ollie/embedding/embedding.go

// Package embedding provides text embedding using ONNX-based sentence transformers.
// It loads an all-MiniLM-L6-v2 model and tokenizer, and provides functions to
// embed text and compute similarity scores.
package embedding

import (
	"encoding/json"
	"fmt"
	"math"
	"os"
	"path/filepath"
	"strings"
	"sync"
	"unicode"

	ort "github.com/yalue/onnxruntime_go"
)

// Model holds the ONNX model and tokenizer state.
type Model struct {
	session   *ort.DynamicAdvancedSession
	tokenizer *Tokenizer
	mu        sync.Mutex
}

// Vector is a 384-dimensional embedding vector (MiniLM output size).
type Vector []float32

var initOnce sync.Once
var initErr error

// LoadModel loads the ONNX model and tokenizer from the given directory.
// The directory must contain model.onnx, tokenizer.json, and libonnxruntime.so.
func LoadModel(modelDir string) (*Model, error) {
	modelPath := filepath.Join(modelDir, "model.onnx")
	tokenizerPath := filepath.Join(modelDir, "tokenizer.json")
	libPath := filepath.Join(modelDir, "libonnxruntime.so")

	// Initialize ONNX runtime once
	initOnce.Do(func() {
		ort.SetSharedLibraryPath(libPath)
		initErr = ort.InitializeEnvironment()
	})
	if initErr != nil {
		return nil, fmt.Errorf("init onnx environment: %w", initErr)
	}

	// Load tokenizer
	tok, err := loadTokenizer(tokenizerPath)
	if err != nil {
		return nil, fmt.Errorf("load tokenizer: %w", err)
	}

	// Create ONNX session
	inputs := []string{"input_ids", "attention_mask", "token_type_ids"}
	outputs := []string{"last_hidden_state"}

	session, err := ort.NewDynamicAdvancedSession(modelPath, inputs, outputs, nil)
	if err != nil {
		return nil, fmt.Errorf("create onnx session: %w", err)
	}

	return &Model{
		session:   session,
		tokenizer: tok,
	}, nil
}

// Close releases model resources.
func (m *Model) Close() error {
	m.mu.Lock()
	defer m.mu.Unlock()
	if m.session != nil {
		return m.session.Destroy()
	}
	return nil
}

// Embed returns the embedding vector for the given text.
func (m *Model) Embed(text string) (Vector, error) {
	m.mu.Lock()
	defer m.mu.Unlock()

	// Tokenize
	inputIDs, attentionMask, tokenTypeIDs := m.tokenizer.Encode(text)
	seqLen := int64(len(inputIDs))

	// Create input tensors with shape [1, seqLen]
	shape := ort.Shape{1, seqLen}

	inputIDsTensor, err := ort.NewTensor(shape, toInt64(inputIDs))
	if err != nil {
		return nil, fmt.Errorf("create input_ids tensor: %w", err)
	}
	defer inputIDsTensor.Destroy()

	attentionTensor, err := ort.NewTensor(shape, toInt64(attentionMask))
	if err != nil {
		return nil, fmt.Errorf("create attention_mask tensor: %w", err)
	}
	defer attentionTensor.Destroy()

	tokenTypeTensor, err := ort.NewTensor(shape, toInt64(tokenTypeIDs))
	if err != nil {
		return nil, fmt.Errorf("create token_type_ids tensor: %w", err)
	}
	defer tokenTypeTensor.Destroy()

	// Create output tensor with shape [1, seqLen, 384]
	outputShape := ort.Shape{1, seqLen, 384}
	outputTensor, err := ort.NewEmptyTensor[float32](outputShape)
	if err != nil {
		return nil, fmt.Errorf("create output tensor: %w", err)
	}
	defer outputTensor.Destroy()

	// Run inference
	err = m.session.Run(
		[]ort.ArbitraryTensor{inputIDsTensor, attentionTensor, tokenTypeTensor},
		[]ort.ArbitraryTensor{outputTensor},
	)
	if err != nil {
		return nil, fmt.Errorf("run inference: %w", err)
	}

	// Mean pooling: average over sequence length, taking attention mask into account
	output := outputTensor.GetData()
	return meanPool(output, attentionMask, int(seqLen), 384), nil
}

// EmbedBatch is reserved for future batch optimization.
// Currently calls Embed sequentially.

// CosineSimilarity computes the cosine similarity between two vectors.
func CosineSimilarity(a, b Vector) float32 {
	if len(a) != len(b) {
		return 0
	}
	var dot, normA, normB float64
	for i := range a {
		dot += float64(a[i]) * float64(b[i])
		normA += float64(a[i]) * float64(a[i])
		normB += float64(b[i]) * float64(b[i])
	}
	if normA == 0 || normB == 0 {
		return 0
	}
	return float32(dot / (math.Sqrt(normA) * math.Sqrt(normB)))
}

// meanPool performs mean pooling over the sequence dimension with attention masking.
func meanPool(output []float32, mask []int, seqLen, hiddenSize int) Vector {
	result := make(Vector, hiddenSize)
	var count float32
	for i := 0; i < seqLen; i++ {
		if mask[i] == 0 {
			continue
		}
		count++
		for j := 0; j < hiddenSize; j++ {
			result[j] += output[i*hiddenSize+j]
		}
	}
	if count > 0 {
		for j := range result {
			result[j] /= count
		}
	}
	// L2 normalize
	var norm float64
	for _, v := range result {
		norm += float64(v) * float64(v)
	}
	norm = math.Sqrt(norm)
	if norm > 0 {
		for j := range result {
			result[j] = float32(float64(result[j]) / norm)
		}
	}
	return result
}

func toInt64(ints []int) []int64 {
	out := make([]int64, len(ints))
	for i, v := range ints {
		out[i] = int64(v)
	}
	return out
}

// --- Tokenizer ---

// Tokenizer handles WordPiece tokenization for BERT-style models.
type Tokenizer struct {
	vocab     map[string]int
	maxLen    int
	clsID     int
	sepID     int
	unkID     int
	lowercase bool
}

// tokenizerJSON is the HuggingFace tokenizers JSON format.
type tokenizerJSON struct {
	Truncation *struct {
		MaxLength int `json:"max_length"`
	} `json:"truncation"`
	Normalizer *struct {
		Lowercase bool `json:"lowercase"`
	} `json:"normalizer"`
	Model struct {
		Vocab map[string]int `json:"vocab"`
	} `json:"model"`
	AddedTokens []struct {
		ID      int    `json:"id"`
		Content string `json:"content"`
	} `json:"added_tokens"`
}

func loadTokenizer(path string) (*Tokenizer, error) {
	data, err := os.ReadFile(path)
	if err != nil {
		return nil, err
	}
	var tj tokenizerJSON
	if err := json.Unmarshal(data, &tj); err != nil {
		return nil, err
	}

	tok := &Tokenizer{
		vocab:  tj.Model.Vocab,
		maxLen: 128, // default
		unkID:  100, // [UNK]
		clsID:  101, // [CLS]
		sepID:  102, // [SEP]
	}
	if tj.Truncation != nil {
		tok.maxLen = tj.Truncation.MaxLength
	}
	if tj.Normalizer != nil {
		tok.lowercase = tj.Normalizer.Lowercase
	}

	// Override IDs from added_tokens if present
	for _, at := range tj.AddedTokens {
		switch at.Content {
		case "[UNK]":
			tok.unkID = at.ID
		case "[CLS]":
			tok.clsID = at.ID
		case "[SEP]":
			tok.sepID = at.ID
		}
	}

	return tok, nil
}

// Encode tokenizes text and returns input_ids, attention_mask, and token_type_ids.
func (t *Tokenizer) Encode(text string) (inputIDs, attentionMask, tokenTypeIDs []int) {
	if t.lowercase {
		text = strings.ToLower(text)
	}

	// Basic whitespace + punctuation tokenization
	words := tokenizeBasic(text)

	// WordPiece tokenization
	var tokens []int
	tokens = append(tokens, t.clsID)
	for _, word := range words {
		wordTokens := t.tokenizeWord(word)
		tokens = append(tokens, wordTokens...)
	}
	tokens = append(tokens, t.sepID)

	// Truncate if needed (keep [CLS] and [SEP])
	if len(tokens) > t.maxLen {
		tokens = append(tokens[:t.maxLen-1], t.sepID)
	}

	// Build masks
	seqLen := len(tokens)
	inputIDs = tokens
	attentionMask = make([]int, seqLen)
	tokenTypeIDs = make([]int, seqLen)
	for i := 0; i < seqLen; i++ {
		attentionMask[i] = 1
		tokenTypeIDs[i] = 0
	}

	return inputIDs, attentionMask, tokenTypeIDs
}

func (t *Tokenizer) tokenizeWord(word string) []int {
	var tokens []int
	remaining := word
	for len(remaining) > 0 {
		found := false
		for end := len(remaining); end > 0; end-- {
			subword := remaining[:end]
			if len(tokens) > 0 {
				subword = "##" + subword
			}
			if id, ok := t.vocab[subword]; ok {
				tokens = append(tokens, id)
				remaining = remaining[end:]
				found = true
				break
			}
		}
		if !found {
			tokens = append(tokens, t.unkID)
			break
		}
	}
	return tokens
}

// tokenizeBasic splits on whitespace and punctuation.
func tokenizeBasic(text string) []string {
	var words []string
	var current strings.Builder
	for _, r := range text {
		if unicode.IsSpace(r) {
			if current.Len() > 0 {
				words = append(words, current.String())
				current.Reset()
			}
		} else if unicode.IsPunct(r) {
			if current.Len() > 0 {
				words = append(words, current.String())
				current.Reset()
			}
			words = append(words, string(r))
		} else {
			current.WriteRune(r)
		}
	}
	if current.Len() > 0 {
		words = append(words, current.String())
	}
	return words
}