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Similarity Searching with Vectors: Deep Dive for Leaders (Chapter 8)

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Manage episode 523922847 series 3705593
Content provided by Keith Bourne. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Keith Bourne or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Discover how similarity searching with vectors is revolutionizing information retrieval beyond traditional keyword search. In this episode, we break down the business impact, technology trade-offs, and strategic considerations leaders need to harness this powerful approach. Drawing from Chapter 8 of Keith Bourne's "Unlocking Data with Generative AI and RAG," we explore how vector search drives smarter AI, faster results, and better customer experiences.

In this episode:

- Understand the fundamentals of vector similarity search and why it matters for modern AI-powered search

- Compare semantic, keyword, and hybrid search approaches and their business implications

- Explore the role of Approximate Nearest Neighbor (ANN) algorithms in scaling search performance

- Review leading tools and managed services like FAISS, Pinecone, and Google Vertex AI Vector Search

- Hear real-world use cases from retail, customer support, and AI applications

- Discuss challenges such as embedding drift, ranking, and infrastructure complexity

Key tools and technologies mentioned:

FAISS, Pinecone, Google Vertex AI Vector Search, LangChain EnsembleRetriever, Weaviate, pgvector, BM25Retriever, Reciprocal Rank Fusion, sentence_transformers, Chroma

Timestamps:

0:00 - Introduction and episode overview

2:15 - What is similarity searching with vectors?

5:00 - Why now: The rise of unstructured data and AI reliance

7:30 - Core concepts: Vector embeddings and distance metrics

10:00 - Comparing search approaches: Keyword vs Semantic vs Hybrid

13:00 - Under the hood: ANN algorithms and indexing techniques

15:30 - Real-world impact and business use cases

17:30 - Challenges and managing expectations

19:00 - Closing thoughts and resources

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Visit Memriq.ai for practical AI guides, research breakdowns, and leadership resources

Thanks for listening to Memriq Inference Digest - Leadership Edition. Stay tuned for more insights that empower strategic AI decision-making.

  continue reading

22 episodes

Artwork
iconShare
 
Manage episode 523922847 series 3705593
Content provided by Keith Bourne. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Keith Bourne or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Discover how similarity searching with vectors is revolutionizing information retrieval beyond traditional keyword search. In this episode, we break down the business impact, technology trade-offs, and strategic considerations leaders need to harness this powerful approach. Drawing from Chapter 8 of Keith Bourne's "Unlocking Data with Generative AI and RAG," we explore how vector search drives smarter AI, faster results, and better customer experiences.

In this episode:

- Understand the fundamentals of vector similarity search and why it matters for modern AI-powered search

- Compare semantic, keyword, and hybrid search approaches and their business implications

- Explore the role of Approximate Nearest Neighbor (ANN) algorithms in scaling search performance

- Review leading tools and managed services like FAISS, Pinecone, and Google Vertex AI Vector Search

- Hear real-world use cases from retail, customer support, and AI applications

- Discuss challenges such as embedding drift, ranking, and infrastructure complexity

Key tools and technologies mentioned:

FAISS, Pinecone, Google Vertex AI Vector Search, LangChain EnsembleRetriever, Weaviate, pgvector, BM25Retriever, Reciprocal Rank Fusion, sentence_transformers, Chroma

Timestamps:

0:00 - Introduction and episode overview

2:15 - What is similarity searching with vectors?

5:00 - Why now: The rise of unstructured data and AI reliance

7:30 - Core concepts: Vector embeddings and distance metrics

10:00 - Comparing search approaches: Keyword vs Semantic vs Hybrid

13:00 - Under the hood: ANN algorithms and indexing techniques

15:30 - Real-world impact and business use cases

17:30 - Challenges and managing expectations

19:00 - Closing thoughts and resources

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Visit Memriq.ai for practical AI guides, research breakdowns, and leadership resources

Thanks for listening to Memriq Inference Digest - Leadership Edition. Stay tuned for more insights that empower strategic AI decision-making.

  continue reading

22 episodes

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