[Question]: Querying images from document for the given prompt

Prem-Nitin commented 7 months ago

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Question

I have a use case where for a given question asked to the rag, i need it to fetch me the related image and display it. How to implement this using llamaindex?

dosubot[bot] commented 7 months ago

Hey @Prem-Nitin, great to see you back here! 🚀

To implement a feature in LlamaIndex that fetches and displays related images for a given question, you can follow these steps:

Keyword Extraction: Utilize the DEFAULT_QUERY_KEYWORD_EXTRACT_TEMPLATE to extract keywords from the given question. This will help in identifying the main topics or subjects of the question which can be used to search for related images.

DEFAULT_QUERY_KEYWORD_EXTRACT_TEMPLATE_TMPL = (
    "A question is provided below. Given the question, extract up to {max_keywords} "
    "keywords from the text. Focus on extracting the keywords that we can use "
    "to best lookup answers to the question. Avoid stopwords.\n"
    "---------------------\n"
    "{question}\n"
    "---------------------\n"
    "Provide keywords in the following comma-separated format: 'KEYWORDS: <keywords>'\n"
)

Image Search API Integration: After extracting the keywords, integrate an image search API (such as Unsplash, Pixabay, or Google Custom Search API) into LlamaIndex. Use the extracted keywords as the search query to fetch related images. Each of these APIs will require you to register for an API key and adhere to their usage guidelines.
Displaying Images: Once you have the search results, you can display the images in your application's UI. Ensure you handle the image metadata properly, including credits or source information as required by the API's terms of service.
Caching for Performance: Consider caching the results for frequently asked questions or keywords to improve the performance of your feature and reduce the number of API calls.
User Interface Considerations: Design the UI in a way that the images are presented in a user-friendly manner, possibly with options to view more images or refine the search.

Here's a simplified example of how you might integrate an image search API using Python, assuming you have extracted keywords and chosen an API:

import requests

def fetch_images(keywords):
    API_KEY = 'YOUR_API_KEY'  # Replace with your actual API key
    SEARCH_URL = 'https://api.unsplash.com/search/photos'  # Example API endpoint
    query = ','.join(keywords)  # Assuming keywords is a list of extracted keywords
    params = {
        'query': query,
        'client_id': API_KEY,
    }
    response = requests.get(SEARCH_URL, params=params)
    if response.status_code == 200:
        data = response.json()
        images = [item['urls']['regular'] for item in data['results']]  # Extracting image URLs
        return images
    else:
        return []

# Example usage
keywords = ['llama', 'nature']  # Extracted keywords
images = fetch_images(keywords)
# Now, you can display these images URLs in your application

This example uses the Unsplash API for demonstration purposes. You'll need to adapt it based on the actual API you choose to use, including handling authentication, rate limits, and response parsing as per the API's documentation.

You can find more details about the keyword extraction template in the default_prompts.py file in the LlamaIndex repository.

Please note that this is a high-level overview and the actual implementation may vary based on your specific requirements and the API you choose to use for image search.

Sources

#### About Dosu

This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.

Prem-Nitin commented 7 months ago

@dosu-bot the image search need to happen within the document given to the rag.

dosubot[bot] commented 7 months ago

Based on your request, it seems you want to fetch and display related images from a given document based on a specific question, without using an external API. Here's a step-by-step guide on how you can achieve this:

Extract Image References and Descriptions: The current structure of the document provided to the LlamaIndex, as defined in llama_index/core/data_structs/document_summary.py, does not explicitly include references to images along with descriptions or captions. You will need to ensure that your document includes references to images along with descriptions or captions. These references could be in a structured format within the document, such as a list or a dictionary, where each entry contains the image's file path (or identifier) and a description or caption.
Modify the Query Processing: You need to modify the query processing part of the LlamaIndex code to handle image-related queries. This involves parsing the query to identify when a user is asking for images related to a specific topic or question. Currently, LlamaIndex uses a GPT model to extract keywords from a query, as seen in the KeywordTableIndex class. You can modify this process to identify image-related queries.
Search for Related Images: Implement a search mechanism within the document's content to find images related to the query. This could involve keyword extraction from the query and matching these keywords with the descriptions or captions of the images in the document.
Display the Images: Once the related images are identified, implement a way to display these images. If you are working in a console application, you might need to return the file paths or identifiers of the images. For a web or graphical application, you could display the images directly. Currently, LlamaIndex displays or returns the results of a query to the user by synthesizing a response based on the SQL query results if the synthesize_response flag is set to True. You can modify this behavior to return or display the images.

Here's a simplified example of how you might implement these steps in Python, assuming the document is represented as a dictionary with image descriptions:

document_images = {
    'image1.jpg': 'A beautiful sunset over the mountains.',
    'image2.jpg': 'A group of friends camping under the stars.',
    'image3.jpg': 'A clear blue sky on a sunny day.'
}

def find_related_images(query, document_images):
    # Simple keyword extraction from the query
    keywords = query.lower().split()

    # Find images with descriptions containing any of the keywords
    related_images = [image for image, description in document_images.items()
                      if any(keyword in description.lower() for keyword in keywords)]

    return related_images

# Example usage
query = "sunset"
related_images = find_related_images(query, document_images)
print(f"Related images: {related_images}")

This example is quite basic and uses a simple keyword matching approach. For more complex documents and queries, you might need to implement more sophisticated natural language processing techniques to understand the context and semantics of the query and the image descriptions better.

Please note that this approach requires modifications to your document structure to include image references and descriptions, as well as changes to the query processing, image search, and result display mechanisms in the LlamaIndex code. Be sure to thoroughly test your changes to ensure they work as expected and do not introduce new issues.

For more information, you can refer to the following sources:

I hope this helps! If you have any further questions or need more clarification, feel free to ask.

Sources

#### About Dosu

This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.

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