Storage: Support the s3, azure blob as the object storage of ragflow. (#2278)

### What problem does this PR solve?

issue: https://github.com/infiniflow/ragflow/issues/2277

_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._

### Type of change

- [ ] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

api/db/services/document_service.py

@@ -34,7 +34,7 @@ from api.utils.file_utils import get_project_base_directory
 from graphrag.mind_map_extractor import MindMapExtractor
 from rag.settings import SVR_QUEUE_NAME
 from rag.utils.es_conn import ELASTICSEARCH
-from rag.utils.minio_conn import MINIO
+from rag.utils.storage_factory import STORAGE_IMPL
 from rag.nlp import search, rag_tokenizer
 
 from api.db import FileType, TaskStatus, ParserType, LLMType
@@ -473,7 +473,7 @@ def doc_upload_and_parse(conversation_id, file_objs, user_id):
             else:
                 d["image"].save(output_buffer, format='JPEG')
 
-            MINIO.put(kb.id, d["_id"], output_buffer.getvalue())
+            STORAGE_IMPL.put(kb.id, d["_id"], output_buffer.getvalue())
             d["img_id"] = "{}-{}".format(kb.id, d["_id"])
             del d["image"]
             docs.append(d)