Weaviate Podcast

Weaviate Podcast

By WeaviateTechnology
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Weaviate Podcast episodes

  • Farshad Farahbakhshian and Etienne Dilocker on Weaviate and AWS - Weaviate Podcast #67!

    Hey everyone! Thank you so much for watching the 67th Weaviate Podcast, announcing Weaviate on the AWS Marketplace! This was one of my favorite podcasts to date with a deep dive on the details of running RAG applications in the cloud, our general understanding of LLM Fine-Tuning and RAG, as well as a really interesting discussion on VPCs and Hybrid SaaS! I hope you find the podcast useful, as always we are more than happy to answer any questions or discuss any ideas you have about the content presented in the podcast!

    Learn more here: https://aws.amazon.com/marketplace/seller-profile?id=seller-jxgfug62rvpxs
    As well as here: https://weaviate.io/developers/weaviate/installation/aws-marketplace
    Chapters
    0:00 Welcome Farshad
    0:38 Weaviate’s Journey to AWS
    2:05 Retrieval-Augmented Generation and Vector DBs
    3:44 Running AI in the Cloud
    9:40 Fine-Tuning LLMs vs. RAG
    10:30 Skill vs. Knowledge (Lawyer Example)
    14:28 Continual Learning of LLMs
    16:50 Searching through multiple sources
    19:58 Hybrid Search controlled by LLMs
    22:10 Classes versus Filters
    25:00 SQL and Vector Search
    25:55 Favorite RAG Use Cases
    31:55 Cloud Benchmarking
    37:00 Price Performance
    38:20 Tuning HNSW
    42:15 Horizontal Scalability on AWS Marketplace
    47:00 Privacy Requirements
    54:45 Weaviate Hybrid SaaS
    59:00 AWS Marketplace

    1 hr 2 min
  • Hybrid SaaS in Weaviate Explained!

    Hey everyone! Here is a clip from our newest Weaviate podcast with Farshad Farahbakhshian, Gen AI specialist at AWS and Etienne Dilocker, CTO and Co-Founder of Weaviate! This podcast announces Weaviate on the AWS marketplace and is packed with info on running Weaviate in the cloud such as this clip explaining how Hybrid SaaS works! I hope you find the clip useful, we are more than happy to answer any questions you have about the content in this clip!

    Chapters
    0:00 Quick Intro for Context
    0:29 Etienne Dilocker on Hybrid SaaS

    5 min
  • David Garnitz on VectorFlow - Weaviate Podcast #66!

    Hey everyone! Thank you so much for watching the 66th Weaviate Podcast with David Garnitz, the creator of VectorFlow! VectorFlow (open-sourced on GH and linked below) is a new tool for ingesting data into Vector Databases such as Weaviate! There is quite an interesting End-to-End stack emerging at the ingestion layer, from retrieving data from misc. sources such as Slack, Salesforce, GitHub, Google Drive, Notion, ... to then Chunking the Text (maybe with the use of Visual Document Layout parsers like what Unstructured is imagining), extracting Metadata potentially (say the "age" of an NBA player as in the Evaporate-Code+ research) -- then sending this data off to embedding model inference and unpacking that can of worms from inference acceleration to load balancing, and finally -- importing the vectors themselves to Weaviate! I learned so much from this conversation, I really hope you enjoy listening and please check out VectorFlow below!

    VectorFlow: https://github.com/dgarnitz/vectorflow
    Chapters
    0:00 VectorFlow on GitHub!
    0:52 Welcome David Garnitz!
    1:17 Vector Flow, Founding Vision
    2:00 Billions of Vectors in Weaviate!
    4:20 End-to-end data importing
    6:30 Metadata Extraction in Vector Database Flows
    10:15 Vectorizing 100s of millions of billions of chunks
    15:58 Fine-Tuning Embedding Models
    23:50 Zero-Shot Models in Metadata and Chunking
    36:36 Vector + SQL
    42:45 Self-Driving Databases
    49:23 Generative Feedback Loop REST API
    51:38 GPT Cache
    55:55 Building VectorFlow

    1 hr 5 min
  • Ofir Press on AliBi and Self-Ask - Weaviate Podcast #65!

    Hey everyone! Thank you so much for watching the Weaviate Podcast! I am SUPER excited to publish my conversation with Ofir Press! Ofir has done incredible work pioneering AliBi attention and Self-Ask prompting and I learned so much from speaking with him! As always we are more than happy to answer any questions or discuss any ideas you have about the content in the podcast!

    +Huge Congratulations on your Ph.D. Ofir!
    AliBi Attention: https://arxiv.org/abs/2108.12409
    Self-Ask Prompting: https://arxiv.org/abs/2210.03350
    Ofir Pres on YouTube: https://www.youtube.com/@ofirpress
    Chapters
    0:00 Welcome Ofir Press
    0:41 Large Context LLMs
    12:38 Quadratic Complexity of Attention
    19:12 AliBi Attention, Visual Demo!
    24:53 Recency Bias in LLMs
    28:57 RAG in Long Context LLM Training
    36:27 Self-Ask Prompting
    46:07 Chain-of-Thought and Self-Ask
    50:47 Gorilla LLMs
    58:42 New Directions for New Training Data

    1 hr 8 min
  • Shishir Patil and Tianjun Zhang on Gorilla - Weaviate Podcast #64!

    Hey everyone! Thank you so much for watching the 64th Weaviate Podcast with Shishir Patil and Tianjun Zhang, co-authors of Gorilla: Large Language Models Connected with Massive APIs! I learned so much about Gorilla from Shishir and Tianjun, from the APIBench dataset to the continually evolving APIZoo, how the models are trained with Retrieval-Aware Training, Self-Instruct Training data and how the authors think of fine-tuning LLaMA-7B models for tasks such as this, and many more! I hope you enjoy the podcast! As always I am more than happy to answer any questions or discuss any ideas you have about the content in the podcast!

    Please check out the paper here! https://arxiv.org/abs/2305.15334
    Chapters
    0:00 Welcome Shishir and Tianjun
    0:25 Gorilla LLM Story
    1:50 API Examples
    7:40 The APIZoo
    10:55 Gorilla vs. OpenAI Funcs
    12:50 Retrieval-Aware Training
    19:55 Mixing APIs, Gorilla for Integration
    25:12 LlaMA-7B Fine-Tuning vs. GPT-4
    29:08 Weaviate Gorilla
    33:52 Gorilla and Baby Gorillas
    35:40 Gorilla vs. HuggingFace
    38:32 Structured Output Parsing
    41:14 Reflexion Prompting for Debugging
    44:00 Directions for the Future

    50 min
  • Nils Reimers on Cohere Search AI - Weaviate Podcast #63!

    Hey everyone! Thank you so much for watching the 63rd Weaviate Podcast, I couldn't be more excited to welcome Nils Reimers back to the podcast!! Similar to our debut episode together, we began by describing the latest collaboration of Weaviate and Cohere (episode 1, new multilingual embedding models; episode 2, rerankers!), and then continued into some of the key questions around search technology. In this one, we discussed the importance of temporal queries and metadata extraction, long document representation, and future directions for Retrieval-Augmented Generation! I hope you enjoy the podcast, as always I am more than happy to answer any questions or discuss any ideas you have about the content in the podcast! Thank you so much for watching!

    Learn more about Cohere Rerankers and how to use it in Weaviate here: https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/reranker-cohere
    Chapters
    0:00 Introduction
    1:30 Cohere Rerankers
    7:02 Dataset Curation at Cohere
    10:30 New Rerankers and XGBoost
    14:35 Temporal Queries
    17:55 Metadata Extraction from Unstructured Text Chunks
    21:52 Soft Filters
    24:58 Chunking and Long Document Representation
    38:00 Retrieval-Augmented Generation
    45:40 Retrieval-Aware Training to solve Hallucinations
    49:50 Learning to Search and End-to-End RAG
    54:35 RETRO
    59:25 Foundation Model for Search

    1 hr 6 min
  • Atai Barkai on PodcastGPT - Weaviate Podcast #62!

    Hey everyone! Thank you so much for watching the 62nd Weaviate Podcast with Atai Barkai! We are stepping into the meta with this one for a podcast about podcasts! Podcasts are one of the biggest opportunities of new technologies, starting with Whisper's ability to transcribe audio to text and advances with speaker diarization, .. the question to be explored is, What Vector Database and LLM applications can we build with this data?! What is the future of podcasting with these new technologies?! I had so much fun discussing all these ideas with Atai! As always we are more than happy to answer any questions or discuss any ideas you have about content discussed in the podcast! Thank you so much for watching!

    Chapters
    0:00 Welcome Atai!
    1:04 TawkitAI and PodcastGPT!
    2:20 Chat with Podcast
    PodcastGPT - https://www.podcastgpt.ai/
    Tawkit AI - https://twitter.com/tawkitapp
    Weaviate Podcast Search Demo!
    https://github.com/weaviate/weaviate-podcast-search

    56 min
  • Rohit Agarwal on Portkey - Weaviate Podcast #61!

    Hey everyone! Thank you so much for watching the 61st episode of the Weaviate Podcast! I am beyond excited to publish this one! I first met Rohit at the Cal Hacks event hosted by UC Berkeley where we had a debate about the impact of Semantic Caching! Rohit taught me a ton about the topic and I think it's going to be one of the most impactful early applications of Generative Feedback Loops! Rohit is building Portkey, a SUPER interesting LLM middleware that does things like load balancing between LLM APIs, and as discussed in the podcast there are all sorts of opportunities for this kind of space whether it be routing to tool-specific LLMs, different cost / accuracy requirements, or multiple models in the HuggingGPT sense. It was amazing chatting with Rohit, this was the best dive into LLMOps I have personally been apart of! As always we are more than happy to answer any questions or discuss any ideas you have about the content in the podcast!

    Check out portkey here! https://portkey.ai/blog
    Chapters
    0:00 Introduction
    0:24 Portkey, Founding Vision
    2:20 LLMOps vs. MLOps
    4:00 Inference Hosting Options
    7:05 3 Layers of LLM Use
    8:35 LLM Load Balancers
    12:45 Fine-Tuning LLMs
    17:08 Retrieval-Aware Tuning
    21:16 Portkey Cost Savings
    23:08 HuggingGPT
    26:28 Semantic Caching
    32:40 Frequently Asked Questions
    34:00 Embeddings vs. Generative Tasks
    35:30 AI Moats, GPT Wrappers
    39:56 Unlocks from Cheaper LLM Inference

    50 min
  • Patrice Bourgougnon on WPSolr - Weaviate Podcast #60

    Hey everyone! Thank you so much for watching the 60th Weaviate podcast with Patrice Bourgougnon! Patrice is the creator of WPSolr, integrating AI search capabilities with Wordpress and Woocommerce. Patrice is one of the most active contributors to Weaviate, filing issues and poking holes in new releases! Patrice shared incredible feedback on Weaviate and how he sees the state of Vector Databases and Search! As always, we are more than happy to answer any questions or ideas you have about the content discussed in the podcast! Thanks for watching!

    Chapters
    0:00 Introduction
    0:45 Vector Databases and Wordpress
    4:50 Weaviate Client Languages
    10:00 Inference and Database Container Management
    21:30 Business Opportunities for Search in Production
    26:40 Testing Search Performance, “Something to sleep on”
    30:50 Zero-Shot Model Ability
    36:05 Make LLMs Stateful
    43:46 Chatbots and Search Boxes
    44:55 Mixing Models in Applications
    47:00 BM25 vs. Vector Search in RETRO RAG

    1 hr 26 min
  • Andriy Mulyar on Nomic AI, Atlas, and GPT4All - Weaviate Podcast #58

    Hey everyone! Thank you so much for watching the 58th episode of the Weaviate Podcast! I am SUPER excited to welcome Andriy Muylar! Andriy is the Co-Founder of Nomic AI, a company fresh off a $17M series A raise! Nomic has created some incredible products such as Atlas and GPT4All! I was really impressed by Andriy's vision of the state and forecasted evolution of these topics! I hope you enjoy the podcast! As always, we are more than happy to answer any questions or discuss any ideas you have about the content discussed in the podcast!

    Integration Tutorial for Weaviate and Nomic AI Atlas! https://docs.nomic.ai/vector_database.html
    This example worked for me if you want to clone it with the podcast transcription dataset: https://github.com/weaviate/weaviate-podcast-search/blob/main/atlas-visualizer.py
    Check out Nomic AI here! https://home.nomic.ai/blog
    Chapters
    0:00 Congrats Nomic and Weaviate Integration!
    2:35 Welcome Andriy Mulyar!
    3:05 Founding Story of Nomic AI
    6:55 Understanding Massive Scale Text Data
    10:14 Topic Modeling
    16:30 Monitoring Model Training

    59 min

About Weaviate Podcast

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