omeyb/movie-plots-nomic-embeddings

🎬 Movie Plot Embeddings Dataset (Nomic Embed v1.5)

This dataset contains vector embeddings for movie plots using the `nomic-embed-text:v1.5` model via Ollama.

The source movie data comes from the Neo4j LLM Fundamentals dataset.

The main puprpose of this dataset is to be used with Neo4j Course on LLM Fundamentals. They did everythig with openai's models. This csv is created using ollama and nomic-embed-text. People who want to use ollama instread of openai, can refer to this csv for embedding movie plots in the course.

📂 Contents

• —movie_embeddings.csv:
• —movieId: Movie identifier refering to neo4j Movie node's property : movieId
• —embedding: JSON array of floats (768-dim)

🧠 Embedding Details

• —Model: nomic-embed-text:v1.5
• —Dimensions: 768
• —Embedding tool: Ollama
• —Embedding date: June 2025

📜 License

This dataset is shared under the MIT License (see below). You are free to use, modify, and distribute it.

✨ Usage Example (in neo4j)

1. 1.Drop old index (if it was already created in course)

DROP INDEX moviePlots;

1. 1.Create new index on 768 dimensions

CREATE VECTOR INDEX moviePlots IF NOT EXISTS
FOR (m:Movie)
ON m.plotEmbedding
OPTIONS {indexConfig: {
 `vector.dimensions`: 768,
 `vector.similarity_function`: 'cosine'
}}

1. 1.Load embeddings to neo4j

LOAD CSV WITH HEADERS
FROM 'https://huggingface.co/datasets/coolomya/movie-plots-nomic-embeddings/resolve/main/movie_embeddings.csv'
AS row
MATCH (m:Movie {movieId: row.movieId})
CALL db.create.setNodeVectorProperty(m, 'plotEmbedding', apoc.convert.fromJsonList(row.embedding))
RETURN count(*)

1. 1.Do vector search on new embeddings

MATCH (m:Movie {title: 'Toy Story'})
CALL db.index.vector.queryNodes('moviePlots', 6, m.plotEmbedding)
YIELD node, score
RETURN node.title AS title, node.plot AS plot, score

Now you can continue your python course on Neo4j LLM with local ollama and this embedding

license: mit ---