Optimize log message
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@@ -1372,11 +1372,12 @@ class PGGraphStorage(BaseGraphStorage):
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if record:
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node = record[0]
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node_dict = node["n"]["properties"]
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# Process string result, parse it to JSON dictionary
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if isinstance(node_dict, str):
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try:
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import json
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node_dict = json.loads(node_dict)
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except json.JSONDecodeError:
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logger.warning(f"Failed to parse node string: {node_dict}")
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@@ -1428,15 +1429,16 @@ class PGGraphStorage(BaseGraphStorage):
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record = await self._query(query)
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if record and record[0] and record[0]["edge_properties"]:
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result = record[0]["edge_properties"]
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# Process string result, parse it to JSON dictionary
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if isinstance(result, str):
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try:
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import json
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result = json.loads(result)
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except json.JSONDecodeError:
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logger.warning(f"Failed to parse edge string: {result}")
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return result
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async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
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@@ -1645,15 +1647,18 @@ class PGGraphStorage(BaseGraphStorage):
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for result in results:
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if result["node_id"] and result["n"]:
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node_dict = result["n"]["properties"]
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# Process string result, parse it to JSON dictionary
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if isinstance(node_dict, str):
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try:
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import json
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node_dict = json.loads(node_dict)
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except json.JSONDecodeError:
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logger.warning(f"Failed to parse node string in batch: {node_dict}")
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logger.warning(
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f"Failed to parse node string in batch: {node_dict}"
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)
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# Remove the 'base' label if present in a 'labels' property
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if "labels" in node_dict:
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node_dict["labels"] = [
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@@ -1806,31 +1811,37 @@ class PGGraphStorage(BaseGraphStorage):
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for result in forward_results:
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if result["source"] and result["target"] and result["edge_properties"]:
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edge_props = result["edge_properties"]
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# Process string result, parse it to JSON dictionary
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if isinstance(edge_props, str):
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try:
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import json
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edge_props = json.loads(edge_props)
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except json.JSONDecodeError:
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logger.warning(f"Failed to parse edge properties string: {edge_props}")
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logger.warning(
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f"Failed to parse edge properties string: {edge_props}"
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)
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continue
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edges_dict[(result["source"], result["target"])] = edge_props
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for result in backward_results:
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if result["source"] and result["target"] and result["edge_properties"]:
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edge_props = result["edge_properties"]
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# Process string result, parse it to JSON dictionary
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if isinstance(edge_props, str):
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try:
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import json
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edge_props = json.loads(edge_props)
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except json.JSONDecodeError:
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logger.warning(f"Failed to parse edge properties string: {edge_props}")
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logger.warning(
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f"Failed to parse edge properties string: {edge_props}"
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)
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continue
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edges_dict[(result["source"], result["target"])] = edge_props
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return edges_dict
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@@ -994,10 +994,14 @@ class LightRAG:
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except Exception as e:
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# Log error and update pipeline status
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error_msg = f"Failed to extrat document {doc_id}: {traceback.format_exc()}"
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logger.error(traceback.format_exc())
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error_msg = f"Failed to extrat document {current_file_number}/{total_files}: {file_path}"
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logger.error(error_msg)
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async with pipeline_status_lock:
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pipeline_status["latest_message"] = error_msg
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pipeline_status["history_messages"].append(
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traceback.format_exc()
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)
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pipeline_status["history_messages"].append(error_msg)
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# Cancel other tasks as they are no longer meaningful
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@@ -1080,10 +1084,14 @@ class LightRAG:
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except Exception as e:
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# Log error and update pipeline status
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error_msg = f"Merging stage failed in document {doc_id}: {traceback.format_exc()}"
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logger.error(traceback.format_exc())
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error_msg = f"Merging stage failed in document {current_file_number}/{total_files}: {file_path}"
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logger.error(error_msg)
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async with pipeline_status_lock:
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pipeline_status["latest_message"] = error_msg
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pipeline_status["history_messages"].append(
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traceback.format_exc()
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)
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pipeline_status["history_messages"].append(error_msg)
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# Persistent llm cache
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