code clean
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@@ -536,7 +536,7 @@ class LightRAG:
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),
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embedding_func=self.embedding_func,
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)
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self.llm_model_func = limit_async_func_call(self.llm_model_max_async)(
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partial(
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self.llm_model_func, # type: ignore
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@@ -955,37 +955,34 @@ class LightRAG:
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# Insert entities into vector storage if needed
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data_for_vdb = {
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compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
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"content": dp["entity_name"] + dp["description"],
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"entity_name": dp["entity_name"],
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}
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for dp in all_entities_data
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compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
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"content": dp["entity_name"] + dp["description"],
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"entity_name": dp["entity_name"],
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}
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for dp in all_entities_data
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}
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await self.entities_vdb.upsert(data_for_vdb)
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# Insert relationships into vector storage if needed
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data_for_vdb = {
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compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): {
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"src_id": dp["src_id"],
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"tgt_id": dp["tgt_id"],
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"content": dp["keywords"]
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+ dp["src_id"]
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+ dp["tgt_id"]
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+ dp["description"],
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}
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for dp in all_relationships_data
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compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): {
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"src_id": dp["src_id"],
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"tgt_id": dp["tgt_id"],
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"content": dp["keywords"]
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+ dp["src_id"]
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+ dp["tgt_id"]
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+ dp["description"],
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}
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for dp in all_relationships_data
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}
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await self.relationships_vdb.upsert(data_for_vdb)
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finally:
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if update_storage:
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await self._insert_done()
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def query(
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self,
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query: str,
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param: QueryParam = QueryParam(),
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prompt: str | None = None
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self, query: str, param: QueryParam = QueryParam(), prompt: str | None = None
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) -> str:
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"""
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Perform a sync query.
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@@ -997,7 +994,7 @@ class LightRAG:
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Returns:
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str: The result of the query execution.
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"""
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"""
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loop = always_get_an_event_loop()
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return loop.run_until_complete(self.aquery(query, param, prompt))
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