from llama_index.core import Document, MockEmbedding, Settings, VectorStoreIndex
from llama_index.core.llms import MockLLM


Settings.llm = MockLLM()
Settings.embed_model = MockEmbedding(embed_dim=8)

documents = [
    Document(
        text=(
            "The Atlas support playbook says refund requests must be "
            "reviewed by the billing team before a credit is issued."
        ),
        metadata={"source": "atlas-support-playbook"},
    )
]

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=1)

response = query_engine.query(
    "Who reviews refund requests before a credit is issued?"
)
source_node = response.source_nodes[0].node

print(f"source_count={len(response.source_nodes)}")
print(f"source={source_node.metadata['source']}")
print(f"source_text={source_node.get_content()}")