96 episodes

Join Connor Shorten as he interviews Weaviate community users, leading machine learning experts, and explores Weaviate use cases from users and customers.

Weaviate Podcast Weaviate

    • Technology

Join Connor Shorten as he interviews Weaviate community users, leading machine learning experts, and explores Weaviate use cases from users and customers.

    Google Cloud Marketplace with Dai Vu and Bob van Luijt - Weaviate Podcast #95!

    Google Cloud Marketplace with Dai Vu and Bob van Luijt - Weaviate Podcast #95!

    Hey everyone, thank you so much for watching the 95th Weaviate Podcast! We are beyond honored to feature Dai Vu from Google on this one, alongside Weaviate Co-Founder Bob van Luijt! This podcast dives into all things Google Cloud Marketplace and the state of AI. Beginning with the proliferation of Open-Source models and how Dai sees the evolving landscape with respect to things like Gemini Pro 1.5, Gemini Nano and Gemma, as well as the integration of 3rd party model providers such as Llama 3 on Google Cloud platforms such as Vertex AI. Bob and Dai continue to unpack the next move for open-source infrastructure providers and perspectives around "AI-Native" applications, trends in data gravity, perspectives on benchmarking, and Dai's "aha" moment in AI!

    • 41 min
    ParlayANN with Magdalen Dobson Manohar

    ParlayANN with Magdalen Dobson Manohar

    As you are graduating from ideas to engineering, one of the key concepts to be aware of is Parallel Computing and Concurrency. I am SUPER excited to share our 94th Weaviate podcast with Magdalen Dobson Manohar! Magdalen is one of the most impressive scientists I have ever met, having completed her undergraduate studies at MIT before joining Carnegie Mellon University to study Approximate Nearest Neighbor Search and develop ParlayANN. ParlayANN is one of the most enlightening works I have come across that studies how to build ANN indexes in parallel without the use of locking.

    In my opinion, this is the most insightful podcast we have ever produced into Vector Search, the core technology behind Vector Databases. The podcast begins with Magdalen’s journey into ANN science, the issue of Lock Contention in HNSW, further detailing HNSW vs. DiskANN vs. HCNNG and pyNNDescent, ParlayIVF, how Parallel Index Construction is achieved, conclusions from experimentation, Filtered Vector Search, Out of Distribution Vector Search, and exciting directions for the future!

    I also want to give a huge thanks to Etienne Dilocker, John Trengrove, Abdel Rodriguez, Asdine El Hrychy, and Zain Hasan. There is no way I would be able to keep up with conversations like this without their leadership and collaboration.

    I hope you find the podcast interesting and useful!

    • 1 hr 3 min
    RAGKit with Kyle Davis - Weaviate Podcast #93!

    RAGKit with Kyle Davis - Weaviate Podcast #93!

    Hey everyone! I am SUPER excited to publish our newest Weaviate podcast with Kyle Davis, the creator of RAGKit! At a high-level, the podcast covers our understanding of RAG systems through 4 key areas: (1) Ingest / ETL, (2) Search, (3) Generate / Agents, and (4) Evaluation. Discussing these lead to all sorts of topics from Knowledge Graph RAG, to Function Calling and Tool Selection, Re-ranking, Quantization, and many more!

    This discussion forced me to re-think many of my previously held beliefs about the current RAG stack, particularly the definition of “Agents”. I came in believing that the best way of viewing “Agents” is an abstraction on top of multiple pipelines, such as an “Email Agent”, but Kyle presented the idea of looking at “Agents” as scoping the tools each LLM call is connected to, such as `read_email` or `calculator`. Would love to know what people think about this one, as I think getting a consensus definition of “Agents” can clarify a lot of the current confusion for people building with LLMs / Generative AI.

    • 1 hr 27 min
    VetRec with David de Matheu - Weaviate Podcast #92!

    VetRec with David de Matheu - Weaviate Podcast #92!

    I've seen a lot of interest around RAG for X application domain, Legal, Accounting, Healthcare, .... David and Kevin are maybe the best example of this I have seen so far, pivoting from Neum AI to VetRec!

    We begin the podcast by discussing the decision to switch gears, the advice given by Y Combinator, and David's experience in learning a new application domain.

    We then continue to discuss technical opportunities around RAG for Veterinarians, such as SOAP notes and Differential Diagnosis!

    We conclude with David's thoughts on the ETL space, companies like Unstructured and LlamaIndex's LlamaParse, advice for specific focus in ETL, and general discussions of ETL for Vector DBs / KGs / SQL.

    David and Kevin have been two of my favorite entrepreneurs I've met during my time at Weaviate! They do an amazing job of writing content that helps you live vicariously through them as they take on this opportunity to apply RAG and AI technologies to help Veterinarians!

    I really hope you enjoy the podcast!

    • 59 min
    Tengyu Ma on Voyage AI - Weaviate Podcast #91!

    Tengyu Ma on Voyage AI - Weaviate Podcast #91!

    Voyage AI is the newest giant in the embedding, reranking, and search model game!

    I am SUPER excited to publish our latest Weaviate podcast with Tengyu Ma, Co-Founder of Voyage AI and Assistant Professor at Stanford University!

    We began the podcast with a deep dive into everything embedding model training and contrastive learning theory. Tengyu delivered a masterclass in everything from scaling laws to multi-vector representations, neural architectures, representation collapse, data augmentation, semantic similarity, and more! I am beyond impressed with Tengyu's extensive knowledge and explanations of all these topics.

    The next chapter dives into a case study Voyage AI did fine-tuning an embedding model for the LangChain documentation. This is an absolutely fascinating example of the role of continual fine-tuning with very new concepts (for example, very few people were talking about chaining together LLM calls 2 years ago), as well as the data efficiency advances in fine-tuning.

    We concluded by discussing ML systems challenges in serving an embeddings API. Particularly the challenge of detecting if a request is for batch or query inference and the optimizations that go into either say ~100ms latency for a query embedding or maximizing throughput for batch embeddings.

    • 1 hr 2 min
    Self-Discover DSPy with Chris Dossman - Weaviate Podcast #90!

    Self-Discover DSPy with Chris Dossman - Weaviate Podcast #90!

    One of the core values of DSPy is the ability to add “reasoning modules” such as Chain-of-Thought to your LLM programs!

    For example, Chain-of-Thought describes prompting the LLM with “Let’s think step by step …”. Interestingly, this meta-prompt around asking the LLM to think this way dramatically improves performance in tasks like question answering or document summarization.

    Self-Discover is a meta-prompting technique that searches for the optimal thinking primitives to integrate into your program! For example, you could “Let’s think out of the box to arrive at a creative solution” or “Please explain your answer in 4 levels of abstraction: as if you are talking to a five year old, a high school student, a college student studying Computer Science, and a software engineer with years of experience in the topic”.

    I am SUPER excited to be publishing our 90th Weaviate Podcast with Chris Dossman! Chris has implemented Self-Discover in DSPy, one of the most fascinating examples so far of what the DSPy framework is capable of!

    Chris is also one of the most talented entrepreneurs I have met during my time at Weaviate thanks to introductions from Bob van Luijt and Byron Voorbach. Chris built one of the earliest RAG systems for government information and is now working on LLM opportunities in marketing with his new startup, Dicer.ai!

    I hope you enjoy the podcast, it was such a fun one and I learned so much!

    • 1 hr 2 min

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