Vector Dimensions & Embeddings

Vector Dimensions & Embeddings: The Foundation of NLP

Embeddings are at the heart of natural language processing (NLP). They define how data is represented and stored for LLMs, enabling them to understand and generate meaningful text.

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Types of Embeddings and Their Differences

Numerous models exist for embedding various data types. For text, popular choices include Word2Vec, OpenAI, GloVe, and BERT. For images, VGG and Inception from the Convolutional Neural Network model are commonly used.

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OpenAI: Powering Your Embeddings

In this lesson, you’ll leverage an OpenAI LLM with LangChain to implement text embedding and extraction. OpenAI is an AI research organization that developed the groundbreaking ChatGPT. Their platform offers API keys for accessing various models.

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LangChain: Simplifying LLM Development

LangChain is a framework designed to streamline the development of LLM applications. It provides a unified interface for combining components from various providers, making it easier to build custom apps. Without LangChain, the complexities of understanding individual components, their APIs, and integration processes can become overwhelming.

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