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* init update * Update terminology.rst * fix the branch to create an index.html, and fix pre-commit issues * Doc update * made several changes to the docs after Shuguang's revision * fixing pre-commit issues * fixed the reference file to the final prompt config file * added google analytics --------- Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-261.local>
70 lines
3.3 KiB
ReStructuredText
70 lines
3.3 KiB
ReStructuredText
.. _arch_agent_guide:
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Agentic Workflow
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==============================
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Arch helps you easily personalize your applications by calling application-specific (API) functions
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via user prompts. This involves any predefined functions or APIs you want to expose to users to perform tasks,
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gather information, or manipulate data. This capability is generally referred to as **function calling**, where
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you have the flexibility to support “agentic” apps tailored to specific use cases - from updating insurance
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claims to creating ad campaigns - via prompts.
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Arch analyzes prompts, extracts critical information from prompts, engages in lightweight conversation with
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the user to gather any missing parameters and makes API calls so that you can focus on writing business logic.
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Arch does this via its purpose-built :ref:`Arch-FC LLM <function_calling>` - the fastest (200ms p90 - 10x faser than GPT-4o)
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and cheapest (100x than GPT-40) function-calling LLM that matches performance with frontier models.
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.. image:: includes/agent/function-calling-flow.jpg
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:width: 100%
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:align: center
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Single Function Call
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--------------------
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In the most common scenario, users will request a single action via prompts, and Arch efficiently processes the
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request by extracting relevant parameters, validating the input, and calling the designated function or API. Here
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is how you would go about enabling this scenario with Arch:
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Step 1: Define prompt targets with functions
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. literalinclude:: includes/agent/function-calling-agent.yaml
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:language: yaml
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:linenos:
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:emphasize-lines: 16-37
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:caption: Define prompt targets that can enable users to engage with API and backened functions of an app
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Step 2: Process request parameters in Flask
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Once the prompt targets are configured as above, handling those parameters is
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.. literalinclude:: includes/agent/parameter_handling.py
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:language: python
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:linenos:
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:caption: Parameter handling with Flask
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Parallel/ Multiple Function Calling
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-----------------------------------
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In more complex use cases, users may request multiple actions or need multiple APIs/functions to be called
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simultaneously or sequentially. With Arch, you can handle these scenarios efficiently using parallel or multiple
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function calling. This allows your application to engage in a broader range of interactions, such as updating
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different datasets, triggering events across systems, or collecting results from multiple services in one prompt.
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Arch-FC1B is built to manage these parallel tasks efficiently, ensuring low latency and high throughput, even
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when multiple functions are invoked. It provides two mechanisms to handle these cases:
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Step 1: Define Multiple Function Targets
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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When enabling multiple function calling, define the prompt targets in a way that supports multiple functions or
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API calls based on the user's prompt. These targets can be triggered in parallel or sequentially, depending on
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the user's intent.
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Example of Multiple Prompt Targets in YAML:
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.. literalinclude:: includes/agent/function-calling-agent.yaml
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:language: yaml
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:linenos:
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:emphasize-lines: 16-37
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:caption: Define prompt targets that can enable users to engage with API and backened functions of an app
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