Script to reproduce the neo4j dimensionality mismatch issue

main.py

from llama_index.core import (
    Settings,
    SimpleDirectoryReader,
    StorageContext,
    VectorStoreIndex,
)
from llama_index.legacy.vector_stores import Neo4jVectorStore
from llama_index.llms.ollama import Ollama

Settings.llm = Ollama(
    model="zephyr:7b-beta",
    request_timeout=60,  # secs
    temperature=0.01,
    additional_kwargs={
        "stop": ["Observation:", "<|user|>", "<|assistant|>", "<|SYSTEM|>"],
        "seed": 42,
    },
)

if __name__ == "__main__":
    vector_store = Neo4jVectorStore(
        url="bolt://localhost:7689",
        username="neo4j",
        password="correct-horse-battery-staple",
        # TODO: This seems to be not respected.
        embedding_dimension=1000,
    )
    # By default, the model is 384-dimensional.
    Settings.embed_model = "local"

    documents = SimpleDirectoryReader(input_files=["document.txt"]).load_data()
    print(f"A total of {len(documents)} documents loaded.")
    index = VectorStoreIndex.from_documents(
        documents=documents,
        storage_context=StorageContext.from_defaults(vector_store=vector_store),
    )
    query_engine = index.as_query_engine()
    response = query_engine.query("lorem ispum")
    # "Index query vector has 384 dimensions, but indexed vectors have 1536."