All [Music] right, welcome everybody. Um, I'm Jeff Huber, the co-founder and CEO of Chroma, and I'm joined by Jason. We're going to do a two-parter here. We're really going to pack in the content.It's the last session of the day, and so we thought I'd give you a lot. Um everything in this presentation today is open source and code available. So we're also not selling you any tools.Um and so there'll be QR codes and stuff throughout to grab the code. So let's talk about how to look at your data. Um all of yo ...

Retrieval Augmented Generation (RAG) & Large Language Models (LLMs) - RAG is essential for enterprises to incorporate proprietary information into LLMs, addressing the limitations of out-of-the-box models [2][3] - RAG is considered a more reliable, faster, and cheaper approach compared to fine-tuning and long context windows for utilizing external knowledge [7] - The industry has seen significant improvements in retrieval accuracy over the past 18 months, driven by advancements in embedding models [11][12] - The industry averages approximately 80% accuracy across 100 datasets, indicating a 20% potential improvement headroom in retrieval tasks [12][13] Vector Embeddings & Storage Optimization - Techniques like matryoshka learning and quantization can reduce vector storage costs by up to 100x with minimal performance loss (5-10%) [15][16][17] - Domain-specific embeddings, such as those customized for code, offer better trade-offs between storage cost and accuracy [21] RAG Enhancement Techniques - Hybrid search, combining lexical and vector search with re-rankers, improves retrieval performance [18] - Query decomposition and document enrichment, including adding metadata and context, enhance retrieval accuracy [18][19][20] Future of RAG - The industry predicts a shift towards more sophisticated models that minimize the need for manual "tricks" to improve RAG performance [29][30] - Multimodal embeddings, which can process screenshots, PDFs, and videos, simplify workflows by eliminating the need for separate data extraction and embedding steps [32] - Context-aware and auto-chunking embeddings aim to automate the trunking process and incorporate cross-trunk information, optimizing retrieval and cost [33][36]