context4ai/intent-router-onnx

Intent Router (ONNX int8)

A 7-class intent classifier for code query routing. Classifies natural language queries (English and Chinese) into structured intents for code intelligence tools.

Intents

Label	Description
locate_symbol	Find symbol definitions
find_references	Trace reverse references / impact
trace_dependencies	Trace forward dependencies / call chains
semantic_search	Semantic search over code and docs
browse_structure	Browse package / module structure
cross_layer_trace	Map between code and business docs
ambiguous	Query cannot be classified

Files

File	Required	Description
onnx/model.onnx	Yes	ONNX model graph
onnx/model.onnx_data	Yes	Model weights (int8 quantized)
model_head.json	Yes	Classification head (weights + bias)
tokenizer.json	Yes	Tokenizer
tokenizer_config.json	Yes	Tokenizer configuration
labels.json	Yes	Intent label list
config.json	Yes	Model configuration

Inference

Requires ONNX Runtime. The model takes tokenized text input and outputs sentence embeddings. The classification head (model_head.json) maps embeddings to intent logits.

input text → tokenizer → ONNX model → embedding → classification head → intent + confidence

Benchmark

Evaluated on a held-out test set of 221 bilingual (Chinese + English) code queries.

Metric	Value
Overall accuracy	96.8% (214/221)
Inference latency (CPU, ONNX Runtime)	~3ms p50

Per-intent performance:

Intent	Precision	Recall	F1
locate_symbol	98.4%	96.8%	0.976
find_references	95.1%	97.5%	0.963
trace_dependencies	90.2%	95.1%	0.926
semantic_search	100.0%	91.5%	0.956
browse_structure	91.7%	100.0%	0.957
cross_layer_trace	100.0%	100.0%	1.000
ambiguous	100.0%	100.0%	1.000

Training set: 284 samples. Test set: 221 samples.

Quantization

int8 (dynamic quantization). Total size ~559MB.

Related Project

This model is fine-tuned for C4A (Context For AI), a knowledge modeling service that indexes code repositories and business documents for developer teams and AI agents.

License

MIT