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152072f900
### What problem does this PR solve? #1594 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
81 lines
2.9 KiB
Python
81 lines
2.9 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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from api.db import LLMType
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from api.db.services.llm_service import LLMBundle
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from agent.component import GenerateParam, Generate
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from rag.utils import num_tokens_from_string, encoder
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class RelevantParam(GenerateParam):
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"""
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Define the Relevant component parameters.
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"""
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def __init__(self):
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super().__init__()
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self.prompt = ""
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self.yes = ""
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self.no = ""
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def check(self):
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super().check()
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self.check_empty(self.yes, "[Relevant] 'Yes'")
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self.check_empty(self.no, "[Relevant] 'No'")
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def get_prompt(self):
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self.prompt = """
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You are a grader assessing relevance of a retrieved document to a user question.
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It does not need to be a stringent test. The goal is to filter out erroneous retrievals.
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If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant.
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Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.
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No other words needed except 'yes' or 'no'.
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"""
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return self.prompt
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class Relevant(Generate, ABC):
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component_name = "Relevant"
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def _run(self, history, **kwargs):
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q = ""
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for r, c in self._canvas.history[::-1]:
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if r == "user":
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q = c
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break
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ans = self.get_input()
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ans = " - ".join(ans["content"]) if "content" in ans else ""
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if not ans:
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return Relevant.be_output(self._param.no)
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ans = "Documents: \n" + ans
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ans = f"Question: {q}\n" + ans
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chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
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if num_tokens_from_string(ans) >= chat_mdl.max_length - 4:
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ans = encoder.decode(encoder.encode(ans)[:chat_mdl.max_length - 4])
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ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": ans}],
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self._param.gen_conf())
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print(ans, ":::::::::::::::::::::::::::::::::")
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if ans.lower().find("yes") >= 0:
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return Relevant.be_output(self._param.yes)
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if ans.lower().find("no") >= 0:
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return Relevant.be_output(self._param.no)
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assert False, f"Relevant component got: {ans}"
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