sdk: embed non-toolchain SDK in third_party, add NoMemory/Cleaner support

- Embed sdk/, example/, meta/, go.mod from homeagent-sdk (no .git)
- Core .gitignore excludes SDK toolchain: bin/, tools/, package/
- RegisterInputChannel + ChannelDef(NoMemory, Cleaner) in SDK
- IOManager input channel registry with GetInputChannelDef
- eventloop: apply channel Cleaner/NoMemory to interrupt text
- context engine: channelDefLookup applied in textForVector
- document store: ChannelCleaner param for archive functions
- All callers/adapters updated with ChannelDef{} default

internal/memory/vector/store.go

@@ -191,12 +191,18 @@ func (v *TFIDFVectorizer) Vectorize(text string) Vector {
 	vec := make(Vector)
 	for f, count := range tf {
 		tfNorm := count / maxTF
-		idf := 1.0
-		if v.totalDocs > 0 {
-			df := v.docFreq[f]
-			if df > 0 {
-				idf = math.Log(float64(v.totalDocs+1)/df+1) + 1
-			}
+		if v.totalDocs < 3 {
+			vec[f] = tfNorm
+			continue
+		}
+		df := v.docFreq[f]
+		if df <= 0 {
+			continue
+		}
+		// 平滑 IDF，高频词趋近 0，低频词趋近 log(N)
+		idf := math.Log(float64(v.totalDocs+1) / (df + 1))
+		if idf < 0.1 {
+			continue
 		}
 		vec[f] = tfNorm * idf
 	}

internal/memory/vector/store_test.go

@@ -149,7 +149,7 @@ func TestStoreInsertAndSearch(t *testing.T) {
 func TestStoreRemove(t *testing.T) {
 	s := NewStore()
 	v := NewTFIDFVectorizer(NGramTokenizer(1))
-	v.Train([]string{"a"})
+	v.Train([]string{"hello world", "hello a", "foo bar", "baz qux", "test doc"})
 
 	s.Insert("1", "a", v.Vectorize("a"), nil)
 	s.Insert("2", "a", v.Vectorize("a"), nil)