Code Intelligence
02 / Graph-Augmented Code Intelligence
Python · Static Analysis · Retrieval
Graph-Augmented
Code Intelligence Engine
A structural code intelligence engine that treats repositories as connected program structure — not flat text chunks.
The system combines semantic code units, program relationships, hybrid retrieval, graph-aware ranking, and context selection for repository-scale code understanding.
Demo coming soon
Code is more than text
Program structure itself is a retrieval signal.
Lexical search finds matching terms. Semantic retrieval finds conceptually similar code. Graph-aware retrieval also uses structural relationships — calls, references, imports, inherits, and contains — as evidence beyond text similarity alone.
Flat retrieval
Structural retrieval
System design
Intelligence pipeline
A layered pipeline separates program analysis, retrieval, ranking, and context compilation from downstream language generation.
Retrieval flow
LLM ≠ Code Intelligence Engine — parsing, graph construction, retrieval, ranking, and evaluation remain independent of an optional downstream LLM consumer.
Language-adapter architecture, beginning with Python.
Engineering highlights
Design principles
Three ideas at the center of the engine's architecture. Designed so graph-aware retrieval can be evaluated against text-only baselines rather than assumed to improve results.
Highlight 01
Semantic code units
Functions, classes, methods, and meaningful symbols preserve program boundaries better than arbitrary fixed text chunks.
Highlight 02
Graph-augmented retrieval
Program relationships — calls, references, imports, and inherits — can provide retrieval evidence that textual similarity alone may miss.
Highlight 03
LLM outside the engine
The intelligence pipeline is designed around deterministic program analysis, retrieval, ranking, and context construction, with an LLM acting only as an optional downstream consumer.
Demo coming soon