Plain text misses structure
Search without AST and symbol information is too weak for large repositories.
Anonymized production case
AST module for semantic code search and RAG
A FOXOPS case around an AST-based module inside a larger AI system for semantic code search and RAG context preparation.
Problem
Why ordinary code search is not enough for AI systems
Context must be assembled
Useful AI answers require symbol, import and graph-level connections.
Repository scale matters
Large codebases need an indexed structural model rather than repeated scanning.
RAG quality depends on retrieval quality
Weak retrieval produces weak context and weak generated answers.
Approach
How the module worked inside the AI contour
Step 01
Directory scan and parsing
Source files were discovered and parsed with language-aware adapters.
Step 02
Symbol and chunk extraction
The module built a structured view of symbols, imports, chunks and graph edges.
Step 03
Hybrid retrieval
Search combined structure, lexical signals and graph relations for better context assembly.
Processing graph
Scan
directories / files
AST
structural parse
Symbol index
relations / chunks
Search
hybrid retrieval
RAG layer
context for answers
38.9K
files discovered
17.4K
files scanned
47.8K
symbols extracted
343.3K
graph edges built
Next Step
If you have a similar research or engineering task, it can be assessed as a separate system contour
FOXOPS can help determine whether your AI tooling needs structural code intelligence, hybrid retrieval and a dedicated indexing layer.