LLM Foundations: Vector Databases for Caching and Retrieval Augmented Generation (RAG)
Focused View
Kumaran Ponnambalam
1:33:18
9 View
01 - GenAI with vector databases.mp4
00:51
02 - Course coverage and prerequisites.mp4
02:08
01 - What is a vector.mp4
01:36
02 - Vectorization in NLP.mp4
02:51
03 - Vector similarity search.mp4
02:26
04 - Vector databases.mp4
02:22
05 - Pros and cons of vector databases.mp4
02:17
01 - Introduction to Milvus DB.mp4
01:53
02 - Milvus architecture.mp4
02:37
03 - Collections in Milvus.mp4
03:35
04 - Partitions in Milvus.mp4
01:15
05 - Indexes in Milvus.mp4
01:50
06 - Managing data in Milvus.mp4
01:38
07 - Query and search in Milvus.mp4
04:05
08 - Set up Milvus and exercise files.mp4
04:55
01 - Create a connection.mp4
01:52
02 - Create databases and users.mp4
02:48
03 - Create collections.mp4
02:43
04 - Insert data into Milvus.mp4
03:05
05 - Build an index.mp4
02:00
06 - Query scalar data.mp4
02:01
07 - Search vector fields.mp4
04:38
08 - Delete objects and entities.mp4
01:10
01 - LLMs and caching.mp4
02:13
02 - Prompt caching workflow.mp4
01:58
03 - Set up the Milvus cache.mp4
01:57
04 - Inference process and caching.mp4
03:41
05 - Cache management.mp4
01:56
01 - LLMs as a knowledge source.mp4
02:03
02 - Introduction to retrieval augmented generation.mp4
02:02
03 - RAG Knowledge curation process.mp4
02:30
04 - RAG question-answering process.mp4
01:21
05 - Applications of RAG.mp4
01:59
01 - Set up Milvus for RAG.mp4
01:20
02 - Prepare data for the knowledge base.mp4
02:09
03 - Populate the Milvus database.mp4
01:24
04 - Answer questions with RAG.mp4
02:01
01 - Choose a vector database.mp4
02:01
02 - Combine vector and scalar data.mp4
01:53
03 - Distance measure considerations.mp4
01:48
04 - Tune vector DB performance.mp4
01:48
01 - Continue with LLMs.mp4
00:38
Description
As large language models grow in popularity, the infrastructure to be used around them also becomes vital to reduce costs, generate accurate responses, and improve efficiency. Vector databases play a vital role in several LLM use cases to help alleviate LLM shortcomings, reduce costs and latency. Knowledge of its basics and applications are vital for any engineer building applications with LLMs, and in this course, Kumaran Ponnambalam teaches you the basics of vector databases and how to use them in LLM caching and retrieval-augmented generation (RAG).
Kumaran begins with a discussion on the basics of vector databases and their applications. He then explores specialized databases for storing vectors and uses the Milvus database as the reference example, and demonstrates read and write operations with the Milvus database. Learn how to use vector databases for LLM caching, with an example use case, along with examples of RAG use cases. Finally, Kumaran concludes with a discussion on optimizing vector databases.More details
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Kumaran Ponnambalam
Instructor's CoursesA seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts
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- language english
- Training sessions 42
- duration 1:33:18
- English subtitles has
- Release Date 2024/05/18
