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2 dépôts

Awesome GitHub RepositoriesModel-Level Consistency

Strategies for ensuring data consistency between memory caches and primary data stores at the object level.

Distinct from Data Consistency Models: Existing candidates focus on distributed system consistency (CAP) or structural integrity; this is about ORM-level memory consistency.

Explore 2 awesome GitHub repositories matching data & databases · Model-Level Consistency. Refine with filters or upvote what's useful.

Awesome Model-Level Consistency GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • hyperf/hyperfAvatar de hyperf

    hyperf/hyperf

    6,855Voir sur GitHub↗

    Hyperf is a high-performance PHP coroutine framework designed for building microservices and middleware. It utilizes non-blocking coroutines to handle high concurrency and low-latency request processing, providing a foundation for scalable distributed systems. The framework is distinguished by an aspect-oriented programming based dependency injector that enables pluggable components and meta-programming. It includes a coroutine-optimized object-relational mapper with integrated model caching and an orchestration toolkit for microservice governance, featuring service discovery, circuit breaker

    Stores database results in fast-access memory to minimize system strain while maintaining data consistency.

    PHP
    Voir sur GitHub↗6,855
  • ucbepic/docetlAvatar de ucbepic

    ucbepic/docetl

    3,597Voir sur GitHub↗

    docetl is an AI-powered document ETL tool and map-reduce orchestrator designed to transform large collections of unstructured documents into structured, queryable tables using language models. It provides a declarative pipeline framework for extracting, cleaning, and transforming data from sources such as PDFs and text files into predefined schemas. The project distinguishes itself through a semantic data integration suite that enables joining datasets and resolving duplicate entities based on embedding-based similarity. It includes an interactive prompt playground for developing and optimizi

    Ensures consistent scoring across datasets by generating reference anchors for the prompt via document sampling.

    Pythonagentsdatadata-pipelines
    Voir sur GitHub↗3,597
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  • Prompt Anchor CalibrationUsing sampled documents to create reference examples that ensure consistent model scoring across a dataset. **Distinct from Model-Level Consistency:** Focuses on prompt-level consistency via anchors rather than memory/cache consistency in databases.