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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 | |