Specifications for asynchronous LLM computations with Raku's "LLM::Graph" detail how to manage complex, multi-step LLM workflows by representing them as graphs. By defining the workflow as a graph, developers can execute LLM function calls concurrently, enabling higher throughput and lower latency than synchronous, step-by-step processes.
"LLM::Graph" uses a graph structure to manage dependencies between tasks, where each node represents a computation and edges dictate the flow. Asynchronous behavior is a default feature, with specific options available for control.
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Specifications for asynchronous LLM computations with Raku's "LLM::Graph" detail how to manage complex, multi-step LLM workflows by representing them as graphs. By defining the workflow as a graph, developers can execute LLM function calls concurrently, enabling higher throughput and lower latency than synchronous, step-by-step processes.
"LLM::Graph" uses a graph structure to manage dependencies between tasks, where each node represents a computation and edges dictate the flow. Asynchronous behavior is a default feature, with specific options available for control.