Ďēv Šhãh 🥑Disclaimer
This blog reflects my learnings from Augment your LLM Using Retrieval Augmented Generation by NVIDIA. The content is based on topics covered in the course and my understanding of them through some practical examples. If you are not sure, what RAG is, I would suggest you to check out my following blog.
Introduction to Vector Embeddings
Vector Embeddings is the process of representing complex data like words, sentences, documents, or images into numerical representations that computers can easily understand. This is the significance of embeddings. The data is passed to the Embedding Model which in turns returns the embeddings.
Image Credit: NVIDIA
Understanding Semantic Relationships
Vector Embeddings encapsulate the semantic relationship between words and comprehend contextual meaning to generate embeddings. Therefore, similar words or entities have vectors that are close to each other, which reflects the semantic similarity.
Example
Consider the following words: “king,” “queen,” “man,” and “woman.”
When these words are processed using an embedding technique, they are transformed into vectors:
- “king” → [0.8, 0.5, 0.3]
- “queen” → [0.7, 0.4, 0.6]
- “man” → [0.6, 0.2, 0.5]
- “woman” → [0.5, 0.1, 0.4]
The vectors for “king” and “queen” are close to each other, indicating they are both royal figures but differ in gender. Similarly, “man” and “woman” are also close, reflecting their relatedness as gender counterparts.
Analogy Using Vectors
If you take the vector difference: “king” - “man” + “woman,” the resulting vector should be close to “queen.” This means that the model understands that just as a king is to a man, a queen is to a woman, demonstrating the semantic relationship between these terms.
These embeddings are stored in a Vector Database before being retrieved and passed to a Large Language Model (LLM).
Citation
I would like to acknowledge that I took help from ChatGPT to structure my blog simplify content and generate relevant examples.
