Fix Dockerfile build issue

autorag/__init__.py

@@ -0,0 +1,113 @@
+import logging
+import logging.config
+import os
+import sys
+from random import random
+from typing import List, Any
+
+from llama_index.core.embeddings.mock_embed_model import MockEmbedding
+from llama_index.core.base.llms.types import CompletionResponse
+from llama_index.core.llms.mock import MockLLM
+from llama_index.llms.bedrock import Bedrock
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.embeddings.openai import OpenAIEmbeddingModelType
+
+from llama_index.llms.openai import OpenAI
+from llama_index.llms.openai_like import OpenAILike
+from langchain_openai.embeddings import OpenAIEmbeddings
+from rich.logging import RichHandler
+
+from llama_index.llms.ollama import Ollama
+
+version_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "VERSION")
+
+with open(version_path, "r") as f:
+	__version__ = f.read().strip()
+
+
+class LazyInit:
+	def __init__(self, factory, *args, **kwargs):
+		self._factory = factory
+		self._args = args
+		self._kwargs = kwargs
+		self._instance = None
+
+	def __call__(self):
+		if self._instance is None:
+			self._instance = self._factory(*self._args, **self._kwargs)
+		return self._instance
+
+	def __getattr__(self, name):
+		if self._instance is None:
+			self._instance = self._factory(*self._args, **self._kwargs)
+		return getattr(self._instance, name)
+
+
+rich_format = "[%(filename)s:%(lineno)s] >> %(message)s"
+logging.basicConfig(
+	level="INFO", format=rich_format, handlers=[RichHandler(rich_tracebacks=True)]
+)
+logger = logging.getLogger("AutoRAG")
+
+
+def handle_exception(exc_type, exc_value, exc_traceback):
+	logger = logging.getLogger("AutoRAG")
+	logger.error("Unexpected exception", exc_info=(exc_type, exc_value, exc_traceback))
+
+
+sys.excepthook = handle_exception
+
+
+class AutoRAGBedrock(Bedrock):
+	async def acomplete(
+		self, prompt: str, formatted: bool = False, **kwargs: Any
+	) -> CompletionResponse:
+		return self.complete(prompt, formatted=formatted, **kwargs)
+
+
+generator_models = {
+	"openai": OpenAI,
+	"openailike": OpenAILike,
+	"mock": MockLLM,
+	"bedrock": AutoRAGBedrock,
+	"ollama": Ollama,
+}
+
+# embedding_models = {
+
+# }
+
+try:
+	from llama_index.llms.huggingface import HuggingFaceLLM
+	from llama_index.llms.ollama import Ollama
+
+	generator_models["huggingfacellm"] = HuggingFaceLLM
+	generator_models["ollama"] = Ollama
+
+except ImportError:
+	logger.info(
+		"You are using API version of AutoRAG. "
+		"To use local version, run pip install 'AutoRAG[gpu]'"
+	)
+
+# try:
+# 	from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+# 	embedding_models["hf_all_mpnet_base_v2"] = HuggingFaceEmbedding # 250312 변경 - 김용연
+# 	embedding_models["hf_KURE-v1"] = HuggingFaceEmbedding # 250312 변경 - 김용연
+# 	embedding_models["hf_snowflake-arctic-embed-l-v2.0-ko"] = HuggingFaceEmbedding # 250313 변경 - 김용연
+
+# except ImportError:
+# 	logger.info(
+# 		"You are using API version of AutoRAG."
+# 		"To use local version, run pip install 'AutoRAG[gpu]'"
+# 	)
+
+try:
+	import transformers
+
+	transformers.logging.set_verbosity_error()
+except ImportError:
+	logger.info(
+		"You are using API version of AutoRAG."
+		"To use local version, run pip install 'AutoRAG[gpu]'"
+	)