Google Gemini Movie Recommendation

Movie Recommendation

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Copyright 2025 Google LLC.

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Movie Recommendation System with Gemini API and Qdrant

⚠️	This notebook requires paid tier rate limits to run properly. (cf. pricing for more details).

Overview

The Gemini API provides access to a family of generative AI models for generating content and solving problems. These models are designed and trained to handle both text and images as input.

Qdrant is an open-source vector similarity search engine designed for efficient and scalable semantic search. It offers a simple yet powerful API to store and search high-dimensional vectors, supports filtering with metadata (payloads), and integrates easily into production systems. Qdrant can be self-hosted or accessed via its managed cloud service, making it quick to set up and ideal for a wide range of AI applications that rely on semantic understanding and retrieval.

In this notebook, you'll learn how to perform a similarity search on data from a website with the help of Gemini API and Qdrant.

This notebook was contributed by Anand Roy.

LinkedIn - See Anand other notebooks here.

Have a cool Gemini example? Feel free to share it too!

Setup

First, you must install the packages and set the necessary environment variables.

Installation

Install google's python client SDK for the Gemini API, google-genai. Next, install Qdrant's Python client SDK, qdrant-client.

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Configure your API key

To run the following cell, your API key must be stored it in a Colab Secret named GEMINI_API_KEY. If you don't already have an API key, or you're not sure how to create a Colab Secret, see Authentication for an example.

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Building the Movie Vector Index

This section covers preparing the movie dataset, generating embeddings using Gemini, and indexing them in Qdrant for similarity search.

1. Load the Dataset from Kaggle

Begin by loading the dataset from Kaggle using the kagglehub library. The dataset used in this notebook is the TMDB Movie Dataset 2024, which contains approximately 1 Million+ movie entries.

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/tmp/ipykernel_26130/2431045845.py:6: DeprecationWarning: load_dataset is deprecated and will be removed in a future version.
  df = kagglehub.load_dataset(

2. Inspect the Dataset Structure

Since the dataset is large, inspecting it helps you identify useful fields and filter out irrelevant data early on.

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Dataset Columns:
Index(['id', 'title', 'vote_average', 'vote_count', 'status', 'release_date',
       'revenue', 'runtime', 'adult', 'backdrop_path', 'budget', 'homepage',
       'imdb_id', 'original_language', 'original_title', 'overview',
       'popularity', 'poster_path', 'tagline', 'genres',
       'production_companies', 'production_countries', 'spoken_languages',
       'keywords'],
      dtype='object')

Missing Values per Column:
id                            0
title                        13
vote_average                  0
vote_count                    0
status                        0
release_date             239109
revenue                       0
runtime                       0
adult                         0
backdrop_path            930599
budget                        0
homepage                1123663
imdb_id                  624300
original_language             0
original_title               13
overview                 270635
popularity                    0
poster_path              418486
tagline                 1078824
genres                   526517
production_companies     702743
production_countries     580808
spoken_languages         557829
keywords                 928832
dtype: int64

Number of rows: 1254611
Number of unique IDs: 1253629

3. Filter and Clean the Dataset

This step filters the dataset to keep only metadata useful for semantic search: id, title, overview, genres, keywords, tagline, and release_date. These fields provide enough context to generate meaningful embeddings.

Entries (rows) missing a title or lacking both overview and genres are removed, as they don’t have enough descriptive data for accurate recommendations.

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Original rows: 1254611
Rows before dropping missing title: 1254611
Rows after dropping missing title and dropping missing (genres and overview): 1109811

Sample data after cleaning (keeping missing overviews):
       id            title                                           overview  \
0   27205        Inception  Cobb, a skilled thief who commits corporate es...   
1  157336     Interstellar  The adventures of a group of explorers who mak...   
2     155  The Dark Knight  Batman raises the stakes in his war on crime. ...   
3   19995           Avatar  In the 22nd century, a paraplegic Marine is di...   
4   24428     The Avengers  When an unexpected enemy emerges and threatens...   

                                        genres  \
0           Action, Science Fiction, Adventure   
1            Adventure, Drama, Science Fiction   
2               Drama, Action, Crime, Thriller   
3  Action, Adventure, Fantasy, Science Fiction   
4           Science Fiction, Action, Adventure   

                                            keywords  \
0  rescue, mission, dream, airplane, paris, franc...   
1  rescue, future, spacecraft, race against time,...   
2  joker, sadism, chaos, secret identity, crime f...   
3  future, society, culture clash, space travel, ...   
4  new york city, superhero, shield, based on com...   

                                             tagline  release_year  
0               Your mind is the scene of the crime.        2010.0  
1  Mankind was born on Earth. It was never meant ...        2014.0  
2                  Welcome to a world without rules.        2008.0  
3                        Enter the world of Pandora.        2009.0  
4                            Some assembly required.        2012.0

4. Prepare Text for Embedding

This step prepares the movie metadata for embedding by combining relevant fields into a single structured text string. This representation includes the title, overview, genres, keywords, tagline, and release year (if available). The output is stored in a new column called text_for_embedding.

Embeddings are numerical vector representations of text that capture semantic meaning and relationships. These vectors can be used for tasks like similarity search and clustering. Learn more about text embeddings and explore the Gemini embedding notebook.

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       id            title                                 text_for_embedding
0   27205        Inception  Title: Inception\nOverview: Cobb, a skilled th...
1  157336     Interstellar  Title: Interstellar\nOverview: The adventures ...
2     155  The Dark Knight  Title: The Dark Knight\nOverview: Batman raise...
3   19995           Avatar  Title: Avatar\nOverview: In the 22nd century, ...
4   24428     The Avengers  Title: The Avengers\nOverview: When an unexpec...

5. Sample a Subset for Development

To keep the notebook easy to run and ensure efficient development, you’ll want to iterate quickly and minimize resource usage. Instead of using the full dataset, this step samples 5,000 movies from the cleaned data, unless the dataset is already smaller, in which case all entries are used.

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Taking a random sample of 5000 movies for development.
Working with 5000 movies for the next steps.
            id               title  release_year
95090   714945              Gather        2020.0
198838  117602    California Girls        1985.0
827619  648297          After Jake        2013.0
801274  637062  Duas vezes Senzala        2017.0
846938  342211                Rise           NaN

Final sample DataFrame structure for embedding/indexing:
<class 'pandas.core.frame.DataFrame'>
Index: 5000 entries, 95090 to 650934
Data columns (total 8 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   id                  5000 non-null   int64  
 1   text_for_embedding  5000 non-null   object 
 2   title               5000 non-null   object 
 3   overview            5000 non-null   object 
 4   genres              5000 non-null   object 
 5   keywords            5000 non-null   object 
 6   tagline             5000 non-null   object 
 7   release_year        4260 non-null   float64
dtypes: float64(1), int64(1), object(6)
memory usage: 351.6+ KB
None

6. Initialize Qdrant for Vector Indexing

With the data prepared, the next step is to set up Qdrant, a vector similarity search engine optimized for storing and querying high-dimensional vectors. It supports fast indexing, filtering, and similarity search across millions of vectors.

Qdrant can run:

Locally as a standalone service
In the cloud for production deployments
Or entirely in-memory for fast, temporary use during development

In this notebook, Qdrant is initialized using in-memory mode by passing ":memory:" to the client. This stores data only in RAM, meaning it will not persist after the session ends. This is suitable for experimentation but not for saving results long-term.

You also configure the following:

COLLECTION_NAME: The name of the Qdrant collection to store movie vectors
VECTOR_SIZE: Set to 3072 to match the dimensionality of the text embeddings generated by Gemini
DISTANCE_METRIC: Set to cosine distance, which is ideal for measuring semantic similarity between embedding vectors

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7. Define Batch Embedding Function

This step defines the get_embeddings_batch function, which generates text embeddings for batches of movie data using the Gemini embedding model (gemini-embedding-001) including automatic retries for robustness.

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MODEL_FOR_EMBEDDING

Example embedding vector: [-0.023393, 0.011092304, 0.007310852, -0.095236674, 0.012521885, 0.0069673047, -0.011544445, -0.02173588, 0.027118236, 0.004006482]

8. Create a Collection in Qdrant

A collection in Qdrant is like a table in a database, it stores vectors along with optional metadata (payload). Each collection has its own configuration, including vector size and similarity metric.

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Existing collection 'tmdb_movies_sample' deleted.
Collection 'tmdb_movies_sample' created successfully.

9. Create Payloads for Metadata Storage

In Qdrant, besides storing vector embeddings, you can attach additional information called payload to each vector. This metadata helps in filtering or retrieving relevant results based on attributes like title, genres, or release year.

The create_payload function prepares the payload by extracting specified columns from each movie record, handling missing values and ensuring data types are compatible with Qdrant.

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10. Batch Embedding and Indexing to Qdrant

This step processes the sampled movies dataset in batches to generate vector embeddings using the Gemini API and upload (upsert) these embeddings along with their metadata payloads to the Qdrant collection.

Key points of this process:

The dataset is divided into batches of size BATCH_SIZE for embedding generation to stay within API limits.
Each batch's text data is sent to the Gemini embedding API with retries handled in the embedding function.
For every successfully embedded batch, the code prepares points (each containing an ID, vector embedding, and metadata payload) to be uploaded to Qdrant.
Points are buffered and uploaded in chunks of size QDRANT_BATCH_SIZE to optimize performance.
The process includes error handling and retry logic to avoid failures halting the entire operation.
At the end, any remaining points in the buffer are uploaded.
Summary statistics of processed, failed, and successfully upserted items are printed.

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Starting batch embedding and indexing process for 5000 movies...
Using Gemini Batch Size: 25, Qdrant Upsert Batch Size: 3072

Processing Batches: 100%|██████████| 200/200 [04:15<00:00,  1.28s/it]

Batch embedding and indexing finished.
Total items processed (attempted embedding): 5000
Total points successfully prepared for upsert: 5000

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Verification: Collection 'tmdb_movies_sample' now contains 5000 points.

11. Search and Recommend Similar Movies Using Vector Embeddings

With all movie vectors indexed in Qdrant, you can now perform semantic search. This allows you to take any user query (such as a phrase, movie description, or concept), convert it into an embedding using the same Gemini model, and retrieve the most similar movie vectors from the collection using cosine similarity.

This recommend_movies function demonstrates how to:

Generate an embedding from your input query using the Gemini API.
Perform a similarity search using Qdrant’s search() method.
Retrieve the top k most similar movie entries, including their metadata and similarity scores.

This is the final step where the vector database functions as a recommendation engine.

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Try Out Your Movie Recommender

You can now try querying your movie recommender by describing a theme, genre, or concept in natural language. The system will return the most semantically similar movies from your dataset based on vector similarity search using Gemini-generated embeddings.

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Searching for recommendations based on: '
  I want to watch something with my girlfriends that’s
  both funny and teaches something.
'
Query embedding: [-0.007658997, -0.00046528463, 0.003591214, -0.060340602, 0.005171188, -0.031223807, -0.012421608, -0.0029493966, 0.015560944, 0.011762511, 0.0043392465, -0.012576682, -0.00040339225, 0.00087798183, 0.104019016, 0.003676365, -0.004047451, -0.02373786, 0.03213893, -0.008110603, -0.010839047, -0.013205221, 0.005924564, 0.0144873345, 0.028277583, -0.0042119334, 0.030671213, 0.007607076, 0.039369747, -0.005576319, 0.029120907, 0.038622007, 0.022760479, 0.03496449, 0.020038292, 0.020724915, 0.008881883, 0.0064935135, -0.015201997, -0.008609423, -0.0074098404, -0.012157845, -0.0015358008, -0.008790521, 0.010938966, 0.015006626, 0.0006905204, -0.015275856, 0.0053447806, 0.02772103, -0.004355093, -0.022345519, 0.0011232442, -0.15648542, -0.012109607, -0.011598385, -0.036081407, -0.037203718, -0.00052338815, -0.024585776, 0.0056603705, 0.012978436, -0.02326816, -0.042064063, -0.0045854757, -0.0038374194, 0.011914898, -0.008569233, -0.00563646, -0.0057406654, 0.003564379, 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Found 5 potential recommendations:

--- Recommendations ---
  - Score: 0.7288
    Title: Girlfriends
    Genre: Comedy
    Year: 2021.0
----------
  - Score: 0.7194
    Title: Mes meilleures copines
    Genre: Comedy
    Year: 2003.0
----------
  - Score: 0.7134
    Title: Funny Birds
    Genre: Comedy, Drama
    Year: 2024.0
----------
  - Score: 0.7103
    Title: Mulheres Alteradas
    Genre: Comedy
    Year: 2018.0
----------
  - Score: 0.7087
    Title: Change of Plans
    Genre: Comedy, Romance
    Year: 2009.0
----------

/tmp/ipykernel_26130/3969524323.py:26: DeprecationWarning: `search` method is deprecated and will be removed in the future. Use `query_points` instead.
  search_result = qdrant_client.search(

Next Steps

This notebook demonstrated how to build a movie recommendation system by combining the Gemini API’s embedding capabilities with Qdrant’s vector search.

Useful API References

For more detailed understanding and to explore advanced features, refer to the official documentation:

Gemini API Embeddings Documentation: Learn how to generate text embeddings, understand model parameters, and use them effectively for semantic search and similarity tasks.
Qdrant Python Client Docs: Understand how to manage collections, insert and search vectors, configure indexing, and interact with the vector database.

Related Examples

To explore more use cases and get additional inspiration, check out these related notebooks in this directory:

Similarity Search using Qdrant: A focused example on building semantic search systems with embeddings and Qdrant.
Google GenAI SDK Overview: Walks you through installing and setting up the SDK, text and multimodal prompting, token counting, safety filters, multi-turn chat, function calling, file uploads, context caching, and more.
Text Embeddings with Gemini API: Focuses on generating and working with text embeddings using the Gemini API, ideal for building vector-based search and recommendation systems.