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