cli/internal/cmd/ml/cmd_ingest.go
Claude 1f3a1bcc47 feat: port 11 LEM data management commands into core ml
Ports all remaining LEM pipeline commands from pkg/lem into core ml,
eliminating the standalone LEM CLI dependency. Each command is split
into reusable business logic (pkg/ml/) and a thin cobra wrapper
(internal/cmd/ml/).

New commands: query, inventory, metrics, ingest, normalize, seed-influx,
consolidate, import-all, approve, publish, coverage.

Adds Path(), Exec(), QueryRowScan() convenience methods to DB type.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 05:53:52 +00:00

54 lines
1.6 KiB
Go

package ml
import (
"fmt"
"os"
"forge.lthn.ai/core/cli/pkg/cli"
"forge.lthn.ai/core/cli/pkg/ml"
)
var ingestCmd = &cli.Command{
Use: "ingest",
Short: "Ingest benchmark scores and training logs into InfluxDB",
Long: "Reads content score, capability score, and training log files and writes measurements to InfluxDB for the lab dashboard.",
RunE: runIngest,
}
var (
ingestContent string
ingestCapability string
ingestTraining string
ingestRunID string
ingestBatchSize int
)
func init() {
ingestCmd.Flags().StringVar(&ingestContent, "content", "", "Content scores JSONL file")
ingestCmd.Flags().StringVar(&ingestCapability, "capability", "", "Capability scores JSONL file")
ingestCmd.Flags().StringVar(&ingestTraining, "training-log", "", "MLX LoRA training log file")
ingestCmd.Flags().StringVar(&ingestRunID, "run-id", "", "Run ID tag (defaults to model name)")
ingestCmd.Flags().IntVar(&ingestBatchSize, "batch-size", 100, "Lines per InfluxDB write batch")
}
func runIngest(cmd *cli.Command, args []string) error {
if modelName == "" {
return fmt.Errorf("--model is required")
}
if ingestContent == "" && ingestCapability == "" && ingestTraining == "" {
return fmt.Errorf("at least one of --content, --capability, or --training-log is required")
}
influx := ml.NewInfluxClient(influxURL, influxDB)
cfg := ml.IngestConfig{
ContentFile: ingestContent,
CapabilityFile: ingestCapability,
TrainingLog: ingestTraining,
Model: modelName,
RunID: ingestRunID,
BatchSize: ingestBatchSize,
}
return ml.Ingest(influx, cfg, os.Stdout)
}