Spaces:
Paused
Paused
File size: 3,317 Bytes
ab2ded1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
#
# 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 RetrievalParam(ComponentParamBase):
"""
Define the Retrieval component parameters.
"""
def __init__(self):
super().__init__()
self.similarity_threshold = 0.2
self.keywords_similarity_weight = 0.5
self.top_n = 8
self.top_k = 1024
self.kb_ids = []
self.rerank_id = ""
self.empty_response = ""
def check(self):
self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold")
self.check_decimal_float(self.keywords_similarity_weight, "[Retrieval] Keywords similarity weight")
self.check_positive_number(self.top_n, "[Retrieval] Top N")
self.check_empty(self.kb_ids, "[Retrieval] Knowledge bases")
class Retrieval(ComponentBase, ABC):
component_name = "Retrieval"
def _run(self, history, **kwargs):
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(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0])
self._canvas.set_embedding_model(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"]:
df = Retrieval.be_output(self._param.empty_response)
df["empty_response"] = True
return df
df = pd.DataFrame(kbinfos["chunks"])
df["content"] = df["content_with_weight"]
del df["content_with_weight"]
print(">>>>>>>>>>>>>>>>>>>>>>>>>>\n", query, df)
return df
|