Embeddings - Enhancing Language Models & Search | Course and Power Point for Bots

The article explores the significance of embeddings in RAG and fine-tuned LLM models, emphasizing their role in representing and understanding natural language through semantic relationships. It discusses creating, storing, and utilizing embeddings for search, context retrieval, and various applications beyond RAG and LLMs.


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Embeddings in RAG and Fine-Tuned LLM Models

Embeddings are essential in the context of RAG (retrieval augmented generation) and when using fine-tuned Large Language Models (LLMs) as they help represent words, phrases, or sentences in a continuous vector space. These embeddings capture semantic relationships between words and enable machines to understand and process natural language more effectively.

Creating Embeddings

To create embeddings, one can utilize pre-trained models like Word2Vec, GloVe, or train custom embeddings using algorithms like Word Embeddings from Language Models (ELMo) or Bidirectional Encoder Representations from Transformers (BERT).

Storing Embeddings

Embeddings can be stored in various ways such as:

As separate files in formats like Word2Vec, GloVe, or TensorFlow's SavedModel format.
Within the model architecture itself, especially in the case of fine-tuned LLMs.

Using Embeddings for Search and Context

Embeddings can be utilized for search and context retrieval by:

Calculating similarity between embeddings to retrieve relevant information.
Using embeddings to provide context for generated text in RAG models.

Other Uses of Embeddings

Aside from RAG and LLMs, embeddings find applications in:

Named Entity Recognition (NER) tasks.
Sentiment analysis and text classification.
Machine translation and language modeling.

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