a43cc099cd
Virgil split go-ai into standalone modules (go-agentic, go-ml, go-mlx, go-rag). This migrates all CLI imports to the new module paths and fixes API mismatches from the split. Key changes: - go-ai/agentic → go-agentic (cmd/ai, cmd/dev) - go-ai/ml → go-ml (31 files in cmd/ml) - go-ai/rag → go-rag (3 files in cmd/rag) - go-ai/mlx → go-mlx (1 file) - Fix go.work path (../core → ../go) - Add all split repos to go.work and go.mod - Simplify daemon to goroutine-based MCP (remove missing supervisor) - Wire go-agentic SQLiteRegistry into dispatch watch (--agent-id flag) - Add `core ai agent fleet` command for local registry status - Fix rag collections API (PointCount, Status string) - Fix ml live/expand-status to use available go-ml API Co-Authored-By: Charon <charon@lethean.io>
54 lines
1.6 KiB
Go
54 lines
1.6 KiB
Go
package ml
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import (
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"fmt"
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"os"
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"forge.lthn.ai/core/go/pkg/cli"
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"forge.lthn.ai/core/go-ml"
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)
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var ingestCmd = &cli.Command{
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Use: "ingest",
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Short: "Ingest benchmark scores and training logs into InfluxDB",
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Long: "Reads content score, capability score, and training log files and writes measurements to InfluxDB for the lab dashboard.",
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RunE: runIngest,
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}
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var (
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ingestContent string
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ingestCapability string
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ingestTraining string
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ingestRunID string
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ingestBatchSize int
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)
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func init() {
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ingestCmd.Flags().StringVar(&ingestContent, "content", "", "Content scores JSONL file")
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ingestCmd.Flags().StringVar(&ingestCapability, "capability", "", "Capability scores JSONL file")
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ingestCmd.Flags().StringVar(&ingestTraining, "training-log", "", "MLX LoRA training log file")
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ingestCmd.Flags().StringVar(&ingestRunID, "run-id", "", "Run ID tag (defaults to model name)")
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ingestCmd.Flags().IntVar(&ingestBatchSize, "batch-size", 100, "Lines per InfluxDB write batch")
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}
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func runIngest(cmd *cli.Command, args []string) error {
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if modelName == "" {
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return fmt.Errorf("--model is required")
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}
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if ingestContent == "" && ingestCapability == "" && ingestTraining == "" {
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return fmt.Errorf("at least one of --content, --capability, or --training-log is required")
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}
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influx := ml.NewInfluxClient(influxURL, influxDB)
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cfg := ml.IngestConfig{
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ContentFile: ingestContent,
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CapabilityFile: ingestCapability,
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TrainingLog: ingestTraining,
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Model: modelName,
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RunID: ingestRunID,
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BatchSize: ingestBatchSize,
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}
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return ml.Ingest(influx, cfg, os.Stdout)
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}
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