Fix Dockerfile build issue

autorag/nodes/queryexpansion/__init__.py

@@ -0,0 +1,4 @@
+from .hyde import HyDE
+from .multi_query_expansion import MultiQueryExpansion
+from .pass_query_expansion import PassQueryExpansion
+from .query_decompose import QueryDecompose

autorag/nodes/queryexpansion/base.py

@@ -0,0 +1,62 @@
+import abc
+import logging
+from pathlib import Path
+from typing import List, Union
+
+import pandas as pd
+
+from autorag.nodes.util import make_generator_callable_param
+from autorag.schema import BaseModule
+from autorag.utils import validate_qa_dataset
+
+logger = logging.getLogger("AutoRAG")
+
+
+class BaseQueryExpansion(BaseModule, metaclass=abc.ABCMeta):
+	def __init__(self, project_dir: Union[str, Path], *args, **kwargs):
+		logger.info(
+			f"Initialize query expansion node - {self.__class__.__name__} module..."
+		)
+		# set generator module for query expansion
+		generator_class, generator_param = make_generator_callable_param(kwargs)
+		self.generator = generator_class(project_dir, **generator_param)
+
+	def __del__(self):
+		del self.generator
+		logger.info(
+			f"Delete query expansion node - {self.__class__.__name__} module..."
+		)
+
+	def cast_to_run(self, previous_result: pd.DataFrame, *args, **kwargs):
+		logger.info(
+			f"Running query expansion node - {self.__class__.__name__} module..."
+		)
+		validate_qa_dataset(previous_result)
+
+		# find queries columns
+		assert (
+			"query" in previous_result.columns
+		), "previous_result must have query column."
+		queries = previous_result["query"].tolist()
+		return queries
+
+	@staticmethod
+	def _check_expanded_query(queries: List[str], expanded_queries: List[List[str]]):
+		return list(
+			map(
+				lambda query, expanded_query_list: check_expanded_query(
+					query, expanded_query_list
+				),
+				queries,
+				expanded_queries,
+			)
+		)
+
+
+def check_expanded_query(query: str, expanded_query_list: List[str]):
+	# check if the expanded query is the same as the original query
+	expanded_query_list = list(map(lambda x: x.strip(), expanded_query_list))
+	return [
+		expanded_query if expanded_query else query
+		for expanded_query in expanded_query_list
+	]

autorag/nodes/queryexpansion/hyde.py

@@ -0,0 +1,43 @@
+from typing import List
+
+import pandas as pd
+
+from autorag.nodes.queryexpansion.base import BaseQueryExpansion
+from autorag.utils import result_to_dataframe
+
+hyde_prompt = "Please write a passage to answer the question"
+
+
+class HyDE(BaseQueryExpansion):
+	@result_to_dataframe(["queries"])
+	def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
+		queries = self.cast_to_run(previous_result, *args, **kwargs)
+
+		# pop prompt from kwargs
+		prompt = kwargs.pop("prompt", hyde_prompt)
+		kwargs.pop("generator_module_type", None)
+
+		expanded_queries = self._pure(queries, prompt, **kwargs)
+		return self._check_expanded_query(queries, expanded_queries)
+
+	def _pure(self, queries: List[str], prompt: str = hyde_prompt, **generator_params):
+		"""
+		HyDE, which inspired by "Precise Zero-shot Dense Retrieval without Relevance Labels" (https://arxiv.org/pdf/2212.10496.pdf)
+		LLM model creates a hypothetical passage.
+		And then, retrieve passages using hypothetical passage as a query.
+		:param queries: List[str], queries to retrieve.
+		:param prompt: Prompt to use when generating hypothetical passage
+		:return: List[List[str]], List of hyde results.
+		"""
+		full_prompts = list(
+			map(
+				lambda x: (prompt if not bool(prompt) else hyde_prompt)
+				+ f"\nQuestion: {x}\nPassage:",
+				queries,
+			)
+		)
+		input_df = pd.DataFrame({"prompts": full_prompts})
+		result_df = self.generator.pure(previous_result=input_df, **generator_params)
+		answers = result_df["generated_texts"].tolist()
+		results = list(map(lambda x: [x], answers))
+		return results

autorag/nodes/queryexpansion/multi_query_expansion.py

@@ -0,0 +1,57 @@
+from typing import List
+
+import pandas as pd
+
+from autorag.nodes.queryexpansion.base import BaseQueryExpansion
+from autorag.utils import result_to_dataframe
+
+multi_query_expansion_prompt = """You are an AI language model assistant.
+    Your task is to generate 3 different versions of the given user
+    question to retrieve relevant documents from a vector  database.
+    By generating multiple perspectives on the user question,
+    your goal is to help the user overcome some of the limitations
+    of distance-based similarity search. Provide these alternative
+    questions separated by newlines. Original question: {query}"""
+
+
+class MultiQueryExpansion(BaseQueryExpansion):
+	@result_to_dataframe(["queries"])
+	def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
+		queries = self.cast_to_run(previous_result, *args, **kwargs)
+
+		# pop prompt from kwargs
+		prompt = kwargs.pop("prompt", multi_query_expansion_prompt)
+		kwargs.pop("generator_module_type", None)
+
+		expanded_queries = self._pure(queries, prompt, **kwargs)
+		return self._check_expanded_query(queries, expanded_queries)
+
+	def _pure(
+		self, queries, prompt: str = multi_query_expansion_prompt, **kwargs
+	) -> List[List[str]]:
+		"""
+		Expand a list of queries using a multi-query expansion approach.
+		LLM model generate 3 different versions queries for each input query.
+
+		:param queries: List[str], queries to decompose.
+		:param prompt: str, prompt to use for multi-query expansion.
+			default prompt comes from langchain MultiQueryRetriever default query prompt.
+		:return: List[List[str]], list of expansion query.
+		"""
+		full_prompts = list(map(lambda x: prompt.format(query=x), queries))
+		input_df = pd.DataFrame({"prompts": full_prompts})
+		result_df = self.generator.pure(previous_result=input_df, **kwargs)
+		answers = result_df["generated_texts"].tolist()
+		results = list(
+			map(lambda x: get_multi_query_expansion(x[0], x[1]), zip(queries, answers))
+		)
+		return results
+
+
+def get_multi_query_expansion(query: str, answer: str) -> List[str]:
+	try:
+		queries = answer.split("\n")
+		queries.insert(0, query)
+		return queries
+	except:
+		return [query]

autorag/nodes/queryexpansion/pass_query_expansion.py

@@ -0,0 +1,22 @@
+import pandas as pd
+
+from autorag.nodes.queryexpansion.base import BaseQueryExpansion
+from autorag.utils import result_to_dataframe
+
+
+class PassQueryExpansion(BaseQueryExpansion):
+	@result_to_dataframe(["queries"])
+	def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
+		"""
+		Do not perform query expansion.
+		Return with the same queries.
+		The dimension will be 2-d list, and the column name will be 'queries'.
+		"""
+		assert (
+			"query" in previous_result.columns
+		), "previous_result must have query column."
+		queries = previous_result["query"].tolist()
+		return list(map(lambda x: [x], queries))
+
+	def _pure(self, *args, **kwargs):
+		pass

autorag/nodes/queryexpansion/query_decompose.py

@@ -0,0 +1,111 @@
+from typing import List
+
+import pandas as pd
+
+from autorag.nodes.queryexpansion.base import BaseQueryExpansion
+from autorag.utils import result_to_dataframe
+
+decompose_prompt = """Decompose a question in self-contained sub-questions. Use \"The question needs no decomposition\" when no decomposition is needed.
+
+    Example 1:
+
+    Question: Is Hamlet more common on IMDB than Comedy of Errors?
+    Decompositions:
+    1: How many listings of Hamlet are there on IMDB?
+    2: How many listing of Comedy of Errors is there on IMDB?
+
+    Example 2:
+
+    Question: Are birds important to badminton?
+
+    Decompositions:
+    The question needs no decomposition
+
+    Example 3:
+
+    Question: Is it legal for a licensed child driving Mercedes-Benz to be employed in US?
+
+    Decompositions:
+    1: What is the minimum driving age in the US?
+    2: What is the minimum age for someone to be employed in the US?
+
+    Example 4:
+
+    Question: Are all cucumbers the same texture?
+
+    Decompositions:
+    The question needs no decomposition
+
+    Example 5:
+
+    Question: Hydrogen's atomic number squared exceeds number of Spice Girls?
+
+    Decompositions:
+    1: What is the atomic number of hydrogen?
+    2: How many Spice Girls are there?
+
+    Example 6:
+
+    Question: {question}
+
+    Decompositions:
+    """
+
+
+class QueryDecompose(BaseQueryExpansion):
+	@result_to_dataframe(["queries"])
+	def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
+		queries = self.cast_to_run(previous_result, *args, **kwargs)
+
+		# pop prompt from kwargs
+		prompt = kwargs.pop("prompt", decompose_prompt)
+		kwargs.pop("generator_module_type", None)
+
+		expanded_queries = self._pure(queries, prompt, **kwargs)
+		return self._check_expanded_query(queries, expanded_queries)
+
+	def _pure(
+		self, queries: List[str], prompt: str = decompose_prompt, *args, **kwargs
+	) -> List[List[str]]:
+		"""
+		decompose query to little piece of questions.
+		:param queries: List[str], queries to decompose.
+		:param prompt: str, prompt to use for query decomposition.
+			default prompt comes from Visconde's StrategyQA few-shot prompt.
+		:return: List[List[str]], list of decomposed query. Return input query if query is not decomposable.
+		"""
+		full_prompts = []
+		for query in queries:
+			if bool(prompt):
+				full_prompt = f"prompt: {prompt}\n\n question: {query}"
+			else:
+				full_prompt = decompose_prompt.format(question=query)
+			full_prompts.append(full_prompt)
+		input_df = pd.DataFrame({"prompts": full_prompts})
+		result_df = self.generator.pure(previous_result=input_df, *args, **kwargs)
+		answers = result_df["generated_texts"].tolist()
+		results = list(
+			map(lambda x: get_query_decompose(x[0], x[1]), zip(queries, answers))
+		)
+		return results
+
+
+def get_query_decompose(query: str, answer: str) -> List[str]:
+	"""
+	decompose query to little piece of questions.
+	:param query: str, query to decompose.
+	:param answer: str, answer from query_decompose function.
+	:return: List[str], list of a decomposed query. Return input query if query is not decomposable.
+	"""
+	if answer.lower() == "the question needs no decomposition":
+		return [query]
+	try:
+		lines = [line.strip() for line in answer.splitlines() if line.strip()]
+		if lines[0].startswith("Decompositions:"):
+			lines.pop(0)
+		questions = [line.split(":", 1)[1].strip() for line in lines if ":" in line]
+		if not questions:
+			return [query]
+		return questions
+	except:
+		return [query]

autorag/nodes/queryexpansion/run.py

@@ -0,0 +1,276 @@
+import logging
+import os
+import pathlib
+from copy import deepcopy
+from typing import List, Dict, Optional
+
+import pandas as pd
+
+from autorag.nodes.retrieval.run import evaluate_retrieval_node
+from autorag.schema.metricinput import MetricInput
+from autorag.strategy import measure_speed, filter_by_threshold, select_best
+from autorag.support import get_support_modules
+from autorag.utils.util import make_combinations, explode
+
+logger = logging.getLogger("AutoRAG")
+
+
+def run_query_expansion_node(
+	modules: List,
+	module_params: List[Dict],
+	previous_result: pd.DataFrame,
+	node_line_dir: str,
+	strategies: Dict,
+) -> pd.DataFrame:
+	"""
+	Run evaluation and select the best module among query expansion node results.
+	Initially, retrieval is run using expanded_queries, the result of the query_expansion module.
+	The retrieval module is run as a combination of the retrieval_modules in strategies.
+	If there are multiple retrieval_modules, run them all and choose the best result.
+	If there are no retrieval_modules, run them with the default of bm25.
+	In this way, the best result is selected for each module, and then the best result is selected.
+
+	:param modules: Query expansion modules to run.
+	:param module_params: Query expansion module parameters.
+	:param previous_result: Previous result dataframe.
+	    In this case, it would be qa data.
+	:param node_line_dir: This node line's directory.
+	:param strategies: Strategies for query expansion node.
+	:return: The best result dataframe.
+	"""
+	if not os.path.exists(node_line_dir):
+		os.makedirs(node_line_dir)
+	node_dir = os.path.join(node_line_dir, "query_expansion")
+	if not os.path.exists(node_dir):
+		os.makedirs(node_dir)
+	project_dir = pathlib.PurePath(node_line_dir).parent.parent
+
+	# run query expansion
+	results, execution_times = zip(
+		*map(
+			lambda task: measure_speed(
+				task[0].run_evaluator,
+				project_dir=project_dir,
+				previous_result=previous_result,
+				**task[1],
+			),
+			zip(modules, module_params),
+		)
+	)
+	average_times = list(map(lambda x: x / len(results[0]), execution_times))
+
+	# save results to folder
+	pseudo_module_params = deepcopy(module_params)
+	for i, module_param in enumerate(pseudo_module_params):
+		if "prompt" in module_params:
+			module_param["prompt"] = str(i)
+	filepaths = list(
+		map(lambda x: os.path.join(node_dir, f"{x}.parquet"), range(len(modules)))
+	)
+	list(
+		map(lambda x: x[0].to_parquet(x[1], index=False), zip(results, filepaths))
+	)  # execute save to parquet
+	filenames = list(map(lambda x: os.path.basename(x), filepaths))
+
+	# make summary file
+	summary_df = pd.DataFrame(
+		{
+			"filename": filenames,
+			"module_name": list(map(lambda module: module.__name__, modules)),
+			"module_params": module_params,
+			"execution_time": average_times,
+		}
+	)
+
+	# Run evaluation when there are more than one module.
+	if len(modules) > 1:
+		# pop general keys from strategies (e.g. metrics, speed_threshold)
+		general_key = ["metrics", "speed_threshold", "strategy"]
+		general_strategy = dict(
+			filter(lambda x: x[0] in general_key, strategies.items())
+		)
+		extra_strategy = dict(
+			filter(lambda x: x[0] not in general_key, strategies.items())
+		)
+
+		# first, filter by threshold if it is enabled.
+		if general_strategy.get("speed_threshold") is not None:
+			results, filenames = filter_by_threshold(
+				results, average_times, general_strategy["speed_threshold"], filenames
+			)
+
+		# check metrics in strategy
+		if general_strategy.get("metrics") is None:
+			raise ValueError(
+				"You must at least one metrics for query expansion evaluation."
+			)
+
+		if extra_strategy.get("top_k") is None:
+			extra_strategy["top_k"] = 10  # default value
+
+		# get retrieval modules from strategy
+		retrieval_callables, retrieval_params = make_retrieval_callable_params(
+			extra_strategy
+		)
+
+		# get retrieval_gt
+		retrieval_gt = pd.read_parquet(
+			os.path.join(project_dir, "data", "qa.parquet"), engine="pyarrow"
+		)["retrieval_gt"].tolist()
+
+		# make rows to metric_inputs
+		metric_inputs = [
+			MetricInput(retrieval_gt=ret_gt, query=query, generation_gt=gen_gt)
+			for ret_gt, query, gen_gt in zip(
+				retrieval_gt,
+				previous_result["query"].tolist(),
+				previous_result["generation_gt"].tolist(),
+			)
+		]
+
+		# run evaluation
+		evaluation_results = list(
+			map(
+				lambda result: evaluate_one_query_expansion_node(
+					retrieval_callables,
+					retrieval_params,
+					[
+						setattr(metric_input, "queries", queries) or metric_input
+						for metric_input, queries in zip(
+							metric_inputs, result["queries"].to_list()
+						)
+					],
+					general_strategy["metrics"],
+					project_dir,
+					previous_result,
+					general_strategy.get("strategy", "mean"),
+				),
+				results,
+			)
+		)
+
+		evaluation_df = pd.DataFrame(
+			{
+				"filename": filenames,
+				**{
+					f"query_expansion_{metric_name}": list(
+						map(lambda x: x[metric_name].mean(), evaluation_results)
+					)
+					for metric_name in general_strategy["metrics"]
+				},
+			}
+		)
+		summary_df = pd.merge(
+			on="filename", left=summary_df, right=evaluation_df, how="left"
+		)
+
+		best_result, best_filename = select_best(
+			evaluation_results,
+			general_strategy["metrics"],
+			filenames,
+			strategies.get("strategy", "mean"),
+		)
+		# change metric name columns to query_expansion_metric_name
+		best_result = best_result.rename(
+			columns={
+				metric_name: f"query_expansion_{metric_name}"
+				for metric_name in strategies["metrics"]
+			}
+		)
+		best_result = best_result.drop(
+			columns=["retrieved_contents", "retrieved_ids", "retrieve_scores"]
+		)
+	else:
+		best_result, best_filename = results[0], filenames[0]
+		best_result = pd.concat([previous_result, best_result], axis=1)
+
+	# add 'is_best' column at summary file
+	summary_df["is_best"] = summary_df["filename"] == best_filename
+
+	# save files
+	summary_df.to_csv(os.path.join(node_dir, "summary.csv"), index=False)
+	best_result.to_parquet(
+		os.path.join(node_dir, f"best_{os.path.splitext(best_filename)[0]}.parquet"),
+		index=False,
+	)
+
+	return best_result
+
+
+def evaluate_one_query_expansion_node(
+	retrieval_funcs: List,
+	retrieval_params: List[Dict],
+	metric_inputs: List[MetricInput],
+	metrics: List[str],
+	project_dir,
+	previous_result: pd.DataFrame,
+	strategy_name: str,
+) -> pd.DataFrame:
+	previous_result["queries"] = [
+		metric_input.queries for metric_input in metric_inputs
+	]
+	retrieval_results = list(
+		map(
+			lambda x: x[0].run_evaluator(
+				project_dir=project_dir, previous_result=previous_result, **x[1]
+			),
+			zip(retrieval_funcs, retrieval_params),
+		)
+	)
+	evaluation_results = list(
+		map(
+			lambda x: evaluate_retrieval_node(
+				x,
+				metric_inputs,
+				metrics,
+			),
+			retrieval_results,
+		)
+	)
+	best_result, _ = select_best(
+		evaluation_results, metrics, strategy_name=strategy_name
+	)
+	best_result = pd.concat([previous_result, best_result], axis=1)
+	return best_result
+
+
+def make_retrieval_callable_params(strategy_dict: Dict):
+	"""
+	strategy_dict looks like this:
+
+	.. Code:: json
+
+	    {
+	        "metrics": ["retrieval_f1", "retrieval_recall"],
+	        "top_k": 50,
+	        "retrieval_modules": [
+	          {"module_type": "bm25"},
+	          {"module_type": "vectordb", "embedding_model": ["openai", "huggingface"]}
+	        ]
+	      }
+
+	"""
+	node_dict = deepcopy(strategy_dict)
+	retrieval_module_list: Optional[List[Dict]] = node_dict.pop(
+		"retrieval_modules", None
+	)
+	if retrieval_module_list is None:
+		retrieval_module_list = [
+			{
+				"module_type": "bm25",
+			}
+		]
+	node_params = node_dict
+	modules = list(
+		map(
+			lambda module_dict: get_support_modules(module_dict.pop("module_type")),
+			retrieval_module_list,
+		)
+	)
+	param_combinations = list(
+		map(
+			lambda module_dict: make_combinations({**module_dict, **node_params}),
+			retrieval_module_list,
+		)
+	)
+	return explode(modules, param_combinations)