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2044bb0039
### What problem does this PR solve? 1. Remove unused code 2. Fix type mismatch, in nlp search and infinity search interface 3. Fix chunk list, get all chunks of this user. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) --------- Signed-off-by: jinhai <haijin.chn@gmail.com>
104 lines
4.6 KiB
Python
104 lines
4.6 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 rag.utils.doc_store_conn import OrderByExpr, FusionExpr
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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: str | list[str], kb_ids: list[str], emb_mdl=None, highlight=False):
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def merge_into_first(sres, title="") -> dict[str, str]:
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if not sres:
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return {}
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content_with_weight = ""
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df, texts = [],[]
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for d in sres.values():
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try:
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df.append(json.loads(d["content_with_weight"]))
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except Exception:
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texts.append(d["content_with_weight"])
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if df:
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content_with_weight = title + "\n" + pd.DataFrame(df).to_csv()
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else:
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content_with_weight = title + "\n" + "\n".join(texts)
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first_id = ""
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first_source = {}
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for k, v in sres.items():
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first_id = id
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first_source = deepcopy(v)
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break
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first_source["content_with_weight"] = content_with_weight
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first_id = next(iter(sres))
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return {first_id: first_source}
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qst = req.get("question", "")
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matchText, keywords = self.qryr.question(qst, min_match=0.05)
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condition = self.get_filters(req)
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## Entity retrieval
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condition.update({"knowledge_graph_kwd": ["entity"]})
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assert emb_mdl, "No embedding model selected"
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matchDense = self.get_vector(qst, emb_mdl, 1024, req.get("similarity", 0.1))
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q_vec = matchDense.embedding_data
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src = req.get("fields", ["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd",
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"doc_id", f"q_{len(q_vec)}_vec", "position_list", "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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fusionExpr = FusionExpr("weighted_sum", 32, {"weights": "0.5, 0.5"})
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ent_res = self.dataStore.search(src, list(), condition, [matchText, matchDense, fusionExpr], OrderByExpr(), 0, 32, idxnm, kb_ids)
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ent_res_fields = self.dataStore.getFields(ent_res, src)
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entities = [d.get["name_kwd"] for d in ent_res_fields.values() if d.get("name_kwd")]
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ent_ids = self.dataStore.getChunkIds(ent_res)
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ent_content = merge_into_first(ent_res_fields, "-Entities-")
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if ent_content:
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ent_ids = list(ent_content.keys())
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## Community retrieval
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condition = self.get_filters(req)
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condition.update({"entities_kwd": entities, "knowledge_graph_kwd": ["community_report"]})
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comm_res = self.dataStore.search(src, list(), condition, [matchText, matchDense, fusionExpr], OrderByExpr(), 0, 32, idxnm, kb_ids)
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comm_res_fields = self.dataStore.getFields(comm_res, src)
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comm_ids = self.dataStore.getChunkIds(comm_res)
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comm_content = merge_into_first(comm_res_fields, "-Community Report-")
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if comm_content:
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comm_ids = list(comm_content.keys())
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## Text content retrieval
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condition = self.get_filters(req)
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condition.update({"knowledge_graph_kwd": ["text"]})
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txt_res = self.dataStore.search(src, list(), condition, [matchText, matchDense, fusionExpr], OrderByExpr(), 0, 6, idxnm, kb_ids)
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txt_res_fields = self.dataStore.getFields(txt_res, src)
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txt_ids = self.dataStore.getChunkIds(txt_res)
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txt_content = merge_into_first(txt_res_fields, "-Original Content-")
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if txt_content:
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txt_ids = list(txt_content.keys())
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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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highlight=None,
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field={**ent_content, **comm_content, **txt_content},
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keywords=[]
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)
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