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v0.7.2: 根目录清理 + Agent 心跳重构 + 内嵌 ONNX 模型
- 根目录清理: branding/docs/knowledge -> assets/, package/tools/deploy -> deploy/ - meta.go: Version 0.7.2, SDKCompatibleVersion 语义改为最高兼容 - Makefile: 版本回退 0.7.2 - registry.go: 系统提示词改用 meta.Version 格式化 - Agent 心跳: reorgGraph 拆分为三个独立循环(archive/merge/review)，各自可配间隔 - GraphDB: 新增 sentences 表 + 关系句子溯源 + ClearSentenceID + CleanupOrphanedSentences - Knowledge: 支持词嵌入向量化器 - NLP 四阶段流水线: Parse -> Extract -> Verify -> Fuse + SentenceRef - 移除远程 HTTP 解析器(remote_parser.go) - 新增内嵌 ONNX 模型(vocab + dep_parser.onnx): +build onnxruntime: 全量 ONNX Runtime 推理 !build onnxruntime: 内嵌词表规则式降级解析器 - config: core.agent.onnx_model_path 替代 dep_parser_url
This commit is contained in:
JianFeeeee
277
internal/nlp/onnx.go
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277
internal/nlp/onnx.go
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@ -0,0 +1,277 @@
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//go:build onnxruntime
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package nlp
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import (
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"embed"
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"encoding/json"
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"fmt"
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"os"
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"path/filepath"
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"gitcode.com/JianFeeeee/HomeAgent/internal/config"
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ort "github.com/yalue/onnxruntime_go"
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)
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//go:embed models/*
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var onnxModelFS embed.FS
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type ONNXParser struct {
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rt *ort.AdvancedSession
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vocab map[string]int64
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posVocab map[string]int64
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Release func()
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}
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type ONNXConfig struct {
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ModelPath string // 留空使用内嵌模型
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DataDir string // 模型解压/缓存目录
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}
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func NewONNXParser(cfg ONNXConfig) (*ONNXParser, error) {
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vocab, err := loadJSONMap[int64]("models/vocab.json", onnxModelFS)
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if err != nil {
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return nil, fmt.Errorf("load vocab: %w", err)
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}
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posVocab, err := loadJSONMap[int64]("models/pos_vocab.json", onnxModelFS)
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if err != nil {
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return nil, fmt.Errorf("load pos_vocab: %w", err)
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}
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modelPath := cfg.ModelPath
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if modelPath == "" {
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modelPath, err = extractEmbeddedModel(cfg.DataDir)
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if err != nil {
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return nil, fmt.Errorf("extract model: %w", err)
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}
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}
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ort.SetSharedLibraryPath(findONNXRuntime())
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if err := ort.InitializeEnvironment(); err != nil {
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return nil, fmt.Errorf("init onnx env: %w", err)
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}
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inputs := ort.NewInputDetails()
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inputs.Append("input_ids", []int64{1, 128})
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outputs := ort.NewOutputDetails()
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outputs.Append("pos_logits", []int64{1, 128, 18})
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outputs.Append("head_logits", []int64{1, 128, 128})
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outputs.Append("rel_logits", []int64{1, 128, 128, 18})
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session, err := ort.NewAdvancedSession(modelPath, inputs, outputs, nil)
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if err != nil {
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return nil, fmt.Errorf("create session: %w", err)
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}
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release := func() {
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session.Destroy()
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ort.DestroyEnvironment()
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}
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return &ONNXParser{
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rt: session,
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vocab: vocab,
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posVocab: posVocab,
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Release: release,
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}, nil
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}
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func (p *ONNXParser) Parse(text string) (*ParseResult, error) {
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if text == "" {
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return &ParseResult{}, nil
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}
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inputIDs := tokenize(text, p.vocab, 128)
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inputIDs = padTo(inputIDs, 128)
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inputTensor, err := ort.NewTensor(ort.NewShape(1, 128), inputIDs)
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if err != nil {
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return nil, fmt.Errorf("create input tensor: %w", err)
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}
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defer inputTensor.Destroy()
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outputs, err := p.rt.Call(inputTensor)
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if err != nil {
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return nil, fmt.Errorf("onnx call: %w", err)
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}
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rawPOS := outputs[0].GetData().([]float32)
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rawHeads := outputs[1].GetData().([]float32)
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rawRels := outputs[2].GetData().([]float32)
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seqLen := actualLen(inputIDs)
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tokens := idsToTokens(inputIDs[:seqLen], p.vocab)
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pos := decodePOS(rawPOS, seqLen, p.posVocab)
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heads := decodeHeads(rawHeads, seqLen)
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rels := decodeRels(rawRels, seqLen)
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return &ParseResult{Tokens: tokens, POS: pos, Heads: heads, DepRels: rels}, nil
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}
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func loadJSONMap[T ~int64 | ~string](path string, fs embed.FS) (map[string]T, error) {
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data, err := fs.ReadFile(path)
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if err != nil {
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return nil, err
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}
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var raw struct {
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Word map[string]T `json:"word"`
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}
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if err := json.Unmarshal(data, &raw); err != nil {
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result := make(map[string]T)
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if err2 := json.Unmarshal(data, &result); err2 != nil {
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return nil, err
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}
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return result, nil
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}
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return raw.Word, nil
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}
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func extractEmbeddedModel(dataDir string) (string, error) {
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if dataDir == "" {
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dataDir = filepath.Join(os.TempDir(), "homeagent-nlp")
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}
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os.MkdirAll(dataDir, 0755)
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dst := filepath.Join(dataDir, "dep_parser.onnx")
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if _, err := os.Stat(dst); err == nil {
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return dst, nil
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}
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data, err := onnxModelFS.ReadFile("models/dep_parser.onnx")
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if err != nil {
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return "", err
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}
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if err := os.WriteFile(dst, data, 0644); err != nil {
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return "", err
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}
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return dst, nil
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}
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func findONNXRuntime() string {
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candidates := []string{
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"onnxruntime.dll",
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"libonnxruntime.so",
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"libonnxruntime.dylib",
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filepath.Join(os.Getenv("ONNXRUNTIME_DIR"), "libonnxruntime.so"),
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filepath.Join(os.Getenv("ONNXRUNTIME_DIR"), "onnxruntime.dll"),
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}
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for _, c := range candidates {
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if _, err := os.Stat(c); err == nil {
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abs, _ := filepath.Abs(c)
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return abs
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}
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}
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return "onnxruntime.dll"
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}
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func tokenize(text string, vocab map[string]int64, maxLen int) []int64 {
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ids := []int64{vocab["<bos>"]}
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runes := []rune(text)
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for i := 0; i < len(runes) && len(ids) < maxLen; i++ {
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if id, ok := vocab[string(runes[i])]; ok {
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ids = append(ids, id)
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} else {
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ids = append(ids, vocab["<unk>"])
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}
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}
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return ids
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}
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func padTo(ids []int64, length int) []int64 {
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for len(ids) < length {
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ids = append(ids, 0)
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}
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return ids
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}
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func actualLen(ids []int64) int {
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for i, id := range ids {
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if id == 0 {
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return i
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}
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}
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return len(ids)
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}
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func idsToTokens(ids []int64, vocab map[string]int64) []string {
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rev := make(map[int64]string)
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for k, v := range vocab {
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rev[v] = k
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}
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var tokens []string
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for _, id := range ids {
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if t, ok := rev[id]; ok {
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tokens = append(tokens, t)
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}
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}
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return tokens
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}
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func decodePOS(raw []float32, seqLen int, posVocab map[string]int64) []string {
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rev := make(map[int64]string)
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for k, v := range posVocab {
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rev[v] = k
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}
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pos := make([]string, seqLen)
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for i := 0; i < seqLen; i++ {
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bestIdx := 0
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bestVal := float32(-1e9)
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for j := 0; j < 18; j++ {
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v := raw[i*18+j]
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if v > bestVal {
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bestVal = v
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bestIdx = j
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}
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}
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if tag, ok := rev[int64(bestIdx)]; ok {
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pos[i] = tag
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}
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}
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return pos
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}
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func decodeHeads(raw []float32, seqLen int) []int {
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heads := make([]int, seqLen)
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for i := 0; i < seqLen; i++ {
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bestIdx := 0
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bestVal := float32(-1e9)
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for j := 0; j < seqLen; j++ {
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v := raw[i*seqLen+j]
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if v > bestVal {
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bestVal = v
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bestIdx = j
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}
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}
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heads[i] = bestIdx
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}
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return heads
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}
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func decodeRels(raw []float32, seqLen int) []string {
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rels := make([]string, seqLen)
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for i := 0; i < seqLen; i++ {
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bestIdx := 0
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bestVal := float32(-1e9)
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for j := 0; j < 18; j++ {
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// average over head dimension for argmax
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var sum float32
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for k := 0; k < seqLen; k++ {
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sum += raw[i*seqLen*18+k*18+j]
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}
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avg := sum / float32(seqLen)
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if avg > bestVal {
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bestVal = avg
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bestIdx = j
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}
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}
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rels[i] = posIDToTag(bestIdx)
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}
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return rels
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}
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func posIDToTag(id int) string {
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tags := []string{"<bos>", "ADJ", "ADP", "ADV", "AUX", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X"}
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if id >= 0 && id < len(tags) {
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return tags[id]
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}
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return "X"
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}
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