Skillset triggered via Indexer is not able to create vector embeddings

I have connected a blob storage to azure AI search via indexer creating the required data source, skillset, index and the indexer.
I have used two skills: **SplitSkill** and **AzureOpenAIEmbeddingSkill**
SplitSkill is working properly as I can see in the index documents being split into chunks but no vector emebdding is being generated and the vector embedding fields remais empty.

What could be the reason? I have checked and verified embedding model, skillset and index.
I have used code present in the azure github samples.

Skillset Code:
```
from azure.search.documents.indexes.models import (
SplitSkill,
InputFieldMappingEntry,
OutputFieldMappingEntry,
AzureOpenAIEmbeddingSkill,
SearchIndexerIndexProjections,
SearchIndexerIndexProjectionSelector,
SearchIndexerIndexProjectionsParameters,
IndexProjectionMode,
SearchIndexerSkillset
)

# Create a skillset
skillset_name = f"{index_name}-skillset"

# Otherwise, use the normal document content.
split_skill_text_source = "/document/content" if not use_ocr else "/document/merged_content"
split_skill = SplitSkill(
description="Split skill to chunk documents",
text_split_mode="pages",
context="/document",
maximum_page_length=2000,
page_overlap_length=500,
inputs=[
InputFieldMappingEntry(name="text", source=split_skill_text_source),
],
outputs=[
OutputFieldMappingEntry(name="textItems", target_name="pages")
],
)

embedding_skill = AzureOpenAIEmbeddingSkill(
description="Skill to generate embeddings via Azure OpenAI",
context="/document/pages/*",
resource_uri=azure_openai_endpoint,
deployment_id=azure_openai_embedding_deployment,
model_name=azure_openai_model_name,
dimensions=dimenson,
api_key=model_key,
inputs=[
InputFieldMappingEntry(name="text", source="/document/pages/*"),
],
outputs=[
OutputFieldMappingEntry(name="embedding", target_name="content_vector")
],
)

index_projections = SearchIndexerIndexProjections(
selectors=[
SearchIndexerIndexProjectionSelector(
target_index_name=index_name,
parent_key_field_name="parent_id",
source_context="/document/pages/*",
mappings=[
InputFieldMappingEntry(name="content", source="/document/pages/*"),
InputFieldMappingEntry(name="content_vector", source="/document/pages/*/vector"),
InputFieldMappingEntry(name="metadata", source="/document/metadata_storage_name"),
],
),
],
parameters=SearchIndexerIndexProjectionsParameters(
projection_mode=IndexProjectionMode.SKIP_INDEXING_PARENT_DOCUMENTS
),
)

skills = [split_skill, embedding_skill]

skillset = SearchIndexerSkillset(
name=skillset_name,
description="Skillset to chunk documents and generating embeddings",
skills=skills,
index_projections=index_projections
)

client = SearchIndexerClient(endpoint, credential)
client.create_or_update_skillset(skillset)
print(f"{skillset.name} created")
```

Start with the provided SplitSkill, AzureOpenAIEmbeddingSkill, and SearchIndexerIndexProjections configuration, focusing on the input and output mapping paths. Check the indexer’s skillset execution and projection results to determine why embeddings are not reaching the vector field. Done means the index documents contain generated vector embeddings.

Written by the indexing model from the issue text.