How AI Is Built

Today we are talking to Michael Günther, a senior machine learning scientist at Jina about his work on JINA Clip.

Some key points:

• Uni-modal embeddings convert a single type of input (text, images, audio) into vectors
• Multimodal embeddings learn a joint embedding space that can handle multiple types of input, enabling cross-modal search (e.g., searching images with text)
• Multimodal models can potentially learn richer representations of the world, including concepts that are difficult or impossible to put into words

Types of Text-Image Models

1. CLIP-like Models
   • Separate vision and text transformer models
   • Each tower maps inputs to a shared vector space
   • Optimized for efficient retrieval
2. Vision-Language Models
   • Process image patches as tokens
   • Use transformer architecture to combine image and text information
   • Better suited for complex document matching
3. Hybrid Models
   • Combine separate encoders with additional transformer components
   • Allow for more complex interactions between modalities
   • Example: Google's Magic Lens model

Training Insights from Jina CLIP

1. Key Learnings
   • Freezing the text encoder during training can significantly hinder performance
   • Short image captions limit the model's ability to learn rich text representations
   • Large batch sizes are crucial for training embedding models effectively
2. Training Process
   • Three-stage training approach: 
     • Stage 1: Training on image captions and text pairs
     • Stage 2: Adding longer image captions
     • Stage 3: Including triplet data with hard negatives

Practical Considerations

• Similarity Scales
  • Different modalities can produce different similarity value scales
  • Important to consider when combining multiple embedding types
  • Can affect threshold-based filtering
• Model Selection
  • Evaluate models based on relevant benchmarks
  • Consider the domain similarity between training data and intended use case
  • Assessment of computational requirements and efficiency needs

Future Directions

1. Areas for Development
   • More comprehensive benchmarks for multimodal tasks
   • Better support for semi-structured data
   • Improved handling of non-photographic images
2. Upcoming Developments at Jina AI
   • Multilingual support for Jina ColBERT
   • New version of text embedding models
   • Focus on complex multimodal search applications

Practical Applications

• E-commerce
  • Product search and recommendations
  • Combined text-image embeddings for better results
  • Synthetic data generation for fine-tuning
• Fine-tuning Strategies
  • Using click data and query logs
  • Generative pseudo-labeling for creating training data
  • Domain-specific adaptations

Key Takeaways for Engineers

1. Be aware of similarity value scales and their implications
2. Establish quantitative evaluation metrics before optimization
3. Consider model limitations (e.g., image resolution, text length)
4. Use performance optimizations like flash attention and activation checkpointing
5. Universal embedding models might not be optimal for specific use cases

Michael Guenther

• LinkedIn
• X (Twitter)
• Jina AI
• New Multilingual Embedding Modal

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Introduction to Uni-modal and Multimodal Embeddings 00:16 Exploring Multimodal Embeddings and Their Applications 01:06 Training Multimodal Embedding Models 02:21 Challenges and Solutions in Embedding Models 07:29 Advanced Techniques and Future Directions 29:19 Understanding Model Interference in Search Specialization 30:17 Fine-Tuning Jina CLIP for E-Commerce 32:18 Synthetic Data Generation and Pseudo-Labeling 33:36 Challenges and Learnings in Embedding Models 40:52 Future Directions and Takeaways

Imagine a world where data bottlenecks, slow data loaders, or memory issues on the VM don't hold back machine learning.

Machine learning and AI success depends on the speed you can iterate. LanceDB is here to to enable fast experiments on top of terabytes of unstructured data. It is the database for AI. Dive with us into how LanceDB was built, what went into the decision to use Rust as the main implementation language, the potential of AI on top of LanceDB, and more.

"LanceDB is the database for AI...to manage their data, to do a performant billion scale vector search."

“We're big believers in the composable data systems vision."

"You can insert data into LanceDB using Panda's data frames...to sort of really large 'embed the internet' kind of workflows."

"We wanted to create a new generation of data infrastructure that makes their [AI engineers] lives a lot easier."

"LanceDB offers up to 1,000 times faster performance than Parquet."

Change She:

• LinkedIn
• X (Twitter)

LanceDB:

• X (Twitter)
• GitHub
• Web
• Discord
• VectorDB Recipes

Nicolay Gerold:

• LinkedIn
• X (Twitter)

00:00 Introduction to Multimodal Embeddings
00:26 Challenges in Storage and Serving
02:51 LanceDB: The Solution for Multimodal Data
04:25 Interview with Chang She: Origins and Vision
10:37 Technical Deep Dive: LanceDB and Rust
18:11 Innovations in Data Storage Formats
19:00 Optimizing Performance in Lakehouse Ecosystems
21:22 Future Use Cases for LanceDB
26:04 Building Effective Recommendation Systems
32:10 Exciting Applications and Future Directions

Today’s guest is Mór Kapronczay. Mór is the Head of ML at superlinked. Superlinked is a compute framework for your information retrieval and feature engineering systems, where they turn anything into embeddings.

When most people think about embeddings, they think about ada, openai.

You just take your text and throw it in there.

But that’s too crude.

OpenAI embeddings are trained on the internet.

But your data set (most likely) is not the internet.

You have different nuances.

And you have more than just text.

So why not use it.

Some highlights:

1. Text Embeddings are Not a Magic Bullet

➡️ Pouring everything into a text embedding model won't yield magical results ➡️ Language is lossy - it's a poor compression method for complex information

1. Embedding Numerical Data

➡️ Direct number embeddings don't work well for vector search ➡️ Consider projecting number ranges onto a quarter circle ➡️ Apply logarithmic transforms for skewed distributions

1. Multi-Modal Embeddings

➡️ Create separate vector parts for different data aspects ➡️ Normalize individual parts ➡️ Weight vector parts based on importance

A Multi-Vector approach can help you understand the contributions of each modality or embedding and give you an easier time to fine-tune your retrieval system without fine-tuning your embedding models by tuning your vector database like you would a search database (like Elastic).

Mór Kapronczay

• LinkedIn
• Superlinked
• X (Twitter)

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Introduction to Embeddings 00:30 Beyond Text: Expanding Embedding Capabilities 02:09 Challenges and Innovations in Embedding Techniques 03:49 Unified Representations and Vector Computers 05:54 Embedding Complex Data Types 07:21 Recommender Systems and Interaction Data 08:59 Combining and Weighing Embeddings 14:58 Handling Numerical and Categorical Data 20:35 Optimizing Embedding Efficiency 22:46 Dynamic Weighting and Evaluation 24:35 Exploring AB Testing with Embeddings 25:08 Joint vs Separate Embedding Spaces 27:30 Understanding Embedding Dimensions 29:59 Libraries and Frameworks for Embeddings 32:08 Challenges in Embedding Models 33:03 Vector Database Connectors 34:09 Balancing Production and Updates 36:50 Future of Vector Search and Modalities 39:36 Building with Embeddings: Tips and Tricks 42:26 Concluding Thoughts and Next Steps

Today we have Jessica Talisman with us, who is working as an Information Architect at Adobe. She is (in my opinion) the expert on taxonomies and ontologies.

That’s what you will learn today in this episode of How AI Is Built. Taxonomies, ontologies, knowledge graphs.

Everyone is talking about them no-one knows how to build them.

But before we look into that, what are they good for in search?

Imagine a large corpus of academic papers. When a user searches for "machine learning in healthcare", the system can:

• Recognize "machine learning" as a subcategory of "artificial intelligence"
• Identify "healthcare" as a broad field with subfields like "diagnostics" and "patient care"
• We can use these to expand the query or narrow it down.
• We can return results that include papers on "neural networks for medical imaging" or "predictive analytics in patient outcomes", even if these exact phrases weren't in the search query
• We can also filter down and remove papers not tagged with AI that might just mention it in a side not.

So we are building the plumbing, the necessary infrastructure for tagging, categorization, query expansion and relexation, filtering.

So how can we build them?

1️⃣ Start with Industry Standards • Leverage established taxonomies (e.g., Google, GS1, IAB) • Audit them for relevance to your project • Use as a foundation, not a final solution

2️⃣ Customize and Fill Gaps • Adapt industry taxonomies to your specific domain • Create a "coverage model" for your unique needs • Mine internal docs to identify domain-specific concepts

3️⃣ Follow Ontology Best Practices • Use clear, unique primary labels for each concept • Include definitions to avoid ambiguity • Provide context for each taxonomy node

Jessica Talisman:

• LinkedIn

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Introduction to Taxonomies and Knowledge Graphs 02:03 Building the Foundation: Metadata to Knowledge Graphs 04:35 Industry Taxonomies and Coverage Models 06:32 Clustering and Labeling Techniques 11:00 Evaluating and Maintaining Taxonomies 31:41 Exploring Taxonomy Granularity 32:18 Differentiating Taxonomies for Experts and Users 33:35 Mapping and Equivalency in Taxonomies 34:02 Best Practices and Examples of Taxonomies 40:50 Building Multilingual Taxonomies 44:33 Creative Applications of Taxonomies 48:54 Overrated and Underappreciated Technologies 53:00 The Importance of Human Involvement in AI 53:57 Connecting with the Speaker 55:05 Final Thoughts and Takeaways

ColPali makes us rethink how we approach document processing.

ColPali revolutionizes visual document search by combining late interaction scoring with visual language models. This approach eliminates the need for extensive text extraction and preprocessing, handling messy real-world data more effectively than traditional methods.

In this episode, Jo Bergum, chief scientist at Vespa, shares his insights on how ColPali is changing the way we approach complex document formats like PDFs and HTML pages.

Introduction to ColPali:

• Combines late interaction scoring from Colbert with visual language model (PoliGemma)
• Represents screenshots of documents as multi-vector representations
• Enables searching across complex document formats (PDFs, HTML)
• Eliminates need for extensive text extraction and preprocessing

Advantages of ColPali:

• Handles messy, real-world data better than traditional methods
• Considers both textual and visual elements in documents
• Potential applications in various domains (finance, medical, legal)
• Scalable to large document collections with proper optimization

Jo Bergum:

• LinkedIn
• Vespa
• X (Twitter)
• PDF Retrieval with Vision Language Models
• Scaling ColPali to billions of PDFs with Vespa

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Messy Data in AI 01:19 Challenges in Search Systems 03:41 Understanding Representational Approaches 08:18 Dense vs Sparse Representations 19:49 Advanced Retrieval Models and ColPali 30:59 Exploring Image-Based AI Progress 32:25 Challenges and Innovations in OCR 33:45 Understanding ColPali and MaxSim 38:13 Scaling and Practical Applications of ColPali 44:01 Future Directions and Use Cases

Today, we're talking to Aamir Shakir, the founder and baker at mixedbread.ai, where he's building some of the best embedding and re-ranking models out there. We go into the world of rerankers, looking at how they can classify, deduplicate documents, prioritize LLM outputs, and delve into models like ColBERT.

We discuss:

• The role of rerankers in retrieval pipelines
• Advantages of late interaction models like ColBERT for interpretability
• Training rerankers vs. embedding models and their impact on performance
• Incorporating metadata and context into rerankers for enhanced relevance
• Creative applications of rerankers beyond traditional search
• Challenges and future directions in the retrieval space

Still not sure whether to listen? Here are some teasers:

• Rerankers can significantly boost your retrieval system's performance without overhauling your existing setup.
• Late interaction models like ColBERT offer greater explainability by allowing token-level comparisons between queries and documents.
• Training a reranker often yields a higher impact on retrieval performance than training an embedding model.
• Incorporating metadata directly into rerankers enables nuanced search results based on factors like recency and pricing.
• Rerankers aren't just for search—they can be used for zero-shot classification, deduplication, and prioritizing outputs from large language models.
• The future of retrieval may involve compound models capable of handling multiple modalities, offering a more unified approach to search.

Aamir Shakir:

• LinkedIn
• X (Twitter)
• Mixedbread.ai

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Introduction and Overview 00:25 Understanding Rerankers 01:46 Maxsim and Token-Level Embeddings 02:40 Setting Thresholds and Similarity 03:19 Guest Introduction: Aamir Shakir 03:50 Training and Using Rerankers (Episode Start) 04:50 Challenges and Solutions in Reranking 08:03 Future of Retrieval and Recommendation 26:05 Multimodal Retrieval and Reranking 38:04 Conclusion and Takeaways

Text embeddings have limitations when it comes to handling long documents and out-of-domain data.

Today, we are talking to Nils Reimers. He is one of the researchers who kickstarted the field of dense embeddings, developed sentence transformers, started HuggingFace’s Neural Search team and now leads the development of search foundational models at Cohere. Tbh, he has too many accolades to count off here.

We talk about the main limitations of embeddings:

• Failing out of domain
• Struggling with long documents
• Very hard to debug
• Hard to find formalize what actually is similar

Are you still not sure whether to listen? Here are some teasers:

• Interpreting embeddings can be challenging, and current models are not easily explainable.
• Fine-tuning is necessary to adapt embeddings to specific domains, but it requires careful consideration of the data and objectives.
• Re-ranking is an effective approach to handle long documents and incorporate additional factors like recency and trustworthiness.
• The future of embeddings lies in addressing scalability issues and exploring new research directions.

Nils Reimers:

• LinkedIn
• X (Twitter)
• Website
• Cohere

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

text embeddings, limitations, long documents, interpretation, fine-tuning, re-ranking, future research

00:00 Introduction and Guest Introduction 00:43 Early Work with BERT and Argument Mining 02:24 Evolution and Innovations in Embeddings 03:39 Constructive Learning and Hard Negatives 05:17 Training and Fine-Tuning Embedding Models 12:48 Challenges and Limitations of Embeddings 18:16 Adapting Embeddings to New Domains 22:41 Handling Long Documents and Re-Ranking 31:08 Combining Embeddings with Traditional ML 45:16 Conclusion and Upcoming Episodes

Hey! Welcome back.

Today we look at how we can get our RAG system ready for scale.

We discuss common problems and their solutions, when you introduce more users and more requests to your system.

For this we are joined by Nirant Kasliwal, the author of fastembed.

Nirant shares practical insights on metadata extraction, evaluation strategies, and emerging technologies like Colipali. This episode is a must-listen for anyone looking to level up their RAG implementations.

"Naive RAG has a lot of problems on the retrieval end and then there's a lot of problems on how LLMs look at these data points as well."

"The first 30 to 50% of gains are relatively quick. The rest 50% takes forever."

"You do not want to give the same answer about company's history to the co-founding CEO and the intern who has just joined."

"Embedding similarity is the signal on which you want to build your entire search is just not quite complete."

Key insights:

• Naive RAG often fails due to limitations of embeddings and LLMs' sensitivity to input ordering.
• Query profiling and expansion: 
  • Use clustering and tools like latent Scope to identify problematic query types
  • Expand queries offline and use parallel searches for better results
• Metadata extraction: 
  • Extract temporal, entity, and other relevant information from queries
  • Use LLMs for extraction, with checks against libraries like Stanford NLP
• User personalization: 
  • Include user role, access privileges, and conversation history
  • Adapt responses based on user expertise and readability scores
• Evaluation and improvement: 
  • Create synthetic datasets and use real user feedback
  • Employ tools like DSPY for prompt engineering
• Advanced techniques: 
  • Query routing based on type and urgency
  • Use smaller models (1-3B parameters) for easier iteration and error spotting
  • Implement error handling and cross-validation for extracted metadata

Nirant Kasliwal:

• X (Twitter)
• LinkedIn
• Search in the LLM Era for AI Engineers (course)

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

query understanding, AI-powered search, Lambda Mart, e-commerce ranking, networking, experts, recommendation, search

In this episode of How AI is Built, Nicolay Gerold interviews Doug Turnbull, a search engineer at Reddit and author on “Relevant Search”. They discuss how methods and technologies, including large language models (LLMs) and semantic search, contribute to relevant search results.

Key Highlights:

• Defining relevance is challenging and depends heavily on user intent and context
• Combining multiple search techniques (keyword, semantic, etc.) in tiers can improve results
• LLMs are emerging as a powerful tool for augmenting traditional search approaches
• Operational concerns often drive architectural decisions in large-scale search systems
• Underappreciated techniques like LambdaMART may see a resurgence

Key Quotes:

"There's not like a perfect measure or definition of what a relevant search result is for a given application. There are a lot of really good proxies, and a lot of really good like things, but you can't just like blindly follow the one objective, if you want to build a good search product." - Doug Turnbull

"I think 10 years ago, what people would do is they would just put everything in Solr, Elasticsearch or whatever, and they would make the query to Elasticsearch pretty complicated to rank what they wanted... What I see people doing more and more these days is that they'll use each retrieval source as like an independent piece of infrastructure." - Doug Turnbull on the evolution of search architecture

"Honestly, I feel like that's a very practical and underappreciated thing. People talk about RAG and I talk, I call this GAR - generative AI augmented retrieval, so you're making search smarter with generative AI." - Doug Turnbull on using LLMs to enhance search

"LambdaMART and gradient boosted decision trees are really powerful, especially for when you're expressing your re-ranking as some kind of structured learning problem... I feel like we'll see that and like you're seeing papers now where people are like finding new ways of making BM25 better." - Doug Turnbull on underappreciated techniques

Doug Turnbull

• LinkedIn
• X (Twitter)
• Web

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Chapters

00:00 Introduction and Guest Introduction 00:52 Understanding Relevant Search Results 01:18 Search Behavior on Social Media 02:14 Challenges in Defining Relevance 05:12 Query Understanding and Ranking Signals 10:57 Evolution of Search Technologies 15:15 Combining Search Techniques 21:49 Leveraging LLMs and Embeddings 25:49 Operational Considerations in Search Systems 39:09 Concluding Thoughts and Future Directions

In this episode, we talk data-driven search optimizations with Charlie Hull.

Charlie is a search expert from Open Source Connections. He has built Flax, one of the leading open source search companies in the UK, has written “Searching the Enterprise”, and is one of the main voices on data-driven search.

We discuss strategies to improve search systems quantitatively and much more.

Key Points:

1. Relevance in search is subjective and context-dependent, making it challenging to measure consistently.
2. Common mistakes in assessing search systems include overemphasizing processing speed and relying solely on user complaints.
3. Three main methods to measure search system performance: 
   • Human evaluation
   • User interaction data analysis
   • AI-assisted judgment (with caution)
4. Importance of balancing business objectives with user needs when optimizing search results.
5. Technical components for assessing search systems: 
   • Query logs analysis
   • Source data quality examination
   • Test queries and cases setup

Resources mentioned:

• Quepid: Open-source tool for search quality testing
• Haystack conference: Upcoming event in Berlin (September 30 - October 1)
• Relevance Slack community
• OpenSource Connections

Charlie Hull:

• LinkedIn
• X (Twitter)

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

search results, search systems, assessing, evaluation, improvement, data quality, user behavior, proactive, test dataset, search engine optimization, SEO, search quality, metadata, query classification, user intent, search results, metrics, business objectives, user objectives, experimentation, continuous improvement, data modeling, embeddings, machine learning, information retrieval

00:00 Introduction
01:35 Challenges in Measuring Search Relevance
02:19 Common Mistakes in Search System Assessment
03:22 Methods to Measure Search System Performance
04:28 Human Evaluation in Search Systems
05:18 Leveraging User Interaction Data
06:04 Implementing AI for Search Evaluation
09:14 Technical Components for Assessing Search Systems
12:07 Improving Search Quality Through Data Analysis
17:16 Proactive Search System Monitoring
24:26 Balancing Business and User Objectives in Search
25:08 Search Metrics and KPIs: A Contract Between Teams
26:56 The Role of Recency and Popularity in Search Algorithms
28:56 Experimentation: The Key to Optimizing Search
30:57 Offline Search Labs and A/B Testing
34:05 Simple Levers to Improve Search
37:38 Data Modeling and Its Importance in Search
43:29 Combining Keyword and Vector Search
44:24 Bridging the Gap Between Machine Learning and Information Retrieval
47:13 Closing Remarks and Contact Information

Welcome back to How AI Is Built. 

We have got a very special episode to kick off season two. 

Daniel Tunkelang is a search consultant currently working with Algolia. He is a leader in the field of information retrieval, recommender systems, and AI-powered search. He worked for Canva, Algolia, Cisco, Gartner, Handshake, to pick a few. 

His core focus is query understanding.  

**Query understanding is about focusing less on the results and more on the query.** The query of the user is the first-class citizen. It is about figuring out what the user wants and than finding, scoring, and ranking results based on it. So most of the work happens before you hit the database. 

**Key Takeaways:**

- The "bag of documents" model for queries and "bag of queries" model for documents are useful approaches for representing queries and documents in search systems.
- Query specificity is an important factor in query understanding. It can be measured using cosine similarity between query vectors and document vectors.
- Query classification into broad categories (e.g., product taxonomy) is a high-leverage technique for improving search relevance and can act as a guardrail for query expansion and relaxation.
- Large Language Models (LLMs) can be useful for search, but simpler techniques like query similarity using embeddings can often solve many problems without the complexity and cost of full LLM implementations.
- Offline processing to enhance document representations (e.g., filling in missing metadata, inferring categories) can significantly improve search quality.

**Daniel Tunkelang**

- [LinkedIn](https://www.linkedin.com/in/dtunkelang/)
- [Medium](https://queryunderstanding.com/)

**Nicolay Gerold:**

- [⁠LinkedIn⁠](https://www.linkedin.com/in/nicolay-gerold/)
- [⁠X (Twitter)](https://twitter.com/nicolaygerold)
- [Substack](https://nicolaygerold.substack.com/)

Query understanding, search relevance, bag of documents, bag of queries, query specificity, query classification, named entity recognition, pre-retrieval processing, caching, large language models (LLMs), embeddings, offline processing, metadata enhancement, FastText, MiniLM, sentence transformers, visualization, precision, recall

[00:00:00] 1. Introduction to Query Understanding

• Definition and importance in search systems
• Evolution of query understanding techniques

[00:05:30] 2. Query Representation Models

• The "bag of documents" model for queries
• The "bag of queries" model for documents
• Advantages of holistic query representation

[00:12:00] 3. Query Specificity and Classification

• Measuring query specificity using cosine similarity
• Importance of query classification in search relevance
• Implementing and leveraging query classifiers

[00:19:30] 4. Named Entity Recognition in Query Understanding

• Role of NER in query processing
• Challenges with unique or tail entities

[00:24:00] 5. Pre-Retrieval Query Processing

• Importance of early-stage query analysis
• Balancing computational resources and impact

[00:28:30] 6. Performance Optimization Techniques

• Caching strategies for query understanding
• Offline processing for document enhancement

[00:33:00] 7. Advanced Techniques: Embeddings and Language Models

• Using embeddings for query similarity
• Role of Large Language Models (LLMs) in search
• When to use simpler techniques vs. complex models

[00:39:00] 8. Practical Implementation Strategies

• Starting points for engineers new to query understanding
• Tools and libraries for query understanding (FastText, MiniLM, etc.)
• Balancing precision and recall in search systems

[00:44:00] 9. Visualization and Analysis of Query Spaces

• Discussion on t-SNE, UMAP, and other visualization techniques
• Limitations and alternatives to embedding visualizations

[00:47:00] 10. Future Directions and Closing Thoughts - Emerging trends in query understanding - Key takeaways for search system engineers

[00:53:00] End of Episode

Today we are launching the season 2 of How AI Is Built.

The last few weeks, we spoke to a lot of regular listeners and past guests and collected feedback. Analyzed our episode data. And we will be applying the learnings to season 2.

This season will be all about search.

We are trying to make it better, more actionable, and more in-depth. The goal is that at the end of this season, you have a full-fleshed course on search in podcast form, which mini-courses on specific elements like RAG.

We will be talking to experts from information retrieval, information architecture, recommendation systems, and RAG; from academia and industry. Fields that do not really talk to each other.

We will try to unify and transfer the knowledge and give you a full tour of search, so you can build your next search application or feature with confidence.

We will be talking to Charlie Hull on how to systematically improve search systems, with Nils Reimers on the fundamental flaws of embeddings and how to fix them, with Daniel Tunkelang on how to actually understand the queries of the user, and many more.

We will try to bridge the gaps. How to use decades of research and practice in iteratively improving traditional search and apply it to RAG. How to take new methods from recommendation systems and vector databases and bring it into traditional search systems. How to use all of the different methods as search signals and combine them to deliver the results your user actually wants.

We will be using two types of episodes:

1. Traditional deep dives, like we have done them so far. Each one will dive into one specific topic within search interviewing an expert on that topic.
2. Supplementary episodes, which answer one additional question; often either complementary or precursory knowledge for the episode, which we did not get to in the deep dive.

We will be starting with episodes next week, looking at the first, last, and overarching action in search: understanding user intent and understanding the queries with Daniel Tunkelang.

I am really excited to kick this off.

I would love to hear from you:

• What would you love to learn in this season?
• What guest should I have on?
• What topics should I make a deep dive on (try to be specific)?

Yeah, let me know in the comments or just slide into my DMs on Twitter or LinkedIn.

I am looking forward to hearing from you guys.

I want to try to be more interactive. So anytime you encounter anything unclear or any question pops up in one of the episode, give me a shout and I will try to answer it to you and to everyone.

Enough of me rambling. Let’s kick this off. I will see you next Thursday, when we start with query understanding.

Shoot me a message and stay up to date:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

In this episode of "How AI is Built," host Nicolay Gerold interviews Jonathan Yarkoni, founder of Reach Latent. Jonathan shares his expertise in extracting value from unstructured data using AI, discussing challenging projects, the impact of ChatGPT, and the future of generative AI. From weather prediction to legal tech, Jonathan provides valuable insights into the practical applications of AI across various industries.

Key Takeaways

• Generative AI projects often require less data cleaning due to the models' tolerance for "dirty" data, allowing for faster implementation in some cases.
• The success of AI projects post-delivery is ensured through monitoring, but automatic retraining of generative AI applications is not yet common due to evaluation challenges.
• Industries ripe for AI disruption include text-heavy fields like legal, education, software engineering, and marketing, as well as biotech and entertainment.
• The adoption of AI is expected to occur in waves, with 2024 likely focusing on internal use cases and 2025 potentially seeing more customer-facing applications as models improve.
• Synthetic data generation, using models like GPT-4, can be a valuable approach for training AI systems when real data is scarce or sensitive.
• Evaluation frameworks like RAGAS and custom metrics are essential for assessing the quality of synthetic data and AI model outputs.
• Jonathan’s ideal tech stack for generative AI projects includes tools like Instructor, Guardrails, Semantic Routing, DSPY, LangChain, and LlamaIndex, with a growing emphasis on evaluation stacks.

Key Quotes

"I think we're going to see another wave in 2024 and another one in 2025. And people are familiarized. That's kind of the wave of 2023. 2024 is probably still going to be a lot of internal use cases because it's a low risk environment and there was a lot of opportunity to be had."

"To really get to production reliably, we have to have these tools evolve further and get more standardized so people can still use the old ways of doing production with the new technology."

Jonathan Yarkoni

• LinkedIn
• YouTube
• X (Twitter)
• Reach Latent

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Chapters

00:00 Introduction: Extracting Value from Unstructured Data 
03:16 Flexible Tailoring Solutions to Client Needs 
05:39 Monitoring and Retraining Models in the Evolving AI Landscape 
09:15 Generative AI: Disrupting Industries and Unlocking New Possibilities 
17:47 Balancing Immediate Results and Cutting-Edge Solutions in AI Development 
28:29 Dream Tech Stack for Generative AI

unstructured data, textual data, automation, weather prediction, data cleaning, chat GPT, AI disruption, legal, education, software engineering, marketing, biotech, immediate results, cutting-edge solutions, tech stack

This episode of "How AI Is Built" is all about data processing for AI. Abhishek Choudhary and Nicolay discuss Spark and alternatives to process data so it is AI-ready.

Spark is a distributed system that allows for fast data processing by utilizing memory. It uses a dataframe representation "RDD" to simplify data processing.

When should you use Spark to process your data for your AI Systems?

→ Use Spark when:

• Your data exceeds terabytes in volume
• You expect unpredictable data growth
• Your pipeline involves multiple complex operations
• You already have a Spark cluster (e.g., Databricks)
• Your team has strong Spark expertise
• You need distributed computing for performance
• Budget allows for Spark infrastructure costs

→ Consider alternatives when:

• Dealing with datasets under 1TB
• In early stages of AI development
• Budget constraints limit infrastructure spending
• Simpler tools like Pandas or DuckDB suffice

Spark isn't always necessary. Evaluate your specific needs and resources before committing to a Spark-based solution for AI data processing.

In today’s episode of How AI Is Built, Abhishek and I discuss data processing:

• When to use Spark vs. alternatives for data processing
• Key components of Spark: RDDs, DataFrames, and SQL
• Integrating AI into data pipelines
• Challenges with LLM latency and consistency
• Data storage strategies for AI workloads
• Orchestration tools for data pipelines
• Tips for making LLMs more reliable in production

Abhishek Choudhary:

• LinkedIn
• GitHub
• X (Twitter)

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

In this episode, Nicolay talks with Rahul Parundekar, founder of AI Hero, about the current state and future of AI agents. Drawing from over a decade of experience working on agent technology at companies like Toyota, Rahul emphasizes the importance of focusing on realistic, bounded use cases rather than chasing full autonomy.

They dive into the key challenges, like effectively capturing expert workflows and decision processes, delivering seamless user experiences that integrate into existing routines, and managing costs through techniques like guardrails and optimized model choices. The conversation also explores potential new paradigms for agent interactions beyond just chat.

Key Takeaways:

• Agents need to focus on realistic use cases rather than trying to be fully autonomous. Enterprises are unlikely to allow agents full autonomy anytime soon.
• Capturing the logic and workflows in the user's head is the key challenge. Shadowing experts and having them demonstrate workflows is more effective than asking them to document processes.
• User experience is crucial - agents must integrate seamlessly into existing user workflows without major disruptions. Interfaces beyond just chat may be needed.
• Cost control is important - techniques like guardrails, context windowing, model choice optimization, and dev vs production modes can help manage costs.
• New paradigms beyond just chat could be powerful - e.g. workflow specification, state/declarative definition of desired end-state.
• Prompt engineering and dynamic prompt improvement based on feedback remain an open challenge.

Key Quotes:

• "Empowering users to create their own workflows is essential for effective agent usage."
• "Capturing workflows accurately is a significant challenge in agent development."
• "Preferences, right? So a lot of the work becomes like, hey, can you do preference learning for this user so that the next time the user doesn't have to enter the same information again, things like that."

Rahul Parundekar:

• AI Hero
• AI Hero Docs

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Exploring the Potential of Autonomous Agents

02:23 Challenges of Accuracy and Repeatability in Agents

08:31 Capturing User Workflows and Improving Prompts

13:37 Tech Stack for Implementing Agents in the Enterprise

agent development, determinism, user experience, agent paradigms, private use, human-agent interaction, user workflows, agent deployment, human-in-the-loop, LLMs, declarative ways, scalability, AI Hero

In this conversation, Nicolay and Richmond Alake discuss various topics related to building AI agents and using MongoDB in the AI space. They cover the use of agents and multi-agents, the challenges of controlling agent behavior, and the importance of prompt compression.

When you are building agents. Build them iteratively. Start with simple LLM calls before moving to multi-agent systems.

Main Takeaways:

1. Prompt Compression: Using techniques like prompt compression can significantly reduce the cost of running LLM-based applications by reducing the number of tokens sent to the model. This becomes crucial when scaling to production.
2. Memory Management: Effective memory management is key for building reliable agents. Consider different memory components like long-term memory (knowledge base), short-term memory (conversation history), semantic cache, and operational data (system logs). Store each in separate collections for easy access and reference.
3. Performance Optimization: Optimize performance across multiple dimensions - output quality (by tuning context and knowledge base), latency (using semantic caching), and scalability (using auto-scaling databases like MongoDB).
4. Prompting Techniques: Leverage prompting techniques like ReAct (observe, plan, act) and structured prompts (JSON, pseudo-code) to improve agent predictability and output quality.
5. Experimentation: Continuous experimentation is crucial in this rapidly evolving field. Try different frameworks (LangChain, Crew AI, Haystack), models (Claude, Anthropic, open-source), and techniques to find the best fit for your use case.

Richmond Alake:

• LinkedIn
• Medium
• Find Richmond on MongoDB
• X (Twitter)
• YouTube
• GenAI Showcase MongoDB
• MongoDB AI Stack

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Reducing the Scope of AI Agents

01:55 Seamless Data Ingestion 

03:20 Challenges and Considerations in Implementing Multi-Agents

06:05 Memory Modeling for Robust Agents with MongoDB

15:05 Performance Optimization in AI Agents

18:19 RAG Setup

AI agents, multi-agents, prompt compression, MongoDB, data storage, data ingestion, performance optimization, tooling, generative AI

In this episode, Kirk Marple, CEO and founder of Graphlit, shares his expertise on building efficient data integrations.

Kirk breaks down his approach using relatable concepts:

1. The "Two-Sided Funnel": This model streamlines data flow by converting various data sources into a standard format before distributing it.
2. Universal Data Streams: Kirk explains how he transforms diverse data into a single, manageable stream of information.
3. Parallel Processing: Learn about the "competing consumer model" that allows for faster data handling.
4. Building Blocks for Success: Discover the importance of well-defined interfaces and actor models in creating robust data systems.
5. Tech Talk: Kirk discusses data normalization techniques and the potential shift towards a more streamlined "Kappa architecture."
6. Reusable Patterns: Find out how Kirk's methods can speed up the integration of new data sources.

Kirk Marple:

• LinkedIn
• X (Twitter)
• Graphlit
• Graphlit Docs

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Chapters

00:00 Building Integrations into Different Tools

00:44 The Two-Sided Funnel Model for Data Flow

04:07 Using Well-Defined Interfaces for Faster Integration

04:36 Managing Feeds and State with Actor Models

06:05 The Importance of Data Normalization

10:54 Tech Stack for Data Flow

11:52 Progression towards a Kappa Architecture

13:45 Reusability of Patterns for Faster Integration

data integration, data sources, data flow, two-sided funnel model, canonical format, stream of ingestible objects, competing consumer model, well-defined interfaces, actor model, data normalization, tech stack, Kappa architecture, reusability of patterns

In our latest episode, we sit down with Derek Tu, Founder and CEO of Carbon, a cutting-edge ETL tool designed specifically for large language models (LLMs).

Carbon is streamlining AI development by providing a platform for integrating unstructured data from various sources, enabling businesses to build innovative AI applications more efficiently while addressing data privacy and ethical concerns.

• "I think people are trying to optimize around the chunking strategy... But for me, that seems a bit maybe not focusing on the right area of optimization. These embedding models themselves have gone just like, so much more advanced over the past five to 10 years that regardless of what representation you're passing in, they do a pretty good job of being able to understand that information semantically and returning the relevant chunks." - Derek Tu on the importance of embedding models over chunking strategies
• "If you are cost conscious and if you're worried about performance, I would definitely look at quantizing your embeddings. I think we've probably been able to, I don't have like the exact numbers here, but I think we might be saving at least half, right, in storage costs by quantizing everything." - Derek Tu on optimizing costs and performance with vector databases

Derek Tu:

• LinkedIn
• Carbon

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Key Takeaways:

• Understand your data sources: Before building your ETL pipeline, thoroughly assess the various data sources you'll be working with, such as Slack, Email, Google Docs, and more. Consider the unique characteristics of each source, including data format, structure, and metadata.
• Normalize and preprocess data: Develop strategies to normalize and preprocess the unstructured data from different sources. This may involve parsing, cleaning, and transforming the data into a standardized format that can be easily consumed by your AI models.
• Experiment with chunking strategies: While there's no one-size-fits-all approach to chunking, it's essential to experiment with different strategies to find what works best for your specific use case. Consider factors like data format, structure, and the desired granularity of the chunks.
• Leverage metadata and tagging: Metadata and tagging can play a crucial role in organizing and retrieving relevant data for your AI models. Implement mechanisms to capture and store important metadata, such as document types, topics, and timestamps, and consider using AI-powered tagging to automatically categorize your data.
• Choose the right embedding model: Embedding models have advanced significantly in recent years, so focus on selecting the right model for your needs rather than over-optimizing chunking strategies. Consider factors like model performance, dimensionality, and compatibility with your data types.
• Optimize vector database usage: When working with vector databases, consider techniques like quantization to reduce storage costs and improve performance. Experiment with different configurations and settings to find the optimal balance for your specific use case.

00:00 Introduction and Optimizing Embedding Models

03:00 The Evolution of Carbon and Focus on Unstructured Data

06:19 Customer Progression and Target Group

09:43 Interesting Use Cases and Handling Different Data Representations

13:30 Chunking Strategies and Normalization

20:14 Approach to Chunking and Choosing a Vector Database

23:06 Tech Stack and Recommended Tools

28:19 Future of Carbon: Multimodal Models and Building a Platform

Carbon, LLMs, RAG, chunking, data processing, global customer base, GDPR compliance, AI founders, AI agents, enterprises

In this episode, Nicolay sits down with Hugo Lu, founder and CEO of Orchestra, a modern data orchestration platform. As data pipelines and analytics workflows become increasingly complex, spanning multiple teams, tools and cloud services, the need for unified orchestration and visibility has never been greater.

Orchestra is a serverless data orchestration tool that aims to provide a unified control plane for managing data pipelines, infrastructure, and analytics across an organization's modern data stack.

The core architecture involves users building pipelines as code which then run on Orchestra's serverless infrastructure. It can orchestrate tasks like data ingestion, transformation, AI calls, as well as monitoring and getting analytics on data products. All with end-to-end visibility, data lineage and governance even when organizations have a scattered, modular data architecture across teams and tools.

Key Quotes:

• Find the right level of abstraction when building data orchestration tasks/workflows. "I think the right level of abstraction is always good. I think like Prefect do this really well, right? Their big sell was, just put a decorator on a function and it becomes a task. That is a great idea. You know, just make tasks modular and have them do all the boilerplate stuff like error logging, monitoring of data, all of that stuff.”
• Modularize data pipeline components: "It's just around understanding what that dev workflow should look like. I think it should be a bit more modular." Having a modular architecture where different components like data ingestion, transformation, model training are decoupled allows better flexibility and scalability.
• Adopt a streaming/event-driven architecture for low-latency AI use cases: "If you've got an event-driven architecture, then, you know, that's not what you use an orchestration tool for...if you're having a conversation with a chatbot, like, you know, you're sending messages, you're sending events, you're getting a response back. That I would argue should be dealt with by microservices."

Hugo Lu:

• LinkedIn
• Newsletter
• Orchestra
• Orchestra Docs

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Introduction to Orchestra and its Focus on Data Products

08:03 Unified Control Plane for Data Stack and End-to-End Control

14:42 Use Cases and Unique Applications of Orchestra

19:31 Retaining Existing Dev Workflows and Best Practices in Orchestra

22:23 Event-Driven Architectures and Monitoring in Orchestra

23:49 Putting Data Products First and Monitoring Health and Usage

25:40 The Future of Data Orchestration: Stream-Based and Cost-Effective

data orchestration, Orchestra, serverless architecture, versatility, use cases, maturity levels, challenges, AI workloads

Ever wondered how AI systems handle images and videos, or how they make lightning-fast recommendations? Tune in as Nicolay chats with Zain Hassan, an expert in vector databases from Weaviate. They break down complex topics like quantization, multi-vector search, and the potential of multimodal search, making them accessible for all listeners. Zain even shares a sneak peek into the future, where vector databases might connect our brains with computers!

Zain Hasan:

• LinkedIn
• X (Twitter)
• Weaviate

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Key Insights:

• Vector databases can handle not just text, but also image, audio, and video data
• Quantization is a powerful technique to significantly reduce costs and enable in-memory search
• Binary quantization allows efficient brute force search for smaller datasets
• Multi-vector search enables retrieval of heterogeneous data types within the same index
• The future lies in multimodal search and recommendations across different senses
• Brain-computer interfaces and EEG foundation models are exciting areas to watch

Key Quotes:

• "Vector databases are pretty much the commercialization and the productization of representation learning."
• "I think quantization, it builds on the assumption that there is still noise in the embeddings. And if I'm looking, it's pretty similar as well to the thought of Matryoshka embeddings that I can reduce the dimensionality."
• "Going from text to multimedia in vector databases is really simple."
• "Vector databases allow you to take all the advances that are happening in machine learning and now just simply turn a switch and use them for your application."

Chapters

00:00 - 01:24 Introduction

01:24 - 03:48 Underappreciated aspects of vector databases

03:48 - 06:06 Quantization trade-offs and techniques

• Various quantization techniques: binary quantization, product quantization, scalar quantization

06:06 - 08:24 Binary quantization

• Reducing vectors from 32-bits per dimension down to 1-bit
• Enables efficient in-memory brute force search for smaller datasets
• Requires normally distributed data between negative and positive values

08:24 - 10:44 Product quantization and other techniques

• Alternative to binary quantization, segments vectors and clusters each segment
• Scalar quantization reduces vectors to 8-bits per dimension

10:44 - 13:08 Quantization as a "superpower" to reduce costs

13:08 - 15:34 Comparing quantization approaches

15:34 - 17:51 Placing vector databases in the database landscape

17:51 - 20:12 Pruning unused vectors and nodes

20:12 - 22:37 Improving precision beyond similarity thresholds

22:37 - 25:03 Multi-vector search

25:03 - 27:11 Impact of vector databases on data interaction

27:11 - 29:35 Interesting and weird use cases

29:35 - 32:00 Future of multimodal search and recommendations

32:00 - 34:22 Extending recommendations to user data

34:22 - 36:39 What's next for Weaviate

36:39 - 38:57 Exciting technologies beyond vector databases and LLMs

vector databases, quantization, hybrid search, multi-vector support, representation learning, cost reduction, memory optimization, multimodal recommender systems, brain-computer interfaces, weather prediction models, AI applications

In this episode of "How AI is Built", data architect Anjan Banerjee provides an in-depth look at the world of data architecture and building complex AI and data systems. Anjan breaks down the basics using simple analogies, explaining how data architecture involves sorting, cleaning, and painting a picture with data, much like organizing Lego bricks to build a structure.

Summary by Section

Introduction

• Anjan Banerjee, a data architect, discusses building complex AI and data systems
• Explains the basics of data architecture using Lego and chat app examples

Sources and Tools

• Identifying data sources is the first step in designing a data architecture
• Pick the right tools to extract data based on use cases (block storage for images, time series DB, etc.)
• Use one tool for most activities if possible, but specialized tools offer benefits
• Multi-modal storage engines are gaining popularity (Snowflake, Databricks, BigQuery)

Airflow and Orchestration

• Airflow is versatile but has a learning curve; good for orgs with Python/data engineering skills
• For less technical orgs, GUI-based tools like Talend, Alteryx may be better
• AWS Step Functions and managed Airflow are improving native orchestration capabilities
• For multi-cloud, prefer platform-agnostic tools like Astronomer, Prefect, Airbyte

AI and Data Processing

• ML is key for data-intensive use cases to avoid storing/processing petabytes in cloud
• TinyML and edge computing enable ML inference on device (drones, manufacturing)
• Cloud batch processing still dominates for user targeting, recommendations

Data Lakes and Storage

• Storage choice depends on data types, use cases, cloud ecosystem
• Delta Lake excels at data versioning and consistency; Iceberg at partitioning and metadata
• Pulling data into separate system often needed for advanced analytics beyond source system

Data Quality and Standardization

• "Poka-yoke" error-proofing of input screens is vital for downstream data quality
• Impose data quality rules and unified schemas (e.g. UTC timestamps) during ingestion
• Complexity arises with multi-region compliance (GDPR, CCPA) requiring encryption, sanitization

Hot Takes and Wishes

• Snowflake is overhyped; great UX but costly at scale. Databricks is preferred.
• Automated data set joining and entity resolution across systems would be a game-changer

Anjan Banerjee:

• LinkedIn

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Understanding Data Architecture

12:36 Choosing the Right Tools

20:36 The Benefits of Serverless Functions

21:34 Integrating AI in Data Acquisition

24:31 The Trend Towards Single Node Engines

26:51 Choosing the Right Database Management System and Storage

29:45 Adding Additional Storage Components

32:35 Reducing Human Errors for Better Data Quality

39:07 Overhyped and Underutilized Tools

Data architecture, AI, data systems, data sources, data extraction, data storage, multi-modal storage engines, data orchestration, Airflow, edge computing, batch processing, data lakes, Delta Lake, Iceberg, data quality, standardization, poka-yoke, compliance, entity resolution

Jorrit Sandbrink, a data engineer specializing on open table formats, discusses the advantages of decoupling storage and compute, the importance of choosing the right table format, and strategies for optimizing your data pipelines. This episode is full of practical advice for anyone looking to build a high-performance data analytics platform.

• Lake house architecture: A blend of data warehouse and data lake, addressing their shortcomings and providing a unified platform for diverse workloads.
• Key components and decisions: Storage options (cloud or on-prem), table formats (Delta Lake, Iceberg, Apache Hoodie), and query engines (Apache Spark, Polars).
• Optimizations: Partitioning strategies, file size considerations, and auto-optimization tools for efficient data layout and query performance.
• Orchestration tools: Airflow, Dagster, Prefect, and their roles in triggering and managing data pipelines.
• Data ingress with DLT: An open-source Python library for building data pipelines, focusing on efficient data extraction and loading.

Key Takeaways:

• Lake houses offer a powerful and flexible architecture for modern data analytics.
• Open-source solutions provide cost-effective and customizable alternatives.
• Carefully consider your specific use cases and preferences when choosing tools and components.
• Tools like DLT simplify data ingress and can be easily integrated with serverless functions.
• The data landscape is constantly evolving, so staying informed about new tools and trends is crucial.

Sound Bites

"The Lake house is sort of a modular setup where you decouple the storage and the compute." "A lake house is an architecture, an architecture for data analytics platforms." "The most popular table formats for a lake house are Delta, Iceberg, and Apache Hoodie."

Jorrit Sandbrink:

• LinkedIn
• dlt

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Chapters

00:00 Introduction to the Lake House Architecture

03:59 Choosing Storage and Table Formats

06:19 Comparing Compute Engines

21:37 Simplifying Data Ingress

25:01 Building a Preferred Data Stack

lake house, data analytics, architecture, storage, table format, query execution engine, document store, DuckDB, Polars, orchestration, Airflow, Dexter, DLT, data ingress, data processing, data storage

Kirk Marple, CEO and founder of Graphlit, discusses the evolution of his company from a data cataloging tool to an platform designed for ETL (Extract, Transform, Load) and knowledge retrieval for Large Language Models (LLMs). Graphlit empowers users to build custom applications on top of its API that go beyond naive RAG.

Key Points:

• Knowledge Graphs: Graphlet utilizes knowledge graphs as a filtering layer on top of keyword metadata and vector search, aiding in information retrieval.
• Storage for KGs: A single piece of content in their data model resides across multiple systems: a document store with JSON, a graph node, and a search index. This hybrid approach creates a virtual entity with representations in different databases.
• Entity Extraction: Azure Cognitive Services and other models are employed to extract entities from text for improved understanding.
• Metadata-first approach: The metadata-first strategy involves extracting comprehensive metadata from various sources, ensuring it is canonicalized and filterable. This approach aids in better indexing and retrieval of data, crucial for effective RAG.
• Challenges: Entity resolution and deduplication remain significant challenges in knowledge graph development.

Notable Quotes:

• "Knowledge graphs is a filtering [mechanism]...but then I think also the kind of spidering and pulling extra content in is the other place this comes into play."
• "Knowledge graphs to me are kind of like index per se...you're providing a new type of index on top of that."
• "[For RAG]...you have to find constraints to make it workable."
• "Entity resolution, deduping, I think is probably the number one thing."
• "I've essentially built a connector infrastructure that would be like a FiveTran or something that Airflow would have..."
• "One of the reasons is because we're a platform as a service, the burstability of it is really important. We can spin up to a hundred instances without any problem, and we don't have to think about it."
• "Once cost and performance become a no-brainer, we're going to start seeing LLMs be more of a compute tool. I think that would be a game-changer for how applications are built in the future."

Kirk Marple:

• LinkedIn
• X (Twitter)
• Graphlit
• Graphlit Docs

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Chapters

00:00 Graphlit’s Hybrid Approach 02:23 Use Cases and Transition to Graphlit 04:19 Knowledge Graphs as a Filtering Mechanism 13:23 Using Gremlin for Querying the Graph 32:36 XML in Prompts for Better Segmentation 35:04 The Future of LLMs and Graphlit 36:25 Getting Started with Graphlit

Graphlit, knowledge graphs, AI, document store, graph database, search index co-pilot, entity extraction, Azure Cognitive Services, XML, event-driven architecture, serverless architecture graph rag, developer portal

From Problem to Requirements to Architecture.

In this episode, Nicolay Gerold and Jon Erich Kemi Warghed discuss the landscape of data engineering, sharing insights on selecting the right tools, implementing effective data governance, and leveraging powerful concepts like software-defined assets. They discuss the challenges of keeping up with the ever-evolving tech landscape and offer practical advice for building sustainable data platforms. Tune in to discover how to simplify complex data pipelines, unlock the power of orchestration tools, and ultimately create more value from your data.

• "Don't overcomplicate what you're actually doing."
• "Getting your basic programming software development skills down is super important to becoming a good data engineer."
• "Who has time to learn 500 new tools? It's like, this is not humanly possible anymore."

Key Takeaways:

• Data Governance: Data governance is about transparency and understanding the data you have. It's crucial for organizations as they scale and data becomes more complex. Tools like dbt and Dagster can help achieve this.
• Open Source Tooling: When choosing open source tools, assess their backing, commit frequency, community support, and ease of use.
• Agile Data Platforms: Focus on the capabilities you want to enable and prioritize solving the core problems of your data engineers and analysts.
• Software Defined Assets: This concept, exemplified by Dagster, shifts the focus from how data is processed to what data should exist. This change in mindset can greatly simplify data orchestration and management.
• The Importance of Fundamentals: Strong programming and software development skills are crucial for data engineers, and understanding the basics of data management and orchestration is essential for success.
• The Importance of Versioning Data: Data has to be versioned so you can easily track changes, revert to previous states if needed, and ensure reproducibility in your data pipelines. lakeFS applies the concepts of Git to your data lake. This gives you the ability to create branches for different development environments, commit changes to specific versions, and merge branches together once changes have been tested and validated.

Jon Erik Kemi Warghed:

• LinkedIn

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Chapters

00:00 The Problem with the Modern Data Stack: Too many tools and buzzwords

00:57 How to Choose the Right Tools: Considerations for startups and large companies

03:13 Evaluating Open Source Tools: Background checks and due diligence

07:52 Defining Data Governance: Transparency and understanding of data

10:15 The Importance of Data Governance: Challenges and solutions

12:21 Data Governance Tools: dbt and Dagster

17:05 The Impact of Dagster: Software-defined assets and declarative thinking

19:31 The Power of Software Defined Assets: How Dagster differs from Airflow and Mage

21:52 State Management and Orchestration in Dagster: Real-time updates and dependency management

26:24 Why Use Orchestration Tools?: The role of orchestration in complex data pipelines

28:47 The Importance of Tool Selection: Thinking about long-term sustainability

31:10 When to Adopt Orchestration: Identifying the need for orchestration tools

In this episode, Nicolay Gerold interviews John Wessel, the founder of Agreeable Data, about data orchestration. They discuss the evolution of data orchestration tools, the popularity of Apache Airflow, the crowded market of orchestration tools, and the key problem that orchestrators solve. They also explore the components of a data orchestrator, the role of AI in data orchestration, and how to choose the right orchestrator for a project. They touch on the challenges of managing orchestrators, the importance of monitoring and optimization, and the need for product people to be more involved in the orchestration space. They also discuss data residency considerations and the future of orchestration tools.

Sound Bites

"The modern era, definitely airflow. Took the market share, a lot of people running it themselves." "It's like people are launching new orchestrators every day. This is a funny one. This was like two weeks ago, somebody launched an orchestrator that was like a meta-orchestrator." "The DAG introduced two other components. It's directed acyclic graph is what DAG means, but direct is like there's a start and there's a finish and the acyclic is there's no loops."

Key Topics

• The evolution of data orchestration: From basic task scheduling to complex DAG-based solutions
• What is a data orchestrator and when do you need one? Understanding the role of orchestrators in handling complex dependencies and scaling data pipelines.
• The crowded market: A look at popular options like Airflow, Daxter, Prefect, and more.
• Best practices: Choosing the right tool, prioritizing serverless solutions when possible, and focusing on solving the use case before implementing complex tools.
• Data residency and GDPR: How regulations influence tool selection, especially in Europe.
• Future of the field: The need for consolidation and finding the right balance between features and usability.

John Wessel:

• LinkedIn
• Data Stack Show
• Agreeable Data

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Data orchestration, data movement, Apache Airflow, orchestrator selection, DAG, AI in orchestration, serverless, Kubernetes, infrastructure as code, monitoring, optimization, data residency, product involvement, generative AI.

Chapters

00:00 Introduction and Overview

00:34 The Evolution of Data Orchestration Tools

04:54 Components and Flow of Data in Orchestrators

08:24 Deployment Options: Serverless vs. Kubernetes

11:14 Considerations for Data Residency and Security

13:02 The Need for a Clear Winner in the Orchestration Space

20:47 Optimization Techniques for Memory and Time-Limited Issues

23:09 Integrating Orchestrators with Infrastructure-as-Code

24:33 Bridging the Gap Between Data and Engineering Practices

27:2 2Exciting Technologies Outside of Data Orchestration

30:09 The Feature of Dagster

In this episode of "How AI is Built", we learn how to build and evaluate real-world language model applications with Shahul and Jithin, creators of Ragas. Ragas is a powerful open-source library that helps developers test, evaluate, and fine-tune Retrieval Augmented Generation (RAG) applications, streamlining their path to production readiness.

Main Insights

• Challenges of Open-Source Models: Open-source large language models (LLMs) can be powerful tools, but require significant post-training optimization for specific use cases.
• Evaluation Before Deployment: Thorough testing and evaluation are key to preventing unexpected behaviors and hallucinations in deployed RAGs. Ragas offers metrics and synthetic data generation to support this process.
• Data is Key: The quality and distribution of data used to train and evaluate LLMs dramatically impact their performance. Ragas is enabling novel synthetic data generation techniques to make this process more effective and cost-efficient.
• RAG Evolution: Techniques for improving RAGs are continuously evolving. Developers must be prepared to experiment and keep up with the latest advancements in chunk embedding, query transformation, and model alignment.

Practical Takeaways

• Start with a solid testing strategy: Before launching, define the quality metrics aligned with your RAG's purpose. Ragas helps in this process.
• Embrace synthetic data: Manually creating test data sets is time-consuming. Tools within Ragas help automate the creation of synthetic data to mirror real-world use cases.
• RAGs are iterative: Be prepared for continuous improvement as better techniques and models emerge.

Interesting Quotes

• "...models are very stochastic and grading it directly would rather trigger them to give some random number..." - Shahul, on the dangers of naive model evaluation.
• "Reducing the developer time in acquiring these test data sets by 90%." - Shahul, on the efficiency gains of Ragas' synthetic data generation.
• "We want to ensure maximum diversity..." - Shahul, on creating realistic and challenging test data for RAG evaluation.

Ragas:

• Web
• Docs

Jithin James:

• LinkedIn

Shahul ES:

• LinkedIn
• X (Twitter)

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Introduction

02:03 Introduction to Open Assistant project

04:05 Creating Customizable and Fine-Tunable Models

06:07 Ragas and the LLM Use Case

08:09 Introduction to Language Model Metrics (LLMs)

11:12 Reducing the Cost of Data Generation

13:19 Evaluation of Components at Melvess

15:40 Combining Ragas Metrics with AutoML Providers

20:08 Improving Performance with Fine-tuning and Reranking

22:56 End-to-End Metrics and Component-Specific Metrics

25:14 The Importance of Deep Knowledge and Understanding

25:53 Robustness vs Optimization

26:32 Challenges of Evaluating Models

27:18 Creating a Dream Tech Stack

27:47 The Future Roadmap for Ragas

28:02 Doubling Down on Grid Data Generation

28:12 Open-Source Models and Expanded Support

28:20 More Metrics for Different Applications

RAG, Ragas, LLM, Evaluation, Synthetic Data, Open-Source, Language Model Applications, Testing.

In this episode of Changelog, Weston Pace dives into the latest updates to LanceDB, an open-source vector database and file format. Lance's new V2 file format redefines the traditional notion of columnar storage, allowing for more efficient handling of large multimodal datasets like images and embeddings. Weston discusses the goals driving LanceDB's development, including null value support, multimodal data handling, and finding an optimal balance for search performance.

Sound Bites

"A little bit more power to actually just try." "We're becoming a little bit more feature complete with returns of arrow." "Weird data representations that are actually really optimized for your use case."

Key Points

• Weston introduces LanceDB, an open-source multimodal vector database and file format.
• The goals behind LanceDB's design: handling null values, multimodal data, and finding the right balance between point lookups and full dataset scan performance.
• Lance V2 File Format:
• Potential Use Cases

Conversation Highlights

• On the benefits of Arrow integration: Strengthening the connection with the Arrow data ecosystem for seamless data handling.
• Why "columnar container format"?: A broader definition than "table format" to encompass more unconventional use cases.
• Tackling multimodal data: How LanceDB V2 enables storage of large multimodal data efficiently and without needing tons of memory.
• Python's role in encoding experimentation: Providing a way to rapidly prototype custom encodings and plug them into LanceDB.

LanceDB:

• X (Twitter)
• GitHub
• Web
• Discord
• VectorDB Recipes
• Lance V2

Weston Pace:

• LinkedIn
• GitHub

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Chapters

00:00 Introducing Lance: A New File Format

06:46 Enabling Custom Encodings in Lance

11:51 Exploring the Relationship Between Lance and Arrow

20:04 New Chapter

Lance file format, nulls, round-tripping data, optimized data representations, full-text search, encodings, downsides, multimodal data, compression, point lookups, full scan performance, non-contiguous columns, custom encodings

Had a fantastic conversation with Christopher Williams, Solutions Architect at Supabase, about setting up Postgres the right way for AI. We dug deep into Supabase, exploring:

• Core components and how they power real-time AI solutions
• Optimizing Postgres for AI workloads
• The magic of PG Vector and other key extensions
• Supabase’s future and exciting new features

Had a fantastic conversation with Christopher Williams, Solutions Architect at Supabase, about setting up Postgres the right way for AI. We dug deep into Supabase, exploring:

• Core components and how they power real-time AI solutions
• Optimizing Postgres for AI workloads
• The magic of PG Vector and other key extensions
• Supabase’s future and exciting new features

If you've ever wanted a simpler way to integrate AI directly into your database, SuperDuperDB might be the answer. SuperDuperDB lets you easily apply AI processes to your data while keeping everything up-to-date with real-time calculations. It works with various databases and aims to make AI development less of a headache.

In this podcast, we explore:

• How SuperDuperDB bridges the gap between AI and databases.
• The benefits of real-time AI processes within your data deployment.
• SuperDuperDB's framework for configuring AI workflows.
• The future of AI-powered databases.

Takeaways

• SuperDuperDB enables developers to apply AI processes directly to their data stores
• The platform supports real-time computation of embeddings or classifications, keeping the data deployment up to date
• SuperDuperDB provides a framework for configuring AI processes that work in close combination with the data deployment
• The platform supports a variety of databases, including operational and analytical databases
• SuperDuperDB aims to simplify AI development by abstracting the data layer and infrastructure

Duncan Blythe:

• LinkedIn

SuperDuperDB:

• Docs
• Website
• LinkedIn

Nicolay Gerold:

• LinkedIn
• X (Twitter)

Chapters

00:00 Introduction to SuperDuperDB

04:19 Real-time Computation and Data Deployment

13:46 Bringing Compute and Database Closer Together

29:30 Declarative Machine Learning with SuperDuperDB

35:09 Future Plans for SuperDuperDB

SuperDuperDB, AI, databases, embeddings, classifications, data deployment, operational databases, analytical databases, AI development, data science

Supabase just acquired OrioleDB, a storage engine for PostgreSQL.

Oriole gets creative with MVCC! It uses an UNDO log rather than keeping multiple versions of an entire data row (tuple). This means when you update data, Oriole tracks the changes needed to "undo" the update if necessary. Think of this like the "undo" function in a text editor. Instead of keeping a full copy of the old text, it just remembers what changed. This can be much smaller. This also saves space by eliminating the need for a garbage collection process.

It also has a bunch of additional performance boosters like data compression, easy integration with data lakes, and index-organized tables.

Show notes:

• Oriole joins Supabase
• Oriole Git
• Percona Talk on OrioleDB
• Supabase

Chris Gwilliams:

• LinkedIn

Nicolay Gerold:

• LinkedIn
• X (Twitter)

00:42 Introduction to OrioleDB

04:38 The Undo Log Approach

08:39 Improving Performance for High Throughput Databases

11:08 My take on OrioleDB

OrioleDB, storage engine, Postgres, table access methods, undo log, high throughput databases, automated features, new use cases, S3, data migration

Today’s guest is Antonio Bustamante, a serial entrepreneur who previously built Kite and Silo and is now working to fix bad data. He is building bem, the data tool to transform any data into the schema your AI and software needs.

bem.ai is a data tool that focuses on transforming any data into the schema needed for AI and software. It acts as a system's interoperability layer, allowing systems that couldn't communicate before to exchange information. Learn what place LLMs play in data transformation, how to build reliable data infrastructure and more.

"Surprisingly, the hardest was semi-structured data. That is data that should be structured, but is unreliable, undocumented, hard to work with."

"We were spending close to four or five million dollars a year just in integrations, which is no small budget for a company that size. So I was pretty much determined to fix this problem once and for all."

"bem focuses on being the system's interoperability layer."

"We basically take in anything you send us, we transform it exactly into your internal data schema so that you don't have to parse, process, transform anything of that sort."

"LLMs are a 30% of it... A lot of it is very, very like thorough validation layers, great infrastructure, just ensuring reliability and connection to our user systems.”

"You can use a million token context window and feed an entire document to an LLM. I can guarantee you if you don't, semantically chunk it out before you're not going to get the right results.”

"We're obsessed with time to value... Our milestone is basically five minute onboarding max, and then you're ready to go."

Antonio Bustamante

• LinkedIn
• X (Twitter)

bem.ai

• LinkedIn
• Website

Nicolay Gerold:

• LinkedIn
• X (Twitter)

Semi-structured data, Data integrations, Large language models (LLMs), Data transformation, Schema interoperability, Fault tolerance, Validation layers, System reliability, Schema evolution, Enterprise software, Data pipelines.

Chapters

00:00 The Problem of Integrations

05:58 Building Fault Tolerant Systems

13:51 Versioning and Semantic Validation

27:33 BEM in the Data Ecosystem

34:40 Future Plans and Onboarding

Imagine a world where data bottlenecks, slow data loaders, or memory issues on the VM don't hold back machine learning.

Machine learning and AI success depends on the speed you can iterate. LanceDB is here to to enable fast experiments on top of terabytes of unstructured data. It is the database for AI. Dive with us into how LanceDB was built, what went into the decision to use Rust as the main implementation language, the potential of AI on top of LanceDB, and more.

"LanceDB is the database for AI...to manage their data, to do a performant billion scale vector search."

“We're big believers in the composable data systems vision."

"You can insert data into LanceDB using Panda's data frames...to sort of really large 'embed the internet' kind of workflows."

"We wanted to create a new generation of data infrastructure that makes their [AI engineers] lives a lot easier."

"LanceDB offers up to 1,000 times faster performance than Parquet."

Chang She:

• LinkedIn
• X (Twitter)

LanceDB:

• X (Twitter)
• GitHub
• Web
• Discord
• VectorDB Recipes

Nicolay Gerold:

• LinkedIn
• X (Twitter)

Chapters:

00:00 Introduction to LanceDB

02:16 Building LanceDB in Rust

12:10 Optimizing Data Infrastructure

26:20 Surprising Use Cases for LanceDB

32:01 The Future of LanceDB

LanceDB, AI, database, Rust, multimodal AI, data infrastructure, embeddings, images, performance, Parquet, machine learning, model database, function registries, agents.