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83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from abc import ABC
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import pandas as pd
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from api.db import LLMType
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.services.llm_service import LLMBundle
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from api.settings import retrievaler
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from graph.component.base import ComponentBase, ComponentParamBase
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class SwitchParam(ComponentParamBase):
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"""
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Define the Switch component parameters.
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"""
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def __init__(self):
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super().__init__()
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"""
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{
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"cpn_id": "categorize:0",
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"not": False,
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"operator": "gt/gte/lt/lte/eq/in",
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"value": "",
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"to": ""
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}
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"""
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self.conditions = []
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self.default = ""
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def check(self):
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self.check_empty(self.conditions, "[Switch] conditions")
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self.check_empty(self.default, "[Switch] Default path")
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for cond in self.conditions:
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if not cond["to"]: raise ValueError(f"[Switch] 'To' can not be empty!")
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def operators(self, field, op, value):
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if op == "gt":
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return float(field) > float(value)
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if op == "gte":
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return float(field) >= float(value)
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if op == "lt":
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return float(field) < float(value)
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if op == "lte":
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return float(field) <= float(value)
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if op == "eq":
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return str(field) == str(value)
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if op == "in":
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return str(field).find(str(value)) >= 0
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return False
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class Switch(ComponentBase, ABC):
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component_name = "Switch"
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def _run(self, history, **kwargs):
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for cond in self._param.conditions:
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input = self._canvas.get_component(cond["cpn_id"])["obj"].output()[1]
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if self._param.operators(input.iloc[0, 0], cond["operator"], cond["value"]):
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if not cond["not"]:
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return pd.DataFrame([{"content": cond["to"]}])
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return pd.DataFrame([{"content": self._param.default}])
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