openviking

You can organize AI agent memory using a filesystem paradigm by structuring context with viking:// URIs. This approach replaces flat vector storage by treating memories and resources as navigable directories, enabling more accurate recursive retrieval for scalable knowledge bases.

Tiered context loading in RAG workflows abstracts content into L0, L1, and L2 tiers: abstract, overview, and full content. This mechanism loads context on demand, conserving AI agent memory while scaling knowledge retrieval operations efficiently.

Yes, you need an MCP server to run end-to-end RAG semantic search pipelines locally. The Model Context Protocol server handles the configuration, dependency installation, and on-demand loading required for AI agents to retrieve knowledge.

To set up a local knowledge base with semantic search, run the MCP setup script to clone the repository and install dependencies via uv and git. This configures the API keys and starts the server for end-to-end RAG workflows.

Directory recursive retrieval provides more accurate results than flat vector search by organizing context hierarchically. This filesystem paradigm allows the RAG pipeline to navigate structured directories, returning targeted knowledge rather than unrelated vectors.

Yes, you can use viking:// URIs to load specific resources on demand for AI context. These URIs act as filesystem-like pointers within the MCP server, allowing AI agents to fetch tiered context levels precisely when needed.