"# How to combine GPT-4V with RAG - Create a Clothing Matchmaker App\n",
"\n",
"Welcome to the Clothing Matchmaker App Jupyter Notebook! This project demonstrates the power of the GPT-4-vision model in analyzing images of clothing items and extracting key features such as color, style, and type. The core of our app relies on this advanced image analysis model developed by OpenAI, which enables us to accurately identify the characteristics of the input clothing item.\n",
"Welcome to the Clothing Matchmaker App Jupyter Notebook! This project demonstrates the power of the GPT-4V model in analyzing images of clothing items and extracting key features such as color, style, and type. The core of our app relies on this advanced image analysis model developed by OpenAI, which enables us to accurately identify the characteristics of the input clothing item.\n",
"\n",
"GPT-4-vision is a model that combines natural language processing with image recognition, allowing it to understand and generate responses based on both text and visual inputs.\n",
"GPT-4V is a model that combines natural language processing with image recognition, allowing it to understand and generate responses based on both text and visual inputs.\n",
"\n",
"Building on the capabilities of the GPT-4-vision model, we employ a custom matching algorithm and the RAG technique to search our knowledge base for items that complement the identified features. This algorithm takes into account factors like color compatibility and style coherence to provide users with suitable recommendations. Through this notebook, we aim to showcase the practical application of these technologies in creating a clothing recommendation system.\n",
"Building on the capabilities of the GPT-4V model, we employ a custom matching algorithm and the RAG technique to search our knowledge base for items that complement the identified features. This algorithm takes into account factors like color compatibility and style coherence to provide users with suitable recommendations. Through this notebook, we aim to showcase the practical application of these technologies in creating a clothing recommendation system.\n",
"\n",
"Using the combination of GPT-4 Vision + RAG (Retrieval-Augmented Generation) offers several advantages:\n",
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"source": [
"### Guardrails\n",
"\n",
"In the context of using Large Language Models (LLMs) like GPT-4v, \"guardrails\" refer to mechanisms or checks put in place to ensure that the model's output remains within desired parameters or boundaries. These guardrails are crucial for maintaining the quality and relevance of the model's responses, especially when dealing with complex or nuanced tasks.\n",
"In the context of using Large Language Models (LLMs) like GPT-4V, \"guardrails\" refer to mechanisms or checks put in place to ensure that the model's output remains within desired parameters or boundaries. These guardrails are crucial for maintaining the quality and relevance of the model's responses, especially when dealing with complex or nuanced tasks.\n",
"\n",
"Guardrails are useful for several reasons:\n",
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"3. **Safety**: They prevent the model from generating harmful, offensive, or inappropriate content.\n",
"4. **Contextual Relevance**: They ensure that the model's output is contextually relevant to the specific task or domain it is being used for.\n",
"\n",
"In our case, we are using GPT-4v to analyze fashion images and suggest items that would complement an original outfit. To implement guardrails, we can **refine results**: After obtaining initial suggestions from GPT-4v, we can send the original image and the suggested items back to the model. We can then ask GPT-4v to evaluate whether each suggested item would indeed be a good fit for the original outfit.\n",
"In our case, we are using GPT-4V to analyze fashion images and suggest items that would complement an original outfit. To implement guardrails, we can **refine results**: After obtaining initial suggestions from GPT-4V, we can send the original image and the suggested items back to the model. We can then ask GPT-4V to evaluate whether each suggested item would indeed be a good fit for the original outfit.\n",
"\n",
"This gives the model the ability to self-correct and adjust its own output based on feedback or additional information. By implementing these guardrails and enabling self-correction, we can enhance the reliability and usefulness of the model's output in the context of fashion analysis and recommendation.\n",
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"source": [
"### Conclusion\n",
"\n",
"In this Jupyter Notebook, we explored the application of GPT-4-vision and other machine learning techniques to the domain of fashion. We demonstrated how to analyze images of clothing items, extract relevant features, and use this information to find matching items that complement an original outfit. Through the implementation of guardrails and self-correction mechanisms, we refined the model's suggestions to ensure they are accurate and contextually relevant.\n",
"In this Jupyter Notebook, we explored the application of GPT-4 with Vision and other machine learning techniques to the domain of fashion. We demonstrated how to analyze images of clothing items, extract relevant features, and use this information to find matching items that complement an original outfit. Through the implementation of guardrails and self-correction mechanisms, we refined the model's suggestions to ensure they are accurate and contextually relevant.\n",
"\n",
"This approach has several practical uses in the real world, including:\n",
Teo Musatoiu
2 months ago
committed by
GitHub