LlamaIndex

Tomorrow! Catch Laurie Voss talking about how to use LlamaIndex with Elastic.

?????? WINNERS Right Here! A huge congrats to 1st place, Timeline of You, 2nd place, Closing.wtf, 3rd place, OilyRags. See their projects here: https://lnkd.in/g_EA8yW9 LlamaIndex VESSL AI Pinecone AI Makerspace 500 Global #ragathon #rag #agenticrag #genai #arize #box #sap #togotherai Toolhouse

Build a multimodal RAG system using Microsoft Azure AI Search, Azure OpenAI, and Arize AI Phoenix with LlamaIndex! This step-by-step guide walks you through contextual retrieval, a technique to improve the accuracy of retrieval by adding global context to each retrieved chunk, benchmarking it against basic retrieval. And don't miss the section on how to use LlamaParse to handle PowerPoint documents! Check out the full tutorial here: https://lnkd.in/gpjMQb_A

Mistral has released some impressive new edge-class models and we have day 0 support as usual! Just run: pip install llama-index-llms-mistralai

As impressed as I was by the projects that were submitted, it was the people that impressed me the most at the LlamaIndex #Agentic RAG-a-thon with Pinecone and VESSL AI event I attended at 500 Global in Palo Alto this past weekend. I wanted to share my gratitude and a few reflections about the weekend. Thank you to all the sponsors first and foremost. Without your participation, these types of events just wouldn’t be possible. To the AI Makerspace team, you truly made putting on this event look easy, even when things weren’t. Thanks to you Mark for the invite! To the leaders of these companies, especially Laurie at LlamaIndex and Daniele at Toolhouse who I had the pleasure of meeting at a previous hackathon, I want to thank you ?? for spending so much 1 on 1 time with myself and others making new releases, real-time product bug fixes, and suggesting workarounds so that projects could move forward. Chris Alexiuk at AI Makerspace, dude you f’n rock. Your workshop on Friday was solid ?? and without your direction and help many projects, including my own would not have been submitted. Thank you! Super proud of the team I joined ??. Our project OpsRocket won ?? the award offered by Arize AI for best use of #Phoenix which was used for observability of our agent workflow. Our project didn’t go as expected on Saturday due to some glitches in the matrix as is often the case, but we got it done. I can’t say enough good things about these guys and I was truly humbled to sit beside you Alton Alexander, Brian Reardon, David Zhou and Patrick OBrien. Even under pressure these guys were cool as a cucumber. I learned a lot from each one of you. I hedged on Sunday and decided at the last minute to submit a solo project I had mostly completed on Friday, after asking my OpsRocket team if they would mind. Literally, 1 second before the deadline I pressed the submit button on the entry form. I didn’t think my solo pitch went well. I didn’t even get all the way through it. Somehow my project made it through finals. I have to thank David and Patrick from the OpsRocket team for helping me refine my pitch and tease out pertinent value points. They didn’t have to help me, but despite OpsRocket not making it to finals, they eagerly jumped right in. This is the type of character these guys have. I really appreciate it! I was truly shocked my AI Mechanic Assistant Catalog project won ??Third Place out of 40+ other projects by 500+ bleeding-edge AI developers from around the world at a hackathon in the heart of Silicon Valley. I am sharing to encourage folks who may not be super technical but have an idea… start attending these events. See if your idea has legs and build a team! Build. Ship. Share. Shout out to all the sponsors. Thanks!! LlamaIndex, Pinecone, VESSL AI, #SFTechWeek, Arize AI, SAP, Box, Together AI, Toolhouse, AI Makerspace, Mistral AI, #SouthBayGenerativeAI, OpenAI Thank you Christine at 500 Global for letting us use your lovely space!

Instead of finetuning your LLMs, try dynamic few-shot prompting instead ?? With dynamic few-shot prompting, instead of injecting a fixed set of examples into the prompt, you retrieve a dynamic set of examples based on the query - so you find relevant examples that are relevant towards solving your input task. This is helpful for use cases like customer support, text-to-SQL, structured output, and more. RS Rohan has a great resource repo showing how this works using LlamaIndex workflows, check it out: https://lnkd.in/g58qxa8X For more details on workflows: https://lnkd.in/giseEZ5q

Like MySQL/MariaDB and want to build gen AI apps? SkySQL can get you up and running! ?? Check out this detailed how-to post from SkySQL: ?? Set up MariaDB Vector in SkySQL ?? Integrate OpenAI's LLM with LlamaIndex ?? Implement a smart product review analysis system https://lnkd.in/gf8EPXxk

?? Excited to share that our team won 1st place at the RAG-A-THON with our project, #TimelineOfYou over the weekend! We built a system that intelligently curates, presents, and lets you chat with your entire professional journey by leveraging a dynamic #knowledgegraph, and #AI agents. Our solution simplifies the process of gathering research, projects, awards, and publications to create tailored documents for higher ed applications, visa petitions for skilled immigration #O1 #EB1A, and executive career advancements. Timeline of You utilizes Retrieval-Augmented Generation (#RAG), and knowledge #graphs to streamline comprehensive career documentation, personalized recommendation letters, #visa petition support, impact maps, and timelines. A special shout-out to my teammates —Ashwani, Aaditya, Rohan, Amala Deshmukh, and Abhinav for their amazing collaboration and creativity. Thanks to LlamaIndex, Pinecone, VESSL AI, TECH WEEK by a16z, and all the other partners/sponsors for supporting this hackathon. Learn more about our project with the devpost submission, in the comments ? #sftechweek

Build a Financial Agent powered by Claude 3.5 Sonnet and Financial Modeling Prep APIs ???? Financial Modeling Prep is a versatile tool for any financial data -stock prices, income statements, company information, and more. In this post, Hanane D. shows you how to build an agent that can consume these APIs through tool calling and natural language. The resulting agent system can compare against previous financial performance, recall data from previous queries, and retrieve from multiple endpoints. https://lnkd.in/gdTamDKf

Multi-agent system for RFP Response Generation ???? We’re excited to release a brand-new guide showing you how to build an agentic workflow that can take in an input RFP template and generate a full response to the RFP, grounded in your knowledge base and adhering to the relevant guidelines. This is much more than a standard RAG or ReAct agent architecture, and requires the careful orchestration of a set of steps + components. 1. Parse the input RFP template using LlamaParse, extract out a set of questions that you would need answered. 2. For each question, use a Research agent (ReAct loop) with access to a set of tools in the knowledge base to retrieve relevant information and generate an answer 3. Aggregate question/answer pairs into a single file 4. Generate the final report with the RFP template and QA pairs as input. Bonuses ??: it’s fully async, and you get back event and final response streaming! Notebook: https://lnkd.in/g_RgrYme LlamaParse signup: https://lnkd.in/gi8dxGnt