[Bug]: ids and docs added to db, but embeddings only appear after restart of Jupyter kernel

### What happened?

Running with Python 3.13.0 on an M3 Macbook

```
Macbook M3
MacOS 15.3.1
Python 3.13.0
VS Code 1.97.0
VS Code Jupyter Extension 2025.1.0
Chroma 0.6.3
```

Execute cell 1 of notebook `_t1.ipynb` running in VS Code:

```
import chromadb, os
op = os.path
curr_dir = op.abspath(os.getcwd())
chroma_path = op.join(curr_dir, 'chroma')
client = chromadb.PersistentClient(chroma_path)
```

From a separate terminal, execute the script `_t2.py`

```
import chromadb, os

op = os.path
curr_dir = op.abspath(op.dirname(__file__))
chroma_path = op.join(curr_dir, 'chroma')
client = chromadb.PersistentClient(chroma_path)

t = client.get_or_create_collection('test')

for n in range(1,6):
new_id = f'id_{n}'
new_doc = f'doc_{n}'
t.upsert(ids=[new_id], documents=[new_doc])

result = t.get(include=['documents','embeddings'])

print(f'{result["ids"] = }')
print(f'{result["documents"] = }')
print(f'{len(result["embeddings"]) = }')
print(f'{len(result["embeddings"][0]) = }')
```

Confirm the expected output in the console:

```
result["ids"] = ['id_1', 'id_2', 'id_3', 'id_4', 'id_5']
result["documents"] = ['doc_1', 'doc_2', 'doc_3', 'doc_4', 'doc_5']
len(result["embeddings"]) = 5
len(result["embeddings"][0]) = 384
```

Run cell 2 of `_t1.ipynb`:

```
t = client.get_or_create_collection('test')

result = t.get(include=['documents','embeddings'])

print(f'{result["ids"] = }')
print(f'{result["documents"] = }')
print(f'{len(result["embeddings"]) = }')
# print(f'{len(result["embeddings"][0]) = }')

result = t.query(
query_texts=['doc_1'],
include=['documents','embeddings'],
n_results=5
)

print(f"\n {'<>' * 10 } \n")
print(f'{result["ids"] = }')
print(f'{result["documents"] = }')
print(f'{len(result["embeddings"]) = }')
print(f'{len(result["embeddings"][0]) = }')
print(f'{result["embeddings"] = }')
```

Confirm that the `ids` and `documents` are there, but `embeddings` are not

```
result["ids"] = ['id_1', 'id_2', 'id_3', 'id_4', 'id_5']
result["documents"] = ['doc_1', 'doc_2', 'doc_3', 'doc_4', 'doc_5']
len(result["embeddings"]) = 0

<><><><><><><><><><>

result["ids"] = [[]]
result["documents"] = [[]]
len(result["embeddings"]) = 1
len(result["embeddings"][0]) = 0
result["embeddings"] = [array([], dtype=float64)]
```

Not only is this buggy, but it is also inconsistent.

On a separate trial, I managed to (somehow) get one embedding properly stored, even though `t.get()` was showing 5 total documents (so 4 were still missing).

The really baffling part came with `t.query()` warning that I only had 4 existing elements, despite the request for `n_results=5`.

```
result["ids"] = ['id_1', 'id_2', 'id_3', 'id_4', 'id_5']
result["documents"] = ['doc_1', 'doc_2', 'doc_3', 'doc_4', 'doc_5']
len(result["embeddings"]) = 1
len(result["embeddings"][0]) = 384

<><><><><><><><><><>

Number of requested results 5 is greater than number of elements in index 4, updating n_results = 4
result["ids"] = [['id_1', 'id_2', 'id_3', 'id_4']]
result["documents"] = [['doc_1', 'doc_2', 'doc_3', 'doc_4']]
len(result["embeddings"]) = 1
len(result["embeddings"][0]) = 1
```

This behavior occurs for image-based collections as well.

The behavior also occurs with `collection.add` (it is not specific to `collection.upsert`, as with my example).

The behavior also occurs if I create the collection for the first time in the notebook cell, as opposed to in the script (as with my current example).

If I restart the kernel and run the notebook cells again, I get the expected output:

```
result["ids"] = ['id_1', 'id_2', 'id_3', 'id_4', 'id_5']
result["documents"] = ['doc_1', 'doc_2', 'doc_3', 'doc_4', 'doc_5']
len(result["embeddings"]) = 5

<><><><><><><><><><>

result["ids"] = [['id_1', 'id_2', 'id_3', 'id_4', 'id_5']]
result["documents"] = [['doc_1', 'doc_2', 'doc_3', 'doc_4', 'doc_5']]
len(result["embeddings"]) = 1
len(result["embeddings"][0]) = 5
```

I have also confirmed that this issue **does not** exist in Google Colab.

I can run the first cell (to instantiate the persistent client), then write my `_t2.py` to the local Colab file system and call it with `%run _t2.py`, and then verify the existence of the newly inserted content (with embeddings) by running the second cell of the still-active notebook.

### Versions

Macbook M3
MacOS 15.3.1

Python 3.13.0
VS Code 1.97.0
VS Code Jupyter Extension 2025.1.0

Chroma 0.6.3

### Relevant log output

```shell

```