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* updated docs (again)

* updated the LLMs section, prompt processing section and the RAG section of the docs

---------

Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-261.local>
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26
docs/source/getting_started/use_cases/function_calling.rst
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@ -1,17 +1,25 @@
.. _arch_function_calling_agentic_guide:
Agentic (Text-to-Action) Apps
==============================
Arch helps you easily personalize your applications by enabling calls to application-specific (API) operations
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. With function calling, you have flexibility to support “agentic” apps
tailored to specific use cases - from updating insurance claims to creating ad campaigns - via prompts.
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 Arch-FC1B LLM - the fastest (200ms p90 - 10x faser than GPT-4o) and cheapest
(100x than GPT-40) function-calling LLM that matches performance with frontier models.
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
@ -54,4 +62,10 @@ When enabling multiple function calling, define the prompt targets in a way that
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:
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

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docs/source/getting_started/use_cases/rag.rst
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.. _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
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/?>`_
@ -65,8 +67,8 @@ You can used the last set of messages that match to an intent to prompt an LLM,
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.
Smarter retrival with parameter extraction
------------------------------------------
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