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Multimodal RAG with Mixpeek and FLUX

Leveraging the power of Mixpeek’s multimodal indexing and Replicate’s FLUX image generation model, we can create a sophisticated pipeline for image indexing, retrieval, and generation. This approach enables dynamic content creation based on existing visual data.

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Key Components

1. Mixpeek: Handles image indexing and retrieval
2. FLUX: Generates high-quality images from text prompts
3. Python: Integrates the components into a cohesive system

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Workflow

1. Index Images: Store and analyze images using Mixpeek’s API
2. Retrieve Images: Search for relevant images using text or image queries
3. Generate Images: Create new images with FLUX based on prompts or retrieved images
4. Integrate Results: Combine retrieved and generated images for various applications

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Use Case Example

Imagine an e-commerce platform that wants to enhance its product recommendations. The system could:

1. Index all product images using Mixpeek
2. Retrieve similar products based on a user’s browsing history
3. Generate new product concepts using FLUX, inspired by popular items
4. Present a mix of existing and AI-generated products to the user

This approach creates a dynamic, ever-evolving catalog that combines real products with AI-generated concepts, potentially increasing user engagement and sales.

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Code Snippet

import requests
import replicate

# Mixpeek setup
mixpeek_api_key = "your_mixpeek_api_key"
collection_id = "products"

# FLUX setup
replicate.api_token = "your_replicate_token"

# Index an image
def index_image(url):
    # Mixpeek indexing code here
    pass

# Retrieve similar images
def find_similar_images(query_image):
    # Mixpeek retrieval code here
    pass

# Generate new image
def generate_image(prompt):
    return replicate.run(
        "black-forest-labs/flux-dev",
        input={"prompt": prompt}
    )

# Integrated workflow
def enhance_product_recommendations(user_history):
    similar_products = find_similar_images(user_history[-1])
    new_concept = generate_image(f"Product similar to {similar_products[0]['description']}")
    return similar_products + [new_concept]

# Usage
recommendations = enhance_product_recommendations(user_browsing_history)