Embeddings returned in payload from retriever

When searching with MMR and a langchain retriever, the full embeddings are returned.

Recommend a pop of embedding in the convert firestore document function:

Working code:

```
def convert_firestore_document(
document: DocumentSnapshot,
page_content_fields: Optional[List[str]] = None,
metadata_fields: Optional[List[str]] = None,
) -> Document:
data_doc = document.to_dict()

# Remove the 'embedding' field if it exists in data_doc
data_doc.pop('embedding', None)

metadata = {
"reference": {
"path": document.reference.path,
FIRESTORE_TYPE: DOC_REF,
}
}

# Check for vector fields and move them from the data_doc to the metadata
vector_keys = [k for k in data_doc if isinstance(data_doc[k], Vector)]
for k in vector_keys:
metadata[k] = _convert_from_firestore(data_doc.pop(k))

set_page_fields = set(
page_content_fields or (data_doc.keys() - set(metadata_fields or []))
)
set_metadata_fields = set(metadata_fields or (data_doc.keys() - set_page_fields))

page_content = {}

for k in sorted(set_metadata_fields):
if k in data_doc:
metadata[k] = _convert_from_firestore(data_doc[k])
for k in sorted(set_page_fields):
if k in data_doc:
page_content[k] = _convert_from_firestore(data_doc[k])

if len(page_content) == 1:
page_content = str(page_content.popitem()[1]) # type: ignore
else:
page_content = json.dumps(page_content) # type: ignore

return Document(page_content=page_content, metadata=metadata) # type: ignore
```