Fix Dockerfile build issue

autorag/nodes/passagecompressor/refine.py

@@ -0,0 +1,54 @@
+from typing import List, Optional
+
+from llama_index.core import PromptTemplate
+from llama_index.core.prompts import PromptType
+from llama_index.core.prompts.utils import is_chat_model
+from llama_index.core.response_synthesizers import Refine as rf
+
+from autorag.nodes.passagecompressor.base import LlamaIndexCompressor
+from autorag.utils.util import get_event_loop, process_batch
+
+
+class Refine(LlamaIndexCompressor):
+	def _pure(
+		self,
+		queries: List[str],
+		contents: List[List[str]],
+		prompt: Optional[str] = None,
+		chat_prompt: Optional[str] = None,
+		batch: int = 16,
+	) -> List[str]:
+		"""
+		Refine a response to a query across text chunks.
+		This function is a wrapper for llama_index.response_synthesizers.Refine.
+		For more information, visit https://docs.llamaindex.ai/en/stable/examples/response_synthesizers/refine/.
+
+		:param queries: The queries for retrieved passages.
+		:param contents: The contents of retrieved passages.
+		:param prompt: The prompt template for refine.
+		    If you want to use chat prompt, you should pass chat_prompt instead.
+		    At prompt, you must specify where to put 'context_msg' and 'query_str'.
+		    Default is None. When it is None, it will use llama index default prompt.
+		:param chat_prompt: The chat prompt template for refine.
+		    If you want to use normal prompt, you should pass prompt instead.
+		    At prompt, you must specify where to put 'context_msg' and 'query_str'.
+		    Default is None. When it is None, it will use llama index default chat prompt.
+		:param batch: The batch size for llm.
+		    Set low if you face some errors.
+		    Default is 16.
+		:return: The list of compressed texts.
+		"""
+		if prompt is not None and not is_chat_model(self.llm):
+			refine_template = PromptTemplate(prompt, prompt_type=PromptType.REFINE)
+		elif chat_prompt is not None and is_chat_model(self.llm):
+			refine_template = PromptTemplate(chat_prompt, prompt_type=PromptType.REFINE)
+		else:
+			refine_template = None
+		summarizer = rf(llm=self.llm, refine_template=refine_template, verbose=True)
+		tasks = [
+			summarizer.aget_response(query, content)
+			for query, content in zip(queries, contents)
+		]
+		loop = get_event_loop()
+		results = loop.run_until_complete(process_batch(tasks, batch_size=batch))
+		return results