Events

Please make sure you are registered using this luma page. Github will email you a form before the event, which you will need to complete for your access pass.

 Connecting your unstructured data with Generative LLMs

Have some food and refreshments. Hear three exciting talks about LLMs and unstructured data.

5:30 - 6:30 - Welcome/Networking/Registration

Anyone interested in talking and learning about Unstructured Data and LLM Apps.

Tech Talk 1: How to Detect and Correct Logical Fallacies in LLM model output

: Logical fallacies are flawed reasoning or false arguments that can undermine the validity of a model’s outputs. LM models are optimized to perform well on specific metrics like cosine similarity, safety, or helpfulness. However, optimizing for metrics alone does not guarantee logically sound reasoning. Users cannot depend on such outputs - if left unchecked, propagating logical fallacies can spread misinformation, confuse users, and lead to harmful real-world consequences when models are deployed in products or services. Eliminating fallacies ensures model outputs remain logically valid and aligned with human reasoning. This maintains user trust and mitigates risks.

How to stream data with Kafka into your RAG application

In this talk, Filip will delve into the integration of Kafka (Confluent Cloud) with Zilliz Cloud (Hosted Milvus), showcasing how this synergy enables real-time data ingestion, parsing, and processing. The primary objective is to enhance the accuracy and performance of your Retrieval Augmented Generation (RAG) applications. Additionally, Filip will provide a comprehensive demonstration, offering practical insights into the capabilities and benefits of this integration.

Tech Talk 3: AI-Powered Pair Programming with GitHub Copilot

: Rob Crystal-Ornelas and Kasia Sitkiewicz, GitHub

: Learn how GitHub’s AI-powered code assistant can help you save time and innovate at any career stage. We’ll demo GitHub Copilot and show the newly beta-released Copilot Chat. Also, we’ll share tips on how to prompt Copilot to make the most of this AI pair-programmer.

Unstructured Data Meetup SF

 Connecting your unstructured data with LLMs

6:00 - 6:30 - Welcome/Networking/Registration

6:30 - 6:50 - Yi Ding, Developer Advocate, LlamaIndex

6:50 - 7:10 - David Garnitz, Founder, VectorFlow

7:10 - 7:30 - Rachel Hu, Founder, CambioML

Tech Talk 1: Advanced Strategies for Production RAG

: Yi Ding, Developer Advocate, LlamaIndex

: As we pass the second anniversary of ChatGPT's knowledge cutoff, it's clearer than ever that retrieving the right data for the LLM is crucial for the majority of applications. What we've seen at LlamaIndex is that once you get past the demo, tuning your RAG strategy involves a lot of domain specific optimization and tradeoffs. I will be discussing a number of more advanced strategies including SubQuestionQueryEngine, "Small to Big," Document Hierarchies, and Milvus's Hybrid Search.

Tech Talk 2: Ingesting Chaos: Handling Unstructured Data Reliably at Scale for RAG & Beyond

: The wide range of scenario and edge cases to account for makes ingesting and processing unstructured data into vector databases difficult. You can offload some of the complexity by using a vector embedding pipeline. This, in combination with an automated evaluation system, will allow you to experiment with different ingestion techniques to see what works best for your data and use-case.

Chris

Events