feat: 完整实现 NLP 三元组提取系统 + token budget 上下文分配

- 重写 extractor.go: 分句、17条 POS 模板、依存模板 + COO 链、ATT合并
- parser.go: 分句循环 + TransE 向量验证(h+r≈t)
- fallback.go: jieba POS 降级解析器
- bridge.go: nlp.Triple ↔ memory.Triple 转换
- pipeline.go: extractKeyTriples 改用 NLP 提取器, 删除5条旧前缀规则
- distill.go: docToTriples 改用 NLP 提取器
- reorgGraph: 语义相似度增强检测, 保持纯 LLM 决断
- Provider 接口加 MaxContextTokens() + 模型窗口映射表
- tokenbudget.go: 中文 token 估算器 + budget 分配(80%利用率)
- process.go/buildSystemPrompt: 按 token 预算截断 memory+timeline

internal/nlp/extractor_test.go

@@ -0,0 +1,99 @@
+package nlp
+
+import (
+	"testing"
+)
+
+func TestFallbackParseDebug(t *testing.T) {
+	cases := []string{"我打球", "我在杭州读书", "小明喜欢吃苹果", "天气很好", "我住在杭州"}
+	p := newFallbackParser()
+	for _, c := range cases {
+		result, err := p.Parse(c)
+		if err != nil || result == nil || len(result.Tokens) == 0 {
+			t.Skip("jieba not available")
+		}
+		t.Logf("%q → tokens=%v pos=%v", c, result.Tokens, result.POS)
+	}
+}
+
+func TestExtractFromPOS(t *testing.T) {
+	p := newFallbackParser()
+
+	tests := []struct {
+		name  string
+		input string
+	}{
+		{"pronoun_prep_ns_noun", "我在杭州读书"},
+		{"pronoun_verb_noun", "我打球"},
+		{"name_verb_noun", "小明喜欢吃苹果"},
+		{"adj_predicate", "天气很好"},
+		{"pronoun_verb_prep_ns", "我住在杭州"},
+		{"empty", ""},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			if tt.input == "" {
+				result, _ := p.Parse("")
+				triples := extractFromPOS(result)
+				if len(triples) != 0 {
+					t.Errorf("expected 0 triples for empty, got %d", len(triples))
+				}
+				return
+			}
+
+			result, err := p.Parse(tt.input)
+			if err != nil || result == nil || len(result.Tokens) == 0 {
+				t.Skip("jieba not available")
+			}
+
+			t.Logf("input=%q tokens=%v pos=%v", tt.input, result.Tokens, result.POS)
+			triples := extractFromPOS(result)
+
+			for _, tr := range triples {
+				if tr.Subject == "" || tr.Relation == "" || tr.Object == "" {
+					t.Errorf("triple has empty field: %+v", tr)
+				}
+				t.Logf("triple: Subject=%q Relation=%q Object=%q score=%.2f", tr.Subject, tr.Relation, tr.Object, tr.Score)
+			}
+
+			if len(triples) == 0 {
+				t.Logf("no triples extracted (may be expected depending on jieba POS tagging)")
+			}
+		})
+	}
+}
+
+func TestExtractorFallback(t *testing.T) {
+	e := NewExtractor(nil)
+	result := e.Extract("我住在杭州")
+	if result == nil {
+		t.Fatal("expected result")
+	}
+	if result.Src == "" {
+		t.Skip("jieba not available")
+	}
+	if len(result.Triples) > 0 {
+		tr := result.Triples[0]
+		t.Logf("extracted: Subject=%q Relation=%q Object=%q (score=%.2f, src=%s)",
+			tr.Subject, tr.Relation, tr.Object, tr.Score, tr.Src)
+	}
+}
+
+func TestExtractorWithDepStub(t *testing.T) {
+	dummy := &dummyParser{}
+	e := NewExtractor(dummy)
+	result := e.Extract("我今天去北京")
+	if result == nil {
+		t.Fatal("expected result")
+	}
+	if len(result.Triples) > 0 {
+		t.Logf("result: src=%s, triples=%+v", result.Src, result.Triples)
+	}
+}
+
+type dummyParser struct{}
+
+func (d *dummyParser) Parse(text string) (*ParseResult, error) {
+	return &ParseResult{}, nil
+}