Integrating TMDB (The Movie Database) API in Langchain

[A-Jatana]

Checked

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Feature request

We propose integrating the TMDB API into Langchain to enhance its capabilities with rich movie data. This integration would enable users to:

• Retrieve detailed information about movies, including titles, synopses, release dates, and user ratings.
• Search for movies based on keywords, genres, or specific attributes such as release year or popularity.
• Access information about actors, directors, and crew members associated with each film.
• Generate personalized movie recommendations based on user preferences and viewing history.

Motivation

The film industry generates immense amounts of data, and accessing this information can be difficult for developers. Many applications lack efficient methods to retrieve comprehensive movie-related data, which can limit creativity and functionality. By integrating the TMDB API, Langchain can facilitate easier access to this data, allowing developers to create engaging applications, such as movie recommendation systems, review aggregators, and more. This would provide users with a seamless way to interact with movie content directly within their applications. There are currently no existing feature requests related to this integration.

Proposal (If applicable)

We plan to implement the TMDB integration by following these steps:

1. API Authentication

• Implement API key authentication to allow Langchain users to securely integrate their TMDB API key.

2. Endpoint Wrappers

• TMDB provides multiple endpoints for different types of movie data. We will create Python wrapper functions that handle these specific API calls:

  • Movie Search: This wrapper will allow users to search for movies based on various criteria (e.g., title, genre, release year, or popularity). The wrapper will format the search query and send it to TMDB's /search/movie endpoint.
  • Movie Details: For a given movie, this wrapper will fetch detailed information like the cast, crew, synopsis, and runtime. It will interact with the /movie/{movie_id} endpoint to return this data in an easy-to-use format.
  • Popular/Top-Rated Movies: This wrapper will request lists of movies that are currently popular or highly rated. TMDB provides endpoints for both /movie/popular and /movie/top_rated.
  • Recommendations: This will generate movie recommendations based on user preferences or a specific movie. The wrapper will interface with the /movie/{movie_id}/recommendations endpoint, which provides suggestions based on a movie the user likes.
  • Movie Reviews: This wrapper will allow users to retrieve reviews for a specific movie. It will interact with the /movie/{movie_id}/reviews endpoint to return user and critic reviews in a structured format.

• The integration is designed to be modular, and we can always add more TMDB endpoints as needed. Potential future expansions could include endpoints for trending movies, upcoming releases, or TV series data. This flexibility ensures that Langchain can continue to evolve with additional TMDB functionality over time.

3. Data Handling

• TMDB responses are typically in JSON format, but these responses can be complex and contain a lot of unnecessary information. We will:
  • Parse the JSON responses from TMDB and extract the relevant data (e.g., title, synopsis, cast) in a cleaner format.
  • Return the data in a consistent structure, likely as Python dictionaries or JSON objects, so that they can be seamlessly integrated into Langchain’s internal systems (e.g., agents, tools).

4. Error Handling

• Build robust error handling to manage invalid API keys, missing data, or network failures.

5. Tool Integration

• We will register the TMDB functionality within Langchain’s tool system. Tools in Langchain are components that can be used by agents to perform specific tasks (in this case, querying TMDB).
• The tool will expose TMDB’s capabilities (e.g., searching for movies, getting details) so that agents can call these functions dynamically during execution.
• This integration will make TMDB an easily accessible data source for a wide range of applications built on Langchain, such as chatbots that recommend movies or AI agents that answer movie-related queries.

6. Testing and Documentation

• Write unit tests to cover the most common use cases, and provide clear documentation for developers.

Modifications to Existing Files

We plan to modify the following files:

• libs/community/langchain_community/agent_toolkits/load_tools.py to register our tool.
• libs/community/langchain_community/tools/__init__.py to register the TMDB tool.
• libs/langchain/langchain/tools/__init__.py to add the TMDB tool to Langchain's core tools.

New Files

We plan to create the following files:

• libs/community/langchain_community/tools/tmdb.py where we will define the TMDBQueryRun class, which will inherit from BaseTool.
• libs/community/langchain_community/utilities/tmdb.py where we will define the TMDBAPIWrapper class, which will handle API requests and interactions with TMDB.
• libs/community/tests/integration_tests/utilities/test_tmdb.py to write test cases ensuring the TMDB tool works as expected.

If we receive the go-ahead, our team of four from the University of Toronto would like to start working on this idea and develop/submit a pull request by mid-November.