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152072f900
### What problem does this PR solve? #1594 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
110 lines
4.3 KiB
Python
110 lines
4.3 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 json
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from copy import deepcopy
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import pandas as pd
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from elasticsearch_dsl import Q, Search
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from rag.nlp.search import Dealer
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class KGSearch(Dealer):
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def search(self, req, idxnm, emb_mdl=None):
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def merge_into_first(sres, title=""):
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df,texts = [],[]
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for d in sres["hits"]["hits"]:
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try:
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df.append(json.loads(d["_source"]["content_with_weight"]))
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except Exception as e:
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texts.append(d["_source"]["content_with_weight"])
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pass
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if not df and not texts: return False
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if df:
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try:
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sres["hits"]["hits"][0]["_source"]["content_with_weight"] = title + "\n" + pd.DataFrame(df).to_csv()
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except Exception as e:
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pass
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else:
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sres["hits"]["hits"][0]["_source"]["content_with_weight"] = title + "\n" + "\n".join(texts)
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return True
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src = req.get("fields", ["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd",
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"image_id", "doc_id", "q_512_vec", "q_768_vec", "position_int", "name_kwd",
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"q_1024_vec", "q_1536_vec", "available_int", "content_with_weight",
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"weight_int", "weight_flt", "rank_int"
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])
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qst = req.get("question", "")
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binary_query, keywords = self.qryr.question(qst, min_match="5%")
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binary_query = self._add_filters(binary_query, req)
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## Entity retrieval
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bqry = deepcopy(binary_query)
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bqry.filter.append(Q("terms", knowledge_graph_kwd=["entity"]))
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s = Search()
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s = s.query(bqry)[0: 32]
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s = s.to_dict()
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q_vec = []
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if req.get("vector"):
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assert emb_mdl, "No embedding model selected"
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s["knn"] = self._vector(
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qst, emb_mdl, req.get(
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"similarity", 0.1), 1024)
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s["knn"]["filter"] = bqry.to_dict()
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q_vec = s["knn"]["query_vector"]
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ent_res = self.es.search(deepcopy(s), idxnm=idxnm, timeout="600s", src=src)
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entities = [d["name_kwd"] for d in self.es.getSource(ent_res)]
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ent_ids = self.es.getDocIds(ent_res)
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if merge_into_first(ent_res, "-Entities-"):
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ent_ids = ent_ids[0:1]
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## Community retrieval
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bqry = deepcopy(binary_query)
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bqry.filter.append(Q("terms", entities_kwd=entities))
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bqry.filter.append(Q("terms", knowledge_graph_kwd=["community_report"]))
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s = Search()
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s = s.query(bqry)[0: 32]
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s = s.to_dict()
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comm_res = self.es.search(deepcopy(s), idxnm=idxnm, timeout="600s", src=src)
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comm_ids = self.es.getDocIds(comm_res)
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if merge_into_first(comm_res, "-Community Report-"):
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comm_ids = comm_ids[0:1]
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## Text content retrieval
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bqry = deepcopy(binary_query)
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bqry.filter.append(Q("terms", knowledge_graph_kwd=["text"]))
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s = Search()
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s = s.query(bqry)[0: 6]
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s = s.to_dict()
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txt_res = self.es.search(deepcopy(s), idxnm=idxnm, timeout="600s", src=src)
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txt_ids = self.es.getDocIds(comm_res)
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if merge_into_first(txt_res, "-Original Content-"):
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txt_ids = comm_ids[0:1]
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return self.SearchResult(
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total=len(ent_ids) + len(comm_ids) + len(txt_ids),
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ids=[*ent_ids, *comm_ids, *txt_ids],
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query_vector=q_vec,
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aggregation=None,
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highlight=None,
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field={**self.getFields(ent_res, src), **self.getFields(comm_res, src), **self.getFields(txt_res, src)},
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keywords=[]
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)
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