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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
import pandas as pd
from api.db import LLMType
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import LLMBundle
from api.settings import retrievaler
from agent.component.base import ComponentBase, ComponentParamBase
class CiteParam(ComponentParamBase):
"""
Define the Retrieval component parameters.
"""
def __init__(self):
super().__init__()
self.cite_sources = []
def check(self):
self.check_empty(self.cite_source, "Please specify where you want to cite from.")
class Cite(ComponentBase, ABC):
component_name = "Cite"
def _run(self, history, **kwargs):
input = "\n- ".join(self.get_input()["content"])
sources = [self._canvas.get_component(cpn_id).output()[1] for cpn_id in self._param.cite_source]
query = []
for role, cnt in history[::-1][:self._param.message_history_window_size]:
if role != "user":continue
query.append(cnt)
query = "\n".join(query)
kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
if not kbs:
raise ValueError("Can't find knowledgebases by {}".format(self._param.kb_ids))
embd_nms = list(set([kb.embd_id for kb in kbs]))
assert len(embd_nms) == 1, "Knowledge bases use different embedding models."
embd_mdl = LLMBundle(kbs[0].tenant_id, LLMType.EMBEDDING, embd_nms[0])
rerank_mdl = None
if self._param.rerank_id:
rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id)
kbinfos = retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
1, self._param.top_n,
self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight,
aggs=False, rerank_mdl=rerank_mdl)
if not kbinfos["chunks"]: return pd.DataFrame()
df = pd.DataFrame(kbinfos["chunks"])
df["content"] = df["content_with_weight"]
del df["content_with_weight"]
return df
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