fix(VectorStore): fix task concurrency and batch processing issue (#2671)

- Refactor task execution to improve concurrency control
- Implement batch processing for vector deletion in Chroma store

# Description

1 ,When chunk a knowledge base file, if there are too many chunks ,
using asyncio.gather(*tasks) will cause the CPU to freeze
2 , When using the Chroma vector database, if the one-time deletion of
embedding documents ids exceeds 5461, it will fail

![0e64098babaa448ec139933fecf5776](https://github.com/user-attachments/assets/f9fa81c1-2914-44e6-8d31-a16d7717c6b3)


# How Has This Been Tested?

# Snapshots:

![ff0ca0d2b35be11dd19aa1983a196ba](https://github.com/user-attachments/assets/4db4202e-5c71-4c97-b075-465ea5419657)

![c8821b068b0c8b73757700d2ecaa23a](https://github.com/user-attachments/assets/b6bc28d0-9406-4b6b-92cf-6a9765288fd2)

Include snapshots for easier review.

# Checklist:

- [x] My code follows the style guidelines of this project
- [x] I have already rebased the commits and make the commit message
conform to the project standard.
- [x] I have performed a self-review of my own code
- [ ] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [ ] Any dependent changes have been merged and published in downstream
modules

packages/dbgpt-core/src/dbgpt/storage/base.py

@@ -1,5 +1,6 @@
 """Index store base class."""
 
+import asyncio
 import logging
 import time
 from abc import ABC, abstractmethod
@@ -182,9 +183,7 @@ class IndexStoreBase(ABC):
         for chunk_group in chunk_groups:
             tasks.append(self.aload_document(chunk_group))
 
-        import asyncio
-
-        results = await asyncio.gather(*tasks)
+        results = await self._run_tasks_with_concurrency(tasks, max_threads)
 
         ids = []
         loaded_cnt = 0
@@ -195,6 +194,14 @@ class IndexStoreBase(ABC):
 
         return ids
 
+    async def _run_tasks_with_concurrency(self, tasks, max_concurrent):
+        results = []
+        for i in range(0, len(tasks), max_concurrent):
+            batch = tasks[i : i + max_concurrent]
+            batch_results = await asyncio.gather(*batch, return_exceptions=True)
+            results.extend(batch_results)
+        return results
+
     def similar_search(
         self, text: str, topk: int, filters: Optional[MetadataFilters] = None
     ) -> List[Chunk]: