---
tags:
- movie-embeddings
- neo4j
- nomic-embed-text
- ollama
- movie-plots
- neo4j-llm-fundamentals
license: mit
language:
- en
pretty_name: Neo4j Movie Plot Embeddings via Nomic‑Embed‑Text and Ollama
---

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

This dataset contains vector embeddings for movie plots using the [`nomic-embed-text:v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text) model via [Ollama](https://ollama.com/).

The source movie data comes from the [Neo4j LLM Fundamentals dataset](https://github.com/neo4j-graph-examples/recommendations).

The main puprpose of this dataset is to be used with [Neo4j Course on LLM Fundamentals](https://graphacademy.neo4j.com/courses/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](https://ollama.com/)
- 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. Drop old index (if it was already created in course)
```sql
DROP INDEX moviePlots;
```

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

3. Load embeddings to neo4j 
```sql
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(*)
```

4. Do vector search on new embeddings
```sql
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
---