Release dec 2025 (#15)

* Resolves issue #13

- Switched model to bge-small-en-v1.5
- Added lazy re-embedding
- Added model version tracking per vector
- Added conversion of vectors to the new model

* Add lfs support to the workflow.

* Implements importance scoring with decay + voting #6

* Resolves issue #5 by marking observations as superseeded and scheduled for deletion

* Implement pattern detection #7

* Improve injections and observations accuracy

- Session start: Recent observations for project context (recency-based)
- User prompt: Semantically relevant observations (similarity-based with threshold)

* Added two stage retrieval with bi and cross encoder #8

* Implement query expansion and reformulation #9

* Knowledge graph and relationships ( resolves #4 )

- File Overlap Detection: Detects relationships when observations modify/read the same files
- Concept Overlap Detection: Detects relationships based on shared semantic concepts
- Type Progression Detection: Infers relationships from natural observation type progressions (e.g., discovery → bugfix = "fixes")
- Temporal Proximity Detection: Detects relationships between observations in the same session within 5 minutes
- Narrative Mention Detection: Detects explicit relationship language in narratives (e.g., "fixes", "depends on", "supersedes")

* Add visualisation of the relations to the dashboard.

* fixup! Add visualisation of the relations to the dashboard.

* Update documentation with new settings and screenshots.

internal/embedding/model.go

@@ -0,0 +1,157 @@
+// Package embedding provides text embedding generation with swappable models.
+package embedding
+
+import (
+	"fmt"
+	"sync"
+)
+
+// PoolingStrategy defines how to pool token embeddings into sentence embeddings.
+type PoolingStrategy string
+
+const (
+	// PoolingNone means the model already outputs sentence embeddings directly.
+	PoolingNone PoolingStrategy = "none"
+	// PoolingMean averages all token embeddings (weighted by attention mask).
+	PoolingMean PoolingStrategy = "mean"
+	// PoolingCLS uses only the [CLS] token embedding.
+	PoolingCLS PoolingStrategy = "cls"
+)
+
+// ONNXConfig describes ONNX-specific model configuration.
+// This allows different models to specify their tensor names and pooling needs.
+type ONNXConfig struct {
+	// InputNames are the ONNX input tensor names in order.
+	InputNames []string
+	// OutputNames are the ONNX output tensor names.
+	OutputNames []string
+	// Pooling specifies how to convert token embeddings to sentence embeddings.
+	// If PoolingNone, the model outputs sentence embeddings directly.
+	Pooling PoolingStrategy
+	// HiddenSize is the embedding dimension (used for pooling calculations).
+	HiddenSize int
+}
+
+// EmbeddingModel represents a text embedding model.
+type EmbeddingModel interface {
+	// Name returns the human-readable model name (e.g., "bge-small-en-v1.5").
+	Name() string
+
+	// Version returns a short version string for storage (e.g., "bge-v1.5").
+	Version() string
+
+	// Dimensions returns the embedding vector size.
+	Dimensions() int
+
+	// Embed generates an embedding for a single text.
+	Embed(text string) ([]float32, error)
+
+	// EmbedBatch generates embeddings for multiple texts.
+	EmbedBatch(texts []string) ([][]float32, error)
+
+	// Close releases model resources.
+	Close() error
+}
+
+// ONNXConfigurer is an optional interface that models can implement
+// to expose their ONNX configuration for introspection.
+type ONNXConfigurer interface {
+	// ONNXConfig returns the model's ONNX configuration.
+	ONNXConfig() ONNXConfig
+}
+
+// ModelMetadata describes an embedding model for UI/config.
+type ModelMetadata struct {
+	Name        string `json:"name"`        // Human-readable name
+	Version     string `json:"version"`     // Short ID for DB storage
+	Dimensions  int    `json:"dimensions"`  // Vector size
+	Description string `json:"description"` // Brief description
+	Default     bool   `json:"default"`     // Is this the default model?
+}
+
+// ModelFactory creates a new instance of an embedding model.
+type ModelFactory func() (EmbeddingModel, error)
+
+// ModelRegistry provides model lookup by version.
+type ModelRegistry struct {
+	mu           sync.RWMutex
+	models       map[string]ModelFactory
+	metadata     map[string]ModelMetadata
+	defaultModel string
+}
+
+// NewModelRegistry creates a new model registry.
+func NewModelRegistry() *ModelRegistry {
+	return &ModelRegistry{
+		models:   make(map[string]ModelFactory),
+		metadata: make(map[string]ModelMetadata),
+	}
+}
+
+// Register adds a model factory to the registry.
+func (r *ModelRegistry) Register(meta ModelMetadata, factory ModelFactory) {
+	r.mu.Lock()
+	defer r.mu.Unlock()
+
+	r.models[meta.Version] = factory
+	r.metadata[meta.Version] = meta
+
+	if meta.Default {
+		r.defaultModel = meta.Version
+	}
+}
+
+// Get creates a new instance of the model with the given version.
+func (r *ModelRegistry) Get(version string) (EmbeddingModel, error) {
+	r.mu.RLock()
+	factory, ok := r.models[version]
+	r.mu.RUnlock()
+
+	if !ok {
+		return nil, fmt.Errorf("unknown model version: %s", version)
+	}
+
+	return factory()
+}
+
+// Default returns the default model version.
+func (r *ModelRegistry) Default() string {
+	r.mu.RLock()
+	defer r.mu.RUnlock()
+	return r.defaultModel
+}
+
+// List returns metadata for all registered models.
+func (r *ModelRegistry) List() []ModelMetadata {
+	r.mu.RLock()
+	defer r.mu.RUnlock()
+
+	result := make([]ModelMetadata, 0, len(r.metadata))
+	for _, meta := range r.metadata {
+		result = append(result, meta)
+	}
+	return result
+}
+
+// DefaultRegistry is the global model registry with all available models.
+var DefaultRegistry = NewModelRegistry()
+
+// RegisterModel adds a model to the default registry.
+func RegisterModel(meta ModelMetadata, factory ModelFactory) {
+	DefaultRegistry.Register(meta, factory)
+}
+
+// GetModel creates a model instance from the default registry.
+func GetModel(version string) (EmbeddingModel, error) {
+	return DefaultRegistry.Get(version)
+}
+
+// GetDefaultModel returns the default model version from the default registry.
+func GetDefaultModel() string {
+	return DefaultRegistry.Default()
+}
+
+// ListModels returns metadata for all models in the default registry.
+func ListModels() []ModelMetadata {
+	return DefaultRegistry.List()
+}