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Multi-Model Inference with Oracle OCI and Baseten

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Linked challenge: LlamaIndex-Powered AI Supply Chain Optimizer with GPT-5 Pro and Multi-Model Inference

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Linked challenge

LlamaIndex-Powered AI Supply Chain Optimizer with GPT-5 Pro and Multi-Model Inference

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Extend your LlamaIndex agent system to incorporate multi-model inference. Use GPT-5 Pro for strategic decision-making and Claude 4 Sonnet for in-depth textual analysis of market reports. Describe how you would deploy and serve these models using Oracle OCI Generative AI and Baseten respectively. How would the LlamaIndex agents dynamically select which model to use for a given sub-task?

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Linked challenge

LlamaIndex-Powered AI Supply Chain Optimizer with GPT-5 Pro and Multi-Model Inference

Construct a multi-agent system using LlamaIndex to analyze and optimize the global supply chain for critical AI hardware components, such as advanced logic wafers (e.g., TSMC N3). This system will integrate diverse structured and unstructured data sources (e.g., market reports, news articles, inventory databases) to identify potential bottlenecks, predict supply shortages, and recommend mitigation strategies. The agents will leverage powerful language models like GPT-5 Pro for strategic reasoning and Claude 4 Sonnet for detailed textual analysis, deploying these models efficiently across different inference platforms. This challenge emphasizes LlamaIndex's capabilities for building agents that can reason over diverse data types and interact with external tools, without relying on traditional multi-agent tool-calling tool orchestration patterns for primary data access. The goal is to create an intelligent system that provides actionable insights to manufacturers and AI developers, helping them navigate and adapt to complex global supply chain dynamics.

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