# # Copyright 2024 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 logging from abc import ABC from api.db import LLMType from api.db.services.llm_service import LLMBundle from agent.component import GenerateParam, Generate class CategorizeParam(GenerateParam): """ Define the Categorize component parameters. """ def __init__(self): super().__init__() self.category_description = {} self.prompt = "" def check(self): super().check() self.check_empty(self.category_description, "[Categorize] Category examples") for k, v in self.category_description.items(): if not k: raise ValueError("[Categorize] Category name can not be empty!") if not v.get("to"): raise ValueError(f"[Categorize] 'To' of category {k} can not be empty!") def get_prompt(self, chat_hist): cate_lines = [] for c, desc in self.category_description.items(): for line in desc.get("examples", "").split("\n"): if not line: continue cate_lines.append("USER: {}\nCategory: {}".format(line, c)) descriptions = [] for c, desc in self.category_description.items(): if desc.get("description"): descriptions.append( "--------------------\nCategory: {}\nDescription: {}\n".format(c, desc["description"])) self.prompt = """ You're a text classifier. You need to categorize the user’s questions into {} categories, namely: {} Here's description of each category: {} You could learn from the following examples: {} You could learn from the above examples. Just mention the category names, no need for any additional words. ---- Real Data ---- {} """.format( len(self.category_description.keys()), "/".join(list(self.category_description.keys())), "\n".join(descriptions), "- ".join(cate_lines), chat_hist ) return self.prompt class Categorize(Generate, ABC): component_name = "Categorize" def _run(self, history, **kwargs): input = self.get_input() chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id) ans = chat_mdl.chat(self._param.get_prompt(input), [{"role": "user", "content": "\nCategory: "}], self._param.gen_conf()) logging.debug(f"input: {input}, answer: {str(ans)}") for c in self._param.category_description.keys(): if ans.lower().find(c.lower()) >= 0: return Categorize.be_output(self._param.category_description[c]["to"]) return Categorize.be_output(list(self._param.category_description.items())[-1][1]["to"]) def debug(self, **kwargs): df = self._run([], **kwargs) cpn_id = df.iloc[0, 0] return Categorize.be_output(self._canvas.get_compnent_name(cpn_id))