A Deep Dive: Embeddings, Vectors & Search Algorithms in LLM's

I have seen that many grasp the concept of embeddings, but the deeper technicalities aren’t as commonly understood. I try to close that gap in this video!

00:00 | Introduction
00:55 | Defining embeddings, vectors, embedding models & applications
03:32 | Embeddings in LLM’s like GPT’s, transformer architecture
04:13 | Input embeddings/positional encodings/encoders/decoders/output embeddings
06:35 | Linear & Softmax
08:56 | Conceptualizing dimensions, interpretability, and dimensionality reduction
14:57 | Visualizing embeddings & dimensions using python, 3D/4D
18:09 | Cosine Similarity, equation breakdown
23:13 | Calculating cosine similarity using python
24:47 | Using embeddings and RAG to build LLM’s with your data
27:21 | Approximate Nearest neighbor & Hierarchical Navigable Small World Algorithms (HNSW)
28:14 | Integrated Vectorization
29:36 | How algorithms like HNSW work for retrieval
31:12 | Equation for node assignment, logarithmic exponential distributions, decay, scaling factor, average degree
34:04 | Visualizing how information is retrieved
36:42 | Azure OpenAI Embedding tutorial

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Resources:
3) Document Embedding for Scientific Articles:

[jackbeats]

Excellent video, concise, articulate, much thanks to this :)

[ethernalspirit]

Your knowledge and explanation is truly next level

[KehrJoris]

Embedding and vectoring are important tools in LLM. They enable large language models to understand and process natural language more effectively.

[MaltzLadd]

Embedding is a technique in natural language processing (NLP) to convert text into numeric vectors. These vectors can be used to represent the meaning of words, phrases or sentences.

[maxtriplex]

Thanks a lot man!very informative and clear ✌🏼

[Canna_Science_and_Technology]

I’m deeply grateful for this detailed exploration of embeddings and vector databases. As someone who has built several RAG systems and transitioned to newer embedding models like BGE for performance, I’ve often worked with these tools without fully grasping their underlying mechanics. Your video has been a revelation, explaining not just the ‘how’ but the ‘why’, especially enlightening me on why the dimensions of my indexes need to match the embedding model I use. It’s incredibly satisfying to understand these concepts that are so crucial to my work. Thank you for shedding light on these complex topics and aiding my professional growth.

[matthew]

Great video ty but did you mean "data" instead of "query" at 25:54?

[Ridz]

I don't know how I got here but this video seems very full of information. I want to ask what level of university is this, and why are you teaching youtube this?

[abdullahnaji]

very informative thank you for posting

[texturalbard]

Could you please make a video about attention mechanisms?

[Canna_Science_and_Technology]

Long winded comment alert.

In a RAG-based Q&A system, the efficiency of query processing and the quality of the results are paramount. One key challenge is the system’s ability to handle vague or context-lacking user queries, which often leads to inaccurate results. To address this, we’ve implemented a fine-tuned LLM to reformat and enrich user queries with contextual information, ensuring more relevant results from the vector database. However, this adds complexity, latency, and cost, especially in systems without high-end GPUs.

Improving algorithmic efficiency is crucial. Integrating techniques like LORA into the LLM can streamline the process, allowing it to handle both context-aware query reformulation and vector searches. This could significantly reduce the need for separate embedding models, enhancing system responsiveness and user experience.

Furthermore, incorporating a feedback mechanism for continuous learning is vital. This would enable the system to adapt and improve over time based on user interactions, leading to progressively more accurate and reliable results. Such a system not only becomes more efficient but also more attuned to the evolving needs and patterns of its users.

[MatthewatCourseCareers]

Hey Abdul - We sent you an email about a paid partnership. Let me know what you think.