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A Beginner's Guide to Getting Started with Multimodality in LangChain
- AI models are advancing in processing multimodal data such as images, audio, video, and PDFs, enhancing system flexibility and intelligence.
- LangChain is integrating multimodality into its stack, enabling chat models to describe images and potentially support audio/video search.
- Langcasts.com is a new resource for AI engineers, offering guides, tips, and classes to help master multimodality in LangChain.
- Multimodality involves working with various types of input data beyond text, offering more natural interactions with AI models.
- LangChain supports multimodal inputs in chat models for images and files, while embedding models and vector stores are evolving to support multimodality.
- Currently, chat models in LangChain allow sending images and text in inputs, paving the way for more interactive and dynamic responses.
- While chat models support multimodal inputs, outputs are mostly text-based, with some exceptions like audio outputs in certain models.
- Embedding models in LangChain focus on text embeddings, with plans to extend support to multimedia embeddings for tasks like image and audio search.
- Vector stores in LangChain cater predominantly to text-based embeddings, but future plans involve supporting image, audio, and video embeddings.
- Developers can start experimenting with multimodal workflows by using external tools for generating embeddings until LangChain fully integrates multimodal support.
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