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Agentic AI Atlas · Codebase as Knowledge Fabric — symbol indexing and code intelligence
knowledge-fabric-impl:codebase-indexa5c.ai
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KnowledgeFabricImpl overview

knowledge-fabric-impl:codebase-index

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Codebase as Knowledge Fabric — symbol indexing and code intelligence overview

The codebase itself as a knowledge fabric via symbol indexing, AST analysis, dependency graphs, and semantic code search. Used by Cursor, GitHub Copilot, Sourcegraph, and IDE language servers to provide code- context-aware retrieval. Knowledge is derived from source code rather than authored separately — function signatures, type hierarchies, import graphs, and usage patterns constitute an implicit knowledge graph. Symbol graphs and LSP provide structural retrieval; semantic embeddings provide similarity-based retrieval. The combination enables agents to understand codebases without reading every file.

KnowledgeFabricImplOutgoing · 1Incoming · 0

Attributes

displayName
Codebase as Knowledge Fabric — symbol indexing and code intelligence
description
The codebase itself as a knowledge fabric via symbol indexing, AST analysis, dependency graphs, and semantic code search. Used by Cursor, GitHub Copilot, Sourcegraph, and IDE language servers to provide code- context-aware retrieval. Knowledge is derived from source code rather than authored separately — function signatures, type hierarchies, import graphs, and usage patterns constitute an implicit knowledge graph. Symbol graphs and LSP provide structural retrieval; semantic embeddings provide similarity-based retrieval. The combination enables agents to understand codebases without reading every file.
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 knowledge is derived rather than authored. Symbol indexing (tree-sitter, LSP) provides structural knowledge. Semantic code embeddings provide similarity-based retrieval. Dependency graphs provide relationship knowledge. The knowledge is always current because it is derived from the current source state. The trade-off is that code knowledge lacks the "why" — rationale, context, and design intent require separate knowledge artifacts (ADRs, comments, documentation).

Outgoing edges

realizes1

Incoming edges

None.