#
#  Copyright 2025 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.
#

import re
import json
import copy
from infinity.common import InfinityException, SortType
from infinity.errors import ErrorCode
from common.decorator import singleton
import pandas as pd
from common.constants import PAGERANK_FLD, TAG_FLD
from common.doc_store.doc_store_base import MatchExpr, MatchTextExpr, MatchDenseExpr, FusionExpr, OrderByExpr
from common.doc_store.infinity_conn_base import InfinityConnectionBase


@singleton
class InfinityConnection(InfinityConnectionBase):
    """
    Dataframe and fields convert
    """

    @staticmethod
    def field_keyword(field_name: str):
        # Treat "*_kwd" tag-like columns as keyword lists except knowledge_graph_kwd; source_id is also keyword-like.
        if field_name == "source_id" or (
                field_name.endswith("_kwd") and field_name not in ["knowledge_graph_kwd", "docnm_kwd", "important_kwd",
                                                                   "question_kwd"]):
            return True
        return False

    def convert_select_fields(self, output_fields: list[str]) -> list[str]:
        need_empty_count = "important_kwd" in output_fields
        for i, field in enumerate(output_fields):
            if field in ["docnm_kwd", "title_tks", "title_sm_tks"]:
                output_fields[i] = "docnm"
            elif field in ["important_kwd", "important_tks"]:
                output_fields[i] = "important_keywords"
            elif field in ["question_kwd", "question_tks"]:
                output_fields[i] = "questions"
            elif field in ["content_with_weight", "content_ltks", "content_sm_ltks"]:
                output_fields[i] = "content"
            elif field in ["authors_tks", "authors_sm_tks"]:
                output_fields[i] = "authors"
        if need_empty_count and "important_kwd_empty_count" not in output_fields:
            output_fields.append("important_kwd_empty_count")
        return list(set(output_fields))

    @staticmethod
    def convert_matching_field(field_weight_str: str) -> str:
        tokens = field_weight_str.split("^")
        field = tokens[0]
        if field == "docnm_kwd" or field == "title_tks":
            field = "docnm@ft_docnm_rag_coarse"
        elif field == "title_sm_tks":
            field = "docnm@ft_docnm_rag_fine"
        elif field == "important_kwd":
            field = "important_keywords@ft_important_keywords_rag_coarse"
        elif field == "important_tks":
            field = "important_keywords@ft_important_keywords_rag_fine"
        elif field == "question_kwd":
            field = "questions@ft_questions_rag_coarse"
        elif field == "question_tks":
            field = "questions@ft_questions_rag_fine"
        elif field == "content_with_weight" or field == "content_ltks":
            field = "content@ft_content_rag_coarse"
        elif field == "content_sm_ltks":
            field = "content@ft_content_rag_fine"
        elif field == "authors_tks":
            field = "authors@ft_authors_rag_coarse"
        elif field == "authors_sm_tks":
            field = "authors@ft_authors_rag_fine"
        elif field == "tag_kwd":
            field = "tag_kwd@ft_tag_kwd_whitespace__"
        tokens[0] = field
        return "^".join(tokens)

    """
    CRUD operations
    """

    def search(
            self,
            select_fields: list[str],
            highlight_fields: list[str],
            condition: dict,
            match_expressions: list[MatchExpr],
            order_by: OrderByExpr,
            offset: int,
            limit: int,
            index_names: str | list[str],
            knowledgebase_ids: list[str],
            agg_fields: list[str] | None = None,
            rank_feature: dict | None = None,
    ) -> tuple[pd.DataFrame, int]:
        """
        BUG: Infinity returns empty for a highlight field if the query string doesn't use that field.
        """
        if isinstance(index_names, str):
            index_names = index_names.split(",")
        assert isinstance(index_names, list) and len(index_names) > 0
        inf_conn = self.connPool.get_conn()
        try:
            db_instance = inf_conn.get_database(self.dbName)
            df_list = list()
            table_list = list()
            output = select_fields.copy()
            output = self.convert_select_fields(output)
            if agg_fields is None:
                agg_fields = []
            for essential_field in ["id"] + agg_fields:
                if essential_field not in output:
                    output.append(essential_field)
            score_func = ""
            score_column = ""
            for matchExpr in match_expressions:
                if isinstance(matchExpr, MatchTextExpr):
                    score_func = "score()"
                    score_column = "SCORE"
                    break
            if not score_func:
                for matchExpr in match_expressions:
                    if isinstance(matchExpr, MatchDenseExpr):
                        score_func = "similarity()"
                        score_column = "SIMILARITY"
                        break
            if match_expressions:
                if score_func and score_func not in output:
                    output.append(score_func)
                if PAGERANK_FLD not in output:
                    output.append(PAGERANK_FLD)
            output = [f for f in output if f and f != "_score"]
            if limit <= 0:
                # ElasticSearch default limit is 10000
                limit = 10000

            # Prepare expressions common to all tables
            filter_cond = None
            filter_fulltext = ""
            if condition:
                # For metadata table (ragflow_doc_meta_), keep kb_id filter
                # For chunk tables, remove kb_id filter as they use table separation per KB
                is_meta_table = any(indexName.startswith("ragflow_doc_meta_") for indexName in index_names)
                if not is_meta_table:
                    condition = {k: v for k, v in condition.items() if k != "kb_id"}

                table_found = False
                for indexName in index_names:
                    if indexName.startswith("ragflow_doc_meta_"):
                        table_names_to_search = [indexName]
                    else:
                        table_names_to_search = [f"{indexName}_{kb_id}" for kb_id in knowledgebase_ids]
                    for table_name in table_names_to_search:
                        try:
                            filter_cond = self.equivalent_condition_to_str(condition, db_instance.get_table(table_name))
                            table_found = True
                            break
                        except Exception:
                            pass
                    if table_found:
                        break
                if not table_found:
                    self.logger.error(
                        f"No valid tables found for indexNames {index_names} and knowledgebaseIds {knowledgebase_ids}")
                    return pd.DataFrame(), 0

            for matchExpr in match_expressions:
                if isinstance(matchExpr, MatchTextExpr):
                    if filter_cond and "filter" not in matchExpr.extra_options:
                        matchExpr.extra_options.update({"filter": filter_cond})
                    matchExpr.fields = [self.convert_matching_field(field) for field in matchExpr.fields]
                    fields = ",".join(matchExpr.fields)
                    filter_fulltext = f"filter_fulltext('{fields}', '{matchExpr.matching_text}')"
                    if filter_cond:
                        filter_fulltext = f"({filter_cond}) AND {filter_fulltext}"
                    minimum_should_match = matchExpr.extra_options.get("minimum_should_match", 0.0)
                    if isinstance(minimum_should_match, float):
                        str_minimum_should_match = str(int(minimum_should_match * 100)) + "%"
                        matchExpr.extra_options["minimum_should_match"] = str_minimum_should_match

                    # Add rank_feature support
                    if rank_feature and "rank_features" not in matchExpr.extra_options:
                        # Convert rank_feature dict to Infinity's rank_features string format
                        # Format: "field^feature_name^weight,field^feature_name^weight"
                        rank_features_list = []
                        for feature_name, weight in rank_feature.items():
                            # Use TAG_FLD as the field containing rank features
                            rank_features_list.append(f"{TAG_FLD}^{feature_name}^{weight}")
                        if rank_features_list:
                            matchExpr.extra_options["rank_features"] = ",".join(rank_features_list)

                    for k, v in matchExpr.extra_options.items():
                        if not isinstance(v, str):
                            matchExpr.extra_options[k] = str(v)
                    self.logger.debug(f"INFINITY search MatchTextExpr: {json.dumps(matchExpr.__dict__)}")
                elif isinstance(matchExpr, MatchDenseExpr):
                    if filter_fulltext and "filter" not in matchExpr.extra_options:
                        matchExpr.extra_options.update({"filter": filter_fulltext})
                    for k, v in matchExpr.extra_options.items():
                        if not isinstance(v, str):
                            matchExpr.extra_options[k] = str(v)
                    similarity = matchExpr.extra_options.get("similarity")
                    if similarity:
                        matchExpr.extra_options["threshold"] = similarity
                        del matchExpr.extra_options["similarity"]
                    self.logger.debug(f"INFINITY search MatchDenseExpr: {json.dumps(matchExpr.__dict__)}")
                elif isinstance(matchExpr, FusionExpr):
                    if matchExpr.method == "weighted_sum":
                        # The default is "minmax" which gives a zero score for the last doc.
                        matchExpr.fusion_params["normalize"] = "atan"
                    self.logger.debug(f"INFINITY search FusionExpr: {json.dumps(matchExpr.__dict__)}")

            order_by_expr_list = list()
            if order_by.fields:
                for order_field in order_by.fields:
                    if order_field[1] == 0:
                        order_by_expr_list.append((order_field[0], SortType.Asc))
                    else:
                        order_by_expr_list.append((order_field[0], SortType.Desc))

            total_hits_count = 0
            # Scatter search tables and gather the results
            for indexName in index_names:
                if indexName.startswith("ragflow_doc_meta_"):
                    table_names_to_search = [indexName]
                else:
                    table_names_to_search = [f"{indexName}_{kb_id}" for kb_id in knowledgebase_ids]
                for table_name in table_names_to_search:
                    try:
                        table_instance = db_instance.get_table(table_name)
                    except Exception:
                        continue
                    table_list.append(table_name)
                    builder = table_instance.output(output)
                    if len(match_expressions) > 0:
                        for matchExpr in match_expressions:
                            if isinstance(matchExpr, MatchTextExpr):
                                fields = ",".join(matchExpr.fields)
                                self.logger.info(f"INFINITY search match_text: {matchExpr.matching_text}")
                                builder = builder.match_text(
                                    fields,
                                    matchExpr.matching_text,
                                    matchExpr.topn,
                                    matchExpr.extra_options.copy(),
                                )
                            elif isinstance(matchExpr, MatchDenseExpr):
                                builder = builder.match_dense(
                                    matchExpr.vector_column_name,
                                    matchExpr.embedding_data,
                                    matchExpr.embedding_data_type,
                                    matchExpr.distance_type,
                                    matchExpr.topn,
                                    matchExpr.extra_options.copy(),
                                )
                            elif isinstance(matchExpr, FusionExpr):
                                builder = builder.fusion(matchExpr.method, matchExpr.topn, matchExpr.fusion_params)
                    else:
                        if filter_cond and len(filter_cond) > 0:
                            builder.filter(filter_cond)
                    if order_by.fields:
                        builder.sort(order_by_expr_list)
                    builder.offset(offset).limit(limit)
                    kb_res, extra_result = builder.option({"total_hits_count": True}).to_df()
                    if extra_result:
                        total_hits_count += int(extra_result["total_hits_count"])
                    self.logger.debug(f"INFINITY search table: {str(table_name)}, result: {str(kb_res)}")
                    df_list.append(kb_res)
            res = self.concat_dataframes(df_list, output)
            if match_expressions and score_column:
                res["_score"] = res[score_column] + res[PAGERANK_FLD]
                res = res.sort_values(by="_score", ascending=False).reset_index(drop=True)
                res = res.head(limit)
            self.logger.debug(f"INFINITY search final result: {str(res)}")
            return res, total_hits_count
        finally:
            self.connPool.release_conn(inf_conn)

    def get(self, chunk_id: str, index_name: str, knowledgebase_ids: list[str]) -> dict | None:
        inf_conn = self.connPool.get_conn()
        try:
            db_instance = inf_conn.get_database(self.dbName)
            df_list = list()
            assert isinstance(knowledgebase_ids, list)
            table_list = list()
            if not knowledgebase_ids:
                self.logger.warning("INFINITY get called with empty knowledgebase_ids for index %s", index_name)
                return None
            table_names_to_search = [f"{index_name}_{kb_id}" for kb_id in knowledgebase_ids if kb_id]
            if not table_names_to_search:
                self.logger.warning("INFINITY get has only blank knowledgebase_ids for index %s", index_name)
                return None
            for table_name in table_names_to_search:
                table_list.append(table_name)
                try:
                    table_instance = db_instance.get_table(table_name)
                except Exception:
                    self.logger.warning(
                        f"Table not found: {table_name}, this dataset isn't created in Infinity. Maybe it is created in other document engine.")
                    continue
                kb_res, _ = table_instance.output(["*"]).filter(f"id = '{chunk_id}'").to_df()
                self.logger.debug(f"INFINITY get table: {str(table_list)}, result: {str(kb_res)}")
                df_list.append(kb_res)
        finally:
            self.connPool.release_conn(inf_conn)
        res = self.concat_dataframes(df_list, ["id"])
        fields = set(res.columns.tolist())
        for field in ["docnm_kwd", "title_tks", "title_sm_tks", "important_kwd", "important_tks", "question_kwd",
                      "question_tks", "content_with_weight", "content_ltks", "content_sm_ltks", "authors_tks",
                      "authors_sm_tks"]:
            fields.add(field)
        res_fields = self.get_fields(res, list(fields))
        chunk = res_fields.get(chunk_id, None)
        if chunk is not None:
            chunk["id"] = chunk_id
        return chunk

    def insert(self, documents: list[dict], index_name: str, knowledgebase_id: str = None) -> list[str]:
        '''
        # Save input to file to test inserting from file in GO
        import datetime
        import os
        debug_file = os.path.join("/var/infinity/tmp", f"insert_{index_name}_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S_%f')}.json")
        with open(debug_file, 'w') as f:
            json.dump({
                "table_name": index_name,
                "knowledgebase_id": knowledgebase_id,
                "chunks": documents
            }, f, indent=2)
        self.logger.debug(f"Saved insert input to {debug_file}")
        '''

        inf_conn = self.connPool.get_conn()
        try:
            db_instance = inf_conn.get_database(self.dbName)
            if index_name.startswith("ragflow_doc_meta_"):
                table_name = index_name
            else:
                table_name = f"{index_name}_{knowledgebase_id}"
            try:
                table_instance = db_instance.get_table(table_name)
            except InfinityException as e:
                # src/common/status.cppm, kTableNotExist = 3022
                if e.error_code != ErrorCode.TABLE_NOT_EXIST:
                    raise
                vector_size = 0
                patt = re.compile(r"q_(?P<vector_size>\d+)_vec")
                for k in documents[0].keys():
                    m = patt.match(k)
                    if m:
                        vector_size = int(m.group("vector_size"))
                        break
                if vector_size == 0:
                    raise ValueError("Cannot infer vector size from documents")

                # Determine parser_id from document structure
                # Table parser documents have 'chunk_data' field
                parser_id = None
                if "chunk_data" in documents[0] and isinstance(documents[0].get("chunk_data"), dict):
                    from common.constants import ParserType
                    parser_id = ParserType.TABLE.value
                    self.logger.debug("Detected TABLE parser from document structure")

                # Fallback: Create table with base schema (shouldn't normally happen as init_kb() creates it)
                self.logger.debug(f"Fallback: Creating table {table_name} with base schema, parser_id: {parser_id}")
                self.create_idx(index_name, knowledgebase_id, vector_size, parser_id)
                table_instance = db_instance.get_table(table_name)

            # embedding fields can't have a default value....
            embedding_clmns = []
            clmns = table_instance.show_columns().rows()
            for n, ty, _, _ in clmns:
                r = re.search(r"Embedding\([a-z]+,([0-9]+)\)", ty)
                if not r:
                    continue
                embedding_clmns.append((n, int(r.group(1))))

            docs = copy.deepcopy(documents)
            for d in docs:
                assert "_id" not in d
                assert "id" in d
                for k, v in list(d.items()):
                    if k == "docnm_kwd":
                        d["docnm"] = v
                    elif k == "title_kwd":
                        if not d.get("docnm_kwd"):
                            d["docnm"] = self.list2str(v)
                    elif k == "title_sm_tks":
                        if not d.get("docnm_kwd"):
                            d["docnm"] = self.list2str(v)
                    elif k == "important_kwd":
                        if isinstance(v, list):
                            empty_count = sum(1 for kw in v if kw == "")
                            tokens = [kw for kw in v if kw != ""]
                            d["important_keywords"] = self.list2str(tokens, ",")
                            d["important_kwd_empty_count"] = empty_count
                        else:
                            d["important_keywords"] = self.list2str(v, ",")
                    elif k == "important_tks":
                        if not d.get("important_kwd"):
                            d["important_keywords"] = v
                    elif k == "content_with_weight":
                        d["content"] = v
                    elif k == "content_ltks":
                        if not d.get("content_with_weight"):
                            d["content"] = v
                    elif k == "content_sm_ltks":
                        if not d.get("content_with_weight"):
                            d["content"] = v
                    elif k == "authors_tks":
                        d["authors"] = v
                    elif k == "authors_sm_tks":
                        if not d.get("authors_tks"):
                            d["authors"] = v
                    elif k == "question_kwd":
                        d["questions"] = self.list2str(v, "\n")
                    elif k == "question_tks":
                        if not d.get("question_kwd"):
                            d["questions"] = self.list2str(v)
                    elif self.field_keyword(k):
                        if isinstance(v, list):
                            d[k] = "###".join(v)
                        else:
                            d[k] = v
                    elif re.search(r"_feas$", k):
                        d[k] = json.dumps(v)
                    elif k == "chunk_data":
                        # Convert data dict to JSON string for storage
                        if isinstance(v, dict):
                            d[k] = json.dumps(v)
                        else:
                            d[k] = v
                    elif k == "extra":
                        # RAPTOR writes {"raptor_method": ...} as a dict; Infinity's
                        # `extra` column is varchar so we serialize on the write path.
                        # The read path (raptor_utils._as_extra_dict) already accepts
                        # both dict and JSON-string. Other backends (OceanBase JSON
                        # column, ES/OpenSearch) keep dict shape — this is Infinity-only.
                        if isinstance(v, dict):
                            d[k] = json.dumps(v)
                        else:
                            d[k] = v if v else ""
                    elif k == "kb_id":
                        if isinstance(d[k], list):
                            d[k] = d[k][0]  # since d[k] is a list, but we need a str
                    elif k == "position_int":
                        assert isinstance(v, list)
                        arr = [num for row in v for num in row]
                        d[k] = "_".join(f"{num:08x}" for num in arr)
                    elif k in ["page_num_int", "top_int"]:
                        assert isinstance(v, list)
                        d[k] = "_".join(f"{num:08x}" for num in v)
                    elif k == "meta_fields":
                        if isinstance(v, dict):
                            d[k] = json.dumps(v, ensure_ascii=False)
                        else:
                            d[k] = v if v else "{}"
                    else:
                        d[k] = v
                for k in ["docnm_kwd", "title_tks", "title_sm_tks", "important_kwd", "important_tks",
                          "content_with_weight",
                          "content_ltks", "content_sm_ltks", "authors_tks", "authors_sm_tks", "question_kwd",
                          "question_tks"]:
                    if k in d:
                        del d[k]

                for n, vs in embedding_clmns:
                    if n in d:
                        continue
                    d[n] = [0] * vs
            ids = ["'{}'".format(d["id"]) for d in docs]
            str_ids = ", ".join(ids)
            str_filter = f"id IN ({str_ids})"
            table_instance.delete(str_filter)
            # for doc in documents:
            #     logger.info(f"insert position_int: {doc['position_int']}")
            # logger.info(f"InfinityConnection.insert {json.dumps(documents)}")
            table_instance.insert(docs)
        finally:
            self.connPool.release_conn(inf_conn)
        self.logger.debug(f"INFINITY inserted into {table_name} {str_ids}.")
        return []

    def update(self, condition: dict, new_value: dict, index_name: str, knowledgebase_id: str) -> bool:
        # if 'position_int' in newValue:
        #     logger.info(f"update position_int: {newValue['position_int']}")
        inf_conn = self.connPool.get_conn()
        try:
            db_instance = inf_conn.get_database(self.dbName)
            if index_name.startswith("ragflow_doc_meta_"):
                table_name = index_name
            else:
                table_name = f"{index_name}_{knowledgebase_id}"
            try:
                table_instance = db_instance.get_table(table_name)
            except InfinityException as e:
                # src/common/status.cppm, kTableNotExist = 3022
                if e.error_code == ErrorCode.TABLE_NOT_EXIST:
                    self.logger.warning(f"Table {table_name} does not exist, skipping update.")
                    return False
                raise
            # if "exists" in condition:
            #    del condition["exists"]

            clmns = {}
            if table_instance:
                for n, ty, de, _ in table_instance.show_columns().rows():
                    clmns[n] = (ty, de)
            filter = self.equivalent_condition_to_str(condition, table_instance)
            removeValue = {}
            for k, v in list(new_value.items()):
                if k == "docnm_kwd":
                    new_value["docnm"] = self.list2str(v)
                elif k == "title_kwd":
                    if not new_value.get("docnm_kwd"):
                        new_value["docnm"] = self.list2str(v)
                elif k == "title_sm_tks":
                    if not new_value.get("docnm_kwd"):
                        new_value["docnm"] = v
                elif k == "important_kwd":
                    if isinstance(v, list):
                        empty_count = sum(1 for kw in v if kw == "")
                        tokens = [kw for kw in v if kw != ""]
                        new_value["important_keywords"] = self.list2str(tokens, ",")
                        new_value["important_kwd_empty_count"] = empty_count
                    else:
                        new_value["important_keywords"] = self.list2str(v, ",")
                elif k == "important_tks":
                    if not new_value.get("important_kwd"):
                        new_value["important_keywords"] = v
                elif k == "content_with_weight":
                    new_value["content"] = v
                elif k == "content_ltks":
                    if not new_value.get("content_with_weight"):
                        new_value["content"] = v
                elif k == "content_sm_ltks":
                    if not new_value.get("content_with_weight"):
                        new_value["content"] = v
                elif k == "authors_tks":
                    new_value["authors"] = v
                elif k == "authors_sm_tks":
                    if not new_value.get("authors_tks"):
                        new_value["authors"] = v
                elif k == "question_kwd":
                    new_value["questions"] = "\n".join(v)
                elif k == "question_tks":
                    if not new_value.get("question_kwd"):
                        new_value["questions"] = self.list2str(v)
                elif self.field_keyword(k):
                    if isinstance(v, list):
                        new_value[k] = "###".join(v)
                    else:
                        new_value[k] = v
                elif re.search(r"_feas$", k):
                    new_value[k] = json.dumps(v)
                elif k == "kb_id":
                    if isinstance(new_value[k], list):
                        new_value[k] = new_value[k][0]  # since d[k] is a list, but we need a str
                elif k == "position_int":
                    assert isinstance(v, list)
                    arr = [num for row in v for num in row]
                    new_value[k] = "_".join(f"{num:08x}" for num in arr)
                elif k in ["page_num_int", "top_int"]:
                    assert isinstance(v, list)
                    new_value[k] = "_".join(f"{num:08x}" for num in v)
                elif k == "remove":
                    if isinstance(v, str):
                        assert v in clmns, f"'{v}' should be in '{clmns}'."
                        ty, de = clmns[v]
                        if ty.lower().find("cha"):
                            if not de:
                                de = ""
                        new_value[v] = de
                    else:
                        for kk, vv in v.items():
                            removeValue[kk] = vv
                        del new_value[k]
                else:
                    new_value[k] = v
            for k in ["docnm_kwd", "title_tks", "title_sm_tks", "important_kwd", "important_tks", "content_with_weight",
                      "content_ltks", "content_sm_ltks", "authors_tks", "authors_sm_tks", "question_kwd",
                      "question_tks"]:
                if k in new_value:
                    del new_value[k]

            remove_opt = {}  # "[k,new_value]": [id_to_update, ...]
            if removeValue:
                col_to_remove = list(removeValue.keys())
                row_to_opt = table_instance.output(col_to_remove + ["id"]).filter(filter).to_df()
                self.logger.debug(
                    f"INFINITY search table {str(table_name)}, filter {filter}, result: {str(row_to_opt[0])}")
                row_to_opt = self.get_fields(row_to_opt, col_to_remove)
                for id, old_v in row_to_opt.items():
                    for k, remove_v in removeValue.items():
                        if remove_v in old_v[k]:
                            new_v = old_v[k].copy()
                            new_v.remove(remove_v)
                            kv_key = json.dumps([k, new_v])
                            if kv_key not in remove_opt:
                                remove_opt[kv_key] = [id]
                            else:
                                remove_opt[kv_key].append(id)

            self.logger.debug(f"INFINITY update table {table_name}, filter {filter}, newValue {new_value}.")
            for update_kv, ids in remove_opt.items():
                k, v = json.loads(update_kv)
                table_instance.update(filter + " AND id in ({0})".format(",".join([f"'{id}'" for id in ids])),
                                      {k: "###".join(v)})

            table_instance.update(filter, new_value)
        finally:
            self.connPool.release_conn(inf_conn)
        return True

    def adjust_chunk_pagerank_fea(
            self,
            chunk_id: str,
            index_name: str,
            knowledgebase_id: str,
            delta: int,
            min_weight: int,
            max_weight: int,
            row_id: int | None = None,
            max_retries: int = 2,
    ) -> bool:
        """Adjust pagerank_fea on one chunk row in Infinity.

        Uses row_id for a targeted update when available. If the row_id is
        stale (concurrent update changed it), re-reads the current row_id and
        retries up to *max_retries* times.
        """
        table_name = f"{index_name}_{knowledgebase_id}"
        for attempt in range(max_retries + 1):
            inf_conn = self.connPool.get_conn()
            try:
                db_instance = inf_conn.get_database(self.dbName)
                table_instance = db_instance.get_table(table_name)

                if row_id is None:
                    df, _ = table_instance.output(
                        [PAGERANK_FLD, "row_id()"]
                    ).filter(f"id = '{chunk_id}'").to_df()
                    if df.empty:
                        self.logger.warning(
                            "adjust_chunk_pagerank_fea: chunk %s not found in %s",
                            chunk_id, table_name,
                        )
                        return False
                    current_weight = int(float(df[PAGERANK_FLD].iloc[0] or 0))
                    row_id = int(df["row_id"].iloc[0])
                else:
                    df, _ = table_instance.output(
                        [PAGERANK_FLD]
                    ).filter(f"id = '{chunk_id}'").to_df()
                    if df.empty:
                        return False
                    current_weight = int(float(df[PAGERANK_FLD].iloc[0] or 0))

                new_weight = max(min_weight, min(max_weight, current_weight + delta))

                table_instance.update(
                    f"_row_id = {row_id}",
                    {PAGERANK_FLD: new_weight},
                )
                self.logger.info(
                    "adjust_chunk_pagerank_fea(chunk=%s, table=%s): %s -> %s via row_id=%s",
                    chunk_id, table_name, current_weight, new_weight, row_id,
                )
                return True

            except InfinityException as e:
                if attempt < max_retries:
                    self.logger.warning(
                        "adjust_chunk_pagerank_fea stale row_id=%s for chunk %s (attempt %s/%s): %s",
                        row_id, chunk_id, attempt + 1, max_retries, e,
                    )
                    row_id = None
                    continue
                self.logger.error(
                    "adjust_chunk_pagerank_fea failed for chunk %s after %s attempts: %s",
                    chunk_id, max_retries + 1, e,
                )
                return False
            except Exception as e:
                self.logger.error(
                    "adjust_chunk_pagerank_fea error for chunk %s: %s", chunk_id, e,
                )
                return False
            finally:
                self.connPool.release_conn(inf_conn)
        return False

    """
    Helper functions for search result
    """

    def get_fields(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, fields: list[str]) -> dict[str, dict]:
        if isinstance(res, tuple):
            res = res[0]
        if not fields:
            return {}
        fields_all = fields.copy()
        fields_all.append("id")
        fields_all = set(fields_all)
        if "docnm" in res.columns:
            for field in ["docnm_kwd", "title_tks", "title_sm_tks"]:
                if field in fields_all:
                    res[field] = res["docnm"]
        if "important_keywords" in res.columns:
            if "important_kwd" in fields_all:
                if "important_kwd_empty_count" in res.columns:
                    base = res["important_keywords"].apply(lambda raw: raw.split(",") if raw else [])
                    counts = res["important_kwd_empty_count"].fillna(0).astype(int)
                    res["important_kwd"] = [
                        tokens + [""] * empty_count
                        for tokens, empty_count in zip(base.tolist(), counts.tolist())
                    ]
                else:
                    res["important_kwd"] = res["important_keywords"].apply(lambda v: v.split(",") if v else [])
            if "important_tks" in fields_all:
                res["important_tks"] = res["important_keywords"]
        if "questions" in res.columns:
            if "question_kwd" in fields_all:
                res["question_kwd"] = res["questions"].apply(lambda v: v.splitlines())
            if "question_tks" in fields_all:
                res["question_tks"] = res["questions"]
        if "content" in res.columns:
            for field in ["content_with_weight", "content_ltks", "content_sm_ltks"]:
                if field in fields_all:
                    res[field] = res["content"]
        if "authors" in res.columns:
            for field in ["authors_tks", "authors_sm_tks"]:
                if field in fields_all:
                    res[field] = res["authors"]

        column_map = {col.lower(): col for col in res.columns}
        # row_id() is returned by infinity as "row_id", add mapping for lookup
        if "row_id()" in fields_all and "row_id" in column_map:
            column_map["row_id()"] = column_map["row_id"]
        matched_columns = {column_map[col.lower()]: col for col in fields_all if col.lower() in column_map}
        none_columns = [col for col in fields_all if col.lower() not in column_map]

        res2 = res[matched_columns.keys()]
        res2 = res2.rename(columns=matched_columns)
        res2.drop_duplicates(subset=["id"], inplace=True)

        for column in list(res2.columns):
            k = column.lower()
            if self.field_keyword(k):
                res2[column] = res2[column].apply(lambda v: [kwd for kwd in v.split("###") if kwd])
            elif re.search(r"_feas$", k):
                res2[column] = res2[column].apply(lambda v: json.loads(v) if v else {})
            elif k == "chunk_data":
                # Parse JSON data back to dict for table parser fields
                res2[column] = res2[column].apply(lambda v: json.loads(v) if v and isinstance(v, str) else v)
            elif k == "position_int":
                def to_position_int(v):
                    if v:
                        arr = [int(hex_val, 16) for hex_val in v.split("_")]
                        v = [arr[i: i + 5] for i in range(0, len(arr), 5)]
                    else:
                        v = []
                    return v

                res2[column] = res2[column].apply(to_position_int)
            elif k in ["page_num_int", "top_int"]:
                res2[column] = res2[column].apply(lambda v: [int(hex_val, 16) for hex_val in v.split("_")] if v else [])
            else:
                pass
        for column in ["docnm", "important_keywords", "questions", "content", "authors"]:
            if column in res2:
                del res2[column]
        for column in none_columns:
            res2[column] = None

        return res2.set_index("id").to_dict(orient="index")
