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