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from collections import defaultdict
from typing import List
from graphgen.bases import BaseLLMWrapper
from graphgen.bases.base_storage import BaseGraphStorage
from graphgen.bases.datatypes import Chunk
from graphgen.models import MMKGBuilder
from graphgen.utils import run_concurrent
def build_mm_kg(
llm_client: BaseLLMWrapper,
kg_instance: BaseGraphStorage,
chunks: List[Chunk],
):
"""
Build multi-modal KG and merge into kg_instance
:param llm_client: Synthesizer LLM model to extract entities and relationships
:param kg_instance
:param chunks
:return:
"""
mm_builder = MMKGBuilder(llm_client=llm_client)
results = run_concurrent(
mm_builder.extract,
chunks,
desc="[2/4] Extracting entities and relationships from multi-modal chunks",
unit="chunk",
)
nodes = defaultdict(list)
edges = defaultdict(list)
for n, e in results:
for k, v in n.items():
nodes[k].extend(v)
for k, v in e.items():
edges[tuple(sorted(k))].extend(v)
run_concurrent(
lambda kv: mm_builder.merge_nodes(kv, kg_instance=kg_instance),
list(nodes.items()),
desc="Inserting entities into storage",
)
run_concurrent(
lambda kv: mm_builder.merge_edges(kv, kg_instance=kg_instance),
list(edges.items()),
desc="Inserting relationships into storage",
)
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