Feat/dify rag (#2528)

Co-authored-by: jyong <jyong@dify.ai>

api/core/rag/datasource/keyword/jieba/jieba.py

@@ -0,0 +1,232 @@
+import json
+from collections import defaultdict
+from typing import Any, Optional
+
+from pydantic import BaseModel
+
+from core.rag.datasource.keyword.jieba.jieba_keyword_table_handler import JiebaKeywordTableHandler
+from core.rag.datasource.keyword.keyword_base import BaseKeyword
+from core.rag.models.document import Document
+from extensions.ext_database import db
+from models.dataset import Dataset, DatasetKeywordTable, DocumentSegment
+
+
+class KeywordTableConfig(BaseModel):
+    max_keywords_per_chunk: int = 10
+
+
+class Jieba(BaseKeyword):
+    def __init__(self, dataset: Dataset):
+        super().__init__(dataset)
+        self._config = KeywordTableConfig()
+
+    def create(self, texts: list[Document], **kwargs) -> BaseKeyword:
+        keyword_table_handler = JiebaKeywordTableHandler()
+        keyword_table = self._get_dataset_keyword_table()
+        for text in texts:
+            keywords = keyword_table_handler.extract_keywords(text.page_content, self._config.max_keywords_per_chunk)
+            self._update_segment_keywords(self.dataset.id, text.metadata['doc_id'], list(keywords))
+            keyword_table = self._add_text_to_keyword_table(keyword_table, text.metadata['doc_id'], list(keywords))
+
+        self._save_dataset_keyword_table(keyword_table)
+
+        return self
+
+    def add_texts(self, texts: list[Document], **kwargs):
+        keyword_table_handler = JiebaKeywordTableHandler()
+
+        keyword_table = self._get_dataset_keyword_table()
+        keywords_list = kwargs.get('keywords_list', None)
+        for i in range(len(texts)):
+            text = texts[i]
+            if keywords_list:
+                keywords = keywords_list[i]
+            else:
+                keywords = keyword_table_handler.extract_keywords(text.page_content, self._config.max_keywords_per_chunk)
+            self._update_segment_keywords(self.dataset.id, text.metadata['doc_id'], list(keywords))
+            keyword_table = self._add_text_to_keyword_table(keyword_table, text.metadata['doc_id'], list(keywords))
+
+        self._save_dataset_keyword_table(keyword_table)
+
+    def text_exists(self, id: str) -> bool:
+        keyword_table = self._get_dataset_keyword_table()
+        return id in set.union(*keyword_table.values())
+
+    def delete_by_ids(self, ids: list[str]) -> None:
+        keyword_table = self._get_dataset_keyword_table()
+        keyword_table = self._delete_ids_from_keyword_table(keyword_table, ids)
+
+        self._save_dataset_keyword_table(keyword_table)
+
+    def delete_by_document_id(self, document_id: str):
+        # get segment ids by document_id
+        segments = db.session.query(DocumentSegment).filter(
+            DocumentSegment.dataset_id == self.dataset.id,
+            DocumentSegment.document_id == document_id
+        ).all()
+
+        ids = [segment.index_node_id for segment in segments]
+
+        keyword_table = self._get_dataset_keyword_table()
+        keyword_table = self._delete_ids_from_keyword_table(keyword_table, ids)
+
+        self._save_dataset_keyword_table(keyword_table)
+
+    def search(
+            self, query: str,
+            **kwargs: Any
+    ) -> list[Document]:
+        keyword_table = self._get_dataset_keyword_table()
+
+        k = kwargs.get('top_k', 4)
+
+        sorted_chunk_indices = self._retrieve_ids_by_query(keyword_table, 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 delete(self) -> None:
+        dataset_keyword_table = self.dataset.dataset_keyword_table
+        if dataset_keyword_table:
+            db.session.delete(dataset_keyword_table)
+            db.session.commit()
+
+    def _save_dataset_keyword_table(self, keyword_table):
+        keyword_table_dict = {
+            '__type__': 'keyword_table',
+            '__data__': {
+                "index_id": self.dataset.id,
+                "summary": None,
+                "table": keyword_table
+            }
+        }
+        self.dataset.dataset_keyword_table.keyword_table = json.dumps(keyword_table_dict, cls=SetEncoder)
+        db.session.commit()
+
+    def _get_dataset_keyword_table(self) -> Optional[dict]:
+        dataset_keyword_table = self.dataset.dataset_keyword_table
+        if dataset_keyword_table:
+            if dataset_keyword_table.keyword_table_dict:
+                return dataset_keyword_table.keyword_table_dict['__data__']['table']
+        else:
+            dataset_keyword_table = DatasetKeywordTable(
+                dataset_id=self.dataset.id,
+                keyword_table=json.dumps({
+                    '__type__': 'keyword_table',
+                    '__data__': {
+                        "index_id": self.dataset.id,
+                        "summary": None,
+                        "table": {}
+                    }
+                }, cls=SetEncoder)
+            )
+            db.session.add(dataset_keyword_table)
+            db.session.commit()
+
+        return {}
+
+    def _add_text_to_keyword_table(self, keyword_table: dict, id: str, keywords: list[str]) -> dict:
+        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:
+        # get set of ids that correspond to node
+        node_idxs_to_delete = set(ids)
+
+        # delete node_idxs from keyword to node idxs mapping
+        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):
+        keyword_table_handler = JiebaKeywordTableHandler()
+        keywords = keyword_table_handler.extract_keywords(query)
+
+        # go through text chunks in order of most matching keywords
+        chunk_indices_count: dict[str, int] = defaultdict(int)
+        keywords = [keyword for keyword in keywords if keyword in set(keyword_table.keys())]
+        for keyword in keywords:
+            for node_id in keyword_table[keyword]:
+                chunk_indices_count[node_id] += 1
+
+        sorted_chunk_indices = sorted(
+            list(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]):
+        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()
+
+    def create_segment_keywords(self, node_id: str, keywords: list[str]):
+        keyword_table = self._get_dataset_keyword_table()
+        self._update_segment_keywords(self.dataset.id, node_id, keywords)
+        keyword_table = self._add_text_to_keyword_table(keyword_table, node_id, keywords)
+        self._save_dataset_keyword_table(keyword_table)
+
+    def multi_create_segment_keywords(self, pre_segment_data_list: list):
+        keyword_table_handler = JiebaKeywordTableHandler()
+        keyword_table = self._get_dataset_keyword_table()
+        for pre_segment_data in pre_segment_data_list:
+            segment = pre_segment_data['segment']
+            if pre_segment_data['keywords']:
+                segment.keywords = pre_segment_data['keywords']
+                keyword_table = self._add_text_to_keyword_table(keyword_table, segment.index_node_id,
+                                                                pre_segment_data['keywords'])
+            else:
+                keywords = keyword_table_handler.extract_keywords(segment.content,
+                                                                  self._config.max_keywords_per_chunk)
+                segment.keywords = list(keywords)
+                keyword_table = self._add_text_to_keyword_table(keyword_table, segment.index_node_id, list(keywords))
+        self._save_dataset_keyword_table(keyword_table)
+
+    def update_segment_keywords_index(self, node_id: str, keywords: list[str]):
+        keyword_table = self._get_dataset_keyword_table()
+        keyword_table = self._add_text_to_keyword_table(keyword_table, node_id, keywords)
+        self._save_dataset_keyword_table(keyword_table)
+
+
+class SetEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, set):
+            return list(obj)
+        return super().default(obj)

api/core/rag/datasource/keyword/jieba/jieba_keyword_table_handler.py

@@ -0,0 +1,32 @@
+import re
+
+import jieba
+from jieba.analyse import default_tfidf
+
+from core.rag.datasource.keyword.jieba.stopwords import STOPWORDS
+
+
+class JiebaKeywordTableHandler:
+
+    def __init__(self):
+        default_tfidf.stop_words = STOPWORDS
+
+    def extract_keywords(self, text: str, max_keywords_per_chunk: int = 10) -> set[str]:
+        """Extract keywords with JIEBA tfidf."""
+        keywords = jieba.analyse.extract_tags(
+            sentence=text,
+            topK=max_keywords_per_chunk,
+        )
+
+        return set(self._expand_tokens_with_subtokens(keywords))
+
+    def _expand_tokens_with_subtokens(self, tokens: set[str]) -> set[str]:
+        """Get subtokens from a list of tokens., filtering for stopwords."""
+        results = set()
+        for token in tokens:
+            results.add(token)
+            sub_tokens = re.findall(r"\w+", token)
+            if len(sub_tokens) > 1:
+                results.update({w for w in sub_tokens if w not in list(STOPWORDS)})
+
+        return results

api/core/rag/datasource/keyword/jieba/stopwords.py

@@ -0,0 +1,90 @@
+STOPWORDS = {
+    "during", "when", "but", "then", "further", "isn", "mustn't", "until", "own", "i", "couldn", "y", "only", "you've",
+    "ours", "who", "where", "ourselves", "has", "to", "was", "didn't", "themselves", "if", "against", "through", "her",
+    "an", "your", "can", "those", "didn", "about", "aren't", "shan't", "be", "not", "these", "again", "so", "t",
+    "theirs", "weren", "won't", "won", "itself", "just", "same", "while", "why", "doesn", "aren", "him", "haven",
+    "for", "you'll", "that", "we", "am", "d", "by", "having", "wasn't", "than", "weren't", "out", "from", "now",
+    "their", "too", "hadn", "o", "needn", "most", "it", "under", "needn't", "any", "some", "few", "ll", "hers", "which",
+    "m", "you're", "off", "other", "had", "she", "you'd", "do", "you", "does", "s", "will", "each", "wouldn't", "hasn't",
+    "such", "more", "whom", "she's", "my", "yours", "yourself", "of", "on", "very", "hadn't", "with", "yourselves",
+    "been", "ma", "them", "mightn't", "shan", "mustn", "they", "what", "both", "that'll", "how", "is", "he", "because",
+    "down", "haven't", "are", "no", "it's", "our", "being", "the", "or", "above", "myself", "once", "don't", "doesn't",
+    "as", "nor", "here", "herself", "hasn", "mightn", "have", "its", "all", "were", "ain", "this", "at", "after",
+    "over", "shouldn't", "into", "before", "don", "wouldn", "re", "couldn't", "wasn", "in", "should", "there",
+    "himself", "isn't", "should've", "doing", "ve", "shouldn", "a", "did", "and", "his", "between", "me", "up", "below",
+    "人民", "末##末", "啊", "阿", "哎", "哎呀", "哎哟", "唉", "俺", "俺们", "按", "按照", "吧", "吧哒", "把", "罢了", "被", "本",
+    "本着", "比", "比方", "比如", "鄙人", "彼", "彼此", "边", "别", "别的", "别说", "并", "并且", "不比", "不成", "不单", "不但",
+    "不独", "不管", "不光", "不过", "不仅", "不拘", "不论", "不怕", "不然", "不如", "不特", "不惟", "不问", "不只", "朝", "朝着",
+    "趁", "趁着", "乘", "冲", "除", "除此之外", "除非", "除了", "此", "此间", "此外", "从", "从而", "打", "待", "但", "但是", "当",
+    "当着", "到", "得", "的", "的话", "等", "等等", "地", "第", "叮咚", "对", "对于", "多", "多少", "而", "而况", "而且", "而是",
+    "而外", "而言", "而已", "尔后", "反过来", "反过来说", "反之", "非但", "非徒", "否则", "嘎", "嘎登", "该", "赶", "个", "各",
+    "各个", "各位", "各种", "各自", "给", "根据", "跟", "故", "故此", "固然", "关于", "管", "归", "果然", "果真", "过", "哈",
+    "哈哈", "呵", "和", "何", "何处", "何况", "何时", "嘿", "哼", "哼唷", "呼哧", "乎", "哗", "还是", "还有", "换句话说", "换言之",
+    "或", "或是", "或者", "极了", "及", "及其", "及至", "即", "即便", "即或", "即令", "即若", "即使", "几", "几时", "己", "既",
+    "既然", "既是", "继而", "加之", "假如", "假若", "假使", "鉴于", "将", "较", "较之", "叫", "接着", "结果", "借", "紧接着",
+    "进而", "尽", "尽管", "经", "经过", "就", "就是", "就是说", "据", "具体地说", "具体说来", "开始", "开外", "靠", "咳", "可",
+    "可见", "可是", "可以", "况且", "啦", "来", "来着", "离", "例如", "哩", "连", "连同", "两者", "了", "临", "另", "另外",
+    "另一方面", "论", "嘛", "吗", "慢说", "漫说", "冒", "么", "每", "每当", "们", "莫若", "某", "某个", "某些", "拿", "哪",
+    "哪边", "哪儿", "哪个", "哪里", "哪年", "哪怕", "哪天", "哪些", "哪样", "那", "那边", "那儿", "那个", "那会儿", "那里", "那么",
+    "那么些", "那么样", "那时", "那些", "那样", "乃", "乃至", "呢", "能", "你", "你们", "您", "宁", "宁可", "宁肯", "宁愿", "哦",
+    "呕", "啪达", "旁人", "呸", "凭", "凭借", "其", "其次", "其二", "其他", "其它", "其一", "其余", "其中", "起", "起见", "岂但",
+    "恰恰相反", "前后", "前者", "且", "然而", "然后", "然则", "让", "人家", "任", "任何", "任凭", "如", "如此", "如果", "如何",
+    "如其", "如若", "如上所述", "若", "若非", "若是", "啥", "上下", "尚且", "设若", "设使", "甚而", "甚么", "甚至", "省得", "时候",
+    "什么", "什么样", "使得", "是", "是的", "首先", "谁", "谁知", "顺", "顺着", "似的", "虽", "虽然", "虽说", "虽则", "随", "随着",
+    "所", "所以", "他", "他们", "他人", "它", "它们", "她", "她们", "倘", "倘或", "倘然", "倘若", "倘使", "腾", "替", "通过", "同",
+    "同时", "哇", "万一", "往", "望", "为", "为何", "为了", "为什么", "为着", "喂", "嗡嗡", "我", "我们", "呜", "呜呼", "乌乎",
+    "无论", "无宁", "毋宁", "嘻", "吓", "相对而言", "像", "向", "向着", "嘘", "呀", "焉", "沿", "沿着", "要", "要不", "要不然",
+    "要不是", "要么", "要是", "也", "也罢", "也好", "一", "一般", "一旦", "一方面", "一来", "一切", "一样", "一则", "依", "依照",
+    "矣", "以", "以便", "以及", "以免", "以至", "以至于", "以致", "抑或", "因", "因此", "因而", "因为", "哟", "用", "由",
+    "由此可见", "由于", "有", "有的", "有关", "有些", "又", "于", "于是", "于是乎", "与", "与此同时", "与否", "与其", "越是",
+    "云云", "哉", "再说", "再者", "在", "在下", "咱", "咱们", "则", "怎", "怎么", "怎么办", "怎么样", "怎样", "咋", "照", "照着",
+    "者", "这", "这边", "这儿", "这个", "这会儿", "这就是说", "这里", "这么", "这么点儿", "这么些", "这么样", "这时", "这些", "这样",
+    "正如", "吱", "之", "之类", "之所以", "之一", "只是", "只限", "只要", "只有", "至", "至于", "诸位", "着", "着呢", "自", "自从",
+    "自个儿", "自各儿", "自己", "自家", "自身", "综上所述", "总的来看", "总的来说", "总的说来", "总而言之", "总之", "纵", "纵令",
+    "纵然", "纵使", "遵照", "作为", "兮", "呃", "呗", "咚", "咦", "喏", "啐", "喔唷", "嗬", "嗯", "嗳", "~", "!", ".", ":",
+    "\"", "'", "(", ")", "*", "A", "白", "社会主义", "--", "..", ">>", " [", " ]", "", "<", ">", "/", "\\", "|", "-", "_",
+    "+", "=", "&", "^", "%", "#", "@", "`", ";", "$", "（", "）", "——", "—", "￥", "·", "...", "‘", "’", "〉", "〈", "…",
+    "　", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "０", "１", "２", "３", "４", "５", "６", "７", "８", "９", "二",
+    "三", "四", "五", "六", "七", "八", "九", "零", "＞", "＜", "＠", "＃", "＄", "％", "︿", "＆", "＊", "＋", "～", "｜", "［",
+    "］", "｛", "｝", "啊哈", "啊呀", "啊哟", "挨次", "挨个", "挨家挨户", "挨门挨户", "挨门逐户", "挨着", "按理", "按期", "按时",
+    "按说", "暗地里", "暗中", "暗自", "昂然", "八成", "白白", "半", "梆", "保管", "保险", "饱", "背地里", "背靠背", "倍感", "倍加",
+    "本人", "本身", "甭", "比起", "比如说", "比照", "毕竟", "必", "必定", "必将", "必须", "便", "别人", "并非", "并肩", "并没",
+    "并没有", "并排", "并无", "勃然", "不", "不必", "不常", "不大", "不但...而且", "不得", "不得不", "不得了", "不得已", "不迭",
+    "不定", "不对", "不妨", "不管怎样", "不会", "不仅...而且", "不仅仅", "不仅仅是", "不经意", "不可开交", "不可抗拒", "不力", "不了",
+    "不料", "不满", "不免", "不能不", "不起", "不巧", "不然的话", "不日", "不少", "不胜", "不时", "不是", "不同", "不能", "不要",
+    "不外", "不外乎", "不下", "不限", "不消", "不已", "不亦乐乎", "不由得", "不再", "不择手段", "不怎么", "不曾", "不知不觉", "不止",
+    "不止一次", "不至于", "才", "才能", "策略地", "差不多", "差一点", "常", "常常", "常言道", "常言说", "常言说得好", "长此下去",
+    "长话短说", "长期以来", "长线", "敞开儿", "彻夜", "陈年", "趁便", "趁机", "趁热", "趁势", "趁早", "成年", "成年累月", "成心",
+    "乘机", "乘胜", "乘势", "乘隙", "乘虚", "诚然", "迟早", "充分", "充其极", "充其量", "抽冷子", "臭", "初", "出", "出来", "出去",
+    "除此", "除此而外", "除此以外", "除开", "除去", "除却", "除外", "处处", "川流不息", "传", "传说", "传闻", "串行", "纯", "纯粹",
+    "此后", "此中", "次第", "匆匆", "从不", "从此", "从此以后", "从古到今", "从古至今", "从今以后", "从宽", "从来", "从轻", "从速",
+    "从头", "从未", "从无到有", "从小", "从新", "从严", "从优", "从早到晚", "从中", "从重", "凑巧", "粗", "存心", "达旦", "打从",
+    "打开天窗说亮话", "大", "大不了", "大大", "大抵", "大都", "大多", "大凡", "大概", "大家", "大举", "大略", "大面儿上", "大事",
+    "大体", "大体上", "大约", "大张旗鼓", "大致", "呆呆地", "带", "殆", "待到", "单", "单纯", "单单", "但愿", "弹指之间", "当场",
+    "当儿", "当即", "当口儿", "当然", "当庭", "当头", "当下", "当真", "当中", "倒不如", "倒不如说", "倒是", "到处", "到底", "到了儿",
+    "到目前为止", "到头", "到头来", "得起", "得天独厚", "的确", "等到", "叮当", "顶多", "定", "动不动", "动辄", "陡然", "都", "独",
+    "独自", "断然", "顿时", "多次", "多多", "多多少少", "多多益善", "多亏", "多年来", "多年前", "而后", "而论", "而又", "尔等",
+    "二话不说", "二话没说", "反倒", "反倒是", "反而", "反手", "反之亦然", "反之则", "方", "方才", "方能", "放量", "非常", "非得",
+    "分期", "分期分批", "分头", "奋勇", "愤然", "风雨无阻", "逢", "弗", "甫", "嘎嘎", "该当", "概", "赶快", "赶早不赶晚", "敢",
+    "敢情", "敢于", "刚", "刚才", "刚好", "刚巧", "高低", "格外", "隔日", "隔夜", "个人", "各式", "更", "更加", "更进一步", "更为",
+    "公然", "共", "共总", "够瞧的", "姑且", "古来", "故而", "故意", "固", "怪", "怪不得", "惯常", "光", "光是", "归根到底",
+    "归根结底", "过于", "毫不", "毫无", "毫无保留地", "毫无例外", "好在", "何必", "何尝", "何妨", "何苦", "何乐而不为", "何须",
+    "何止", "很", "很多", "很少", "轰然", "后来", "呼啦", "忽地", "忽然", "互", "互相", "哗啦", "话说", "还", "恍然", "会", "豁然",
+    "活", "伙同", "或多或少", "或许", "基本", "基本上", "基于", "极", "极大", "极度", "极端", "极力", "极其", "极为", "急匆匆",
+    "即将", "即刻", "即是说", "几度", "几番", "几乎", "几经", "既...又", "继之", "加上", "加以", "间或", "简而言之", "简言之",
+    "简直", "见", "将才", "将近", "将要", "交口", "较比", "较为", "接连不断", "接下来", "皆可", "截然", "截至", "藉以", "借此",
+    "借以", "届时", "仅", "仅仅", "谨", "进来", "进去", "近", "近几年来", "近来", "近年来", "尽管如此", "尽可能", "尽快", "尽量",
+    "尽然", "尽如人意", "尽心竭力", "尽心尽力", "尽早", "精光", "经常", "竟", "竟然", "究竟", "就此", "就地", "就算", "居然", "局外",
+    "举凡", "据称", "据此", "据实", "据说", "据我所知", "据悉", "具体来说", "决不", "决非", "绝", "绝不", "绝顶", "绝对", "绝非",
+    "均", "喀", "看", "看来", "看起来", "看上去", "看样子", "可好", "可能", "恐怕", "快", "快要", "来不及", "来得及", "来讲",
+    "来看", "拦腰", "牢牢", "老", "老大", "老老实实", "老是", "累次", "累年", "理当", "理该", "理应", "历", "立", "立地", "立刻",
+    "立马", "立时", "联袂", "连连", "连日", "连日来", "连声", "连袂", "临到", "另方面", "另行", "另一个", "路经", "屡", "屡次",
+    "屡次三番", "屡屡", "缕缕", "率尔", "率然", "略", "略加", "略微", "略为", "论说", "马上", "蛮", "满", "没", "没有", "每逢",
+    "每每", "每时每刻", "猛然", "猛然间", "莫", "莫不", "莫非", "莫如", "默默地", "默然", "呐", "那末", "奈", "难道", "难得", "难怪",
+    "难说", "内", "年复一年", "凝神", "偶而", "偶尔", "怕", "砰", "碰巧", "譬如", "偏偏", "乒", "平素", "颇", "迫于", "扑通",
+    "其后", "其实", "奇", "齐", "起初", "起来", "起首", "起头", "起先", "岂", "岂非", "岂止", "迄", "恰逢", "恰好", "恰恰", "恰巧",
+    "恰如", "恰似", "千", "千万", "千万千万", "切", "切不可", "切莫", "切切", "切勿", "窃", "亲口", "亲身", "亲手", "亲眼", "亲自",
+    "顷", "顷刻", "顷刻间", "顷刻之间", "请勿", "穷年累月", "取道", "去", "权时", "全都", "全力", "全年", "全然", "全身心", "然",
+    "人人", "仍", "仍旧", "仍然", "日复一日", "日见", "日渐", "日益", "日臻", "如常", "如此等等", "如次", "如今", "如期", "如前所述",
+    "如上", "如下", "汝", "三番两次", "三番五次", "三天两头", "瑟瑟", "沙沙", "上", "上来", "上去", "一个", "月", "日", "\n"
+}

api/core/rag/datasource/keyword/keyword_base.py

@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Any
+
+from core.rag.models.document import Document
+from models.dataset import Dataset
+
+
+class BaseKeyword(ABC):
+
+    def __init__(self, dataset: Dataset):
+        self.dataset = dataset
+
+    @abstractmethod
+    def create(self, texts: list[Document], **kwargs) -> BaseKeyword:
+        raise NotImplementedError
+
+    @abstractmethod
+    def add_texts(self, texts: list[Document], **kwargs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def text_exists(self, id: str) -> bool:
+        raise NotImplementedError
+
+    @abstractmethod
+    def delete_by_ids(self, ids: list[str]) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def delete_by_document_id(self, document_id: str) -> None:
+        raise NotImplementedError
+
+    def delete(self) -> None:
+        raise NotImplementedError
+
+    def search(
+            self, query: str,
+            **kwargs: Any
+    ) -> list[Document]:
+        raise NotImplementedError
+
+    def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
+        for text in texts:
+            doc_id = text.metadata['doc_id']
+            exists_duplicate_node = self.text_exists(doc_id)
+            if exists_duplicate_node:
+                texts.remove(text)
+
+        return texts
+
+    def _get_uuids(self, texts: list[Document]) -> list[str]:
+        return [text.metadata['doc_id'] for text in texts]

api/core/rag/datasource/keyword/keyword_factory.py

@@ -0,0 +1,60 @@
+from typing import Any, cast
+
+from flask import current_app
+
+from core.rag.datasource.keyword.jieba.jieba import Jieba
+from core.rag.datasource.keyword.keyword_base import BaseKeyword
+from core.rag.models.document import Document
+from models.dataset import Dataset
+
+
+class Keyword:
+    def __init__(self, dataset: Dataset):
+        self._dataset = dataset
+        self._keyword_processor = self._init_keyword()
+
+    def _init_keyword(self) -> BaseKeyword:
+        config = cast(dict, current_app.config)
+        keyword_type = config.get('KEYWORD_STORE')
+
+        if not keyword_type:
+            raise ValueError("Keyword store must be specified.")
+
+        if keyword_type == "jieba":
+            return Jieba(
+                dataset=self._dataset
+            )
+        else:
+            raise ValueError(f"Vector store {config.get('VECTOR_STORE')} is not supported.")
+
+    def create(self, texts: list[Document], **kwargs):
+        self._keyword_processor.create(texts, **kwargs)
+
+    def add_texts(self, texts: list[Document], **kwargs):
+        self._keyword_processor.add_texts(texts, **kwargs)
+
+    def text_exists(self, id: str) -> bool:
+        return self._keyword_processor.text_exists(id)
+
+    def delete_by_ids(self, ids: list[str]) -> None:
+        self._keyword_processor.delete_by_ids(ids)
+
+    def delete_by_document_id(self, document_id: str) -> None:
+        self._keyword_processor.delete_by_document_id(document_id)
+
+    def delete(self) -> None:
+        self._keyword_processor.delete()
+
+    def search(
+            self, query: str,
+            **kwargs: Any
+    ) -> list[Document]:
+        return self._keyword_processor.search(query, **kwargs)
+
+    def __getattr__(self, name):
+        if self._keyword_processor is not None:
+            method = getattr(self._keyword_processor, name)
+            if callable(method):
+                return method
+
+        raise AttributeError(f"'Keyword' object has no attribute '{name}'")