diff --git a/api/core/rag/datasource/keyword/keyword_factory.py b/api/core/rag/datasource/keyword/keyword_factory.py
index f1a6ade91f..5e30c873a7 100644
--- a/api/core/rag/datasource/keyword/keyword_factory.py
+++ b/api/core/rag/datasource/keyword/keyword_factory.py
@@ -22,8 +22,10 @@ class Keyword:
         match keyword_type:
             case KeyWordType.JIEBA:
                 from core.rag.datasource.keyword.jieba.jieba import Jieba
-
                 return Jieba
+            case KeyWordType.MECAB:
+                from core.rag.datasource.keyword.mecab.mecab import MeCab
+                return MeCab
             case _:
                 raise ValueError(f"Keyword store {keyword_type} is not supported.")
 
diff --git a/api/core/rag/datasource/keyword/keyword_type.py b/api/core/rag/datasource/keyword/keyword_type.py
index d845c7111d..a4fb63b794 100644
--- a/api/core/rag/datasource/keyword/keyword_type.py
+++ b/api/core/rag/datasource/keyword/keyword_type.py
@@ -3,3 +3,4 @@ from enum import StrEnum
 
 class KeyWordType(StrEnum):
     JIEBA = "jieba"
+    MECAB = "mecab"
diff --git a/api/core/rag/datasource/keyword/mecab/README.md b/api/core/rag/datasource/keyword/mecab/README.md
new file mode 100644
index 0000000000..f589c96d44
--- /dev/null
+++ b/api/core/rag/datasource/keyword/mecab/README.md
@@ -0,0 +1,273 @@
+# MeCab Keyword Processor
+
+A Japanese text keyword extraction module using MeCab morphological analyzer for the Dify RAG system.
+
+## Overview
+
+This module provides Japanese text keyword extraction capabilities using the MeCab morphological analyzer. It's designed to:
+
+- Extract meaningful keywords from Japanese text
+- Handle compound words and technical terms
+- Support custom dictionaries
+- Provide configurable scoring based on parts of speech
+- Handle mixed Japanese-English text
+
+## Components
+
+### 1. MeCabKeywordTableHandler
+
+The core component responsible for keyword extraction using MeCab:
+
+```python
+handler = MeCabKeywordTableHandler(
+    dictionary_path="/path/to/dict",    # Optional custom dictionary
+    user_dictionary_path="/path/to/user_dict"  # Optional user dictionary
+)
+keywords = handler.extract_keywords(text, max_keywords=10)
+```
+
+#### Features:
+
+- **Part of Speech (POS) Weighting**:
+
+  ```python
+  pos_weights = {
+      '名詞': 1.0,      # Nouns
+      '動詞': 0.8,      # Verbs
+      '形容詞': 0.6,    # Adjectives
+      '副詞': 0.4,      # Adverbs
+      '連体詞': 0.3,    # Adnominal adjectives
+      '感動詞': 0.2,    # Interjections
+  }
+  ```
+
+- **Special Term Handling**:
+  - Boosts scores for proper nouns (固有名詞)
+  - Boosts scores for technical terms (専門用語)
+  - Compound word detection (e.g., "機械学習", "自然言語処理")
+
+- **Reading Normalization**:
+  - Handles different forms of the same word
+  - Normalizes compound terms using readings
+
+### 2. Configuration (MeCabConfig)
+
+Configurable settings for the processor:
+
+```python
+class MeCabConfig(BaseModel):
+    max_keywords_per_chunk: int = 10
+    min_keyword_length: int = 2
+    score_threshold: float = 0.3
+    storage_type: str = "database"
+    cache_timeout: int = 3600
+    dictionary_path: str = ""
+    user_dictionary_path: str = ""
+    pos_weights: dict = {...}
+```
+
+### 3. Stopwords
+
+Comprehensive Japanese stopword list including:
+
+- Particles (は, が, の, etc.)
+- Auxiliary verbs (です, ます, etc.)
+- Pronouns (これ, それ, etc.)
+- Common words
+- Numbers and punctuation
+- Common English stopwords for mixed text
+
+## Usage
+
+### Basic Usage
+
+```python
+from core.rag.datasource.keyword.keyword_factory import Keyword
+from models.dataset import Dataset
+
+# Initialize
+dataset = Dataset(...)
+keyword_processor = Keyword(dataset)  # Will use MeCab if KEYWORD_STORE = "mecab"
+
+# Process text
+documents = [
+    Document(
+        page_content="自然言語処理は人工知能の重要な分野です。",
+        metadata={"doc_id": "1", ...}
+    )
+]
+keyword_processor.create(documents)
+
+# Search
+results = keyword_processor.search("自然言語処理について")
+```
+
+### Custom Dictionary Usage
+
+```python
+# In your configuration:
+KEYWORD_PROCESSOR_CONFIG = {
+    "dictionary_path": "/path/to/mecab/dict",
+    "user_dictionary_path": "/path/to/user.dic",
+    "pos_weights": {
+        "名詞": 1.2,
+        "動詞": 0.8,
+        # ... customize weights
+    }
+}
+```
+
+## Features
+
+### 1. Keyword Extraction
+
+- **POS-based Scoring**:
+  - Weights different parts of speech
+  - Boosts important terms
+  - Configurable scoring thresholds
+
+- **Compound Word Detection**:
+
+  ```python
+  # Input text: "自然言語処理の研究"
+  # Detected compounds:
+  # - "自然言語"
+  # - "自然言語処理"
+  # - "言語処理"
+  ```
+
+- **Reading Normalization**:
+
+  ```python
+  # Handles variations:
+  # - "データベース" (katakana)
+  # - "データベース" (with readings)
+  # Both normalize to same term
+  ```
+
+### 2. Storage
+
+- **Flexible Storage Options**:
+  - Database storage
+  - File-based storage
+  - Redis-based locking for concurrency
+
+- **Data Structure**:
+
+  ```python
+  {
+      "__type__": "keyword_table",
+      "__data__": {
+          "index_id": "dataset_id",
+          "table": {
+              "keyword1": ["doc_id1", "doc_id2"],
+              "keyword2": ["doc_id2", "doc_id3"],
+          }
+      }
+  }
+  ```
+
+### 3. Error Handling
+
+- Comprehensive error handling
+- Custom exception classes
+- Logging integration
+- Graceful fallbacks
+
+## Performance Considerations
+
+1. **Memory Usage**:
+   - Efficient keyword table structure
+   - Batch processing support
+   - Caching mechanisms
+
+2. **Concurrency**:
+   - Redis-based locking
+   - Transaction handling
+   - Safe concurrent access
+
+3. **Optimization Tips**:
+   - Use appropriate batch sizes
+   - Configure caching timeouts
+   - Adjust scoring thresholds
+
+## Dependencies
+
+- MeCab and Python bindings:
+
+  ```bash
+  # Ubuntu/Debian
+  apt-get install mecab mecab-ipadic-utf8 python3-mecab
+
+  # macOS
+  brew install mecab mecab-ipadic
+  pip install mecab-python3
+  ```
+
+## Best Practices
+
+1. **Dictionary Management**:
+   - Keep dictionaries updated
+   - Use domain-specific user dictionaries
+   - Regular maintenance of custom terms
+
+2. **Configuration Tuning**:
+   - Adjust POS weights for your use case
+   - Set appropriate thresholds
+   - Monitor and adjust batch sizes
+
+3. **Error Handling**:
+   - Implement proper logging
+   - Monitor extraction quality
+   - Handle edge cases
+
+## Testing
+
+Example test cases:
+
+```python
+def test_basic_extraction():
+    text = "自然言語処理は人工知能の重要な分野です。"
+    keywords = handler.extract_keywords(text)
+    assert "自然言語処理" in keywords
+    assert "人工知能" in keywords
+
+def test_compound_words():
+    text = "機械学習モデルを使った自然言語処理"
+    keywords = handler.extract_keywords(text)
+    assert "機械学習" in keywords
+    assert "自然言語処理" in keywords
+
+def test_mixed_text():
+    text = "AIを使った自然言語処理のResearch"
+    keywords = handler.extract_keywords(text)
+    assert "AI" in keywords
+    assert "自然言語処理" in keywords
+    assert "Research" in keywords
+```
+
+## Common Issues and Solutions
+
+1. **Dictionary Loading Failures**:
+
+   ```python
+   try:
+       handler = MeCabKeywordTableHandler(dictionary_path=path)
+   except RuntimeError as e:
+       # Handle dictionary loading error
+   ```
+
+2. **Memory Usage**:
+
+   ```python
+   # Use batch processing for large datasets
+   for batch in chunks(documents, size=100):
+       process_batch(batch)
+   ```
+
+3. **Concurrent Access**:
+
+   ```python
+   with redis_client.lock(f"lock_{dataset_id}"):
+       # Safe concurrent operations
+   ```
diff --git a/api/core/rag/datasource/keyword/mecab/config.py b/api/core/rag/datasource/keyword/mecab/config.py
new file mode 100644
index 0000000000..0abfc24a3d
--- /dev/null
+++ b/api/core/rag/datasource/keyword/mecab/config.py
@@ -0,0 +1,19 @@
+from pydantic import BaseModel
+
+class MeCabConfig(BaseModel):
+    """Configuration for MeCab keyword processor."""
+    max_keywords_per_chunk: int = 10
+    min_keyword_length: int = 2
+    score_threshold: float = 0.3
+    storage_type: str = "database"
+    cache_timeout: int = 3600
+    
+    # MeCab specific settings
+    dictionary_path: str = ""  # Optional custom dictionary path
+    user_dictionary_path: str = ""  # Optional user dictionary path
+    pos_weights: dict = {
+        '名詞': 1.0,      # Nouns
+        '動詞': 0.8,      # Verbs
+        '形容詞': 0.6,    # Adjectives
+        '副詞': 0.4,      # Adverbs
+    } 
diff --git a/api/core/rag/datasource/keyword/mecab/mecab.py b/api/core/rag/datasource/keyword/mecab/mecab.py
new file mode 100644
index 0000000000..f40e3c229e
--- /dev/null
+++ b/api/core/rag/datasource/keyword/mecab/mecab.py
@@ -0,0 +1,287 @@
+import json
+import logging
+from typing import Any, Optional
+from collections import defaultdict
+
+from core.rag.datasource.keyword.keyword_base import BaseKeyword
+from core.rag.datasource.keyword.mecab.mecab_keyword_table_handler import MeCabKeywordTableHandler
+from core.rag.datasource.keyword.mecab.config import MeCabConfig
+from core.rag.models.document import Document
+from extensions.ext_database import db
+from extensions.ext_redis import redis_client
+from extensions.ext_storage import storage
+from models.dataset import Dataset, DatasetKeywordTable, DocumentSegment
+
+
+logger = logging.getLogger(__name__)
+
+
+class KeywordProcessorError(Exception):
+    """Base error for keyword processing."""
+    pass
+
+
+class KeywordExtractionError(KeywordProcessorError):
+    """Error during keyword extraction."""
+    pass
+
+
+class KeywordStorageError(KeywordProcessorError):
+    """Error during storage operations."""
+    pass
+
+
+class SetEncoder(json.JSONEncoder):
+    """JSON encoder that handles sets."""
+    def default(self, obj):
+        if isinstance(obj, set):
+            return list(obj)
+        return super().default(obj)
+
+
+class MeCab(BaseKeyword):
+    """Japanese keyword processor using MeCab morphological analyzer."""
+    
+    def __init__(self, dataset: Dataset):
+        super().__init__(dataset)
+        self._config = MeCabConfig()
+        self._keyword_handler = None
+        self._init_handler()
+    
+    def _init_handler(self):
+        """Initialize MeCab handler with configuration."""
+        try:
+            self._keyword_handler = MeCabKeywordTableHandler(
+                dictionary_path=self._config.dictionary_path,
+                user_dictionary_path=self._config.user_dictionary_path
+            )
+            if self._config.pos_weights:
+                self._keyword_handler.pos_weights = self._config.pos_weights
+                self._keyword_handler.min_score = self._config.score_threshold
+        except Exception as e:
+            logger.error(f"Failed to initialize MeCab handler: {str(e)}")
+            raise KeywordProcessorError(f"MeCab initialization failed: {str(e)}")
+    
+    def create(self, texts: list[Document], **kwargs) -> BaseKeyword:
+        """Create keyword index for documents."""
+        lock_name = f"keyword_indexing_lock_{self.dataset.id}"
+        with redis_client.lock(lock_name, timeout=600):
+            keyword_table = self._get_dataset_keyword_table()
+            
+            for text in texts:
+                keywords = self._keyword_handler.extract_keywords(
+                    text.page_content,
+                    self._config.max_keywords_per_chunk
+                )
+                if text.metadata is not None:
+                    self._update_segment_keywords(
+                        self.dataset.id,
+                        text.metadata["doc_id"],
+                        list(keywords)
+                    )
+                    keyword_table = self._add_text_to_keyword_table(
+                        keyword_table or {},
+                        text.metadata["doc_id"],
+                        list(keywords)
+                    )
+            
+            self._save_dataset_keyword_table(keyword_table)
+            return self
+    
+    def add_texts(self, texts: list[Document], **kwargs):
+        """Add new texts to existing index."""
+        lock_name = f"keyword_indexing_lock_{self.dataset.id}"
+        with redis_client.lock(lock_name, timeout=600):
+            keyword_table = self._get_dataset_keyword_table()
+            keywords_list = kwargs.get("keywords_list")
+            
+            for i, text in enumerate(texts):
+                if keywords_list:
+                    keywords = keywords_list[i]
+                    if not keywords:
+                        keywords = self._keyword_handler.extract_keywords(
+                            text.page_content,
+                            self._config.max_keywords_per_chunk
+                        )
+                else:
+                    keywords = self._keyword_handler.extract_keywords(
+                        text.page_content,
+                        self._config.max_keywords_per_chunk
+                    )
+                
+                if text.metadata is not None:
+                    self._update_segment_keywords(
+                        self.dataset.id,
+                        text.metadata["doc_id"],
+                        list(keywords)
+                    )
+                    keyword_table = self._add_text_to_keyword_table(
+                        keyword_table or {},
+                        text.metadata["doc_id"],
+                        list(keywords)
+                    )
+            
+            self._save_dataset_keyword_table(keyword_table)
+    
+    def text_exists(self, id: str) -> bool:
+        """Check if text exists in index."""
+        keyword_table = self._get_dataset_keyword_table()
+        if keyword_table is None:
+            return False
+        return id in set.union(*keyword_table.values()) if keyword_table else False
+    
+    def delete_by_ids(self, ids: list[str]) -> None:
+        """Delete texts by IDs."""
+        lock_name = f"keyword_indexing_lock_{self.dataset.id}"
+        with redis_client.lock(lock_name, timeout=600):
+            keyword_table = self._get_dataset_keyword_table()
+            if keyword_table is not None:
+                keyword_table = self._delete_ids_from_keyword_table(keyword_table, ids)
+            self._save_dataset_keyword_table(keyword_table)
+    
+    def delete(self) -> None:
+        """Delete entire index."""
+        lock_name = f"keyword_indexing_lock_{self.dataset.id}"
+        with redis_client.lock(lock_name, timeout=600):
+            dataset_keyword_table = self.dataset.dataset_keyword_table
+            if dataset_keyword_table:
+                db.session.delete(dataset_keyword_table)
+                db.session.commit()
+                if dataset_keyword_table.data_source_type != "database":
+                    file_key = f"keyword_files/{self.dataset.tenant_id}/{self.dataset.id}.txt"
+                    storage.delete(file_key)
+    
+    def search(self, query: str, **kwargs: Any) -> list[Document]:
+        """Search documents using keywords."""
+        keyword_table = self._get_dataset_keyword_table()
+        k = kwargs.get("top_k", 4)
+        
+        sorted_chunk_indices = self._retrieve_ids_by_query(
+            keyword_table or {},
+            query,
+            k
+        )
+        
+        documents = []
+        for chunk_index in sorted_chunk_indices:
+            segment = (
+                db.session.query(DocumentSegment)
+                .filter(
+                    DocumentSegment.dataset_id == self.dataset.id,
+                    DocumentSegment.index_node_id == chunk_index
+                )
+                .first()
+            )
+            
+            if segment:
+                documents.append(
+                    Document(
+                        page_content=segment.content,
+                        metadata={
+                            "doc_id": chunk_index,
+                            "doc_hash": segment.index_node_hash,
+                            "document_id": segment.document_id,
+                            "dataset_id": segment.dataset_id,
+                        },
+                    )
+                )
+        
+        return documents
+    
+    def _get_dataset_keyword_table(self) -> Optional[dict]:
+        """Get keyword table from storage."""
+        dataset_keyword_table = self.dataset.dataset_keyword_table
+        if dataset_keyword_table:
+            keyword_table_dict = dataset_keyword_table.keyword_table_dict
+            if keyword_table_dict:
+                return dict(keyword_table_dict["__data__"]["table"])
+        return {}
+    
+    def _save_dataset_keyword_table(self, keyword_table):
+        """Save keyword table to storage."""
+        table_dict = {
+            "__type__": "keyword_table",
+            "__data__": {
+                "index_id": self.dataset.id,
+                "summary": None,
+                "table": keyword_table
+            }
+        }
+        
+        dataset_keyword_table = self.dataset.dataset_keyword_table
+        data_source_type = dataset_keyword_table.data_source_type
+        
+        if data_source_type == "database":
+            dataset_keyword_table.keyword_table = json.dumps(table_dict, cls=SetEncoder)
+            db.session.commit()
+        else:
+            file_key = f"keyword_files/{self.dataset.tenant_id}/{self.dataset.id}.txt"
+            if storage.exists(file_key):
+                storage.delete(file_key)
+            storage.save(
+                file_key,
+                json.dumps(table_dict, cls=SetEncoder).encode("utf-8")
+            )
+    
+    def _add_text_to_keyword_table(self, keyword_table: dict, id: str, keywords: list[str]) -> dict:
+        """Add text keywords to table."""
+        for keyword in keywords:
+            if keyword not in keyword_table:
+                keyword_table[keyword] = set()
+            keyword_table[keyword].add(id)
+        return keyword_table
+    
+    def _delete_ids_from_keyword_table(self, keyword_table: dict, ids: list[str]) -> dict:
+        """Delete IDs from keyword table."""
+        node_idxs_to_delete = set(ids)
+        keywords_to_delete = set()
+        
+        for keyword, node_idxs in keyword_table.items():
+            if node_idxs_to_delete.intersection(node_idxs):
+                keyword_table[keyword] = node_idxs.difference(node_idxs_to_delete)
+                if not keyword_table[keyword]:
+                    keywords_to_delete.add(keyword)
+        
+        for keyword in keywords_to_delete:
+            del keyword_table[keyword]
+        
+        return keyword_table
+    
+    def _retrieve_ids_by_query(self, keyword_table: dict, query: str, k: int = 4):
+        """Retrieve document IDs by query."""
+        keywords = self._keyword_handler.extract_keywords(query)
+        
+        # Score documents based on matching keywords
+        chunk_indices_count = defaultdict(int)
+        keywords_list = [
+            keyword for keyword in keywords
+            if keyword in set(keyword_table.keys())
+        ]
+        
+        for keyword in keywords_list:
+            for node_id in keyword_table[keyword]:
+                chunk_indices_count[node_id] += 1
+        
+        sorted_chunk_indices = sorted(
+            chunk_indices_count.keys(),
+            key=lambda x: chunk_indices_count[x],
+            reverse=True
+        )
+        
+        return sorted_chunk_indices[:k]
+    
+    def _update_segment_keywords(self, dataset_id: str, node_id: str, keywords: list[str]):
+        """Update segment keywords in database."""
+        document_segment = (
+            db.session.query(DocumentSegment)
+            .filter(
+                DocumentSegment.dataset_id == dataset_id,
+                DocumentSegment.index_node_id == node_id
+            )
+            .first()
+        )
+        
+        if document_segment:
+            document_segment.keywords = keywords
+            db.session.add(document_segment)
+            db.session.commit() 
diff --git a/api/core/rag/datasource/keyword/mecab/mecab_keyword_table_handler.py b/api/core/rag/datasource/keyword/mecab/mecab_keyword_table_handler.py
new file mode 100644
index 0000000000..0eaf230300
--- /dev/null
+++ b/api/core/rag/datasource/keyword/mecab/mecab_keyword_table_handler.py
@@ -0,0 +1,152 @@
+import re
+from typing import Optional, Set
+import MeCab
+from collections import defaultdict
+
+from core.rag.datasource.keyword.mecab.stopwords import STOPWORDS
+
+class MeCabKeywordTableHandler:
+    """Japanese keyword extraction using MeCab morphological analyzer."""
+    
+    def __init__(self, dictionary_path: str = "", user_dictionary_path: str = ""):
+        """Initialize MeCab tokenizer.
+        
+        Args:
+            dictionary_path: Path to custom system dictionary
+            user_dictionary_path: Path to user dictionary
+        """
+        try:
+            # Build MeCab argument string
+            mecab_args = ["-Ochasen"]  # Use ChaSen format for detailed POS info
+            if dictionary_path:
+                mecab_args.append(f"-d {dictionary_path}")
+            if user_dictionary_path:
+                mecab_args.append(f"-u {user_dictionary_path}")
+            
+            self.tagger = MeCab.Tagger(" ".join(mecab_args))
+            self.tagger.parse('')  # Force initialization to catch dictionary errors
+            
+        except RuntimeError as e:
+            raise RuntimeError(f"Failed to initialize MeCab: {str(e)}")
+        
+        # POS weights for scoring
+        self.pos_weights = {
+            '名詞': 1.0,      # Nouns
+            '動詞': 0.8,      # Verbs
+            '形容詞': 0.6,    # Adjectives
+            '副詞': 0.4,      # Adverbs
+            '連体詞': 0.3,    # Adnominal adjectives
+            '感動詞': 0.2,    # Interjections
+        }
+        self.min_score = 0.3
+        
+    def extract_keywords(self, text: str, max_keywords_per_chunk: Optional[int] = 10) -> Set[str]:
+        """Extract keywords from Japanese text using MeCab.
+        
+        Args:
+            text: Input text to extract keywords from
+            max_keywords_per_chunk: Maximum number of keywords to extract
+            
+        Returns:
+            Set of extracted keywords
+        """
+        if not text or not text.strip():
+            return set()
+            
+        try:
+            # Parse text with MeCab
+            self.tagger.parse('')  # Clear tagger state
+            node = self.tagger.parseToNode(text)
+            
+            # Calculate term frequencies and scores
+            term_scores = defaultdict(float)
+            while node:
+                features = node.feature.split(',')
+                if len(features) > 0:
+                    pos = features[0]  # Part of speech
+                    pos_subtype = features[1] if len(features) > 1 else ''
+                    base_form = features[6] if len(features) > 6 else node.surface
+                    
+                    # Score the term based on its POS
+                    if pos in self.pos_weights and base_form not in STOPWORDS:
+                        score = self.pos_weights[pos]
+                        # Boost proper nouns and technical terms
+                        if pos == '名詞' and pos_subtype in ['固有名詞', '専門用語']:
+                            score *= 1.5
+                        if len(base_form) > 1:  # Filter out single characters
+                            term_scores[base_form] += score
+                
+                node = node.next
+                
+            # Get top scoring terms
+            sorted_terms = sorted(
+                term_scores.items(), 
+                key=lambda x: x[1], 
+                reverse=True
+            )
+            
+            # Filter by minimum score and take top N
+            keywords = {
+                term for term, score in sorted_terms 
+                if score >= self.min_score
+            }
+            
+            if max_keywords_per_chunk:
+                keywords = set(list(keywords)[:max_keywords_per_chunk])
+            
+            # Expand with compound terms
+            expanded_keywords = self._expand_tokens_with_compounds(keywords, text)
+            
+            return expanded_keywords
+            
+        except Exception as e:
+            raise RuntimeError(f"Failed to extract keywords: {str(e)}")
+    
+    def _expand_tokens_with_compounds(self, keywords: Set[str], text: str) -> Set[str]:
+        """Expand keywords with compound terms.
+        
+        This method looks for adjacent keywords in the original text to capture
+        compound terms like '機械学習' (machine learning) or '自然言語処理' (natural language processing).
+        """
+        results = set(keywords)
+        
+        try:
+            # Parse again to find compounds
+            node = self.tagger.parseToNode(text)
+            compound = []
+            compound_readings = []  # For handling different forms of the same compound
+            
+            while node:
+                features = node.feature.split(',')
+                if len(features) > 6:
+                    base_form = features[6]
+                    reading = features[7] if len(features) > 7 else None
+                else:
+                    base_form = node.surface
+                    reading = None
+                    
+                if base_form in keywords:
+                    compound.append(base_form)
+                    if reading:
+                        compound_readings.append(reading)
+                else:
+                    if len(compound) > 1:
+                        # Add the compound term
+                        compound_term = ''.join(compound)
+                        if len(compound_term) > 1:
+                            results.add(compound_term)
+                            # If readings are available, add normalized form
+                            if compound_readings:
+                                normalized_term = ''.join(compound_readings)
+                                if normalized_term != compound_term:
+                                    results.add(normalized_term)
+                    compound = []
+                    compound_readings = []
+                
+                node = node.next
+            
+            return results
+            
+        except Exception as e:
+            # If compound expansion fails, return original keywords
+            return keywords 
diff --git a/api/core/rag/datasource/keyword/mecab/stopwords.py b/api/core/rag/datasource/keyword/mecab/stopwords.py
new file mode 100644
index 0000000000..13802ac947
--- /dev/null
+++ b/api/core/rag/datasource/keyword/mecab/stopwords.py
@@ -0,0 +1,36 @@
+STOPWORDS = {
+    # Japanese particles and basic stopwords
+    "は", "が", "の", "に", "を", "で", "へ", "と", "から", "より", "まで", "によって",
+    "あそこ", "あっ", "あの", "あのかた", "あの人", "あり", "あります", "ある", "あれ",
+    "い", "いう", "います", "いる", "う", "うち", "え", "お", "および", "おり", "おります",
+    "か", "かつて", "から", "が", "き", "ここ", "こちら", "こと", "この", "これ", "これら",
+    "さ", "さらに", "し", "しかし", "する", "ず", "せ", "せる", "そこ", "そして", "その",
+    "その他", "その後", "それ", "それぞれ", "それで", "た", "ただし", "たち", "ため", "たり",
+    "だ", "だっ", "だれ", "つ", "て", "で", "でき", "できる", "です", "では", "でも", "と",
+    "という", "といった", "とき", "ところ", "として", "とともに", "とも", "と共に", "どこ",
+    "どの", "な", "ない", "なお", "なかっ", "ながら", "なく", "なっ", "など", "なに", "なら",
+    "なり", "なる", "なん", "に", "において", "における", "について", "にて", "によって", "により",
+    "による", "に対して", "に対する", "に関する", "の", "ので", "のみ", "は", "ば", "へ", "ほか",
+    "ほとんど", "ほど", "ます", "また", "または", "まで", "も", "もの", "ものの", "や", "よう",
+    "より", "ら", "られ", "られる", "れ", "れる", "を", "ん", "何", "及び", "彼", "彼女",
+    "我々", "特に", "私", "私達", "貴方", "貴方方",
+
+    # Japanese auxiliary verbs
+    "です", "ます", "でした", "ました", "である", "だ", "な", "だった",
+
+    # Japanese pronouns
+    "これ", "それ", "あれ", "この", "その", "あの", "ここ", "そこ", "あそこ",
+
+    # Japanese common words
+    "いる", "ある", "なる", "する", "できる", "おる", "いく", "くる",
+
+    # Numbers
+    "一", "二", "三", "四", "五", "六", "七", "八", "九", "十",
+    "1", "2", "3", "4", "5", "6", "7", "8", "9", "0",
+
+    # Punctuation
+    "、", "。", "「", "」", "『", "』", "（", "）", "［", "］",
+
+    # Common English stopwords (for mixed text)
+    "the", "is", "at", "which", "on", "in", "and", "or", "a", "an",
+}