Introducing LlamaIndex.TS version 0.3! Featuring:
⭐️ Agent support including ReAct, Anthropic and OpenAI agents, as well as a generic AgentRunner class
⭐️ Standardized Web Streams compatible with React 19, Deno, and Node 22
⭐️ More comprehensive type system
⭐️ Enhanced support…

Join us and our friends Pulumi on May 8 for a webinar on how to use Pulumi to deploy an AI application using LlamaIndex onto AWS.

Your hosts Engin Diri and Laurie Voss will take you through a detailed, practical use case for how infrastructure as code helps streamline deploying AI…

Learn how to build agentic RAG with semantic caching to speed up common queries! In this collaboration with Redis, Tyler Hutcherson and Laurie Voss explain how to build world-class RAG applications optimized for quality, efficiency and cost.

youtube.com/watch?v=mTNiGf…

🚀 We're excited to announce the first-ever Llama 3 hackathon at SHACK15, in official partnership with AI at Meta

This will be our biggest hackathon of 2024 yet. Two days of hacking, $10k in prizes and credits, and hands-on mentorship from the Llama 3 team

A huge shout-out to…

Performing Complex Financial Calculations with Agentic RAG 🧮📈

Let’s build a financial assistant that can calculate percentage evolution, compound annual growth rate (CAGR), and P/E ratios over unstructured financial reports, without any human data transformation!

This post by…

Streamlining knowledge work with LlamaParse, Fireworks, and MongoDB!

Check out this neat project and blog post from Team CLAB, winners of our recent hackathon, that creates a full-stack documentation bot that uses
✅ LlamaIndex to parse and orchestrate
✅ Nomic embeddings via…

A Reference Architecture for Advanced RAG with LlamaIndex 🦙 and Bedrock 📖

If you’re looking to build advanced RAG in the AWS ecosystem, our code repo gives you the perfect reference material for both advanced parsing and agentic reasoning, while using a full suite of AWS…

Memory is a key ingredient for autonomous agents, but most memory implementations are exceedingly simple.

Check out memary - a reference long-term memory implementation using knowledge graphs 🧠🕸️

1️⃣ Use LLMs to extract agent inputs/responses in a knowledge graph backed by…

Multi-Stage Advanced RAG with LlamaIndex 🦙 and cohere reranking

Making retrieval a multi-hop process 💡 instead of single-shot can ensure better LLM context and reduce hallucinations.

This article by Michael R. (@kxsystems) highlights an example of this multi-stage…

Before you build complex agent systems, I’d recommend building with the individual “agent ingredients” first to gain a better first principles understanding of how they work.

Here are the main ingredients for building an agent (mini 🧵)

Query Planning: Given the task +…

A 9-part series on RAG from Prototype to Production ⭐️

RAG in a notebook is easy, RAG serving live production users is hard. This tutorial series by Marco Bertelli is the perfect step-by-step resource to outline all the architectural components you need to productionize a full…

Check out our first-party guide to building advanced RAG with LlamaIndex 🦙 + the AWS ecosystem ⭐️

✅ integration with Bedrock LLMs/Knowledge Base/Agents
✅ Use S3 and Step Functions with LlamaParse and LlamaCloud
✅ Build Agentic RAG with Bedrock Agents + Lambda functions +…

Team!

Show off your cool open-source AI projects? (bonus if local & private)

Let's make this thread happen!!! Reply below with your project. 🧵

Let's go open-source!

Our co-founder Simon Suo appeared on the MLSecOps podcast! They covered
➡️ The future of LLM-based applications
➡️ How to maintain data security on LLM apps
➡️ LlamaParse and LlamaCloud
and more!

Catch the episode on YouTube or wherever you get your podcasts:…

We’re excited to feature LlamaIndex 🦙 + AWS workshop materials featuring 3+ patterns for building LLM apps on AWS 💫

These include:
1️⃣ Using S3 as a data source for ingestion (with LlamaParse and LlamaCloud)
2️⃣ Use LlamaIndex 🦙 with AWS Bedrock LLMs and embeddings
3️⃣ Using…

The folks at KX are hosting a webinar about how to get the most out of LlamaParse!

You'll see how to use LlamaParse for
➡️ Parsing complex documents
➡️ Advanced preprocessing using natural language instructions
➡️ Table extraction
➡️ Image extraction
➡️ Integration with…

Build a best-in-class RAG application with Qdrant!

In this tutorial you'll see how to use:
✅ LlamaParse from LlamaIndex to parse documents
✅ Jina AI's latest embeddings to encode meaning
✅ Qdrant's hybrid cloud to store embeddings
✅ Mixtral 8x7b from Mistral AI to…

create-llama is the easiest way to get started with a full-stack RAG application and take it all the way to production, and it just hit version 0.1!

There's a ton of updates recently, including:
✅ ollama support, so you can run llama3 and phi3
✅ New vector database support…

Build a UX for your LLM chatbot/agent that not only includes streaming, but also showcases sources as expandable UI elements 📖🔎, similar to Perplexity!

Now possible in one-line of code through create-llama. Amazing work by Marcus Schiesser 💫

Check it out:…