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from typing import List
import gradio as gr
from graphgen.bases import BaseLLMWrapper
from graphgen.bases.base_storage import BaseGraphStorage
from graphgen.bases.datatypes import Chunk
from graphgen.utils import logger
from .build_mm_kg import build_mm_kg
from .build_text_kg import build_text_kg
async def build_kg(
llm_client: BaseLLMWrapper,
kg_instance: BaseGraphStorage,
chunks: List[Chunk],
progress_bar: gr.Progress = None,
):
"""
Build knowledge graph (KG) and merge into kg_instance
:param llm_client: Synthesizer LLM model to extract entities and relationships
:param kg_instance
:param chunks
:param anchor_type: get this type of information from chunks
:param progress_bar: Gradio progress bar to show the progress of the extraction
:return:
"""
text_chunks = [chunk for chunk in chunks if chunk.type == "text"]
mm_chunks = [
chunk
for chunk in chunks
if chunk.type in ("image", "video", "table", "formula")
]
if len(text_chunks) == 0:
logger.info("All text chunks are already in the storage")
else:
logger.info("[Text Entity and Relation Extraction] processing ...")
await build_text_kg(
llm_client=llm_client,
kg_instance=kg_instance,
chunks=text_chunks,
progress_bar=progress_bar,
)
if len(mm_chunks) == 0:
logger.info("All multi-modal chunks are already in the storage")
else:
logger.info("[Multi-modal Entity and Relation Extraction] processing ...")
await build_mm_kg(
llm_client=llm_client,
kg_instance=kg_instance,
chunks=mm_chunks,
progress_bar=progress_bar,
)
return kg_instance
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