Fix Dockerfile build issue

autorag/vectordb/__init__.py

@@ -0,0 +1,75 @@
+import os
+from typing import List
+
+from autorag.support import dynamically_find_function
+from autorag.utils.util import load_yaml_config
+from autorag.vectordb.base import BaseVectorStore
+
+
+def get_support_vectordb(vectordb_name: str):
+	support_vectordb = {
+		"chroma": ("autorag.vectordb.chroma", "Chroma"),
+		"Chroma": ("autorag.vectordb.chroma", "Chroma"),
+		"milvus": ("autorag.vectordb.milvus", "Milvus"),
+		"Milvus": ("autorag.vectordb.milvus", "Milvus"),
+		"weaviate": ("autorag.vectordb.weaviate", "Weaviate"),
+		"Weaviate": ("autorag.vectordb.weaviate", "Weaviate"),
+		"pinecone": ("autorag.vectordb.pinecone", "Pinecone"),
+		"Pinecone": ("autorag.vectordb.pinecone", "Pinecone"),
+		"couchbase": ("autorag.vectordb.couchbase", "Couchbase"),
+		"Couchbase": ("autorag.vectordb.couchbase", "Couchbase"),
+		"qdrant": ("autorag.vectordb.qdrant", "Qdrant"),
+		"Qdrant": ("autorag.vectordb.qdrant", "Qdrant"),
+	}
+	return dynamically_find_function(vectordb_name, support_vectordb)
+
+
+def load_vectordb(vectordb_name: str, **kwargs):
+	vectordb = get_support_vectordb(vectordb_name)
+	return vectordb(**kwargs)
+
+
+def load_vectordb_from_yaml(yaml_path: str, vectordb_name: str, project_dir: str):
+	config_dict = load_yaml_config(yaml_path)
+	vectordb_list = config_dict.get("vectordb", [])
+	if len(vectordb_list) == 0 or vectordb_name == "default":
+		chroma_path = os.path.join(project_dir, "resources", "chroma")
+		return load_vectordb(
+			"chroma",
+			client_type="persistent",
+			embedding_model="openai",
+			collection_name="openai",
+			path=chroma_path,
+		)
+
+	target_dict = list(filter(lambda x: x["name"] == vectordb_name, vectordb_list))
+	target_dict[0].pop("name")  # delete a name key
+	target_vectordb_name = target_dict[0].pop("db_type")
+	target_vectordb_params = target_dict[0]
+	return load_vectordb(target_vectordb_name, **target_vectordb_params)
+
+
+def load_all_vectordb_from_yaml(
+	yaml_path: str, project_dir: str
+) -> List[BaseVectorStore]:
+	config_dict = load_yaml_config(yaml_path)
+	vectordb_list = config_dict.get("vectordb", [])
+	if len(vectordb_list) == 0:
+		chroma_path = os.path.join(project_dir, "resources", "chroma")
+		return [
+			load_vectordb(
+				"chroma",
+				client_type="persistent",
+				embedding_model="openai",
+				collection_name="openai",
+				path=chroma_path,
+			)
+		]
+
+	result_vectordbs = []
+	for vectordb_dict in vectordb_list:
+		_ = vectordb_dict.pop("name")
+		vectordb_type = vectordb_dict.pop("db_type")
+		vectordb = load_vectordb(vectordb_type, **vectordb_dict)
+		result_vectordbs.append(vectordb)
+	return result_vectordbs