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最近文章：

Massive Search: LLM Structured Outputs is All You Need

Executing complex searches across entities with diverse attributes —such as text, numbers, booleans, and images—can be challenging. These searches often require intricate queries, potentially involving joins across multiple data sources. For example, searching for a book might involve filtering by its title, description, price, user comments, and cover image simultaneously.

Massive Search provides a method for querying such complex but logically grouped data by leveraging LLM Structured Outputs. “Logically grouped” means all the data pertains to the same core entity, like a specific book product.

Smart Diagnosis Solution

Smart Diagnosis Solution, stack and layers

Manufacturer-Executor-Evaluator: A General LLM Agentic Pattern for Collective Intelligence

Manufacturer is responsible for generating the task specification based on the task examples, which is the system start point and objective,

the Executor is responsible for executing the task based on the task specification, it is the final solution output,

and the Evaluator is responsible for evaluating the execution result to make sure the task specification meets the objective, feedback or comments from Evaluator will be used to improve the task specification in the next iteration.