Showing 4 open source projects for "configuration"

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  • 1
    AnySearch Skill

    AnySearch Skill

    Unified real-time search engine skill for AI agents

    AnySearch Skill is a real-time search engine skill for AI agents. It gives agents a structured way to search the web, run vertical searches, perform parallel batch searches, and extract full-page content. The project is packaged as a skill rather than a standalone search application, so it is meant to be installed into compatible AI-agent environments. It supports multiple domain-specific search categories, making it useful when general web search is too broad. The skill can also fetch page...
    Downloads: 15 This Week
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  • 2
    dirsearch

    dirsearch

    Web path scanner

    ...To use multiple wordlists, you can separate your wordlists with commas. Example: wordlist1.txt,wordlist2.txt. Default values for dirsearch flags can be edited in the configuration file: default.conf. The thread number (-t | --threads) reflects the number of separated brute force processes. And so the bigger the thread number is, the faster dirsearch runs. By default, the number of threads is 30, but you can increase it if you want to speed up the progress.
    Downloads: 10 This Week
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  • 3
    rag-search

    rag-search

    RAG Search API

    ...The project integrates web search, vector embeddings, and reranking logic to retrieve relevant context before passing it to a language model for response generation. It is built to be easily deployable, requiring only environment configuration and dependency installation to run a functional RAG service. The system supports configurable filtering, scoring thresholds, and reranking options, allowing developers to fine-tune retrieval quality. Its architecture is modular, separating handlers, services, and utilities to support customization and extension. Overall, rag-search serves as a practical starter backend for teams building AI search or question-answering applications on their own data.
    Downloads: 0 This Week
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  • 4
    ENAS in PyTorch

    ENAS in PyTorch

    PyTorch implementation of "Efficient Neural Architecture Search

    ENAS in PyTorch is a PyTorch implementation of Efficient Neural Architecture Search (ENAS), a method that automates the design of neural network architectures through reinforcement learning and parameter sharing. The repository demonstrates how a controller network can explore a large search space and discover high-performing architectures while dramatically reducing the computational cost traditionally associated with neural architecture search. It is primarily intended as a research and...
    Downloads: 0 This Week
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