{
  "id": "knowledge-fabric-impl:codebase-index",
  "_kind": "KnowledgeFabricImpl",
  "_file": "agent-stack/knowledge-fabric-impls/codebase-knowledge-fabrics.yaml",
  "_cluster": "agent-stack",
  "attributes": {
    "displayName": "Codebase as Knowledge Fabric — symbol indexing and code intelligence",
    "description": "The codebase itself as a knowledge fabric via symbol indexing, AST\nanalysis, dependency graphs, and semantic code search. Used by Cursor,\nGitHub Copilot, Sourcegraph, and IDE language servers to provide code-\ncontext-aware retrieval. Knowledge is derived from source code rather\nthan authored separately — function signatures, type hierarchies, import\ngraphs, and usage patterns constitute an implicit knowledge graph. Symbol\ngraphs and LSP provide structural retrieval; semantic embeddings provide\nsimilarity-based retrieval. The combination enables agents to understand\ncodebases without reading every file.\n",
    "knowledgeFileFormats": [
      "source-code",
      "ast-nodes",
      "symbol-tables"
    ],
    "retrievalStrategy": "hybrid",
    "knowledgePersistence": "derived-from-source",
    "knowledgeScopes": [
      "project",
      "organization"
    ],
    "autoExtractionSupport": true,
    "notes": "Codebase-as-knowledge is unique among knowledge fabrics because the\nknowledge is derived rather than authored. Symbol indexing (tree-sitter,\nLSP) provides structural knowledge. Semantic code embeddings provide\nsimilarity-based retrieval. Dependency graphs provide relationship\nknowledge. The knowledge is always current because it is derived from\nthe current source state. The trade-off is that code knowledge lacks\nthe \"why\" — rationale, context, and design intent require separate\nknowledge artifacts (ADRs, comments, documentation).\n"
  },
  "outgoingEdges": [
    {
      "from": "knowledge-fabric-impl:codebase-index",
      "to": "layer:12-knowledge-fabric",
      "kind": "realizes",
      "attributes": {}
    }
  ],
  "incomingEdges": []
}