diff --git a/internal/common/kg_scoring.go b/internal/common/kg_scoring.go
new file mode 100644
index 0000000000..977ad32644
--- /dev/null
+++ b/internal/common/kg_scoring.go
@@ -0,0 +1,282 @@
+//
+//  Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
+//
+//  Licensed under the Apache License, Version 2.0 (the "License");
+//  you may not use this file except in compliance with the License.
+//  You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+//  Unless required by applicable law or agreed to in writing, software
+//  distributed under the License is distributed on an "AS IS" BASIS,
+//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//  See the License for the specific language governing permissions and
+//  limitations under the License.
+//
+
+package common
+
+import (
+	"fmt"
+	"sort"
+	"strings"
+)
+
+// KGEntity represents a knowledge graph entity with its scores.
+type KGEntity struct {
+	Sim         float64
+	PageRank    float64
+	Description string
+	NhopEnts    []NhopEntity
+}
+
+// NhopEntity represents an N-hop neighbor path.
+type NhopEntity struct {
+	Path    []string  // entity names along the path
+	Weights []float64 // pagerank weights per hop
+}
+
+// KGRelation represents a knowledge graph relation with its scores.
+type KGRelation struct {
+	Sim         float64
+	PageRank    float64
+	Description string
+}
+
+// Edge represents a directed (from_entity, to_entity) pair.
+type Edge struct {
+	From, To string
+}
+
+// EdgeScore represents the accumulated score for an edge from N-hop analysis.
+type EdgeScore struct {
+	Sim      float64
+	PageRank float64
+}
+
+// ScoredEntity is a scored entity ready for output.
+type ScoredEntity struct {
+	Entity      string
+	Score       float64
+	Description string
+}
+
+// ScoredRelation is a scored relation ready for output.
+type ScoredRelation struct {
+	From        string
+	To          string
+	Score       float64
+	Description string
+}
+
+// AnalyzeNHopPaths decomposes N-hop paths into edges with distance-decayed scores.
+// Python equivalent: rag/graphrag/search.py lines 172-187
+func AnalyzeNHopPaths(entsFromQuery map[string]*KGEntity) map[Edge]EdgeScore {
+	nhopPathes := make(map[Edge]EdgeScore)
+	for _, ent := range entsFromQuery {
+		for _, nbr := range ent.NhopEnts {
+			path := nbr.Path
+			weights := nbr.Weights
+			for i := 0; i < len(path)-1; i++ {
+				f, t := path[i], path[i+1]
+				edge := Edge{From: f, To: t}
+				es := nhopPathes[edge]
+				es.Sim += ent.Sim / (2.0 + float64(i))
+				if i < len(weights) {
+					es.PageRank = weights[i]
+				}
+				nhopPathes[edge] = es
+			}
+		}
+	}
+	return nhopPathes
+}
+
+// DoubleHitBoost doubles the similarity of entities found in both
+// keyword search and type search. Python equivalent: lines 194-198
+func DoubleHitBoost(entsFromQuery map[string]*KGEntity, entsFromTypes map[string]struct{}) {
+	for ent := range entsFromQuery {
+		if _, ok := entsFromTypes[ent]; ok {
+			entsFromQuery[ent].Sim *= 2
+		}
+	}
+}
+
+// FuseRelationScores integrates N-hop contributions and type boosts
+// into relation scores. New edges from N-hop are added as relations.
+// Python equivalent: lines 200-222
+func FuseRelationScores(
+	relsFromText map[Edge]*KGRelation,
+	entsFromTypes map[string]struct{},
+	nhopPathes map[Edge]EdgeScore,
+) {
+	// Boost existing relations with N-hop and type scores
+	for edge, rel := range relsFromText {
+		s := 0.0
+		if np, ok := nhopPathes[edge]; ok {
+			s += np.Sim
+			delete(nhopPathes, edge)
+		}
+		if _, ok := entsFromTypes[edge.From]; ok {
+			s += 1
+		}
+		if _, ok := entsFromTypes[edge.To]; ok {
+			s += 1
+		}
+		rel.Sim *= s + 1
+	}
+
+	// N-hop discovered edges become new relations
+	for edge, np := range nhopPathes {
+		s := 0.0
+		if _, ok := entsFromTypes[edge.From]; ok {
+			s += 1
+		}
+		if _, ok := entsFromTypes[edge.To]; ok {
+			s += 1
+		}
+		relsFromText[edge] = &KGRelation{
+			Sim:      np.Sim * (s + 1),
+			PageRank: np.PageRank,
+		}
+	}
+}
+
+// SortAndTrimEntities sorts entities by sim*pagerank and takes top N.
+// Python equivalent: lines 224-225
+func SortAndTrimEntities(entsFromQuery map[string]*KGEntity, topN int) []ScoredEntity {
+	if topN <= 0 {
+		topN = 6
+	}
+	var scored []ScoredEntity
+	for name, ent := range entsFromQuery {
+		scored = append(scored, ScoredEntity{
+			Entity:      name,
+			Score:       ent.Sim * ent.PageRank,
+			Description: ent.Description,
+		})
+	}
+	sort.Slice(scored, func(i, j int) bool {
+		return scored[i].Score > scored[j].Score
+	})
+	if len(scored) > topN {
+		scored = scored[:topN]
+	}
+	return scored
+}
+
+// SortAndTrimRelations sorts relations by sim*pagerank and takes top N.
+// Python equivalent: lines 226-227
+func SortAndTrimRelations(relsFromText map[Edge]*KGRelation, topN int) []ScoredRelation {
+	if topN <= 0 {
+		topN = 6
+	}
+	var scored []ScoredRelation
+	for edge, rel := range relsFromText {
+		scored = append(scored, ScoredRelation{
+			From:        edge.From,
+			To:          edge.To,
+			Score:       rel.Sim * rel.PageRank,
+			Description: rel.Description,
+		})
+	}
+	sort.Slice(scored, func(i, j int) bool {
+		return scored[i].Score > scored[j].Score
+	})
+	if len(scored) > topN {
+		scored = scored[:topN]
+	}
+	return scored
+}
+
+// NumTokensFromString estimates the number of tokens in a string.
+// Uses a simple approximation: len/4 characters per token (roughly matching cl100k_base).
+func NumTokensFromString(s string) int {
+	return len(s) / 4
+}
+
+// FormatEntitiesToCSV formats scored entities as a CSV string and tracks token count.
+func FormatEntitiesToCSV(entities []ScoredEntity, maxToken int) (csv string, remainingToken int) {
+	if len(entities) == 0 {
+		return "", maxToken
+	}
+	var b strings.Builder
+	b.WriteString("---- Entities ----\n")
+	b.WriteString("Entity,Score,Description\n")
+	for i, ent := range entities {
+		desc := extractDescription(ent.Description)
+		line := fmt.Sprintf("%s,%.2f,%s\n", ent.Entity, ent.Score, desc)
+		tokens := NumTokensFromString(line)
+		if maxToken-tokens <= 0 {
+			entities = entities[:i]
+			break
+		}
+		b.WriteString(line)
+		maxToken -= tokens
+	}
+	return b.String(), maxToken
+}
+
+// FormatRelationsToCSV formats scored relations as a CSV string and tracks token count.
+func FormatRelationsToCSV(relations []ScoredRelation, maxToken int) (csv string, remainingToken int) {
+	if len(relations) == 0 {
+		return "", maxToken
+	}
+	var b strings.Builder
+	b.WriteString("---- Relations ----\n")
+	b.WriteString("From Entity,To Entity,Score,Description\n")
+	for i, rel := range relations {
+		desc := extractDescription(rel.Description)
+		line := fmt.Sprintf("%s,%s,%.2f,%s\n", rel.From, rel.To, rel.Score, desc)
+		tokens := NumTokensFromString(line)
+		if maxToken-tokens <= 0 {
+			relations = relations[:i]
+			break
+		}
+		b.WriteString(line)
+		maxToken -= tokens
+	}
+	return b.String(), maxToken
+}
+
+// BuildKGContent assembles the final knowledge graph content string.
+// Python equivalent: lines 267-291
+func BuildKGContent(
+	entities []ScoredEntity,
+	relations []ScoredRelation,
+	maxToken int,
+) string {
+	entityCSV, remaining := FormatEntitiesToCSV(entities, maxToken)
+	relCSV, _ := FormatRelationsToCSV(relations, remaining)
+	return entityCSV + relCSV
+}
+
+// extractDescription tries to parse a description from a JSON-like string.
+// Python equivalent: json.loads(desc).get("description", "")
+func extractDescription(desc string) string {
+	if desc == "" {
+		return ""
+	}
+	// If the description looks like JSON, try to extract the "description" field
+	desc = strings.TrimSpace(desc)
+	if strings.HasPrefix(desc, "{") && strings.HasSuffix(desc, "}") {
+		// Simple extraction: find "description" key value
+		// This matches Python's json.loads(desc).get("description", "") behavior
+		idx := strings.Index(desc, `"description"`)
+		if idx >= 0 {
+			remain := desc[idx+len(`"description"`):]
+			colonIdx := strings.Index(remain, ":")
+			if colonIdx >= 0 {
+				valPart := strings.TrimSpace(remain[colonIdx+1:])
+				if strings.HasPrefix(valPart, `"`) {
+					valPart = strings.TrimPrefix(valPart, `"`)
+					endQuote := strings.Index(valPart, `"`)
+					if endQuote >= 0 {
+						return valPart[:endQuote]
+					}
+				}
+			}
+		}
+	}
+	return desc
+}
diff --git a/internal/common/kg_scoring_test.go b/internal/common/kg_scoring_test.go
new file mode 100644
index 0000000000..12da289004
--- /dev/null
+++ b/internal/common/kg_scoring_test.go
@@ -0,0 +1,263 @@
+//
+//  Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
+//
+//  Licensed under the Apache License, Version 2.0 (the "License");
+//  you may not use this file except in compliance with the License.
+//  You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+//  Unless required by applicable law or agreed to in writing, software
+//  distributed under the License is distributed on an "AS IS" BASIS,
+//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//  See the License for the specific language governing permissions and
+//  limitations under the License.
+//
+
+package common
+
+import (
+	"strings"
+	"testing"
+)
+
+// --- AnalyzeNHopPaths ---
+
+func TestAnalyzeNHopPaths_Basic(t *testing.T) {
+	ents := map[string]*KGEntity{
+		"A": {
+			Sim: 0.9,
+			NhopEnts: []NhopEntity{
+				{Path: []string{"A", "B", "C"}, Weights: []float64{0.8, 0.5}},
+			},
+		},
+	}
+	result := AnalyzeNHopPaths(ents)
+	// A→B: 0.9 / (2+0) = 0.45
+	// B→C: 0.9 / (2+1) = 0.3
+	if len(result) != 2 {
+		t.Fatalf("expected 2 edges, got %d", len(result))
+	}
+	if result[Edge{"A", "B"}].Sim != 0.45 {
+		t.Errorf("expected A→B sim=0.45, got %f", result[Edge{"A", "B"}].Sim)
+	}
+	if result[Edge{"B", "C"}].Sim != 0.3 {
+		t.Errorf("expected B→C sim=0.3, got %f", result[Edge{"B", "C"}].Sim)
+	}
+}
+
+func TestAnalyzeNHopPaths_MultipleContributors(t *testing.T) {
+	ents := map[string]*KGEntity{
+		"A": {
+			Sim: 0.8,
+			NhopEnts: []NhopEntity{
+				{Path: []string{"A", "B"}, Weights: []float64{0.7}},
+			},
+		},
+		"X": {
+			Sim: 0.6,
+			NhopEnts: []NhopEntity{
+				{Path: []string{"X", "B"}, Weights: []float64{0.5}},
+			},
+		},
+	}
+	result := AnalyzeNHopPaths(ents)
+	// A→B: 0.8 / 2 = 0.4
+	// X→B: 0.6 / 2 = 0.3
+	if result[Edge{"A", "B"}].Sim != 0.4 {
+		t.Errorf("expected A→B sim=0.4, got %f", result[Edge{"A", "B"}].Sim)
+	}
+	if result[Edge{"X", "B"}].Sim != 0.3 {
+		t.Errorf("expected X→B sim=0.3, got %f", result[Edge{"X", "B"}].Sim)
+	}
+}
+
+func TestAnalyzeNHopPaths_Empty(t *testing.T) {
+	result := AnalyzeNHopPaths(nil)
+	if len(result) != 0 {
+		t.Errorf("expected empty, got %d", len(result))
+	}
+}
+
+// --- DoubleHitBoost ---
+
+func TestDoubleHitBoost(t *testing.T) {
+	ents := map[string]*KGEntity{
+		"A": {Sim: 0.5},
+		"B": {Sim: 0.3},
+	}
+	types := map[string]struct{}{"A": {}}
+	DoubleHitBoost(ents, types)
+	if ents["A"].Sim != 1.0 {
+		t.Errorf("expected A sim=1.0 after boost, got %f", ents["A"].Sim)
+	}
+	if ents["B"].Sim != 0.3 {
+		t.Errorf("expected B sim unchanged at 0.3, got %f", ents["B"].Sim)
+	}
+}
+
+func TestDoubleHitBoost_Empty(t *testing.T) {
+	ents := map[string]*KGEntity{"A": {Sim: 0.5}}
+	DoubleHitBoost(ents, map[string]struct{}{})
+	if ents["A"].Sim != 0.5 {
+		t.Errorf("expected unchanged, got %f", ents["A"].Sim)
+	}
+}
+
+// --- FuseRelationScores ---
+
+func TestFuseRelationScores_NhopContribution(t *testing.T) {
+	rels := map[Edge]*KGRelation{
+		{"A", "B"}: {Sim: 0.5, PageRank: 0.8},
+	}
+	types := map[string]struct{}{}
+	nhop := map[Edge]EdgeScore{
+		{"A", "B"}: {Sim: 0.3},
+	}
+	FuseRelationScores(rels, types, nhop)
+	// sim = 0.5 * (0.3 + 1) = 0.65
+	if rels[Edge{"A", "B"}].Sim != 0.65 {
+		t.Errorf("expected 0.65, got %f", rels[Edge{"A", "B"}].Sim)
+	}
+}
+
+func TestFuseRelationScores_TypeBoost(t *testing.T) {
+	rels := map[Edge]*KGRelation{
+		{"A", "B"}: {Sim: 0.5},
+	}
+	types := map[string]struct{}{"A": {}, "B": {}}
+	nhop := map[Edge]EdgeScore{}
+	FuseRelationScores(rels, types, nhop)
+	// Both endpoints in types: s=2, sim = 0.5 * (2+1) = 1.5
+	if rels[Edge{"A", "B"}].Sim != 1.5 {
+		t.Errorf("expected 1.5, got %f", rels[Edge{"A", "B"}].Sim)
+	}
+}
+
+func TestFuseRelationScores_NhopNewEdge(t *testing.T) {
+	rels := map[Edge]*KGRelation{}
+	types := map[string]struct{}{}
+	nhop := map[Edge]EdgeScore{
+		{"A", "B"}: {Sim: 0.4, PageRank: 0.7},
+	}
+	FuseRelationScores(rels, types, nhop)
+	if _, ok := rels[Edge{"A", "B"}]; !ok {
+		t.Fatal("expected new edge from N-hop")
+	}
+	if rels[Edge{"A", "B"}].Sim != 0.4 {
+		t.Errorf("expected sim=0.4, got %f", rels[Edge{"A", "B"}].Sim)
+	}
+}
+
+// --- SortAndTrim ---
+
+func TestSortAndTrimEntities(t *testing.T) {
+	ents := map[string]*KGEntity{
+		"A": {Sim: 0.5, PageRank: 0.9},
+		"B": {Sim: 0.8, PageRank: 0.3},
+		"C": {Sim: 0.9, PageRank: 0.1},
+	}
+	result := SortAndTrimEntities(ents, 2)
+	if len(result) != 2 {
+		t.Fatalf("expected 2, got %d", len(result))
+	}
+	// A: 0.45, B: 0.24, C: 0.09 → top 2 should be A, B
+	if result[0].Entity != "A" {
+		t.Errorf("expected A first (0.45), got %s (%f)", result[0].Entity, result[0].Score)
+	}
+}
+
+func TestSortAndTrimEntities_DefaultTopN(t *testing.T) {
+	ents := map[string]*KGEntity{
+		"A": {Sim: 0.5, PageRank: 0.9},
+		"B": {Sim: 0.8, PageRank: 0.3},
+	}
+	result := SortAndTrimEntities(ents, 0)
+	if len(result) != 2 {
+		t.Errorf("expected default topN to include all, got %d", len(result))
+	}
+}
+
+func TestSortAndTrimRelations(t *testing.T) {
+	rels := map[Edge]*KGRelation{
+		{"A", "B"}: {Sim: 0.9, PageRank: 0.1},
+		{"C", "D"}: {Sim: 0.3, PageRank: 0.8},
+	}
+	result := SortAndTrimRelations(rels, 1)
+	if len(result) != 1 {
+		t.Fatalf("expected 1, got %d", len(result))
+	}
+	// A→B: 0.09, C→D: 0.24 → C→D should be first
+	if result[0].From != "C" {
+		t.Errorf("expected C first (0.24), got %s (%f)", result[0].From, result[0].Score)
+	}
+}
+
+// --- Format and Build ---
+
+func TestBuildKGContent_Basic(t *testing.T) {
+	entities := []ScoredEntity{
+		{Entity: "A", Score: 0.45, Description: `{"description": "Entity A desc"}`},
+	}
+	relations := []ScoredRelation{
+		{From: "A", To: "B", Score: 0.3, Description: `{"description": "rel A-B"}`},
+	}
+	result := BuildKGContent(entities, relations, 10000)
+	if !contains(result, "Entity A desc") {
+		t.Errorf("expected entity description in output, got: %s", result)
+	}
+	if !contains(result, "rel A-B") {
+		t.Errorf("expected relation description in output, got: %s", result)
+	}
+}
+
+func TestBuildKGContent_TokenBudget(t *testing.T) {
+	longDesc := strings.Repeat("This is a very long description. ", 50)
+	entities := []ScoredEntity{
+		{Entity: "LongEntityName", Score: 1.0, Description: longDesc},
+	}
+	relations := []ScoredRelation{
+		{From: "X", To: "Y", Score: 1.0, Description: "relation desc"},
+	}
+	result := BuildKGContent(entities, relations, 50)
+	// Token budget is very small, should truncate and not include relations
+	if contains(result, "relation desc") {
+		t.Log("Note: relations included despite small budget (depending on token count)")
+	}
+}
+
+func TestExtractDescription_JSON(t *testing.T) {
+	result := extractDescription(`{"description": "Entity A description", "other": "value"}`)
+	if result != "Entity A description" {
+		t.Errorf("expected 'Entity A description', got %q", result)
+	}
+}
+
+func TestExtractDescription_Plain(t *testing.T) {
+	result := extractDescription("plain description")
+	if result != "plain description" {
+		t.Errorf("expected 'plain description', got %q", result)
+	}
+}
+
+func TestNumTokensFromString(t *testing.T) {
+	s := "This is a test string with multiple words"
+	tokens := NumTokensFromString(s)
+	if tokens <= 0 {
+		t.Errorf("expected positive token count, got %d", tokens)
+	}
+}
+
+// contains checks if a string contains a substring.
+func contains(s, substr string) bool {
+	return len(s) >= len(substr) && containsStr(s, substr)
+}
+
+func containsStr(s, substr string) bool {
+	for i := 0; i <= len(s)-len(substr); i++ {
+		if s[i:i+len(substr)] == substr {
+			return true
+		}
+	}
+	return false
+}