Datasets:

RobinMillford
/

movie-embeddings-rag-db

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
version: string
total_movies: int64
date_range: struct<earliest: string, latest: string>
movies_by_year: struct<>
top_genres: struct<Drama: int64, Comedy: int64, Thriller: int64, Crime: int64, Animation: int64, Mystery: int64, Action: int64, Horror: int64, Sci-Fi & Fantasy: int64, Romance: int64>
last_updated: string
embedding_model: string
embedding_dimension: int64
vs
id: int64
title: string
original_title: string
tagline: string
overview: string
release_date: string
runtime: int64
status: string
vote_average: double
vote_count: int64
popularity: double
budget: int64
revenue: int64
original_language: string
spoken_languages: list<item: string>
production_countries: list<item: string>
genres: list<item: string>
keywords: list<item: string>
production_companies: list<item: string>
belongs_to_collection: int64
collection_name: string
cast: list<item: struct<character: string, name: string, order: int64>>
director: list<item: string>
writers: list<item: string>
producers: list<item: string>
cinematographer: list<item: string>
composer: list<item: string>
certification: string
reviews: list<item: struct<author: string, content: string, rating: double>>
trailers: list<item: struct<key: string, name: string, site: string, type: string>>
similar_movies: list<item: struct<id: int64, title: string>>
recommended_movies: list<item: struct<id: int64, title: string>>
alternative_titles: list<item: string>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 249, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              version: string
              total_movies: int64
              date_range: struct<earliest: string, latest: string>
              movies_by_year: struct<>
              top_genres: struct<Drama: int64, Comedy: int64, Thriller: int64, Crime: int64, Animation: int64, Mystery: int64, Action: int64, Horror: int64, Sci-Fi & Fantasy: int64, Romance: int64>
              last_updated: string
              embedding_model: string
              embedding_dimension: int64
              vs
              id: int64
              title: string
              original_title: string
              tagline: string
              overview: string
              release_date: string
              runtime: int64
              status: string
              vote_average: double
              vote_count: int64
              popularity: double
              budget: int64
              revenue: int64
              original_language: string
              spoken_languages: list<item: string>
              production_countries: list<item: string>
              genres: list<item: string>
              keywords: list<item: string>
              production_companies: list<item: string>
              belongs_to_collection: int64
              collection_name: string
              cast: list<item: struct<character: string, name: string, order: int64>>
              director: list<item: string>
              writers: list<item: string>
              producers: list<item: string>
              cinematographer: list<item: string>
              composer: list<item: string>
              certification: string
              reviews: list<item: struct<author: string, content: string, rating: double>>
              trailers: list<item: struct<key: string, name: string, site: string, type: string>>
              similar_movies: list<item: struct<id: int64, title: string>>
              recommended_movies: list<item: struct<id: int64, title: string>>
              alternative_titles: list<item: string>

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Movie Embeddings RAG Dataset

This dataset contains movie embeddings for Retrieval-Augmented Generation (RAG) in a movie recommendation chatbot.

Dataset Information

Total Movies: 7733
Date Range: Unknown - Unknown
Last Updated: 2025-11-26
Embedding Model: sentence-transformers/all-MiniLM-L6-v2
Embedding Dimension: 384

Files

chroma.sqlite3.gz - Compressed ChromaDB database with embeddings
movies.json.gz - Compressed movie metadata (title, overview, cast, crew, etc.)
metadata.json - Dataset statistics and version info

Top Genres

Drama: 3940 movies
Comedy: 2441 movies
Thriller: 1150 movies
Crime: 1138 movies
Animation: 933 movies

Usage

Download and Extract

from huggingface_hub import hf_hub_download
import gzip
import shutil

# Download compressed database
db_path = hf_hub_download(
    repo_id="YOUR_USERNAME/movie-embeddings-rag",
    filename="chroma.sqlite3.gz",
    repo_type="dataset"
)

# Decompress
with gzip.open(db_path, 'rb') as f_in:
    with open('chroma.sqlite3', 'wb') as f_out:
        shutil.copyfileobj(f_in, f_out)

Use with ChromaDB

import chromadb

client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_collection("movies")

# Search for movies
results = collection.query(
    query_texts=["science fiction movie about space"],
    n_results=5
)

Automated Updates

This dataset is automatically updated monthly via GitHub Actions with new movie releases.

License

MIT License - Free to use for any purpose.

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