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c5da3cdd97
### What problem does this PR solve? #4367 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
90 lines
3.3 KiB
Python
90 lines
3.3 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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import logging
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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.dialog_service import label_question
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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 import settings
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from agent.component.base import ComponentBase, ComponentParamBase
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class RetrievalParam(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.similarity_threshold = 0.2
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self.keywords_similarity_weight = 0.5
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self.top_n = 8
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self.top_k = 1024
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self.kb_ids = []
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self.rerank_id = ""
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self.empty_response = ""
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def check(self):
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self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold")
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self.check_decimal_float(self.keywords_similarity_weight, "[Retrieval] Keywords similarity weight")
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self.check_positive_number(self.top_n, "[Retrieval] Top N")
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class Retrieval(ComponentBase, ABC):
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component_name = "Retrieval"
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def _run(self, history, **kwargs):
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query = self.get_input()
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query = str(query["content"][0]) if "content" in query else ""
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kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
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if not kbs:
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return Retrieval.be_output("")
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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(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0])
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self._canvas.set_embedding_model(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 = settings.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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rank_feature=label_question(query, kbs))
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if not kbinfos["chunks"]:
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df = Retrieval.be_output("")
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if self._param.empty_response and self._param.empty_response.strip():
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df["empty_response"] = self._param.empty_response
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return df
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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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logging.debug("{} {}".format(query, df))
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return df
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