Distributed AI Function Calling With Datacenter-Local Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional AI model function calling in cloud computing networks involves multiple network communications, leading to increased latency due to the need for back-and-forth requests between users, application servers, AI inference providers, and external APIs.
Innovation Solution
A distributed cloud computing network system where AI models and function calls are executed within the same datacenter, utilizing helper code to streamline communication by reducing the number of network requests through direct interaction with AI models and external functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional function calling is used with multiple back-and-forth network requests, then AI models can interact with external data sources, but latency increases significantly due to multiple network communications
Solution Approach 1:
The patent merges the AI model execution environment with the function execution environment by deploying both in the same datacenter. The helper code executes locally on the compute server alongside the AI model, eliminating the need for multiple network round-trips between different services. This spatial merging of previously distributed components directly reduces latency while maintaining the ability to query external data sources.
Solution Approach 2:
The patent introduces helper code as an intermediary component that bridges the AI model and external data sources. This helper code executes locally on the compute server, receiving function calls from the AI model and coordinating direct invocations of external functions without requiring multiple network requests through the application server. The intermediary enables efficient local coordination while maintaining external data access capabilities.
2Adaptability or versatility
If multiple network communications are used for function calling, then AI models can execute functions and query external data, but system complexity increases due to multiple coordination steps
Solution Approach 1:
The patent combines multiple previously separate communication protocols into a unified local execution model. Instead of coordinating between AI model service, application server, and external APIs through multiple network protocols, the system uses a single unified execution context where the AI model, helper code, and external functions all interact through local process calls. This merging simplifies the communication layer while maintaining full functionality.
Solution Approach 2:
The helper code serves as a local intermediary that abstracts the complexity of function invocation from the AI model. It handles the coordination of external function calls, error handling, and data formatting locally, eliminating the need for complex multi-step network coordination protocols. This intermediary layer simplifies the overall system architecture by consolidating coordination logic.
3Productivity
If AI models execute in a different datacenter from the application server, then AI computing resources can be leveraged, but network latency increases due to geographical distance
Solution Approach 1:
The patent applies local quality by deploying the AI model and helper code in the same datacenter as the application server, creating a localized execution environment. This geographic co-location eliminates long-distance network transmission for AI inference operations, significantly reducing latency while still allowing access to cloud-based AI computing resources through the unified execution context. The local quality approach optimizes for speed by placing critical components close together.
Data Source
AI summary
A compute server of a distributed cloud computing network receives an inference request. The inference request triggers execution of code that is related to an AI application that interacts with the inference request and causes its input to be run through an AI model. Helper code executing on the compute server transmits an inference request to the AI model, where this inference request includes a prompt and one or more function definitions, and where the AI model is executing in the same datacenter as the compute server. The helper code receives a result of the AI model executing the inference request, where it includes structured data for calling a function. The helper code calls the function using the structured data. The helper code receives a response to the called function. The code processes a response to the inference request based at least on the received response to the called function.