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2 repositorios

Awesome GitHub RepositoriesDirectory-Based Retrieval

Retrieval systems that resolve knowledge resources based on the filesystem hierarchy.

Distinct from Context-Aware Knowledge Managers: Distinct from Context-Aware Knowledge Managers: focuses on filesystem directory context rather than LLM token optimization.

Explore 2 awesome GitHub repositories matching software engineering & architecture · Directory-Based Retrieval. Refine with filters or upvote what's useful.

Awesome Directory-Based Retrieval GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • cheat/cheatAvatar de cheat

    cheat/cheat

    13,361Ver en GitHub↗

    Cheat is a command-line cheatsheet manager and terminal reference tool used for creating and viewing searchable, interactive guides. It functions as a plain-text knowledge base that organizes technical notes and command snippets through a hierarchical directory structure and metadata tags. The tool provides context-aware note management by discovering and displaying project-specific documentation based on the current working directory. It allows for the creation of a personalized collection of reference files that can be retrieved directly within a shell environment to assist with command opt

    Discovers and displays the most relevant technical documentation by traversing up the current directory tree.

    Go
    Ver en GitHub↗13,361
  • volcengine/openvikingAvatar de volcengine

    volcengine/OpenViking

    2,993Ver en GitHub↗

    OpenViking is a multi-tenant context server and knowledge base administration system designed to provide AI agents with persistent long-term memory. It enables the indexing of diverse documents and codebases to support retrieval-augmented generation, allowing agents to recall past interactions, user preferences, and learned experiences across sessions. The project is distinguished by its use of a URI-based virtual filesystem to organize memories, resources, and skills. It implements a tiered context loading system that balances retrieval precision with token budgets by structuring data into a

    Drills down through folder hierarchies using intent analysis and vector search for context retrieval.

    Pythonagentagentic-ragai-agents
    Ver en GitHub↗2,993
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