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Understanding LLM Embeddings and Vector Databases

Apr 2, 2025

Lecture Notes on LLM Embeddings and Vector Databases

Introduction

  • Discussion focuses on LLM (Large Language Model) embeddings and their integration into systems, specifically through Bing's capabilities.
  • Purpose of embeddings: Encapsulate text into n-dimensional vectors for various applications.

Understanding LLM Embeddings

  • Embeddings: Produced by LLMs for text inputs, resulting in one or more n-dimensional vectors.
    • Applicable to full documents, sentences, or individual words.
  • Tokenization Process:
    • Step 1: Conversion of raw text into tokens by LLM tokenizer.
    • Step 2: Each token is transformed into an n-dimensional vector via the embedding layer, generating a sequence of vectors.

Context Sensitivity of Embeddings

  • Embeddings are context-sensitive; same words can yield different embeddings based on surrounding text.
    • Example: "Cool" in different contexts (trendy vs. temperature).

Importance of LLM Embeddings

  • Facilitates integration of proprietary organizational data into LLM systems.
  • Provides a means for system builders to augment third-party LLMs with custom data.

Storing Embeddings in Vector Databases

  • Vector Databases:
    • Designed specifically for efficient vector storage and operations (e.g., similarity searches, vector manipulations).
    • Capable of creating n-dimensional vector indexes, supporting ML and LLM use cases.
    • Optimized for system-specific query patterns, promoting performance and cost-effectiveness.
  • Growing variety of databases (relational, key-value, columnar, graph, blockchain, and now vector).

LLM Driven Systems with Vector Databases

  • RAG (Retrieval Augmented Generation) Systems:
    • User sends a prompt to an LLM.
    • LLM tokenizer processes the prompt into tokens.
    • An embedding LLM generates a query embedding for a similarity search in the vector database.
    • Relevant documents from the database are combined with the original prompt to generate a response.
    • Response is returned after conversion from embedding to output tokens.

Conclusion

  • LLM embeddings are crucial for capturing and integrating proprietary data in systems, providing a foundation for RAG systems.
  • Vector databases play a central role in storing and managing these embeddings.

Final Thoughts

  • Encourage balance between technology engagement and real-world activities like swimming, hiking, etc.
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Lecture covered in a video part of a broader playlist on machine learning and AI concepts.

Full transcript