apunkt/plano
1
0
Fork 0
mirror of https://github.com/katanemo/plano.git synced 2026-06-17 15:25:17 +02:00
Code Issues Projects Releases Packages Wiki Activity

removed more references of the word Arch

Browse source
This commit is contained in:
Salman Paracha 2025-12-22 16:15:01 -08:00
parent cdfd8d5726
commit 0890e5c5ed
4 changed files with 21 additions and 21 deletions
Download patch file Download diff file Expand all files Collapse all files

2
docs/source/get_started/overview.rst
View file

@ -55,7 +55,7 @@ Deep dive into essential ideas and mechanisms behind Plano:
.. grid-item-card:: :octicon:`webhook` Model Providers
:link: ../concepts/llm_providers/llm_providers.html
Explore Arch’s LLM integration options
Explore Plano's LLM integration options
.. grid-item-card:: :octicon:`workflow` Prompt Target
:link: ../concepts/prompt_target.html

30
docs/source/guides/function_calling.rst
View file

@ -3,7 +3,7 @@
Function Calling
================
**Function Calling** is a powerful feature in Arch that allows your application to dynamically execute backend functions or services based on user prompts.
**Function Calling** is a powerful feature in Plano that allows your application to dynamically execute backend functions or services based on user prompts.
This enables seamless integration between natural language interactions and backend operations, turning user inputs into actionable results.
@ -18,15 +18,15 @@ Function Calling Workflow
#. **Prompt Parsing**
When a user submits a prompt, Arch analyzes it to determine the intent. Based on this intent, the system identifies whether a function needs to be invoked and which parameters should be extracted.
When a user submits a prompt, Plano analyzes it to determine the intent. Based on this intent, the system identifies whether a function needs to be invoked and which parameters should be extracted.
#. **Parameter Extraction**
Arch’s advanced natural language processing capabilities automatically extract parameters from the prompt that are necessary for executing the function. These parameters can include text, numbers, dates, locations, or other relevant data points.
Plano’s advanced natural language processing capabilities automatically extract parameters from the prompt that are necessary for executing the function. These parameters can include text, numbers, dates, locations, or other relevant data points.
#. **Function Invocation**
Once the necessary parameters have been extracted, Arch invokes the relevant backend function. This function could be an API, a database query, or any other form of backend logic. The function is executed with the extracted parameters to produce the desired output.
Once the necessary parameters have been extracted, Plano invokes the relevant backend function. This function could be an API, a database query, or any other form of backend logic. The function is executed with the extracted parameters to produce the desired output.
#. **Response Handling**
@ -64,11 +64,11 @@ Key Features
Implementing Function Calling
-----------------------------
Here’s a step-by-step guide to configuring function calling within your Arch setup:
Here’s a step-by-step guide to configuring function calling within your Plano setup:
Step 1: Define the Function
~~~~~~~~~~~~~~~~~~~~~~~~~~~
First, create or identify the backend function you want Arch to call. This could be an API endpoint, a script, or any other executable backend logic.
First, create or identify the backend function you want Plano to call. This could be an API endpoint, a script, or any other executable backend logic.
.. code-block:: python
@ -96,8 +96,8 @@ First, create or identify the backend function you want Arch to call. This could
Step 2: Configure Prompt Targets
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Next, map the function to a prompt target, defining the intent and parameters that Arch will extract from the user’s prompt.
Specify the parameters your function needs and how Arch should interpret these.
Next, map the function to a prompt target, defining the intent and parameters that Plano will extract from the user’s prompt.
Specify the parameters your function needs and how Plano should interpret these.
.. code-block:: yaml
:caption: Prompt Target Example Configuration
@ -121,22 +121,22 @@ Specify the parameters your function needs and how Arch should interpret these.
.. Note::
For a complete refernce of attributes that you can configure in a prompt target, see :ref:`here <defining_prompt_target_parameters>`.
Step 3: Arch Takes Over
Step 3: Plano Takes Over
~~~~~~~~~~~~~~~~~~~~~~~
Once you have defined the functions and configured the prompt targets, Arch Gateway takes care of the remaining work.
Once you have defined the functions and configured the prompt targets, Plano takes care of the remaining work.
It will automatically validate parameters, and ensure that the required parameters (e.g., location) are present in the prompt, and add validation rules if necessary.
.. figure:: /_static/img/plano_network_diagram_high_level.png
:width: 100%
:align: center
High-level network flow of where Arch Gateway sits in your agentic stack. Managing incoming and outgoing prompt traffic
High-level network flow of where Plano sits in your agentic stack. Managing incoming and outgoing prompt traffic
Once a downstream function (API) is called, Arch Gateway takes the response and sends it an upstream LLM to complete the request (for summarization, Q/A, text generation tasks).
For more details on how Arch Gateway enables you to centralize usage of LLMs, please read :ref:`LLM providers <llm_providers>`.
Once a downstream function (API) is called, Plano takes the response and sends it an upstream LLM to complete the request (for summarization, Q/A, text generation tasks).
For more details on how Plano enables you to centralize usage of LLMs, please read :ref:`LLM providers <llm_providers>`.
By completing these steps, you enable Arch to manage the process from validation to response, ensuring users receive consistent, reliable results - and that you are focused
By completing these steps, you enable Plano to manage the process from validation to response, ensuring users receive consistent, reliable results - and that you are focused
on the stuff that matters most.
Example Use Cases
@ -152,7 +152,7 @@ Here are some common use cases where Function Calling can be highly beneficial:
Best Practices and Tips
-----------------------
When integrating function calling into your generative AI applications, keep these tips in mind to get the most out of our Arch-Function models:
When integrating function calling into your generative AI applications, keep these tips in mind to get the most out of our Plano-Function models:
- **Keep it clear and simple**: Your function names and parameters should be straightforward and easy to understand. Think of it like explaining a task to a smart colleague - the clearer you are, the better the results.

4
docs/source/resources/configuration_reference.rst
View file

@ -3,11 +3,11 @@
Configuration Reference
=======================
The following is a complete reference of the ``arch_config.yml`` that controls the behavior of a single instance of
The following is a complete reference of the ``plano_config.yml`` that controls the behavior of a single instance of
the Arch gateway. This where you enable capabilities like routing to upstream LLm providers, defining prompt_targets
where prompts get routed to, apply guardrails, and enable critical agent observability features.
.. literalinclude:: includes/arch_config_full_reference.yaml
:language: yaml
:linenos:
:caption: :download:`Arch Configuration - Full Reference <includes/arch_config_full_reference.yaml>`
:caption: :download:`Plano Configuration - Full Reference <includes/arch_config_full_reference.yaml>`

6
docs/source/resources/deployment.rst
View file

@ -3,12 +3,12 @@
Deployment
==========
This guide shows how to deploy Arch directly using Docker without the archgw CLI, including basic runtime checks for routing and health monitoring.
This guide shows how to deploy Plano directly using Docker without the archgw CLI, including basic runtime checks for routing and health monitoring.
Docker Deployment
-----------------
Below is a minimal, production-ready example showing how to deploy the Arch Docker image directly and run basic runtime checks. Adjust image names, tags, and the ``arch_config.yaml`` path to match your environment.
Below is a minimal, production-ready example showing how to deploy the Plano Docker image directly and run basic runtime checks. Adjust image names, tags, and the ``arch_config.yaml`` path to match your environment.
.. note::
You will need to pass all required environment variables that are referenced in your ``arch_config.yaml`` file.
@ -65,7 +65,7 @@ Test the chat completion endpoint with automatic routing:
.. code-block:: bash
# Request handled by the gateway. 'model: "none"' lets Arch decide routing
# Request handled by the gateway. 'model: "none"' lets Plano decide routing
curl --header 'Content-Type: application/json' \
--data '{"messages":[{"role":"user","content":"tell me a joke"}], "model":"none"}' \
http://localhost:12000/v1/chat/completions | jq .model