Fix Dockerfile build issue

autorag/node_line.py

@@ -0,0 +1,73 @@
+import os
+import pathlib
+from typing import Dict, List, Optional
+from rich.progress import Progress
+
+import pandas as pd
+
+from autorag.schema import Node
+from autorag.utils.util import load_summary_file
+
+
+def make_node_lines(node_line_dict: Dict) -> List[Node]:
+	"""
+	This method makes a list of nodes from node line dictionary.
+	:param node_line_dict: Node_line_dict loaded from yaml file, or get from user input.
+	:return: List of Nodes inside this node line.
+	"""
+	nodes = node_line_dict.get("nodes")
+	if nodes is None:
+		raise ValueError("Node line must have 'nodes' key.")
+	node_objects = list(map(lambda x: Node.from_dict(x), nodes))
+	return node_objects
+
+
+def run_node_line(
+	nodes: List[Node],
+	node_line_dir: str,
+	previous_result: Optional[pd.DataFrame] = None,
+	progress: Progress = None,
+	task_eval: Progress.tasks = None,
+):
+	"""
+	Run the whole node line by running each node.
+
+	:param nodes: A list of nodes.
+	:param node_line_dir: This node line's directory.
+	:param previous_result: A result of the previous node line.
+	    If None, it loads qa data from data/qa.parquet.
+	:param progress: Rich Progress object.
+	:param task_eval: Progress task object
+	:return: The final result of the node line.
+	"""
+	if previous_result is None:
+		project_dir = pathlib.PurePath(node_line_dir).parent.parent
+		qa_path = os.path.join(project_dir, "data", "qa.parquet")
+		if not os.path.exists(qa_path):
+			raise ValueError(f"qa.parquet does not exist in {qa_path}.")
+		previous_result = pd.read_parquet(qa_path, engine="pyarrow")
+
+	summary_lst = []
+	for node in nodes:
+		previous_result = node.run(previous_result, node_line_dir)
+		node_summary_df = load_summary_file(
+			os.path.join(node_line_dir, node.node_type, "summary.csv")
+		)
+		best_node_row = node_summary_df.loc[node_summary_df["is_best"]]
+		summary_lst.append(
+			{
+				"node_type": node.node_type,
+				"best_module_filename": best_node_row["filename"].values[0],
+				"best_module_name": best_node_row["module_name"].values[0],
+				"best_module_params": best_node_row["module_params"].values[0],
+				"best_execution_time": best_node_row["execution_time"].values[0],
+			}
+		)
+		# Update progress for each node
+		if progress:
+			progress.update(task_eval, advance=1)
+
+	pd.DataFrame(summary_lst).to_csv(
+		os.path.join(node_line_dir, "summary.csv"), index=False
+	)
+	return previous_result