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Feat: add delete all support for delete operations (#13530)
### What problem does this PR solve? Add delete all support for delete operations. ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Documentation Update --------- Co-authored-by: writinwaters <cai.keith@gmail.com>
This commit is contained in:
Yongteng Lei
GitHub
@ -122,151 +122,153 @@ class InfinityConnection(InfinityConnectionBase):
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index_names = index_names.split(",")
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assert isinstance(index_names, list) and len(index_names) > 0
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inf_conn = self.connPool.get_conn()
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db_instance = inf_conn.get_database(self.dbName)
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df_list = list()
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table_list = list()
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if hide_forgotten:
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condition.update({"must_not": {"exists": "forget_at_flt"}})
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output = select_fields.copy()
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if agg_fields is None:
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agg_fields = []
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for essential_field in ["id"] + agg_fields:
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if essential_field not in output:
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output.append(essential_field)
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score_func = ""
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score_column = ""
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for matchExpr in match_expressions:
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if isinstance(matchExpr, MatchTextExpr):
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score_func = "score()"
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score_column = "SCORE"
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break
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if not score_func:
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try:
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db_instance = inf_conn.get_database(self.dbName)
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df_list = list()
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table_list = list()
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if hide_forgotten:
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condition.update({"must_not": {"exists": "forget_at_flt"}})
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output = select_fields.copy()
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if agg_fields is None:
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agg_fields = []
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for essential_field in ["id"] + agg_fields:
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if essential_field not in output:
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output.append(essential_field)
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score_func = ""
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score_column = ""
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for matchExpr in match_expressions:
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if isinstance(matchExpr, MatchDenseExpr):
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score_func = "similarity()"
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score_column = "SIMILARITY"
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if isinstance(matchExpr, MatchTextExpr):
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score_func = "score()"
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score_column = "SCORE"
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break
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if match_expressions:
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if score_func not in output:
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output.append(score_func)
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output = [f for f in output if f != "_score"]
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if limit <= 0:
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# ElasticSearch default limit is 10000
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limit = 10000
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# Prepare expressions common to all tables
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filter_cond = None
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filter_fulltext = ""
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if condition:
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condition_dict = {self.convert_condition_and_order_field(k): v for k, v in condition.items()}
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table_found = False
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for indexName in index_names:
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for mem_id in memory_ids:
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table_name = f"{indexName}_{mem_id}"
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try:
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filter_cond = self.equivalent_condition_to_str(condition_dict, db_instance.get_table(table_name))
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table_found = True
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if not score_func:
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for matchExpr in match_expressions:
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if isinstance(matchExpr, MatchDenseExpr):
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score_func = "similarity()"
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score_column = "SIMILARITY"
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break
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if match_expressions:
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if score_func not in output:
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output.append(score_func)
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output = [f for f in output if f != "_score"]
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if limit <= 0:
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# ElasticSearch default limit is 10000
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limit = 10000
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# Prepare expressions common to all tables
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filter_cond = None
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filter_fulltext = ""
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if condition:
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condition_dict = {self.convert_condition_and_order_field(k): v for k, v in condition.items()}
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table_found = False
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for indexName in index_names:
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for mem_id in memory_ids:
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table_name = f"{indexName}_{mem_id}"
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try:
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filter_cond = self.equivalent_condition_to_str(condition_dict, db_instance.get_table(table_name))
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table_found = True
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break
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except Exception:
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pass
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if table_found:
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break
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if not table_found:
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self.logger.error(f"No valid tables found for indexNames {index_names} and memoryIds {memory_ids}")
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return pd.DataFrame(), 0
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for matchExpr in match_expressions:
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if isinstance(matchExpr, MatchTextExpr):
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if filter_cond and "filter" not in matchExpr.extra_options:
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matchExpr.extra_options.update({"filter": filter_cond})
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matchExpr.fields = [self.convert_matching_field(field) for field in matchExpr.fields]
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fields = ",".join(matchExpr.fields)
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filter_fulltext = f"filter_fulltext('{fields}', '{matchExpr.matching_text}')"
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if filter_cond:
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filter_fulltext = f"({filter_cond}) AND {filter_fulltext}"
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minimum_should_match = matchExpr.extra_options.get("minimum_should_match", 0.0)
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if isinstance(minimum_should_match, float):
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str_minimum_should_match = str(int(minimum_should_match * 100)) + "%"
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matchExpr.extra_options["minimum_should_match"] = str_minimum_should_match
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for k, v in matchExpr.extra_options.items():
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if not isinstance(v, str):
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matchExpr.extra_options[k] = str(v)
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self.logger.debug(f"INFINITY search MatchTextExpr: {json.dumps(matchExpr.__dict__)}")
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elif isinstance(matchExpr, MatchDenseExpr):
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if filter_fulltext and "filter" not in matchExpr.extra_options:
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matchExpr.extra_options.update({"filter": filter_fulltext})
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for k, v in matchExpr.extra_options.items():
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if not isinstance(v, str):
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matchExpr.extra_options[k] = str(v)
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similarity = matchExpr.extra_options.get("similarity")
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if similarity:
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matchExpr.extra_options["threshold"] = similarity
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del matchExpr.extra_options["similarity"]
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self.logger.debug(f"INFINITY search MatchDenseExpr: {json.dumps(matchExpr.__dict__)}")
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elif isinstance(matchExpr, FusionExpr):
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if matchExpr.method == "weighted_sum":
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# The default is "minmax" which gives a zero score for the last doc.
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matchExpr.fusion_params["normalize"] = "atan"
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self.logger.debug(f"INFINITY search FusionExpr: {json.dumps(matchExpr.__dict__)}")
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order_by_expr_list = list()
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if order_by.fields:
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for order_field in order_by.fields:
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order_field_name = self.convert_condition_and_order_field(order_field[0])
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if order_field[1] == 0:
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order_by_expr_list.append((order_field_name, SortType.Asc))
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else:
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order_by_expr_list.append((order_field_name, SortType.Desc))
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total_hits_count = 0
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# Scatter search tables and gather the results
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column_name_list = []
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for indexName in index_names:
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for memory_id in memory_ids:
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table_name = f"{indexName}_{memory_id}"
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try:
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table_instance = db_instance.get_table(table_name)
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except Exception:
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pass
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if table_found:
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break
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if not table_found:
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self.logger.error(f"No valid tables found for indexNames {index_names} and memoryIds {memory_ids}")
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return pd.DataFrame(), 0
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for matchExpr in match_expressions:
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if isinstance(matchExpr, MatchTextExpr):
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if filter_cond and "filter" not in matchExpr.extra_options:
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matchExpr.extra_options.update({"filter": filter_cond})
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matchExpr.fields = [self.convert_matching_field(field) for field in matchExpr.fields]
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fields = ",".join(matchExpr.fields)
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filter_fulltext = f"filter_fulltext('{fields}', '{matchExpr.matching_text}')"
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if filter_cond:
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filter_fulltext = f"({filter_cond}) AND {filter_fulltext}"
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minimum_should_match = matchExpr.extra_options.get("minimum_should_match", 0.0)
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if isinstance(minimum_should_match, float):
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str_minimum_should_match = str(int(minimum_should_match * 100)) + "%"
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matchExpr.extra_options["minimum_should_match"] = str_minimum_should_match
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for k, v in matchExpr.extra_options.items():
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if not isinstance(v, str):
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matchExpr.extra_options[k] = str(v)
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self.logger.debug(f"INFINITY search MatchTextExpr: {json.dumps(matchExpr.__dict__)}")
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elif isinstance(matchExpr, MatchDenseExpr):
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if filter_fulltext and "filter" not in matchExpr.extra_options:
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matchExpr.extra_options.update({"filter": filter_fulltext})
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for k, v in matchExpr.extra_options.items():
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if not isinstance(v, str):
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matchExpr.extra_options[k] = str(v)
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similarity = matchExpr.extra_options.get("similarity")
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if similarity:
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matchExpr.extra_options["threshold"] = similarity
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del matchExpr.extra_options["similarity"]
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self.logger.debug(f"INFINITY search MatchDenseExpr: {json.dumps(matchExpr.__dict__)}")
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elif isinstance(matchExpr, FusionExpr):
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if matchExpr.method == "weighted_sum":
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# The default is "minmax" which gives a zero score for the last doc.
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matchExpr.fusion_params["normalize"] = "atan"
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self.logger.debug(f"INFINITY search FusionExpr: {json.dumps(matchExpr.__dict__)}")
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order_by_expr_list = list()
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if order_by.fields:
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for order_field in order_by.fields:
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order_field_name = self.convert_condition_and_order_field(order_field[0])
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if order_field[1] == 0:
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order_by_expr_list.append((order_field_name, SortType.Asc))
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else:
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order_by_expr_list.append((order_field_name, SortType.Desc))
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total_hits_count = 0
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# Scatter search tables and gather the results
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column_name_list = []
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for indexName in index_names:
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for memory_id in memory_ids:
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table_name = f"{indexName}_{memory_id}"
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try:
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table_instance = db_instance.get_table(table_name)
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except Exception:
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continue
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table_list.append(table_name)
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if not column_name_list:
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column_name_list = [r[0] for r in table_instance.show_columns().rows()]
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output = self.convert_select_fields(output, column_name_list)
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builder = table_instance.output(output)
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if len(match_expressions) > 0:
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for matchExpr in match_expressions:
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if isinstance(matchExpr, MatchTextExpr):
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fields = ",".join(matchExpr.fields)
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builder = builder.match_text(
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fields,
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matchExpr.matching_text,
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matchExpr.topn,
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matchExpr.extra_options.copy(),
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)
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elif isinstance(matchExpr, MatchDenseExpr):
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builder = builder.match_dense(
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matchExpr.vector_column_name,
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matchExpr.embedding_data,
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matchExpr.embedding_data_type,
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matchExpr.distance_type,
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matchExpr.topn,
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matchExpr.extra_options.copy(),
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)
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elif isinstance(matchExpr, FusionExpr):
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builder = builder.fusion(matchExpr.method, matchExpr.topn, matchExpr.fusion_params)
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else:
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if filter_cond and len(filter_cond) > 0:
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builder.filter(filter_cond)
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if order_by.fields:
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builder.sort(order_by_expr_list)
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builder.offset(offset).limit(limit)
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mem_res, extra_result = builder.option({"total_hits_count": True}).to_df()
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if extra_result:
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total_hits_count += int(extra_result["total_hits_count"])
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self.logger.debug(f"INFINITY search table: {str(table_name)}, result: {str(mem_res)}")
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df_list.append(mem_res)
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self.connPool.release_conn(inf_conn)
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continue
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table_list.append(table_name)
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if not column_name_list:
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column_name_list = [r[0] for r in table_instance.show_columns().rows()]
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output = self.convert_select_fields(output, column_name_list)
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builder = table_instance.output(output)
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if len(match_expressions) > 0:
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for matchExpr in match_expressions:
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if isinstance(matchExpr, MatchTextExpr):
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fields = ",".join(matchExpr.fields)
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builder = builder.match_text(
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fields,
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matchExpr.matching_text,
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matchExpr.topn,
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matchExpr.extra_options.copy(),
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)
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elif isinstance(matchExpr, MatchDenseExpr):
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builder = builder.match_dense(
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matchExpr.vector_column_name,
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matchExpr.embedding_data,
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matchExpr.embedding_data_type,
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matchExpr.distance_type,
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matchExpr.topn,
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matchExpr.extra_options.copy(),
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)
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elif isinstance(matchExpr, FusionExpr):
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builder = builder.fusion(matchExpr.method, matchExpr.topn, matchExpr.fusion_params)
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else:
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if filter_cond and len(filter_cond) > 0:
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builder.filter(filter_cond)
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if order_by.fields:
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builder.sort(order_by_expr_list)
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builder.offset(offset).limit(limit)
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mem_res, extra_result = builder.option({"total_hits_count": True}).to_df()
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if extra_result:
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total_hits_count += int(extra_result["total_hits_count"])
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self.logger.debug(f"INFINITY search table: {str(table_name)}, result: {str(mem_res)}")
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df_list.append(mem_res)
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finally:
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self.connPool.release_conn(inf_conn)
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res = self.concat_dataframes(df_list, output)
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if match_expressions:
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res["_score"] = res[score_column]
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@ -281,28 +283,30 @@ class InfinityConnection(InfinityConnectionBase):
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order_by.asc("forget_at_flt")
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# query
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inf_conn = self.connPool.get_conn()
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db_instance = inf_conn.get_database(self.dbName)
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table_name = f"{index_name}_{memory_id}"
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table_instance = db_instance.get_table(table_name)
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column_name_list = [r[0] for r in table_instance.show_columns().rows()]
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output_fields = [self.convert_message_field_to_infinity(f, column_name_list) for f in select_fields]
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builder = table_instance.output(output_fields)
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filter_cond = self.equivalent_condition_to_str(condition, db_instance.get_table(table_name))
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builder.filter(filter_cond)
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order_by_expr_list = list()
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if order_by.fields:
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for order_field in order_by.fields:
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order_field_name = self.convert_condition_and_order_field(order_field[0])
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if order_field[1] == 0:
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order_by_expr_list.append((order_field_name, SortType.Asc))
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else:
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order_by_expr_list.append((order_field_name, SortType.Desc))
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builder.sort(order_by_expr_list)
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builder.offset(0).limit(limit)
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mem_res, _ = builder.option({"total_hits_count": True}).to_df()
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res = self.concat_dataframes(mem_res, output_fields)
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res.head(limit)
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self.connPool.release_conn(inf_conn)
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try:
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db_instance = inf_conn.get_database(self.dbName)
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table_name = f"{index_name}_{memory_id}"
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table_instance = db_instance.get_table(table_name)
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column_name_list = [r[0] for r in table_instance.show_columns().rows()]
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output_fields = [self.convert_message_field_to_infinity(f, column_name_list) for f in select_fields]
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builder = table_instance.output(output_fields)
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filter_cond = self.equivalent_condition_to_str(condition, db_instance.get_table(table_name))
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builder.filter(filter_cond)
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order_by_expr_list = list()
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if order_by.fields:
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for order_field in order_by.fields:
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order_field_name = self.convert_condition_and_order_field(order_field[0])
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if order_field[1] == 0:
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order_by_expr_list.append((order_field_name, SortType.Asc))
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else:
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order_by_expr_list.append((order_field_name, SortType.Desc))
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builder.sort(order_by_expr_list)
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builder.offset(0).limit(limit)
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mem_res, _ = builder.option({"total_hits_count": True}).to_df()
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res = self.concat_dataframes(mem_res, output_fields)
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res.head(limit)
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finally:
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self.connPool.release_conn(inf_conn)
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return res
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def get_missing_field_message(self, select_fields: list[str], index_name: str, memory_id: str, field_name: str, limit: int=512):
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@ -311,48 +315,52 @@ class InfinityConnection(InfinityConnectionBase):
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order_by.asc("valid_at_flt")
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# query
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inf_conn = self.connPool.get_conn()
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db_instance = inf_conn.get_database(self.dbName)
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||||
table_name = f"{index_name}_{memory_id}"
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table_instance = db_instance.get_table(table_name)
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||||
column_name_list = [r[0] for r in table_instance.show_columns().rows()]
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output_fields = [self.convert_message_field_to_infinity(f, column_name_list) for f in select_fields]
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||||
builder = table_instance.output(output_fields)
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||||
filter_cond = self.equivalent_condition_to_str(condition, db_instance.get_table(table_name))
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||||
builder.filter(filter_cond)
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order_by_expr_list = list()
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||||
if order_by.fields:
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||||
for order_field in order_by.fields:
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||||
order_field_name = self.convert_condition_and_order_field(order_field[0])
|
||||
if order_field[1] == 0:
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||||
order_by_expr_list.append((order_field_name, SortType.Asc))
|
||||
else:
|
||||
order_by_expr_list.append((order_field_name, SortType.Desc))
|
||||
builder.sort(order_by_expr_list)
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||||
builder.offset(0).limit(limit)
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mem_res, _ = builder.option({"total_hits_count": True}).to_df()
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res = self.concat_dataframes(mem_res, output_fields)
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||||
res.head(limit)
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self.connPool.release_conn(inf_conn)
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try:
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db_instance = inf_conn.get_database(self.dbName)
|
||||
table_name = f"{index_name}_{memory_id}"
|
||||
table_instance = db_instance.get_table(table_name)
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||||
column_name_list = [r[0] for r in table_instance.show_columns().rows()]
|
||||
output_fields = [self.convert_message_field_to_infinity(f, column_name_list) for f in select_fields]
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builder = table_instance.output(output_fields)
|
||||
filter_cond = self.equivalent_condition_to_str(condition, db_instance.get_table(table_name))
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||||
builder.filter(filter_cond)
|
||||
order_by_expr_list = list()
|
||||
if order_by.fields:
|
||||
for order_field in order_by.fields:
|
||||
order_field_name = self.convert_condition_and_order_field(order_field[0])
|
||||
if order_field[1] == 0:
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order_by_expr_list.append((order_field_name, SortType.Asc))
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else:
|
||||
order_by_expr_list.append((order_field_name, SortType.Desc))
|
||||
builder.sort(order_by_expr_list)
|
||||
builder.offset(0).limit(limit)
|
||||
mem_res, _ = builder.option({"total_hits_count": True}).to_df()
|
||||
res = self.concat_dataframes(mem_res, output_fields)
|
||||
res.head(limit)
|
||||
finally:
|
||||
self.connPool.release_conn(inf_conn)
|
||||
return res
|
||||
|
||||
def get(self, message_id: str, index_name: str, memory_ids: list[str]) -> dict | None:
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
df_list = list()
|
||||
assert isinstance(memory_ids, list)
|
||||
table_list = list()
|
||||
for memoryId in memory_ids:
|
||||
table_name = f"{index_name}_{memoryId}"
|
||||
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 memory isn't created in Infinity. Maybe it is created in other document engine.")
|
||||
continue
|
||||
mem_res, _ = table_instance.output(["*"]).filter(f"id = '{message_id}'").to_df()
|
||||
self.logger.debug(f"INFINITY get table: {str(table_list)}, result: {str(mem_res)}")
|
||||
df_list.append(mem_res)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
try:
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
df_list = list()
|
||||
assert isinstance(memory_ids, list)
|
||||
table_list = list()
|
||||
for memoryId in memory_ids:
|
||||
table_name = f"{index_name}_{memoryId}"
|
||||
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 memory isn't created in Infinity. Maybe it is created in other document engine.")
|
||||
continue
|
||||
mem_res, _ = table_instance.output(["*"]).filter(f"id = '{message_id}'").to_df()
|
||||
self.logger.debug(f"INFINITY get table: {str(table_list)}, result: {str(mem_res)}")
|
||||
df_list.append(mem_res)
|
||||
finally:
|
||||
self.connPool.release_conn(inf_conn)
|
||||
res = self.concat_dataframes(df_list, ["id"])
|
||||
fields = set(res.columns.tolist())
|
||||
res_fields = self.get_fields(res, list(fields))
|
||||
@ -362,102 +370,106 @@ class InfinityConnection(InfinityConnectionBase):
|
||||
if not documents:
|
||||
return []
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
table_name = f"{index_name}_{memory_id}"
|
||||
vector_size = int(len(documents[0]["content_embed"]))
|
||||
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
|
||||
if vector_size == 0:
|
||||
raise ValueError("Cannot infer vector size from documents")
|
||||
self.create_idx(index_name, memory_id, vector_size)
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
table_name = f"{index_name}_{memory_id}"
|
||||
vector_size = int(len(documents[0]["content_embed"]))
|
||||
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
|
||||
if vector_size == 0:
|
||||
raise ValueError("Cannot infer vector size from documents")
|
||||
self.create_idx(index_name, memory_id, vector_size)
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
|
||||
# embedding fields can't have a default value....
|
||||
embedding_columns = []
|
||||
table_columns = table_instance.show_columns().rows()
|
||||
for n, ty, _, _ in table_columns:
|
||||
r = re.search(r"Embedding\([a-z]+,([0-9]+)\)", ty)
|
||||
if not r:
|
||||
continue
|
||||
embedding_columns.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 == "content_embed":
|
||||
d[f"q_{vector_size}_vec"] = d["content_embed"]
|
||||
d.pop("content_embed")
|
||||
# embedding fields can't have a default value....
|
||||
embedding_columns = []
|
||||
table_columns = table_instance.show_columns().rows()
|
||||
for n, ty, _, _ in table_columns:
|
||||
r = re.search(r"Embedding\([a-z]+,([0-9]+)\)", ty)
|
||||
if not r:
|
||||
continue
|
||||
field_name = self.convert_message_field_to_infinity(k)
|
||||
if field_name in ["valid_at", "invalid_at", "forget_at"]:
|
||||
d[f"{field_name}_flt"] = date_string_to_timestamp(v) if v else 0
|
||||
if v is None:
|
||||
d[field_name] = ""
|
||||
elif self.field_keyword(k):
|
||||
if isinstance(v, list):
|
||||
d[k] = "###".join(v)
|
||||
embedding_columns.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 == "content_embed":
|
||||
d[f"q_{vector_size}_vec"] = d["content_embed"]
|
||||
d.pop("content_embed")
|
||||
continue
|
||||
field_name = self.convert_message_field_to_infinity(k)
|
||||
if field_name in ["valid_at", "invalid_at", "forget_at"]:
|
||||
d[f"{field_name}_flt"] = date_string_to_timestamp(v) if v else 0
|
||||
if v is None:
|
||||
d[field_name] = ""
|
||||
elif self.field_keyword(k):
|
||||
if isinstance(v, list):
|
||||
d[k] = "###".join(v)
|
||||
else:
|
||||
d[k] = v
|
||||
elif k == "memory_id":
|
||||
if isinstance(d[k], list):
|
||||
d[k] = d[k][0] # since d[k] is a list, but we need a str
|
||||
else:
|
||||
d[k] = v
|
||||
elif k == "memory_id":
|
||||
if isinstance(d[k], list):
|
||||
d[k] = d[k][0] # since d[k] is a list, but we need a str
|
||||
else:
|
||||
d[field_name] = v
|
||||
if k != field_name:
|
||||
d.pop(k)
|
||||
d[field_name] = v
|
||||
if k != field_name:
|
||||
d.pop(k)
|
||||
|
||||
for n, vs in embedding_columns:
|
||||
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)
|
||||
table_instance.insert(docs)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
for n, vs in embedding_columns:
|
||||
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)
|
||||
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, memory_id: str) -> bool:
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
table_name = f"{index_name}_{memory_id}"
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
try:
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
table_name = f"{index_name}_{memory_id}"
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
|
||||
columns = {}
|
||||
if table_instance:
|
||||
for n, ty, de, _ in table_instance.show_columns().rows():
|
||||
columns[n] = (ty, de)
|
||||
condition_dict = {self.convert_condition_and_order_field(k): v for k, v in condition.items()}
|
||||
filter = self.equivalent_condition_to_str(condition_dict, table_instance)
|
||||
update_dict = {self.convert_message_field_to_infinity(k): v for k, v in new_value.items()}
|
||||
date_floats = {}
|
||||
for k, v in update_dict.items():
|
||||
if k in ["valid_at", "invalid_at", "forget_at"]:
|
||||
date_floats[f"{k}_flt"] = date_string_to_timestamp(v) if v else 0
|
||||
elif self.field_keyword(k):
|
||||
if isinstance(v, list):
|
||||
update_dict[k] = "###".join(v)
|
||||
columns = {}
|
||||
if table_instance:
|
||||
for n, ty, de, _ in table_instance.show_columns().rows():
|
||||
columns[n] = (ty, de)
|
||||
condition_dict = {self.convert_condition_and_order_field(k): v for k, v in condition.items()}
|
||||
filter = self.equivalent_condition_to_str(condition_dict, table_instance)
|
||||
update_dict = {self.convert_message_field_to_infinity(k): v for k, v in new_value.items()}
|
||||
date_floats = {}
|
||||
for k, v in update_dict.items():
|
||||
if k in ["valid_at", "invalid_at", "forget_at"]:
|
||||
date_floats[f"{k}_flt"] = date_string_to_timestamp(v) if v else 0
|
||||
elif self.field_keyword(k):
|
||||
if isinstance(v, list):
|
||||
update_dict[k] = "###".join(v)
|
||||
else:
|
||||
update_dict[k] = v
|
||||
elif k == "memory_id":
|
||||
if isinstance(update_dict[k], list):
|
||||
update_dict[k] = update_dict[k][0] # since d[k] is a list, but we need a str
|
||||
else:
|
||||
update_dict[k] = v
|
||||
elif k == "memory_id":
|
||||
if isinstance(update_dict[k], list):
|
||||
update_dict[k] = update_dict[k][0] # since d[k] is a list, but we need a str
|
||||
else:
|
||||
update_dict[k] = v
|
||||
if date_floats:
|
||||
update_dict.update(date_floats)
|
||||
if date_floats:
|
||||
update_dict.update(date_floats)
|
||||
|
||||
self.logger.debug(f"INFINITY update table {table_name}, filter {filter}, newValue {new_value}.")
|
||||
table_instance.update(filter, update_dict)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
self.logger.debug(f"INFINITY update table {table_name}, filter {filter}, newValue {new_value}.")
|
||||
table_instance.update(filter, update_dict)
|
||||
finally:
|
||||
self.connPool.release_conn(inf_conn)
|
||||
return True
|
||||
|
||||
"""
|
||||
|
||||
Reference in New Issue
Block a user