeyPrefix + group
+        );
+        if (previousId === id) {
+          // console.log(
+          //   "sphinx-design: Selecting tab from session storage: " + id
+          // );
+          // @ts-ignore
+          label.previousElementSibling.checked = true;
+        }
+      }
+    }
+  });
+}
+
+/**
+ *  Activate other tabs with the same sync id.
+ *
+ * @this {HTMLElement} - The element that was clicked.
+ */
+function onSDLabelClick() {
+  let data = create_key(this);
+  if (!data) return;
+  let [group, id, key] = data;
+  for (const label of sd_id_to_elements[key]) {
+    if (label === this) continue;
+    // @ts-ignore
+    label.previousElementSibling.checked = true;
+  }
+  window.sessionStorage.setItem(storageKeyPrefix + group, id);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);

_static/awesome-docsearch.css

@@ -0,0 +1 @@
+:root{--docsearch-primary-color:hsl(var(--primary));--docsearch-muted-color:hsl(var(--muted-foreground));--docsearch-key-gradient:transparent;--docsearch-key-shadow:transparent;--docsearch-text-color:hsl(var(--popover-foreground));--docsearch-modal-width:760px;--docsearch-modal-background:hsl(var(--popover));--docsearch-footer-background:hsl(var(--popover));--docsearch-searchbox-focus-background:hsl(var(--popover));--docsearch-container-background:hsl(var(--background)/0.8);--docsearch-spacing:0.5rem;--docsearch-hit-active-color:hsl(var(--accent-foreground));--docsearch-hit-background:transparent;--docsearch-searchbox-shadow:none;--docsearch-hit-shadow:none;--docsearch-modal-shadow:none;--docsearch-footer-shadow:none}.DocSearch-Button{background-color:transparent;border-color:hsl(var(--input));border-radius:.5em;border-style:solid;border-width:1px;display:flex;font-size:.875rem;line-height:1.25rem;width:90%;--tw-ring-offset-color:hsl(var(--background));transition-duration:.15s;transition-property:color,background-color,border-color,text-decoration-color,fill,stroke;transition-timing-function:cubic-bezier(.4,0,.2,1)}.DocSearch-Button:hover{--tw-shadow:0 0 transparent;--tw-shadow-colored:0 0 transparent;box-shadow:0 0 transparent,0 0 transparent,0 0 transparent;box-shadow:var(--tw-ring-offset-shadow,0 0 transparent),var(--tw-ring-shadow,0 0 transparent),var(--tw-shadow)}.DocSearch-Button:focus,.DocSearch-Button:hover{background-color:hsl(var(--accent));color:hsl(var(--accent-foreground))}.DocSearch-Button:focus-visible{outline:2px solid transparent;outline-offset:2px;--tw-ring-offset-shadow:var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);--tw-ring-shadow:var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color);box-shadow:var(--tw-ring-offset-shadow),var(--tw-ring-shadow),0 0 transparent;box-shadow:var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent);--tw-ring-color:hsl(var(--ring));--tw-ring-offset-width:2px}.DocSearch-Button-Placeholder{display:block;font-size:.875rem;font-weight:500;line-height:1.25rem}.DocSearch-Button-Key{background-color:hsl(var(--muted));border-color:hsl(var(--border));border-radius:.25rem;border-style:solid;border-width:1px;color:hsl(var(--muted-foreground));font-size:12px}.DocSearch-Container{position:fixed;--tw-backdrop-blur:blur(4px);-webkit-backdrop-filter:blur(4px) var(--tw-backdrop-brightness) var(--tw-backdrop-contrast) var(--tw-backdrop-grayscale) var(--tw-backdrop-hue-rotate) var(--tw-backdrop-invert) var(--tw-backdrop-opacity) var(--tw-backdrop-saturate) var(--tw-backdrop-sepia);-webkit-backdrop-filter:var(--tw-backdrop-blur) var(--tw-backdrop-brightness) var(--tw-backdrop-contrast) var(--tw-backdrop-grayscale) var(--tw-backdrop-hue-rotate) var(--tw-backdrop-invert) var(--tw-backdrop-opacity) var(--tw-backdrop-saturate) var(--tw-backdrop-sepia);backdrop-filter:blur(4px) var(--tw-backdrop-brightness) var(--tw-backdrop-contrast) var(--tw-backdrop-grayscale) var(--tw-backdrop-hue-rotate) var(--tw-backdrop-invert) var(--tw-backdrop-opacity) var(--tw-backdrop-saturate) var(--tw-backdrop-sepia);backdrop-filter:var(--tw-backdrop-blur) var(--tw-backdrop-brightness) var(--tw-backdrop-contrast) var(--tw-backdrop-grayscale) var(--tw-backdrop-hue-rotate) var(--tw-backdrop-invert) var(--tw-backdrop-opacity) var(--tw-backdrop-saturate) var(--tw-backdrop-sepia)}.DocSearch-Modal{border-color:hsl(var(--border));border-radius:var(--radius);border-width:1px}.DocSearch-SearchBar{border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-width:1px;border-color:hsl(var(--input));border-top-left-radius:var(--radius);border-top-right-radius:var(--radius);padding:0}.DocSearch-Form{border-bottom-left-radius:0;border-bottom-right-radius:0;border-top-left-radius:var(--radius);border-top-right-radius:var(--radius)}.DocSearch-Cancel{color:hsl(var(--muted-foreground));font-size:.875rem;line-height:1.25rem;padding-left:.5rem;padding-right:.5rem}.DocSearch-MagnifierLabel,.DocSearch-Search-Icon{stroke-width:2;opacity:.5}.DocSearch-Hit-source{color:hsl(var(--muted-foreground))}.DocSearch-Hit,.DocSearch-Hit a{border-radius:calc(var(--radius) - 4px)}.DocSearch-Hit a:focus-visible{outline-offset:-2px}.DocSearch-Hit[aria-selected=true] a{background-color:hsl(var(--accent));color:hsl(var(--accent-foreground))}.DocSearch-Commands{display:none}.DocSearch-Footer{border-color:hsl(var(--border));border-top-width:1px}

_static/awesome-sphinx-design.css

@@ -0,0 +1 @@
+:root{--sd-color-tabs-label-active:hsl(var(--foreground));--sd-color-tabs-underline-active:hsl(var(--accent-foreground));--sd-color-tabs-label-hover:hsl(var(--accent-foreground));--sd-color-tabs-overline:hsl(var(--border));--sd-color-tabs-underline:hsl(var(--border))}.sd-card{background-color:hsl(var(--card));border-color:hsl(var(--border));border-radius:var(--radius);border-width:1px;color:hsl(var(--card-foreground));margin-top:1.5rem}.sd-container-fluid{margin-bottom:1.5rem;margin-top:1.5rem}.sd-card-title{font-weight:600!important}.sd-summary-title{color:hsl(var(--muted-foreground));font-weight:500!important}.sd-card-footer,.sd-card-header{font-size:.875rem;line-height:1.25rem}.sd-tab-set{margin-top:1.5rem}.sd-tab-content>p{margin-bottom:1.5rem}.sd-tab-content pre:first-of-type{margin-top:0}.sd-tab-set>label{font-weight:500;letter-spacing:.05em}details.sd-dropdown,details.sd-dropdown:not([open])>.sd-card-header{border-color:hsl(var(--border))}details.sd-dropdown summary:focus{outline-style:solid}.sd-cards-carousel{overflow-x:auto}.sd-shadow-sm{--tw-shadow:0 0 transparent!important;--tw-shadow-colored:0 0 transparent!important;box-shadow:0 0 transparent,0 0 transparent,0 0 transparent!important;box-shadow:var(--tw-ring-offset-shadow,0 0 transparent),var(--tw-ring-shadow,0 0 transparent),var(--tw-shadow)!important}

_static/basic.css

@@ -55,7 +55,7 @@ div.sphinxsidebarwrapper {
 
 div.sphinxsidebar {
     float: left;
-    width: 270px;
+    width: 230px;
     margin-left: -100%;
     font-size: 90%;
     word-wrap: break-word;

_static/check-solid.svg

@@ -1,4 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <path d="M5 12l5 5l10 -10" />
-</svg>

_static/copy-button.svg

@@ -1,5 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <rect x="8" y="8" width="12" height="12" rx="2" />
-  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
-</svg>

_static/copybutton.css

@@ -1,94 +0,0 @@
-/* Copy buttons */
-button.copybtn {
-    position: absolute;
-    display: flex;
-    top: .3em;
-    right: .3em;
-    width: 1.7em;
-    height: 1.7em;
-	opacity: 0;
-    transition: opacity 0.3s, border .3s, background-color .3s;
-    user-select: none;
-    padding: 0;
-    border: none;
-    outline: none;
-    border-radius: 0.4em;
-    /* The colors that GitHub uses */
-    border: #1b1f2426 1px solid;
-    background-color: #f6f8fa;
-    color: #57606a;
-}
-
-button.copybtn.success {
-    border-color: #22863a;
-    color: #22863a;
-}
-
-button.copybtn svg {
-    stroke: currentColor;
-    width: 1.5em;
-    height: 1.5em;
-    padding: 0.1em;
-}
-
-div.highlight  {
-    position: relative;
-}
-
-/* Show the copybutton */
-.highlight:hover button.copybtn, button.copybtn.success {
-	opacity: 1;
-}
-
-.highlight button.copybtn:hover {
-    background-color: rgb(235, 235, 235);
-}
-
-.highlight button.copybtn:active {
-    background-color: rgb(187, 187, 187);
-}
-
-/**
- * A minimal CSS-only tooltip copied from:
- *   https://codepen.io/mildrenben/pen/rVBrpK
- *
- * To use, write HTML like the following:
- *
- * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
- */
- .o-tooltip--left {
-  position: relative;
- }
-
- .o-tooltip--left:after {
-    opacity: 0;
-    visibility: hidden;
-    position: absolute;
-    content: attr(data-tooltip);
-    padding: .2em;
-    font-size: .8em;
-    left: -.2em;
-    background: grey;
-    color: white;
-    white-space: nowrap;
-    z-index: 2;
-    border-radius: 2px;
-    transform: translateX(-102%) translateY(0);
-    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
-}
-
-.o-tooltip--left:hover:after {
-    display: block;
-    opacity: 1;
-    visibility: visible;
-    transform: translateX(-100%) translateY(0);
-    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
-    transition-delay: .5s;
-}
-
-/* By default the copy button shouldn't show up when printing a page */
-@media print {
-    button.copybtn {
-        display: none;
-    }
-}

_static/copybutton.js

@@ -1,248 +0,0 @@
-// Localization support
-const messages = {
-  'en': {
-    'copy': 'Copy',
-    'copy_to_clipboard': 'Copy to clipboard',
-    'copy_success': 'Copied!',
-    'copy_failure': 'Failed to copy',
-  },
-  'es' : {
-    'copy': 'Copiar',
-    'copy_to_clipboard': 'Copiar al portapapeles',
-    'copy_success': '¡Copiado!',
-    'copy_failure': 'Error al copiar',
-  },
-  'de' : {
-    'copy': 'Kopieren',
-    'copy_to_clipboard': 'In die Zwischenablage kopieren',
-    'copy_success': 'Kopiert!',
-    'copy_failure': 'Fehler beim Kopieren',
-  },
-  'fr' : {
-    'copy': 'Copier',
-    'copy_to_clipboard': 'Copier dans le presse-papier',
-    'copy_success': 'Copié !',
-    'copy_failure': 'Échec de la copie',
-  },
-  'ru': {
-    'copy': 'Скопировать',
-    'copy_to_clipboard': 'Скопировать в буфер',
-    'copy_success': 'Скопировано!',
-    'copy_failure': 'Не удалось скопировать',
-  },
-  'zh-CN': {
-    'copy': '复制',
-    'copy_to_clipboard': '复制到剪贴板',
-    'copy_success': '复制成功!',
-    'copy_failure': '复制失败',
-  },
-  'it' : {
-    'copy': 'Copiare',
-    'copy_to_clipboard': 'Copiato negli appunti',
-    'copy_success': 'Copiato!',
-    'copy_failure': 'Errore durante la copia',
-  }
-}
-
-let locale = 'en'
-if( document.documentElement.lang !== undefined
-    && messages[document.documentElement.lang] !== undefined ) {
-  locale = document.documentElement.lang
-}
-
-let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
-if (doc_url_root == '#') {
-    doc_url_root = '';
-}
-
-/**
- * SVG files for our copy buttons
- */
-let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
-  <title>${messages[locale]['copy_success']}</title>
-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <path d="M5 12l5 5l10 -10" />
-</svg>`
-
-// If the user specified their own SVG use that, otherwise use the default
-let iconCopy = ``;
-if (!iconCopy) {
-  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
-  <title>${messages[locale]['copy_to_clipboard']}</title>
-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <rect x="8" y="8" width="12" height="12" rx="2" />
-  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
-</svg>`
-}
-
-/**
- * Set up copy/paste for code blocks
- */
-
-const runWhenDOMLoaded = cb => {
-  if (document.readyState != 'loading') {
-    cb()
-  } else if (document.addEventListener) {
-    document.addEventListener('DOMContentLoaded', cb)
-  } else {
-    document.attachEvent('onreadystatechange', function() {
-      if (document.readyState == 'complete') cb()
-    })
-  }
-}
-
-const codeCellId = index => `codecell${index}`
-
-// Clears selected text since ClipboardJS will select the text when copying
-const clearSelection = () => {
-  if (window.getSelection) {
-    window.getSelection().removeAllRanges()
-  } else if (document.selection) {
-    document.selection.empty()
-  }
-}
-
-// Changes tooltip text for a moment, then changes it back
-// We want the timeout of our `success` class to be a bit shorter than the
-// tooltip and icon change, so that we can hide the icon before changing back.
-var timeoutIcon = 2000;
-var timeoutSuccessClass = 1500;
-
-const temporarilyChangeTooltip = (el, oldText, newText) => {
-  el.setAttribute('data-tooltip', newText)
-  el.classList.add('success')
-  // Remove success a little bit sooner than we change the tooltip
-  // So that we can use CSS to hide the copybutton first
-  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
-  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
-}
-
-// Changes the copy button icon for two seconds, then changes it back
-const temporarilyChangeIcon = (el) => {
-  el.innerHTML = iconCheck;
-  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
-}
-
-const addCopyButtonToCodeCells = () => {
-  // If ClipboardJS hasn't loaded, wait a bit and try again. This
-  // happens because we load ClipboardJS asynchronously.
-  if (window.ClipboardJS === undefined) {
-    setTimeout(addCopyButtonToCodeCells, 250)
-    return
-  }
-
-  // Add copybuttons to all of our code cells
-  const COPYBUTTON_SELECTOR = 'div.highlight pre';
-  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
-  codeCells.forEach((codeCell, index) => {
-    const id = codeCellId(index)
-    codeCell.setAttribute('id', id)
-
-    const clipboardButton = id =>
-    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
-      ${iconCopy}
-    </button>`
-    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
-  })
-
-function escapeRegExp(string) {
-    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-    var regexp;
-    var match;
-
-    // Do we check for line continuation characters and "HERE-documents"?
-    var useLineCont = !!lineContinuationChar
-    var useHereDoc = !!hereDocDelim
-
-    // create regexp to capture prompt and remaining line
-    if (isRegexp) {
-        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
-    } else {
-        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
-    }
-
-    const outputLines = [];
-    var promptFound = false;
-    var gotLineCont = false;
-    var gotHereDoc = false;
-    const lineGotPrompt = [];
-    for (const line of textContent.split('\n')) {
-        match = line.match(regexp)
-        if (match || gotLineCont || gotHereDoc) {
-            promptFound = regexp.test(line)
-            lineGotPrompt.push(promptFound)
-            if (removePrompts && promptFound) {
-                outputLines.push(match[2])
-            } else {
-                outputLines.push(line)
-            }
-            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
-            if (line.includes(hereDocDelim) & useHereDoc)
-                gotHereDoc = !gotHereDoc
-        } else if (!onlyCopyPromptLines) {
-            outputLines.push(line)
-        } else if (copyEmptyLines && line.trim() === '') {
-            outputLines.push(line)
-        }
-    }
-
-    // If no lines with the prompt were found then just use original lines
-    if (lineGotPrompt.some(v => v === true)) {
-        textContent = outputLines.join('\n');
-    }
-
-    // Remove a trailing newline to avoid auto-running when pasting
-    if (textContent.endsWith("\n")) {
-        textContent = textContent.slice(0, -1)
-    }
-    return textContent
-}
-
-
-var copyTargetText = (trigger) => {
-  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
-
-  // get filtered text
-  let exclude = '.linenos';
-
-  let text = filterText(target, exclude);
-  return formatCopyText(text, '', false, true, true, true, '', '')
-}
-
-  // Initialize with a callback so we can modify the text before copy
-  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
-
-  // Update UI with error/success messages
-  clipboard.on('success', event => {
-    clearSelection()
-    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
-    temporarilyChangeIcon(event.trigger)
-  })
-
-  clipboard.on('error', event => {
-    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
-  })
-}
-
-runWhenDOMLoaded(addCopyButtonToCodeCells)

_static/copybutton_funcs.js

@@ -1,73 +0,0 @@
-function escapeRegExp(string) {
-    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-export function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-    var regexp;
-    var match;
-
-    // Do we check for line continuation characters and "HERE-documents"?
-    var useLineCont = !!lineContinuationChar
-    var useHereDoc = !!hereDocDelim
-
-    // create regexp to capture prompt and remaining line
-    if (isRegexp) {
-        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
-    } else {
-        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
-    }
-
-    const outputLines = [];
-    var promptFound = false;
-    var gotLineCont = false;
-    var gotHereDoc = false;
-    const lineGotPrompt = [];
-    for (const line of textContent.split('\n')) {
-        match = line.match(regexp)
-        if (match || gotLineCont || gotHereDoc) {
-            promptFound = regexp.test(line)
-            lineGotPrompt.push(promptFound)
-            if (removePrompts && promptFound) {
-                outputLines.push(match[2])
-            } else {
-                outputLines.push(line)
-            }
-            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
-            if (line.includes(hereDocDelim) & useHereDoc)
-                gotHereDoc = !gotHereDoc
-        } else if (!onlyCopyPromptLines) {
-            outputLines.push(line)
-        } else if (copyEmptyLines && line.trim() === '') {
-            outputLines.push(line)
-        }
-    }
-
-    // If no lines with the prompt were found then just use original lines
-    if (lineGotPrompt.some(v => v === true)) {
-        textContent = outputLines.join('\n');
-    }
-
-    // Remove a trailing newline to avoid auto-running when pasting
-    if (textContent.endsWith("\n")) {
-        textContent = textContent.slice(0, -1)
-    }
-    return textContent
-}

_static/css/arch.css

@@ -1,5 +0,0 @@
-@import url("theme.css");
-
-body {
-  font-size: 1em;
-}

_static/design-tabs.js

@@ -0,0 +1,101 @@
+// @ts-check
+
+// Extra JS capability for selected tabs to be synced
+// The selection is stored in local storage so that it persists across page loads.
+
+/**
+ * @type {Record<string, HTMLElement[]>}
+ */
+let sd_id_to_elements = {};
+const storageKeyPrefix = "sphinx-design-tab-id-";
+
+/**
+ * Create a key for a tab element.
+ * @param {HTMLElement} el - The tab element.
+ * @returns {[string, string, string] | null} - The key.
+ *
+ */
+function create_key(el) {
+  let syncId = el.getAttribute("data-sync-id");
+  let syncGroup = el.getAttribute("data-sync-group");
+  if (!syncId || !syncGroup) return null;
+  return [syncGroup, syncId, syncGroup + "--" + syncId];
+}
+
+/**
+ * Initialize the tab selection.
+ *
+ */
+function ready() {
+  // Find all tabs with sync data
+
+  /** @type {string[]} */
+  let groups = [];
+
+  document.querySelectorAll(".sd-tab-label").forEach((label) => {
+    if (label instanceof HTMLElement) {
+      let data = create_key(label);
+      if (data) {
+        let [group, id, key] = data;
+
+        // add click event listener
+        // @ts-ignore
+        label.onclick = onSDLabelClick;
+
+        // store map of key to elements
+        if (!sd_id_to_elements[key]) {
+          sd_id_to_elements[key] = [];
+        }
+        sd_id_to_elements[key].push(label);
+
+        if (groups.indexOf(group) === -1) {
+          groups.push(group);
+          // Check if a specific tab has been selected via URL parameter
+          const tabParam = new URLSearchParams(window.location.search).get(
+            group
+          );
+          if (tabParam) {
+            console.log(
+              "sphinx-design: Selecting tab id for group '" +
+                group +
+                "' from URL parameter: " +
+                tabParam
+            );
+            window.sessionStorage.setItem(storageKeyPrefix + group, tabParam);
+          }
+        }
+
+        // Check is a specific tab has been selected previously
+        let previousId = window.sessionStorage.getItem(
+          storageKeyPrefix + group
+        );
+        if (previousId === id) {
+          // console.log(
+          //   "sphinx-design: Selecting tab from session storage: " + id
+          // );
+          // @ts-ignore
+          label.previousElementSibling.checked = true;
+        }
+      }
+    }
+  });
+}
+
+/**
+ *  Activate other tabs with the same sync id.
+ *
+ * @this {HTMLElement} - The element that was clicked.
+ */
+function onSDLabelClick() {
+  let data = create_key(this);
+  if (!data) return;
+  let [group, id, key] = data;
+  for (const label of sd_id_to_elements[key]) {
+    if (label === this) continue;
+    // @ts-ignore
+    label.previousElementSibling.checked = true;
+  }
+  window.sessionStorage.setItem(storageKeyPrefix + group, id);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);

_static/documentation_options.js

@@ -1,12 +1,12 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: '0.1-beta',
+    VERSION: ' v0.1',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
     FILE_SUFFIX: '.html',
     LINK_SUFFIX: '.html',
-    HAS_SOURCE: true,
-    SOURCELINK_SUFFIX: '',
+    HAS_SOURCE: false,
+    SOURCELINK_SUFFIX: '.txt',
     NAVIGATION_WITH_KEYS: false,
     SHOW_SEARCH_SUMMARY: true,
     ENABLE_SEARCH_SHORTCUTS: true,

_static/images/logo_binder.svg

@@ -1,19 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
-<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
-	 viewBox="0 0 44.4 44.4" style="enable-background:new 0 0 44.4 44.4;" xml:space="preserve">
-<style type="text/css">
-	.st0{fill:none;stroke:#F5A252;stroke-width:5;stroke-miterlimit:10;}
-	.st1{fill:none;stroke:#579ACA;stroke-width:5;stroke-miterlimit:10;}
-	.st2{fill:none;stroke:#E66581;stroke-width:5;stroke-miterlimit:10;}
-</style>
-<title>logo</title>
-<g>
-	<path class="st0" d="M33.9,6.4c3.6,3.9,3.4,9.9-0.5,13.5s-9.9,3.4-13.5-0.5s-3.4-9.9,0.5-13.5l0,0C24.2,2.4,30.2,2.6,33.9,6.4z"/>
-	<path class="st1" d="M35.1,27.3c2.6,4.6,1.1,10.4-3.5,13c-4.6,2.6-10.4,1.1-13-3.5s-1.1-10.4,3.5-13l0,0
-		C26.6,21.2,32.4,22.7,35.1,27.3z"/>
-	<path class="st2" d="M25.9,17.8c2.6,4.6,1.1,10.4-3.5,13s-10.4,1.1-13-3.5s-1.1-10.4,3.5-13l0,0C17.5,11.7,23.3,13.2,25.9,17.8z"/>
-	<path class="st1" d="M19.2,26.4c3.1-4.3,9.1-5.2,13.3-2.1c1.1,0.8,2,1.8,2.7,3"/>
-	<path class="st0" d="M19.9,19.4c-3.6-3.9-3.4-9.9,0.5-13.5s9.9-3.4,13.5,0.5"/>
-</g>
-</svg>

_static/images/logo_deepnote.svg

@@ -1 +0,0 @@
-<svg viewBox="0 0 128 128" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M0 128h52.512l29.539-11.077-11.077-43.487-34.051 3.693L0 128Z" fill="#0076D4"/><path fill-rule="evenodd" clip-rule="evenodd" d="M52.513 128s16.6-8.759 19.673-24.277c3.072-15.517-12.091-26.594-35.263-26.594 0-.41 20.343-28.718 20.343-28.718l49.4 1.435L95.71 107.7l-20.452 15.978L52.513 128Z" fill="#002868"/><path fill-rule="evenodd" clip-rule="evenodd" d="M0 60.718 41.025.001s1.006.01 3.282 0c16.082-.068 81.23 3.12 81.23 60.368 0 65.352-73.025 67.631-73.025 67.631s30.495-5.839 30.495-34.816c0-28.978-27.541-32.466-45.264-32.466H0Z" fill="#00A9FF"/></svg>

_static/images/logo_jupyterhub.svg

@@ -1 +0,0 @@
-<svg id="Layer_1" data-name="Layer 1" xmlns="http://www.w3.org/2000/svg" width="38.73" height="50" viewBox="0 0 38.73 50"><defs><style>.cls-1{fill:#767677;}.cls-2{fill:#f37726;}.cls-3{fill:#9e9e9e;}.cls-4{fill:#616262;}.cls-5{font-size:17.07px;fill:#fff;font-family:Roboto-Regular, Roboto;}</style></defs><title>logo_jupyterhub</title><g id="Canvas"><path id="path7_fill" data-name="path7 fill" class="cls-1" d="M39.51,3.53a3,3,0,0,1-1.7,2.9A3,3,0,0,1,34.48,6a3,3,0,0,1-.82-3.26,3,3,0,0,1,1.05-1.41A3,3,0,0,1,37.52.86a2.88,2.88,0,0,1,1,.6,3,3,0,0,1,.7.93,3.18,3.18,0,0,1,.28,1.14Z" transform="translate(-1.87 -0.69)"/><path id="path8_fill" data-name="path8 fill" class="cls-2" d="M21.91,38.39c-8,0-15.06-2.87-18.7-7.12a19.93,19.93,0,0,0,37.39,0C37,35.52,30,38.39,21.91,38.39Z" transform="translate(-1.87 -0.69)"/><path id="path9_fill" data-name="path9 fill" class="cls-2" d="M21.91,10.78c8,0,15.05,2.87,18.69,7.12a19.93,19.93,0,0,0-37.39,0C6.85,13.64,13.86,10.78,21.91,10.78Z" transform="translate(-1.87 -0.69)"/><path id="path10_fill" data-name="path10 fill" class="cls-3" d="M10.88,46.66a3.86,3.86,0,0,1-.52,2.15,3.81,3.81,0,0,1-1.62,1.51,3.93,3.93,0,0,1-2.19.34,3.79,3.79,0,0,1-2-.94,3.73,3.73,0,0,1-1.14-1.9,3.79,3.79,0,0,1,.1-2.21,3.86,3.86,0,0,1,1.33-1.78,3.92,3.92,0,0,1,3.54-.53,3.85,3.85,0,0,1,2.14,1.93,3.74,3.74,0,0,1,.37,1.43Z" transform="translate(-1.87 -0.69)"/><path id="path11_fill" data-name="path11 fill" class="cls-4" d="M4.12,9.81A2.18,2.18,0,0,1,2.9,9.48a2.23,2.23,0,0,1-.84-1A2.26,2.26,0,0,1,1.9,7.26a2.13,2.13,0,0,1,.56-1.13,2.18,2.18,0,0,1,2.36-.56,2.13,2.13,0,0,1,1,.76,2.18,2.18,0,0,1,.42,1.2A2.22,2.22,0,0,1,4.12,9.81Z" transform="translate(-1.87 -0.69)"/></g><text class="cls-5" transform="translate(5.24 30.01)">Hub</text></svg>

_static/locales/ar/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ar\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "طباعة إلى PDF"
-
-msgid "Theme by the"
-msgstr "موضوع بواسطة"
-
-msgid "Download source file"
-msgstr "تنزيل ملف المصدر"
-
-msgid "open issue"
-msgstr "قضية مفتوحة"
-
-msgid "Contents"
-msgstr "محتويات"
-
-msgid "previous page"
-msgstr "الصفحة السابقة"
-
-msgid "Download notebook file"
-msgstr "تنزيل ملف دفتر الملاحظات"
-
-msgid "Copyright"
-msgstr "حقوق النشر"
-
-msgid "Download this page"
-msgstr "قم بتنزيل هذه الصفحة"
-
-msgid "Source repository"
-msgstr "مستودع المصدر"
-
-msgid "By"
-msgstr "بواسطة"
-
-msgid "repository"
-msgstr "مخزن"
-
-msgid "Last updated on"
-msgstr "آخر تحديث في"
-
-msgid "Toggle navigation"
-msgstr "تبديل التنقل"
-
-msgid "Sphinx Book Theme"
-msgstr "موضوع كتاب أبو الهول"
-
-msgid "suggest edit"
-msgstr "أقترح تحرير"
-
-msgid "Open an issue"
-msgstr "افتح قضية"
-
-msgid "Launch"
-msgstr "إطلاق"
-
-msgid "Fullscreen mode"
-msgstr "وضع ملء الشاشة"
-
-msgid "Edit this page"
-msgstr "قم بتحرير هذه الصفحة"
-
-msgid "By the"
-msgstr "بواسطة"
-
-msgid "next page"
-msgstr "الصفحة التالية"

_static/locales/bg/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: bg\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Печат в PDF"
-
-msgid "Theme by the"
-msgstr "Тема от"
-
-msgid "Download source file"
-msgstr "Изтеглете изходния файл"
-
-msgid "open issue"
-msgstr "отворен брой"
-
-msgid "Contents"
-msgstr "Съдържание"
-
-msgid "previous page"
-msgstr "предишна страница"
-
-msgid "Download notebook file"
-msgstr "Изтеглете файла на бележника"
-
-msgid "Copyright"
-msgstr "Авторско право"
-
-msgid "Download this page"
-msgstr "Изтеглете тази страница"
-
-msgid "Source repository"
-msgstr "Хранилище на източника"
-
-msgid "By"
-msgstr "От"
-
-msgid "repository"
-msgstr "хранилище"
-
-msgid "Last updated on"
-msgstr "Последна актуализация на"
-
-msgid "Toggle navigation"
-msgstr "Превключване на навигацията"
-
-msgid "Sphinx Book Theme"
-msgstr "Тема на книгата Sphinx"
-
-msgid "suggest edit"
-msgstr "предложи редактиране"
-
-msgid "Open an issue"
-msgstr "Отворете проблем"
-
-msgid "Launch"
-msgstr "Стартиране"
-
-msgid "Fullscreen mode"
-msgstr "Режим на цял екран"
-
-msgid "Edit this page"
-msgstr "Редактирайте тази страница"
-
-msgid "By the"
-msgstr "По"
-
-msgid "next page"
-msgstr "Следваща страница"

_static/locales/bn/LC_MESSAGES/booktheme.po

@@ -1,63 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: bn\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "পিডিএফ প্রিন্ট করুন"
-
-msgid "Theme by the"
-msgstr "থিম দ্বারা"
-
-msgid "Download source file"
-msgstr "উত্স ফাইল ডাউনলোড করুন"
-
-msgid "open issue"
-msgstr "খোলা সমস্যা"
-
-msgid "previous page"
-msgstr "আগের পৃষ্ঠা"
-
-msgid "Download notebook file"
-msgstr "নোটবুক ফাইল ডাউনলোড করুন"
-
-msgid "Copyright"
-msgstr "কপিরাইট"
-
-msgid "Download this page"
-msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন"
-
-msgid "Source repository"
-msgstr "উত্স সংগ্রহস্থল"
-
-msgid "By"
-msgstr "দ্বারা"
-
-msgid "Last updated on"
-msgstr "সর্বশেষ আপডেট"
-
-msgid "Toggle navigation"
-msgstr "নেভিগেশন টগল করুন"
-
-msgid "Sphinx Book Theme"
-msgstr "স্পিনিক্স বুক থিম"
-
-msgid "Open an issue"
-msgstr "একটি সমস্যা খুলুন"
-
-msgid "Launch"
-msgstr "শুরু করা"
-
-msgid "Edit this page"
-msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন"
-
-msgid "By the"
-msgstr "দ্বারা"
-
-msgid "next page"
-msgstr "পরবর্তী পৃষ্ঠা"

_static/locales/ca/LC_MESSAGES/booktheme.po

@@ -1,66 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ca\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Imprimeix a PDF"
-
-msgid "Theme by the"
-msgstr "Tema del"
-
-msgid "Download source file"
-msgstr "Baixeu el fitxer font"
-
-msgid "open issue"
-msgstr "número obert"
-
-msgid "previous page"
-msgstr "Pàgina anterior"
-
-msgid "Download notebook file"
-msgstr "Descarregar fitxer de quadern"
-
-msgid "Copyright"
-msgstr "Copyright"
-
-msgid "Download this page"
-msgstr "Descarregueu aquesta pàgina"
-
-msgid "Source repository"
-msgstr "Dipòsit de fonts"
-
-msgid "By"
-msgstr "Per"
-
-msgid "Last updated on"
-msgstr "Darrera actualització el"
-
-msgid "Toggle navigation"
-msgstr "Commuta la navegació"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema del llibre Esfinx"
-
-msgid "suggest edit"
-msgstr "suggerir edició"
-
-msgid "Open an issue"
-msgstr "Obriu un número"
-
-msgid "Launch"
-msgstr "Llançament"
-
-msgid "Edit this page"
-msgstr "Editeu aquesta pàgina"
-
-msgid "By the"
-msgstr "Per la"
-
-msgid "next page"
-msgstr "pàgina següent"

_static/locales/cs/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: cs\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Tisk do PDF"
-
-msgid "Theme by the"
-msgstr "Téma od"
-
-msgid "Download source file"
-msgstr "Stáhněte si zdrojový soubor"
-
-msgid "open issue"
-msgstr "otevřené číslo"
-
-msgid "Contents"
-msgstr "Obsah"
-
-msgid "previous page"
-msgstr "předchozí stránka"
-
-msgid "Download notebook file"
-msgstr "Stáhnout soubor poznámkového bloku"
-
-msgid "Copyright"
-msgstr "autorská práva"
-
-msgid "Download this page"
-msgstr "Stáhněte si tuto stránku"
-
-msgid "Source repository"
-msgstr "Zdrojové úložiště"
-
-msgid "By"
-msgstr "Podle"
-
-msgid "repository"
-msgstr "úložiště"
-
-msgid "Last updated on"
-msgstr "Naposledy aktualizováno"
-
-msgid "Toggle navigation"
-msgstr "Přepnout navigaci"
-
-msgid "Sphinx Book Theme"
-msgstr "Téma knihy Sfinga"
-
-msgid "suggest edit"
-msgstr "navrhnout úpravy"
-
-msgid "Open an issue"
-msgstr "Otevřete problém"
-
-msgid "Launch"
-msgstr "Zahájení"
-
-msgid "Fullscreen mode"
-msgstr "Režim celé obrazovky"
-
-msgid "Edit this page"
-msgstr "Upravit tuto stránku"
-
-msgid "By the"
-msgstr "Podle"
-
-msgid "next page"
-msgstr "další strana"

_static/locales/da/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: da\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Udskriv til PDF"
-
-msgid "Theme by the"
-msgstr "Tema af"
-
-msgid "Download source file"
-msgstr "Download kildefil"
-
-msgid "open issue"
-msgstr "åbent nummer"
-
-msgid "Contents"
-msgstr "Indhold"
-
-msgid "previous page"
-msgstr "forrige side"
-
-msgid "Download notebook file"
-msgstr "Download notesbog-fil"
-
-msgid "Copyright"
-msgstr "ophavsret"
-
-msgid "Download this page"
-msgstr "Download denne side"
-
-msgid "Source repository"
-msgstr "Kildelager"
-
-msgid "By"
-msgstr "Ved"
-
-msgid "repository"
-msgstr "lager"
-
-msgid "Last updated on"
-msgstr "Sidst opdateret den"
-
-msgid "Toggle navigation"
-msgstr "Skift navigation"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx bogtema"
-
-msgid "suggest edit"
-msgstr "foreslå redigering"
-
-msgid "Open an issue"
-msgstr "Åbn et problem"
-
-msgid "Launch"
-msgstr "Start"
-
-msgid "Fullscreen mode"
-msgstr "Fuldskærmstilstand"
-
-msgid "Edit this page"
-msgstr "Rediger denne side"
-
-msgid "By the"
-msgstr "Ved"
-
-msgid "next page"
-msgstr "Næste side"

_static/locales/de/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: de\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "In PDF drucken"
-
-msgid "Theme by the"
-msgstr "Thema von der"
-
-msgid "Download source file"
-msgstr "Quelldatei herunterladen"
-
-msgid "open issue"
-msgstr "offenes Thema"
-
-msgid "Contents"
-msgstr "Inhalt"
-
-msgid "previous page"
-msgstr "vorherige Seite"
-
-msgid "Download notebook file"
-msgstr "Notebook-Datei herunterladen"
-
-msgid "Copyright"
-msgstr "Urheberrechte ©"
-
-msgid "Download this page"
-msgstr "Laden Sie diese Seite herunter"
-
-msgid "Source repository"
-msgstr "Quell-Repository"
-
-msgid "By"
-msgstr "Durch"
-
-msgid "repository"
-msgstr "Repository"
-
-msgid "Last updated on"
-msgstr "Zuletzt aktualisiert am"
-
-msgid "Toggle navigation"
-msgstr "Navigation umschalten"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx-Buch-Thema"
-
-msgid "suggest edit"
-msgstr "vorschlagen zu bearbeiten"
-
-msgid "Open an issue"
-msgstr "Öffnen Sie ein Problem"
-
-msgid "Launch"
-msgstr "Starten"
-
-msgid "Fullscreen mode"
-msgstr "Vollbildmodus"
-
-msgid "Edit this page"
-msgstr "Bearbeite diese Seite"
-
-msgid "By the"
-msgstr "Bis zum"
-
-msgid "next page"
-msgstr "Nächste Seite"

_static/locales/el/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: el\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Εκτύπωση σε PDF"
-
-msgid "Theme by the"
-msgstr "Θέμα από το"
-
-msgid "Download source file"
-msgstr "Λήψη αρχείου προέλευσης"
-
-msgid "open issue"
-msgstr "ανοιχτό ζήτημα"
-
-msgid "Contents"
-msgstr "Περιεχόμενα"
-
-msgid "previous page"
-msgstr "προηγούμενη σελίδα"
-
-msgid "Download notebook file"
-msgstr "Λήψη αρχείου σημειωματάριου"
-
-msgid "Copyright"
-msgstr "Πνευματική ιδιοκτησία"
-
-msgid "Download this page"
-msgstr "Λήψη αυτής της σελίδας"
-
-msgid "Source repository"
-msgstr "Αποθήκη πηγής"
-
-msgid "By"
-msgstr "Με"
-
-msgid "repository"
-msgstr "αποθήκη"
-
-msgid "Last updated on"
-msgstr "Τελευταία ενημέρωση στις"
-
-msgid "Toggle navigation"
-msgstr "Εναλλαγή πλοήγησης"
-
-msgid "Sphinx Book Theme"
-msgstr "Θέμα βιβλίου Sphinx"
-
-msgid "suggest edit"
-msgstr "προτείνω επεξεργασία"
-
-msgid "Open an issue"
-msgstr "Ανοίξτε ένα ζήτημα"
-
-msgid "Launch"
-msgstr "Εκτόξευση"
-
-msgid "Fullscreen mode"
-msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ"
-
-msgid "Edit this page"
-msgstr "Επεξεργαστείτε αυτήν τη σελίδα"
-
-msgid "By the"
-msgstr "Από το"
-
-msgid "next page"
-msgstr "επόμενη σελίδα"

_static/locales/eo/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: eo\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Presi al PDF"
-
-msgid "Theme by the"
-msgstr "Temo de la"
-
-msgid "Download source file"
-msgstr "Elŝutu fontodosieron"
-
-msgid "open issue"
-msgstr "malferma numero"
-
-msgid "Contents"
-msgstr "Enhavo"
-
-msgid "previous page"
-msgstr "antaŭa paĝo"
-
-msgid "Download notebook file"
-msgstr "Elŝutu kajeran dosieron"
-
-msgid "Copyright"
-msgstr "Kopirajto"
-
-msgid "Download this page"
-msgstr "Elŝutu ĉi tiun paĝon"
-
-msgid "Source repository"
-msgstr "Fonto-deponejo"
-
-msgid "By"
-msgstr "De"
-
-msgid "repository"
-msgstr "deponejo"
-
-msgid "Last updated on"
-msgstr "Laste ĝisdatigita la"
-
-msgid "Toggle navigation"
-msgstr "Ŝalti navigadon"
-
-msgid "Sphinx Book Theme"
-msgstr "Sfinksa Libro-Temo"
-
-msgid "suggest edit"
-msgstr "sugesti redaktadon"
-
-msgid "Open an issue"
-msgstr "Malfermu numeron"
-
-msgid "Launch"
-msgstr "Lanĉo"
-
-msgid "Fullscreen mode"
-msgstr "Plenekrana reĝimo"
-
-msgid "Edit this page"
-msgstr "Redaktu ĉi tiun paĝon"
-
-msgid "By the"
-msgstr "Per la"
-
-msgid "next page"
-msgstr "sekva paĝo"

_static/locales/es/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: es\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Imprimir en PDF"
-
-msgid "Theme by the"
-msgstr "Tema por el"
-
-msgid "Download source file"
-msgstr "Descargar archivo fuente"
-
-msgid "open issue"
-msgstr "Tema abierto"
-
-msgid "Contents"
-msgstr "Contenido"
-
-msgid "previous page"
-msgstr "pagina anterior"
-
-msgid "Download notebook file"
-msgstr "Descargar archivo de cuaderno"
-
-msgid "Copyright"
-msgstr "Derechos de autor"
-
-msgid "Download this page"
-msgstr "Descarga esta pagina"
-
-msgid "Source repository"
-msgstr "Repositorio de origen"
-
-msgid "By"
-msgstr "Por"
-
-msgid "repository"
-msgstr "repositorio"
-
-msgid "Last updated on"
-msgstr "Ultima actualización en"
-
-msgid "Toggle navigation"
-msgstr "Navegación de palanca"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema del libro de la esfinge"
-
-msgid "suggest edit"
-msgstr "sugerir editar"
-
-msgid "Open an issue"
-msgstr "Abrir un problema"
-
-msgid "Launch"
-msgstr "Lanzamiento"
-
-msgid "Fullscreen mode"
-msgstr "Modo de pantalla completa"
-
-msgid "Edit this page"
-msgstr "Edita esta página"
-
-msgid "By the"
-msgstr "Por el"
-
-msgid "next page"
-msgstr "siguiente página"

_static/locales/et/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: et\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Prindi PDF-i"
-
-msgid "Theme by the"
-msgstr "Teema"
-
-msgid "Download source file"
-msgstr "Laadige alla lähtefail"
-
-msgid "open issue"
-msgstr "avatud küsimus"
-
-msgid "Contents"
-msgstr "Sisu"
-
-msgid "previous page"
-msgstr "eelmine leht"
-
-msgid "Download notebook file"
-msgstr "Laadige sülearvuti fail alla"
-
-msgid "Copyright"
-msgstr "Autoriõigus"
-
-msgid "Download this page"
-msgstr "Laadige see leht alla"
-
-msgid "Source repository"
-msgstr "Allikahoidla"
-
-msgid "By"
-msgstr "Kõrval"
-
-msgid "repository"
-msgstr "hoidla"
-
-msgid "Last updated on"
-msgstr "Viimati uuendatud"
-
-msgid "Toggle navigation"
-msgstr "Lülita navigeerimine sisse"
-
-msgid "Sphinx Book Theme"
-msgstr "Sfinksiraamatu teema"
-
-msgid "suggest edit"
-msgstr "soovita muuta"
-
-msgid "Open an issue"
-msgstr "Avage probleem"
-
-msgid "Launch"
-msgstr "Käivitage"
-
-msgid "Fullscreen mode"
-msgstr "Täisekraanirežiim"
-
-msgid "Edit this page"
-msgstr "Muutke seda lehte"
-
-msgid "By the"
-msgstr "Autor"
-
-msgid "next page"
-msgstr "järgmine leht"

_static/locales/fi/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: fi\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Tulosta PDF-tiedostoon"
-
-msgid "Theme by the"
-msgstr "Teeman tekijä"
-
-msgid "Download source file"
-msgstr "Lataa lähdetiedosto"
-
-msgid "open issue"
-msgstr "avoin ongelma"
-
-msgid "Contents"
-msgstr "Sisällys"
-
-msgid "previous page"
-msgstr "Edellinen sivu"
-
-msgid "Download notebook file"
-msgstr "Lataa muistikirjatiedosto"
-
-msgid "Copyright"
-msgstr "Tekijänoikeus"
-
-msgid "Download this page"
-msgstr "Lataa tämä sivu"
-
-msgid "Source repository"
-msgstr "Lähteen arkisto"
-
-msgid "By"
-msgstr "Tekijä"
-
-msgid "repository"
-msgstr "arkisto"
-
-msgid "Last updated on"
-msgstr "Viimeksi päivitetty"
-
-msgid "Toggle navigation"
-msgstr "Vaihda navigointia"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx-kirjan teema"
-
-msgid "suggest edit"
-msgstr "ehdottaa muokkausta"
-
-msgid "Open an issue"
-msgstr "Avaa ongelma"
-
-msgid "Launch"
-msgstr "Tuoda markkinoille"
-
-msgid "Fullscreen mode"
-msgstr "Koko näytön tila"
-
-msgid "Edit this page"
-msgstr "Muokkaa tätä sivua"
-
-msgid "By the"
-msgstr "Mukaan"
-
-msgid "next page"
-msgstr "seuraava sivu"

_static/locales/fr/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: fr\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Imprimer au format PDF"
-
-msgid "Theme by the"
-msgstr "Thème par le"
-
-msgid "Download source file"
-msgstr "Télécharger le fichier source"
-
-msgid "open issue"
-msgstr "signaler un problème"
-
-msgid "Contents"
-msgstr "Contenu"
-
-msgid "previous page"
-msgstr "page précédente"
-
-msgid "Download notebook file"
-msgstr "Télécharger le fichier notebook"
-
-msgid "Copyright"
-msgstr "droits d'auteur"
-
-msgid "Download this page"
-msgstr "Téléchargez cette page"
-
-msgid "Source repository"
-msgstr "Dépôt source"
-
-msgid "By"
-msgstr "Par"
-
-msgid "repository"
-msgstr "dépôt"
-
-msgid "Last updated on"
-msgstr "Dernière mise à jour le"
-
-msgid "Toggle navigation"
-msgstr "Basculer la navigation"
-
-msgid "Sphinx Book Theme"
-msgstr "Thème du livre Sphinx"
-
-msgid "suggest edit"
-msgstr "suggestion de modification"
-
-msgid "Open an issue"
-msgstr "Ouvrez un problème"
-
-msgid "Launch"
-msgstr "lancement"
-
-msgid "Fullscreen mode"
-msgstr "Mode plein écran"
-
-msgid "Edit this page"
-msgstr "Modifier cette page"
-
-msgid "By the"
-msgstr "Par le"
-
-msgid "next page"
-msgstr "page suivante"

_static/locales/hr/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: hr\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Ispis u PDF"
-
-msgid "Theme by the"
-msgstr "Tema autora"
-
-msgid "Download source file"
-msgstr "Preuzmi izvornu datoteku"
-
-msgid "open issue"
-msgstr "otvoreno izdanje"
-
-msgid "Contents"
-msgstr "Sadržaj"
-
-msgid "previous page"
-msgstr "Prethodna stranica"
-
-msgid "Download notebook file"
-msgstr "Preuzmi datoteku bilježnice"
-
-msgid "Copyright"
-msgstr "Autorska prava"
-
-msgid "Download this page"
-msgstr "Preuzmite ovu stranicu"
-
-msgid "Source repository"
-msgstr "Izvorno spremište"
-
-msgid "By"
-msgstr "Po"
-
-msgid "repository"
-msgstr "spremište"
-
-msgid "Last updated on"
-msgstr "Posljednje ažuriranje:"
-
-msgid "Toggle navigation"
-msgstr "Uključi / isključi navigaciju"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema knjige Sphinx"
-
-msgid "suggest edit"
-msgstr "predloži uređivanje"
-
-msgid "Open an issue"
-msgstr "Otvorite izdanje"
-
-msgid "Launch"
-msgstr "Pokrenite"
-
-msgid "Fullscreen mode"
-msgstr "Način preko cijelog zaslona"
-
-msgid "Edit this page"
-msgstr "Uredite ovu stranicu"
-
-msgid "By the"
-msgstr "Od strane"
-
-msgid "next page"
-msgstr "sljedeća stranica"

_static/locales/id/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: id\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Cetak ke PDF"
-
-msgid "Theme by the"
-msgstr "Tema oleh"
-
-msgid "Download source file"
-msgstr "Unduh file sumber"
-
-msgid "open issue"
-msgstr "masalah terbuka"
-
-msgid "Contents"
-msgstr "Isi"
-
-msgid "previous page"
-msgstr "halaman sebelumnya"
-
-msgid "Download notebook file"
-msgstr "Unduh file notebook"
-
-msgid "Copyright"
-msgstr "hak cipta"
-
-msgid "Download this page"
-msgstr "Unduh halaman ini"
-
-msgid "Source repository"
-msgstr "Repositori sumber"
-
-msgid "By"
-msgstr "Oleh"
-
-msgid "repository"
-msgstr "gudang"
-
-msgid "Last updated on"
-msgstr "Terakhir diperbarui saat"
-
-msgid "Toggle navigation"
-msgstr "Alihkan navigasi"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema Buku Sphinx"
-
-msgid "suggest edit"
-msgstr "menyarankan edit"
-
-msgid "Open an issue"
-msgstr "Buka masalah"
-
-msgid "Launch"
-msgstr "Meluncurkan"
-
-msgid "Fullscreen mode"
-msgstr "Mode layar penuh"
-
-msgid "Edit this page"
-msgstr "Edit halaman ini"
-
-msgid "By the"
-msgstr "Oleh"
-
-msgid "next page"
-msgstr "halaman selanjutnya"

_static/locales/it/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: it\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Stampa in PDF"
-
-msgid "Theme by the"
-msgstr "Tema di"
-
-msgid "Download source file"
-msgstr "Scarica il file sorgente"
-
-msgid "open issue"
-msgstr "questione aperta"
-
-msgid "Contents"
-msgstr "Contenuti"
-
-msgid "previous page"
-msgstr "pagina precedente"
-
-msgid "Download notebook file"
-msgstr "Scarica il file del taccuino"
-
-msgid "Copyright"
-msgstr "Diritto d'autore"
-
-msgid "Download this page"
-msgstr "Scarica questa pagina"
-
-msgid "Source repository"
-msgstr "Repository di origine"
-
-msgid "By"
-msgstr "Di"
-
-msgid "repository"
-msgstr "repository"
-
-msgid "Last updated on"
-msgstr "Ultimo aggiornamento il"
-
-msgid "Toggle navigation"
-msgstr "Attiva / disattiva la navigazione"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema del libro della Sfinge"
-
-msgid "suggest edit"
-msgstr "suggerisci modifica"
-
-msgid "Open an issue"
-msgstr "Apri un problema"
-
-msgid "Launch"
-msgstr "Lanciare"
-
-msgid "Fullscreen mode"
-msgstr "Modalità schermo intero"
-
-msgid "Edit this page"
-msgstr "Modifica questa pagina"
-
-msgid "By the"
-msgstr "Dal"
-
-msgid "next page"
-msgstr "pagina successiva"

_static/locales/iw/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: iw\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "הדפס לקובץ PDF"
-
-msgid "Theme by the"
-msgstr "נושא מאת"
-
-msgid "Download source file"
-msgstr "הורד את קובץ המקור"
-
-msgid "open issue"
-msgstr "בעיה פתוחה"
-
-msgid "Contents"
-msgstr "תוכן"
-
-msgid "previous page"
-msgstr "עמוד קודם"
-
-msgid "Download notebook file"
-msgstr "הורד קובץ מחברת"
-
-msgid "Copyright"
-msgstr "זכויות יוצרים"
-
-msgid "Download this page"
-msgstr "הורד דף זה"
-
-msgid "Source repository"
-msgstr "מאגר המקורות"
-
-msgid "By"
-msgstr "על ידי"
-
-msgid "repository"
-msgstr "מאגר"
-
-msgid "Last updated on"
-msgstr "עודכן לאחרונה ב"
-
-msgid "Toggle navigation"
-msgstr "החלף ניווט"
-
-msgid "Sphinx Book Theme"
-msgstr "נושא ספר ספינקס"
-
-msgid "suggest edit"
-msgstr "מציע לערוך"
-
-msgid "Open an issue"
-msgstr "פתח גיליון"
-
-msgid "Launch"
-msgstr "לְהַשִׁיק"
-
-msgid "Fullscreen mode"
-msgstr "מצב מסך מלא"
-
-msgid "Edit this page"
-msgstr "ערוך דף זה"
-
-msgid "By the"
-msgstr "דרך"
-
-msgid "next page"
-msgstr "עמוד הבא"

_static/locales/ja/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ja\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "PDFに印刷"
-
-msgid "Theme by the"
-msgstr "のテーマ"
-
-msgid "Download source file"
-msgstr "ソースファイルをダウンロード"
-
-msgid "open issue"
-msgstr "未解決の問題"
-
-msgid "Contents"
-msgstr "目次"
-
-msgid "previous page"
-msgstr "前のページ"
-
-msgid "Download notebook file"
-msgstr "ノートブックファイルをダウンロード"
-
-msgid "Copyright"
-msgstr "Copyright"
-
-msgid "Download this page"
-msgstr "このページをダウンロード"
-
-msgid "Source repository"
-msgstr "ソースリポジトリ"
-
-msgid "By"
-msgstr "著者"
-
-msgid "repository"
-msgstr "リポジトリ"
-
-msgid "Last updated on"
-msgstr "最終更新日"
-
-msgid "Toggle navigation"
-msgstr "ナビゲーションを切り替え"
-
-msgid "Sphinx Book Theme"
-msgstr "スフィンクスの本のテーマ"
-
-msgid "suggest edit"
-msgstr "編集を提案する"
-
-msgid "Open an issue"
-msgstr "問題を報告"
-
-msgid "Launch"
-msgstr "起動"
-
-msgid "Fullscreen mode"
-msgstr "全画面モード"
-
-msgid "Edit this page"
-msgstr "このページを編集"
-
-msgid "By the"
-msgstr "によって"
-
-msgid "next page"
-msgstr "次のページ"

_static/locales/ko/LC_MESSAGES/booktheme.po

@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ko\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "PDF로 인쇄"
-
-msgid "Theme by the"
-msgstr "테마별"
-
-msgid "Download source file"
-msgstr "소스 파일 다운로드"
-
-msgid "open issue"
-msgstr "열린 문제"
-
-msgid "Contents"
-msgstr "내용"
-
-msgid "previous page"
-msgstr "이전 페이지"
-
-msgid "Download notebook file"
-msgstr "노트북 파일 다운로드"
-
-msgid "Copyright"
-msgstr "저작권"
-
-msgid "Download this page"
-msgstr "이 페이지 다운로드"
-
-msgid "Source repository"
-msgstr "소스 저장소"
-
-msgid "By"
-msgstr "으로"
-
-msgid "repository"
-msgstr "저장소"
-
-msgid "Last updated on"
-msgstr "마지막 업데이트"
-
-msgid "Toggle navigation"
-msgstr "탐색 전환"
-
-msgid "Sphinx Book Theme"
-msgstr "스핑크스 도서 테마"
-
-msgid "suggest edit"
-msgstr "편집 제안"
-
-msgid "Open an issue"
-msgstr "이슈 열기"
-
-msgid "Launch"
-msgstr "시작하다"
-
-msgid "Fullscreen mode"
-msgstr "전체 화면으로보기"
-
-msgid "Edit this page"
-msgstr "이 페이지 편집"
-
-msgid "By the"
-msgstr "에 의해"
-
-msgid "next page"
-msgstr "다음 페이지"