How to do Video Question Answering using Python

In this guide, we’ll show you how to build a video question answering system using Python.

Whether you're extracting insights from video content, answering questions about actions or events, or enhancing multimodal AI applications, this tutorial covers essential techniques for processing and understanding video data.

By the end, you’ll be able to link natural language questions to relevant visual information, enabling intelligent, time-aware responses from videos in your Python-based projects.

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What is Video Question Answering?

Video Question Answering focuses on answering natural language questions based on video content.

It combines techniques from computer vision and natural language processing to understand both the visual and temporal aspects of a video—such as objects, actions, and events over time—and generate relevant answers.

Unlike image-based QA, Video QA must reason over sequences of frames, making it more complex and suitable for tasks like summarizing scenes, identifying actions, or tracking changes across time.

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Implementing Video Question Answering in Python

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Get Access to Eden AI API

1. Sign up: Visit Eden AI and sign up for an account for free. Once registered, navigate to the API section to obtain your API key. This key will give you access to a wide range of AI services including Video Question Answering.

2. Navigate to Video Technologies – After logging in, go to the Video section of the platform.

3. Select Video Question Answering– Choose the Video Question Answering feature or explore advanced options based on your needs.

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Live Test Models on Eden AI

Eden AI offers a platform that lets you experiment with various AI models before integration, making it easy to compare providers and choose the one that best fits your requirements.

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Install Python's Requests Module

To make HTTP requests in Python, you'll need the requests library. You can install it using pip:

pip install requests

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Prepare the Code

Here’s a Python example showing how to send a video and a question to Eden AI’s API. Video question answering now goes through a multimodal LLM, called with the OpenAI-compatible chat completions endpoint:

import requests

headers = {
    "Authorization": "Bearer YOUR_API_KEY_HERE"
}

url = "https://api.edenai.run/v3/chat/completions"
json_payload = {
    "model": "google/gemini-2.5-flash",  # Any model that accepts video input
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this video?"},
                {"type": "image_url", "image_url": {"url": "🔗 URL of your video"}},
            ],
        }
    ],
}

response = requests.post(url, json=json_payload, headers=headers)

result = response.json()
print(result["choices"][0]["message"]["content"])

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• Authorization Header: You must include your API key in the header for authentication.
• model: The multimodal LLM that answers, in the provider/model format. It must accept video as input: check for video in input_modalities at https://api.edenai.run/v3/models.
• messages: The natural language question you want the model to answer goes in a text block, and the video in an image_url block, as a direct link to your video file (hosted online).
• Local files: To use a video stored on your machine, upload it first with POST https://api.edenai.run/v3/upload, then send a file block with the returned file_id instead of the image_url block.

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Interpreting the Results

The API response is a JSON object. Here’s an example of what a successful output might look like:

{
  "status": "success",
  "model": "gemini-2.5-flash",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "This video depicts an intense, animated fight sequence between two characters in a desolate, snowy, mountainous environment. ..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {"prompt_tokens": 3219, "completion_tokens": 620, "total_tokens": 3839},
  "provider": "google",
  "cost": 0.0027404
}

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Why Eden AI Is the Best Tool for Entity Sentiment Analysis

Multi-Provider Support

‍Access leading NLP providers like Google, IBM, and Amazon through a single API. This unified approach simplifies the integration of advanced language processing features into your applications.

Easy Integration

‍Eden AI offers a clean and straightforward API structure, making implementation quick and hassle-free. You can add powerful AI capabilities to your system with minimal setup.

Scalability

‍Whether you're building a small prototype or a large-scale application, Eden AI scales effortlessly to meet your project’s needs.

Flexible Pricing

‍Start with free credits and choose a pay-as-you-go plan that fits your usage. Eden AI’s pricing is designed to be cost-effective and grow with your requirements.

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Conclusion

In this guide, you learned how to implement video question answering in Python using the Eden AI API. We walked through how to get your API key, test providers, and integrate the feature in just a few lines of code.

Video QA is a powerful tool that can unlock deeper understanding from video content. Thanks to Eden AI, it’s now easier than ever to access and experiment with this capability without needing to build complex AI pipelines yourself.

FAQ — do Video Question Answering using Python