refactor: replace Git LFS with runtime model download from Hugging Face

Remove ~170MB of model files from the repository (LFS + committed).
Models are now downloaded at runtime from Hugging Face on first use
and cached to the OS cache directory with progress reporting and retries.

- Add internal/models/download.go: runtime downloader with retry, progress bar, checksums
- Remove go:embed for ONNX models (keep tokenizers embedded)
- Use file-based ONNX session loading instead of byte-slice
- Add scripts/download-models.sh for dev/CI model setup
- Update Makefile with setup-models target
- Update workflow-prepare.sh to download models in CI
- Set lfs: false in all CI workflows
- SHA256: bge=828e14..., cross-encoder=5d3e70...

internal/embedding/assets.go

@@ -1,14 +1,12 @@
-// Package embedding provides text embedding generation using all-MiniLM-L6-v2.
+// Package embedding provides text embedding generation using bge-small-en-v1.5.
 package embedding
 
 import (
 	_ "embed"
 )
 
-// Model and tokenizer files - embedded for all platforms
+// Tokenizer file - embedded for all platforms (small, not in LFS).
+// The ONNX model is downloaded at runtime to ~/.claude-mnemonic/models/.
 //
-//go:embed assets/model.onnx
-var modelData []byte
-
 //go:embed assets/tokenizer.json
 var tokenizerData []byte

internal/embedding/service.go

@@ -12,6 +12,7 @@ import (
 	"strings"
 	"sync"
 
+	"github.com/lukaszraczylo/claude-mnemonic/internal/models"
 	"github.com/sugarme/tokenizer"
 	"github.com/sugarme/tokenizer/pretrained"
 	ort "github.com/yalue/onnxruntime_go"
@@ -29,6 +30,13 @@ const (
 	BGEModelName = "bge-small-en-v1.5"
 	// DefaultModelVersion is the default model to use
 	DefaultModelVersion = BGEModelVersion
+
+	// BGEAssetName is the local filename for the BGE model.
+	BGEAssetName = "bge-small-en-v1.5-model.onnx"
+	// BGEAssetURL is the Hugging Face URL for the BGE ONNX model.
+	BGEAssetURL = "https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/onnx/model.onnx"
+	// BGEModelSHA256 is the expected SHA-256 of the BGE model file.
+	BGEModelSHA256 = "828e1496d7fabb79cfa4dcd84fa38625c0d3d21da474a00f08db0f559940cf35"
 )
 
 // MaxSequenceLength is the maximum token sequence length for the model.
@@ -60,6 +68,7 @@ var _ EmbeddingModel = (*bgeModel)(nil)
 var _ ONNXConfigurer = (*bgeModel)(nil)
 
 // newBGEModel creates a new BGE embedding model using bundled ONNX runtime and model.
+// The ONNX model is downloaded at runtime from GitHub Releases and cached to disk.
 func newBGEModel() (EmbeddingModel, error) {
 	// Extract ONNX runtime library to temp directory
 	libDir, err := extractONNXLibrary()
@@ -84,9 +93,15 @@ func newBGEModel() (EmbeddingModel, error) {
 		return nil, fmt.Errorf("load tokenizer: %w", err)
 	}
 
-	// Create ONNX session using model-specific configuration
+	// Download model on first use (cached to ~/.claude-mnemonic/models/)
+	modelPath, err := models.EnsureModel(BGEAssetName, BGEAssetURL, BGEModelSHA256)
+	if err != nil {
+		return nil, fmt.Errorf("ensure embedding model: %w", err)
+	}
+
+	// Create ONNX session from file path using model-specific configuration
 	config := bgeONNXConfig
-	session, err := ort.NewDynamicAdvancedSessionWithONNXData(modelData, config.InputNames, config.OutputNames, nil)
+	session, err := ort.NewDynamicAdvancedSession(modelPath, config.InputNames, config.OutputNames, nil)
 	if err != nil {
 		return nil, fmt.Errorf("create ONNX session: %w", err)
 	}