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How to Build a Multi-Modal Search App with Chroma?
Original article seen at: www.analyticsvidhya.com on October 25, 2023
tldr
- 🔑 Multi-modal search in AI involves neural networks that can process and understand different data types.
- 💡 OpenAI's CLIP is an example of a multi-modal neural network.
- 🛠 The article provides a step-by-step guide on building a multi-modal search app using the Chroma vector database and CLIP.
summary
The article discusses the concept of multi-modal search in AI, which involves neural networks that can process and understand different data types. The article uses the example of OpenAI's CLIP, a multi-modal neural network trained on a large dataset of image-text pairs. The article then delves into the technical aspects of building a multi-modal search app using the Chroma vector database and CLIP. It explains the process of transforming raw data into numerical form, known as vectors or embeddings, and the need for vector databases to store and query these embeddings. The article also discusses the use of specialized embedding models for different data types and the use of indexing methods for efficient querying. The article concludes with a step-by-step guide on how to build a restaurant dish recommender app using Gradio, Chroma, and CLIP.starlaneai's full analysis
The technology discussed in the article represents a significant advancement in the field of AI, particularly in the area of multi-modal search. The use of OpenAI's CLIP and the Chroma vector database could revolutionize search capabilities by enabling neural networks to process and understand different data types. However, the implementation of this technology requires a high level of technical expertise, which could limit its adoption. Furthermore, while the technology has the potential to attract investment, its current applications are limited. Therefore, further research and development are needed to fully realize the potential of this technology.
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starlaneai's Ratings & Analysis
Technical Advancement
80 The article discusses a significant technical advancement in the field of AI, particularly in the area of multi-modal search. The use of OpenAI's CLIP and the Chroma vector database represents a significant step forward in the ability to process and understand different data types.
Adoption Potential
60 The adoption potential of the technology discussed in the article is moderate. While the technology is advanced, its implementation requires a high level of technical expertise.
Public Impact
50 The public impact of the technology is moderate. While the technology has the potential to revolutionize search capabilities, its current applications are limited.
Innovation/Novelty
70 The novelty of the technology is high. The use of multi-modal models in AI is a relatively new concept, and the article provides a detailed explanation of this innovative technology.
Article Accessibility
40 The accessibility of the information in the article is moderate. While the article provides a detailed explanation of the technology, it requires a certain level of technical expertise to fully understand.
Global Impact
50 The global impact of the technology is moderate. While the technology has the potential to revolutionize search capabilities, its current applications are limited.
Ethical Consideration
30 The article does not discuss ethical considerations in detail, resulting in a low rating in this category.
Collaboration Potential
60 The collaboration potential of the technology is high. The use of OpenAI's CLIP and the Chroma vector database suggests potential for collaboration between different entities in the AI industry.
Ripple Effect
50 The ripple effect of the technology is moderate. While the technology has the potential to revolutionize search capabilities, its current applications are limited.
Investment Landscape
40 The potential of the AI innovation to affect the AI investment landscape is moderate. The technology represents a significant advancement in the field of AI, which could attract investment.