Fix LlamaIndexEmbeddingOperator returning None vectors for all chunks

[bujjibabukatta]

Problem

LlamaIndexEmbeddingOperator was returning vector: None for every chunk in its output, making the results unusable for downstream vector storage tasks.

Root cause: VectorStoreIndex._get_node_with_embedding() in llama-index-core calls node.copy() internally before attaching embedding vectors. This means embeddings are only stored on the internal copies, The original node objects in the nodes list retain embedding=None.

Minimal reproduction:

from llama_index.core import Document, VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.embeddings.mock_embed_model import MockEmbedding

docs = [Document(text="hello world")]
nodes = SentenceSplitter(chunk_size=512).get_nodes_from_documents(docs)
index = VectorStoreIndex(nodes, embed_model=MockEmbedding(embed_dim=8))

print(nodes[0].embedding)  # None  ← bug
print(index.vector_store.data.embedding_dict)  # {node_id: [...]}  ← vector is here, not on the node

Fix

Pre-embed the nodes using embed_model.get_text_embedding_batch() before building the index and assign the results directly to the original node objects. Since VectorStoreIndex skips re-embedding nodes that already carry a vector, this avoids redundant API calls while ensuring node.embedding is correctly set on the objects we read from later.

Changes

providers/common/ai/.../operators/llamaindex_embedding.py - added pre-embedding step before VectorStoreIndex construction
providers/common/ai/tests/.../test_llamaindex_embedding.py - updated existing tests to mock get_text_embedding_batch, added regression test