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Light GraphRAG (#4585)
### What problem does this PR solve? #4543 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu
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graphrag/general/index.py
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197
graphrag/general/index.py
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#
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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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import logging
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from functools import reduce, partial
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import networkx as nx
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from api import settings
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from graphrag.general.community_reports_extractor import CommunityReportsExtractor
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from graphrag.entity_resolution import EntityResolution
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from graphrag.general.extractor import Extractor
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from graphrag.general.graph_extractor import DEFAULT_ENTITY_TYPES
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from graphrag.utils import graph_merge, set_entity, get_relation, set_relation, get_entity, get_graph, set_graph, \
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chunk_id, update_nodes_pagerank_nhop_neighbour
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from rag.nlp import rag_tokenizer, search
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from rag.utils.redis_conn import RedisDistributedLock
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class Dealer:
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def __init__(self,
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extractor: Extractor,
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tenant_id: str,
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kb_id: str,
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llm_bdl,
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chunks: list[tuple[str, str]],
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language,
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entity_types=DEFAULT_ENTITY_TYPES,
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embed_bdl=None,
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callback=None
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):
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docids = list(set([docid for docid,_ in chunks]))
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self.llm_bdl = llm_bdl
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self.embed_bdl = embed_bdl
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ext = extractor(self.llm_bdl, language=language,
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entity_types=entity_types,
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get_entity=partial(get_entity, tenant_id, kb_id),
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set_entity=partial(set_entity, tenant_id, kb_id, self.embed_bdl),
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get_relation=partial(get_relation, tenant_id, kb_id),
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set_relation=partial(set_relation, tenant_id, kb_id, self.embed_bdl)
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)
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ents, rels = ext(chunks, callback)
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self.graph = nx.Graph()
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for en in ents:
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self.graph.add_node(en["entity_name"], entity_type=en["entity_type"])#, description=en["description"])
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for rel in rels:
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self.graph.add_edge(
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rel["src_id"],
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rel["tgt_id"],
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weight=rel["weight"],
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#description=rel["description"]
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)
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with RedisDistributedLock(kb_id, 60*60):
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old_graph, old_doc_ids = get_graph(tenant_id, kb_id)
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if old_graph is not None:
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logging.info("Merge with an exiting graph...................")
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self.graph = reduce(graph_merge, [old_graph, self.graph])
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update_nodes_pagerank_nhop_neighbour(tenant_id, kb_id, self.graph, 2)
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if old_doc_ids:
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docids.extend(old_doc_ids)
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docids = list(set(docids))
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set_graph(tenant_id, kb_id, self.graph, docids)
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class WithResolution(Dealer):
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def __init__(self,
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tenant_id: str,
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kb_id: str,
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llm_bdl,
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embed_bdl=None,
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callback=None
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):
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self.llm_bdl = llm_bdl
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self.embed_bdl = embed_bdl
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with RedisDistributedLock(kb_id, 60*60):
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self.graph, doc_ids = get_graph(tenant_id, kb_id)
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if not self.graph:
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logging.error(f"Faild to fetch the graph. tenant_id:{kb_id}, kb_id:{kb_id}")
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if callback:
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callback(-1, msg="Faild to fetch the graph.")
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return
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if callback:
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callback(msg="Fetch the existing graph.")
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er = EntityResolution(self.llm_bdl,
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get_entity=partial(get_entity, tenant_id, kb_id),
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set_entity=partial(set_entity, tenant_id, kb_id, self.embed_bdl),
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get_relation=partial(get_relation, tenant_id, kb_id),
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set_relation=partial(set_relation, tenant_id, kb_id, self.embed_bdl))
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reso = er(self.graph)
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self.graph = reso.graph
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logging.info("Graph resolution is done. Remove {} nodes.".format(len(reso.removed_entities)))
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if callback:
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callback(msg="Graph resolution is done. Remove {} nodes.".format(len(reso.removed_entities)))
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update_nodes_pagerank_nhop_neighbour(tenant_id, kb_id, self.graph, 2)
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set_graph(tenant_id, kb_id, self.graph, doc_ids)
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settings.docStoreConn.delete({
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"knowledge_graph_kwd": "relation",
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"kb_id": kb_id,
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"from_entity_kwd": reso.removed_entities
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}, search.index_name(tenant_id), kb_id)
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settings.docStoreConn.delete({
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"knowledge_graph_kwd": "relation",
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"kb_id": kb_id,
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"to_entity_kwd": reso.removed_entities
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}, search.index_name(tenant_id), kb_id)
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settings.docStoreConn.delete({
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"knowledge_graph_kwd": "entity",
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"kb_id": kb_id,
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"entity_kwd": reso.removed_entities
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}, search.index_name(tenant_id), kb_id)
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class WithCommunity(Dealer):
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def __init__(self,
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tenant_id: str,
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kb_id: str,
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llm_bdl,
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embed_bdl=None,
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callback=None
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):
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self.community_structure = None
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self.community_reports = None
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self.llm_bdl = llm_bdl
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self.embed_bdl = embed_bdl
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with RedisDistributedLock(kb_id, 60*60):
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self.graph, doc_ids = get_graph(tenant_id, kb_id)
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if not self.graph:
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logging.error(f"Faild to fetch the graph. tenant_id:{kb_id}, kb_id:{kb_id}")
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if callback:
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callback(-1, msg="Faild to fetch the graph.")
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return
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if callback:
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callback(msg="Fetch the existing graph.")
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cr = CommunityReportsExtractor(self.llm_bdl,
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get_entity=partial(get_entity, tenant_id, kb_id),
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set_entity=partial(set_entity, tenant_id, kb_id, self.embed_bdl),
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get_relation=partial(get_relation, tenant_id, kb_id),
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set_relation=partial(set_relation, tenant_id, kb_id, self.embed_bdl))
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cr = cr(self.graph, callback=callback)
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self.community_structure = cr.structured_output
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self.community_reports = cr.output
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set_graph(tenant_id, kb_id, self.graph, doc_ids)
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if callback:
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callback(msg="Graph community extraction is done. Indexing {} reports.".format(len(cr.structured_output)))
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settings.docStoreConn.delete({
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"knowledge_graph_kwd": "community_report",
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"kb_id": kb_id
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}, search.index_name(tenant_id), kb_id)
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for stru, rep in zip(self.community_structure, self.community_reports):
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obj = {
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"report": rep,
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"evidences": "\n".join([f["explanation"] for f in stru["findings"]])
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}
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chunk = {
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"docnm_kwd": stru["title"],
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"title_tks": rag_tokenizer.tokenize(stru["title"]),
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"content_with_weight": json.dumps(obj, ensure_ascii=False),
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"content_ltks": rag_tokenizer.tokenize(obj["report"] +" "+ obj["evidences"]),
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"knowledge_graph_kwd": "community_report",
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"weight_flt": stru["weight"],
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"entities_kwd": stru["entities"],
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"important_kwd": stru["entities"],
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"kb_id": kb_id,
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"source_id": doc_ids,
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"available_int": 0
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}
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chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(chunk["content_ltks"])
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#try:
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# ebd, _ = self.embed_bdl.encode([", ".join(community["entities"])])
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# chunk["q_%d_vec" % len(ebd[0])] = ebd[0]
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#except Exception as e:
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# logging.exception(f"Fail to embed entity relation: {e}")
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settings.docStoreConn.insert([{"id": chunk_id(chunk), **chunk}], search.index_name(tenant_id))
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