Fix Dockerfile build issue

autorag/data/legacy/qacreation/ragas.py

@@ -0,0 +1,75 @@
+import uuid
+from typing import Optional
+
+import pandas as pd
+from langchain_core.embeddings import Embeddings
+from langchain_core.language_models import BaseChatModel
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+
+from autorag.data.utils.util import corpus_df_to_langchain_documents
+from autorag.utils import cast_qa_dataset
+
+
+def generate_qa_ragas(
+	corpus_df: pd.DataFrame,
+	test_size: int,
+	distributions: Optional[dict] = None,
+	generator_llm: Optional[BaseChatModel] = None,
+	critic_llm: Optional[BaseChatModel] = None,
+	embedding_model: Optional[Embeddings] = None,
+	**kwargs,
+) -> pd.DataFrame:
+	"""
+	QA dataset generation using RAGAS.
+	Returns qa dataset dataframe.
+
+	:param corpus_df: Corpus dataframe.
+	:param test_size: Number of queries to generate.
+	:param distributions: Distributions of different types of questions.
+	    Default is "simple is 0.5, multi_context is 0.4, and reasoning is 0.1."
+	    Each type of questions refers to Ragas evolution types.
+	:param generator_llm: Generator language model from Langchain.
+	:param critic_llm: Critic language model from Langchain.
+	:param embedding_model: Embedding model from Langchain.
+	:param kwargs: The additional option to pass to the 'generate_with_langchain_docs' method.
+	    You can input 'with_debugging_logs', 'is_async', 'raise_exceptions', and 'run_config'.
+	:return: QA dataset dataframe.
+	"""
+	from ragas.testset import TestsetGenerator
+	from ragas.testset.evolutions import simple, reasoning, multi_context
+
+	if generator_llm is None:
+		generator_llm = ChatOpenAI(model="gpt-3.5-turbo-16k")
+	if critic_llm is None:
+		critic_llm = ChatOpenAI(model="gpt-4-turbo")
+	if embedding_model is None:
+		embedding_model = OpenAIEmbeddings()
+	if distributions is None:
+		distributions = {simple: 0.5, multi_context: 0.4, reasoning: 0.1}
+
+	assert sum(list(distributions.values())) == 1.0, "Sum of distributions must be 1.0"
+
+	generator = TestsetGenerator.from_langchain(
+		generator_llm, critic_llm, embedding_model
+	)
+
+	langchain_docs = corpus_df_to_langchain_documents(corpus_df)
+
+	test_df = generator.generate_with_langchain_docs(
+		langchain_docs, test_size, distributions=distributions, **kwargs
+	).to_pandas()
+
+	result_df = pd.DataFrame(
+		{
+			"qid": [str(uuid.uuid4()) for _ in range(len(test_df))],
+			"query": test_df["question"].tolist(),
+			"generation_gt": list(map(lambda x: x, test_df["ground_truth"].tolist())),
+		}
+	)
+
+	result_df["retrieval_gt"] = test_df["metadata"].apply(
+		lambda x: list(map(lambda y: y["filename"], x))
+	)
+	result_df = cast_qa_dataset(result_df)
+
+	return result_df