Unstructured

Building Multimodal AI Agents From Scratch — Apoorva Joshi, MongoDB

Jun 27, 2025 · 36:58

Apoorva Joshi, an AI developer advocate at MongoDB, leads a hands-on workshop on building multimodal AI agents from scratch. She defines agents as systems using LLMs to reason, plan, and execute tasks with tools, contrasting them with simple prompting and RAG. The workshop focuses on processing mixed-media documents (text and images) by converting each page to a screenshot, embedding it with Voyage AI's multimodal (VLM-based) model to avoid the modality gap of CLIP, and storing embeddings in MongoDB for vector search. The agent uses Google's Gemini 2.0 Flash as the multimodal LLM, implements REACT (reasoning and acting) for planning with feedback, and manages short-term memory via session IDs. Participants build a tool-calling agent that retrieves relevant page screenshots, passes them with conversation history to Gemini, and generates answers for queries like analyzing charts or extracting insights from documents.

[Full Workshop] Llama 3 at 1,000 tok/s on the SambaNova AI Platform

Feb 7, 2025 · 1:00:58

Michelle Matern and Petro Milan of SambaNova present their full-stack AI platform, built on the SN40L RDU chip with a three-tiered memory architecture capable of storing up to 5 trillion parameters. They demonstrate Samba1 Composition of Experts (CoE), a trillion-parameter model combining 92 expert models behind a single endpoint, and show Llama-3-8B achieving 1,000 tokens per second with a time-to-first-token of 0.09 seconds and total inference time of 0.65 seconds—far exceeding GPU-based providers. The workshop includes a hands-on basic inference call using LangChain and SambaStudio API, and a RAG-based Q&A system for enterprise search that integrates Unstructured for document loading, E5-large-v2 embeddings, ChromaDB vector store, and the high-speed Llama-3 endpoint. Attendees learn to configure prompts with special Llama-3 tags, set chunk size and overlap, and optionally run embeddings on SambaNova's RDU hardware for faster processing.

Build enterprise generative AI apps using Llama 3 at 1,000 tokens/s on the SambaNova AI platform

Sep 11, 2024 · 54:34

SambaNova’s Michelle Matern and Petro Milan present their full-stack AI platform, demonstrating how the SN40L RDU chip enables Llama 3 inference at 1,000 tokens per second. They introduce Samba-1, a composition of 92 expert models behind a single endpoint, and benchmark it against GPT-3.5 and GPT-4 on enterprise tasks like information extraction and text-to-SQL. The workshop then builds a RAG-based Q&A system using LangChain, Unstructured, E5-large-v2 embeddings, ChromaDB, and Llama-3-8B-Instruct at 1,000 tokens per second. Attendees set up the environment, load documents, and run inference with real-time metrics showing time to first token of 0.09 seconds and total inference time of 0.65 seconds.