agent/component/keyword.py

#
# 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 re
from abc import ABC
from api.db import LLMType
from api.db.services.llm_service import LLMBundle
from agent.component import GenerateParam, Generate
from agent.settings import DEBUG


class KeywordExtractParam(GenerateParam):
 """
 Define the KeywordExtract component parameters.
 """

 def __init__(self):
 super().__init__()
 self.top_n = 1

 def check(self):
 super().check()
 self.check_positive_integer(self.top_n, "Top N")

 def get_prompt(self):
 self.prompt = """
- Role: You're a question analyzer. 
- Requirements: 
 - Summarize user's question, and give top %s important keyword/phrase.
 - Use comma as a delimiter to separate keywords/phrases.
- Answer format: (in language of user's question)
 - keyword: 
""" % self.top_n
 return self.prompt


class KeywordExtract(Generate, ABC):
 component_name = "KeywordExtract"

 def _run(self, history, **kwargs):
 q = ""
 for r, c in self._canvas.history[::-1]:
 if r == "user":
 q += c
 break

 chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
 ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": q}],
 self._param.gen_conf())

 ans = re.sub(r".*keyword:", "", ans).strip()
 if DEBUG: print(ans, ":::::::::::::::::::::::::::::::::")
 return KeywordExtract.be_output(ans)