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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.
#
import json
from copy import deepcopy
from typing import Dict
import pandas as pd
from rag.utils.doc_store_conn import OrderByExpr, FusionExpr
from rag.nlp.search import Dealer
class KGSearch(Dealer):
def search(self, req, idxnm, kb_ids, emb_mdl, highlight=False):
def merge_into_first(sres, title="") -> Dict[str, str]:
if not sres:
return {}
content_with_weight = ""
df, texts = [],[]
for d in sres.values():
try:
df.append(json.loads(d["content_with_weight"]))
except Exception:
texts.append(d["content_with_weight"])
if df:
content_with_weight = title + "\n" + pd.DataFrame(df).to_csv()
else:
content_with_weight = title + "\n" + "\n".join(texts)
first_id = ""
first_source = {}
for k, v in sres.items():
first_id = id
first_source = deepcopy(v)
break
first_source["content_with_weight"] = content_with_weight
first_id = next(iter(sres))
return {first_id: first_source}
qst = req.get("question", "")
matchText, keywords = self.qryr.question(qst, min_match=0.05)
condition = self.get_filters(req)
## Entity retrieval
condition.update({"knowledge_graph_kwd": ["entity"]})
assert emb_mdl, "No embedding model selected"
matchDense = self.get_vector(qst, emb_mdl, 1024, req.get("similarity", 0.1))
q_vec = matchDense.embedding_data
src = req.get("fields", ["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd",
"doc_id", f"q_{len(q_vec)}_vec", "position_list", "name_kwd",
"q_1024_vec", "q_1536_vec", "available_int", "content_with_weight",
"weight_int", "weight_flt", "rank_int"
])
fusionExpr = FusionExpr("weighted_sum", 32, {"weights": "0.5, 0.5"})
ent_res = self.dataStore.search(src, list(), condition, [matchText, matchDense, fusionExpr], OrderByExpr(), 0, 32, idxnm, kb_ids)
ent_res_fields = self.dataStore.getFields(ent_res, src)
entities = [d["name_kwd"] for d in ent_res_fields.values()]
ent_ids = self.dataStore.getChunkIds(ent_res)
ent_content = merge_into_first(ent_res_fields, "-Entities-")
if ent_content:
ent_ids = list(ent_content.keys())
## Community retrieval
condition = self.get_filters(req)
condition.update({"entities_kwd": entities, "knowledge_graph_kwd": ["community_report"]})
comm_res = self.dataStore.search(src, list(), condition, [matchText, matchDense, fusionExpr], OrderByExpr(), 0, 32, idxnm, kb_ids)
comm_res_fields = self.dataStore.getFields(comm_res, src)
comm_ids = self.dataStore.getChunkIds(comm_res)
comm_content = merge_into_first(comm_res_fields, "-Community Report-")
if comm_content:
comm_ids = list(comm_content.keys())
## Text content retrieval
condition = self.get_filters(req)
condition.update({"knowledge_graph_kwd": ["text"]})
txt_res = self.dataStore.search(src, list(), condition, [matchText, matchDense, fusionExpr], OrderByExpr(), 0, 6, idxnm, kb_ids)
txt_res_fields = self.dataStore.getFields(txt_res, src)
txt_ids = self.dataStore.getChunkIds(txt_res)
txt_content = merge_into_first(txt_res_fields, "-Original Content-")
if txt_content:
txt_ids = list(txt_content.keys())
return self.SearchResult(
total=len(ent_ids) + len(comm_ids) + len(txt_ids),
ids=[*ent_ids, *comm_ids, *txt_ids],
query_vector=q_vec,
highlight=None,
field={**ent_content, **comm_content, **txt_content},
keywords=[]
)
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