Jerry Liu @jerryjliu0
co-founder/CEO @llama_index Careers: https://t.co/EUnMNmbCtx Enterprise: https://t.co/Ht5jwxSrQB llamaindex.ai Joined September 2011-
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Check out our first-party guide to building advanced RAG with @llama_index + 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 +…
Check out our first-party guide to building advanced RAG with @llama_index + 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 +… https://t.co/ByfxGxaBJg
We’re excited to feature @llama_index + 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 @llama_index with AWS Bedrock LLMs and embeddings 3️⃣ Using…
The folks at @kxsystems 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_engine! In this tutorial you'll see how to use: ✅ LlamaParse from LlamaIndex to parse documents ✅ @JinaAI_'s latest embeddings to encode meaning ✅ Qdrant's hybrid cloud to store embeddings ✅ Mixtral 8x7b from @MistralAI to…
create-llama 0.1: the easiest way to build a full-stack LLM application - Specify any popular LLM (incl. llama3, phi3, claude, openai) - Specify any vector store - use either @llama_index python or typescript as the backend - get full-stack streaming + sources built in
create-llama 0.1: the easiest way to build a full-stack LLM application - Specify any popular LLM (incl. llama3, phi3, claude, openai) - Specify any vector store - use either @llama_index python or typescript as the backend - get full-stack streaming + sources built in
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…
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…
We love this work from @clusteredbytes that generates a diagram of a knowledge graph in real time, streaming changes to the diagram to the front-end using the @vercel AI SDK.
We love this work from @clusteredbytes that generates a diagram of a knowledge graph in real time, streaming changes to the diagram to the front-end using the @vercel AI SDK.
Build a UX for your LLM chatbot/agent that not only includes streaming, but also showcases sources as expandable UI elements 📖🔎, similar to @perplexity_ai! Now possible in one-line of code through create-llama. Amazing work by @MarcusSchiesser 💫 Check it out:…
Build a UX for your LLM chatbot/agent that not only includes streaming, but also showcases sources as expandable UI elements 📖🔎, similar to @perplexity_ai! Now possible in one-line of code through create-llama. Amazing work by @MarcusSchiesser 💫 Check it out:… https://t.co/BaK3p9vu7f
Excited to launch the beta for /search in Firecrawl🔥 Given a search query, output the top results with full page content in markdown - ready for your llm. All in 10 seconds
Llama 3 🤜🤛 Phi 3 There's been some exciting advancements in the open-source LLM space, and @akshay_pachaar provides a fantastic reference architecture to compare the two models in RAG capabilities. Check out the detailed architecture diagram and link to the open-source code…
Llama 3 🤜🤛 Phi 3 There's been some exciting advancements in the open-source LLM space, and @akshay_pachaar provides a fantastic reference architecture to compare the two models in RAG capabilities. Check out the detailed architecture diagram and link to the open-source code… https://t.co/FbN2BdMeA3
Language Agent Tree Search 🤖🌲 As LLMs get faster, better, cheaper, developers will be able to compose agentic systems that are able to plan out an entire tree of possible futures, instead of just sequentially planning the next state (e.g. in ReAct). This is crucial for higher…
Use `npx create-llama` to quickly create RAG applications using @llama_index. Now, you can show the sources of the retrieved context (think of @perplexity_ai). Watch the video below to see the feature in action. 📃 Code: github.com/run-llama/crea…
Let's compare Llama-3 & Phi-3 using RAG:
Corrective RAG == retrieval with reflection 🔎🪞 A way to make RAG more advanced is to expand the decision making capabilities in the middle of the RAG process. - Pre-retrieval: use the LLM to do query decomposition, API parameter inference, etc. - Post-retrieval (as shown…
Corrective RAG == retrieval with reflection 🔎🪞 A way to make RAG more advanced is to expand the decision making capabilities in the middle of the RAG process. - Pre-retrieval: use the LLM to do query decomposition, API parameter inference, etc. - Post-retrieval (as shown… https://t.co/RUSMZ4aVFf
Phi-3 is the most capable ~4B model out there today - we're fast approaching a world where small models can reliably perform agentic reasoning ⚡️ This has the potential to make complex agent applications much more feasible in terms of cost and latency. There's still a slight…
Phi-3 is the most capable ~4B model out there today - we're fast approaching a world where small models can reliably perform agentic reasoning ⚡️ This has the potential to make complex agent applications much more feasible in terms of cost and latency. There's still a slight… https://t.co/WLwfGDBAPv
A way to fix bad retrieval issues in RAG is to add a “reflection” layer on the retrieved context 💡 Corrective RAG (CRAG, Yan et al.) adds a retrieval eval module that categorizes gathered information into “Correct”, “Incorrect”, and “Ambiguous”. In the event that context is…
Want to run Phi-3 on your laptop? @ollama has got you with day 0 support as usual! Check out this quick notebook showing completely local RAG using LlamaIndex and Ollama: colab.research.google.com/drive/1RoZzbL8… And Ollama's announcement tweet: x.com/ollama/status/…
Want to run Phi-3 on your laptop? @ollama has got you with day 0 support as usual! Check out this quick notebook showing completely local RAG using LlamaIndex and Ollama: colab.research.google.com/drive/1RoZzbL8… And Ollama's announcement tweet: x.com/ollama/status/… https://t.co/hX8tduGSjP
Phi-3 Mini (3.8B) from @Microsoft was released today, claiming to match Llama 3 8B's performance! But how does it handle RAG, Routing, Query Planning, Text2SQL, Pydantic Program, and Agentic tasks? Thanks to @ravithejads, our benchmark cookbook offers an initial analysis: ✅ RAG…
Created a documentation chatbot: Gemini4Docs (troubleshoot: discord.gg/Ry2kYuxJSf). Search any documentation directly in the CLI and get relevant code snippets in under 10 seconds. Inspired by @jerryjliu0, @jason_lopatecki, @AlexReibman @agihouse_org @vibhuuuus
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We’re excited to feature @llama_index + 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 @llama_index with AWS Bedrock LLMs and embeddings 3️⃣ Using…
Win your free ticket to the AI Quality Conference in San Francisco! It features @chipro, @jerryjliu0, @RichardSocher and 40+ speakers. 👇🏼
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The folks at @kxsystems 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_engine! In this tutorial you'll see how to use: ✅ LlamaParse from LlamaIndex to parse documents ✅ @JinaAI_'s latest embeddings to encode meaning ✅ Qdrant's hybrid cloud to store embeddings ✅ Mixtral 8x7b from @MistralAI 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…
Use `npx create-llama` to quickly create RAG applications using @llama_index. Now, you can show the sources of the retrieved context (think of @perplexity_ai). Watch the video below to see the feature in action. 📃 Code: github.com/run-llama/crea…
@jerryjliu0 @akshay_pachaar thanks for sharing @jerryjliu0
Thanks for the shout out Jerry! 🚀
Llama 3 🤜🤛 Phi 3 There's been some exciting advancements in the open-source LLM space, and @akshay_pachaar provides a fantastic reference architecture to compare the two models in RAG capabilities. Check out the detailed architecture diagram and link to the open-source code…
I built and sold Frame.io for $1.3 billion to Adobe, and along the way, I received plenty of advice that turned out to be entirely off the mark. Here's a look at some of the most glaring misdirections: 1. "As CEO, focus on executive business tasks." This couldn't…
Build a UX for your LLM chatbot/agent that not only includes streaming, but also showcases sources as expandable UI elements 📖🔎, similar to @perplexity_ai! Now possible in one-line of code through create-llama. Amazing work by @MarcusSchiesser 💫 Check it out:…
Use `npx create-llama` to quickly create RAG applications using @llama_index. Now, you can show the sources of the retrieved context (think of @perplexity_ai). Watch the video below to see the feature in action. 📃 Code: github.com/run-llama/crea…
(If you speak English and are curious: we've started a LlamaIndex user group in Korea!)
라마인덱스 한인 커뮤니티, 라마인덱스 KR이 2024년 4월 23일 탄생했습니다! 활발한 커뮤니티를 만들어 LlamaIndex의 강력한 기능과 확장성을 함께 탐색하고 경험하고 싶습니다. 한국어 자료 공유, 활용사례 공유, 궁금한 사항 토론 등 자유로운 소통과 함께 다양한 프로젝트 진행 및 협업 기회를…
Excited to launch the beta for /search in Firecrawl🔥 Given a search query, output the top results with full page content in markdown - ready for your llm. All in 10 seconds
We love this work from @clusteredbytes that generates a diagram of a knowledge graph in real time, streaming changes to the diagram to the front-end using the @vercel AI SDK.
Open source AI Diagram Generator 🔥 Uses @llama_index Pydantic program with partial JSON parsing and @vercel AI SDK to send intermediate diagrams during generation for improved UX 🚀 Repo: github.com/rsrohan99/ai-d… Full tutorial under 2.5 minutes 👇
Language Agent Tree Search 🤖🌲 As LLMs get faster, better, cheaper, developers will be able to compose agentic systems that are able to plan out an entire tree of possible futures, instead of just sequentially planning the next state (e.g. in ReAct). This is crucial for higher…
Why do people get confused by DSPy, part 1: This confused me when I first got started. Let's dive in.
Use `npx create-llama` to quickly create RAG applications using @llama_index. Now, you can show the sources of the retrieved context (think of @perplexity_ai). Watch the video below to see the feature in action. 📃 Code: github.com/run-llama/crea…
Before we begin, take a look at what we're about to create! As always, I'll be using my favourite stack: - @ollama for locally serving a LLM (Llama-3) - @llama_index for orchestration - @streamlit for building the UI - @LightningAI for development & hosting Let's go! 🚀