LlamaIndex on LinkedIn: QA over massive tabular data without hallucinations (Caltrain schedule… (2024)

Text-to-SQL - fully local edition 🔐The latest local LLMs are not only capable of RAG synthesis, but also querying structured databases. Diptiman Raichaudhuri has a great tutorial on how to build a fully local text-to-SQL setup, letting you query local databases without an internet connection. Stack:🐤 DuckDB as the database🦙Ollama + Mixtral-8x7B as the model🦙 LlamaIndex as the text-to-sql orchestrationCheck out the full tutorial here: https://lnkd.in/gi_J7eNB

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Multi-Document Agentic RAG using LlamaIndex and MistralThis is a great article by Plaban Nayak on how to build a multi-document agent that can reason about multi-part questions over multiple documents.It does this by modeling each document as a set of tools (summarization and vector search). Since there can be many documents (so tools will overflow context), we can do tool retrieval first in order to pre-fetch the relevant tools that the agent will operate over. The end result is an advanced agent powered by Mistral AI function calling that goes many steps beyond what naive RAG systems can do. https://lnkd.in/gcuMfJJP

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Get 32x faster performance on your vector search at only a 4% cost in accuracy!Building production apps is all about tradeoffs. In vector search, your data is encoded as 32-bit vectors, which can use a lot of storage and compute to search. In this blog post from Jina AI, they show you how to get dramatically faster vector search by reducing them to binary digits, at a small cost in accuracy of retrieval.They also demonstrate how to get it working in LlamaIndex: it's as simple as adding a single `encoding_queries='binary'` parameter to your embedding call!https://lnkd.in/e4P82Dwk

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Structured Image Extraction with GPT-4o 🖼️GPT-4o is state-of-the-art in integrating image/text understanding, and we’ve created a full cookbook showing you how to use GPT-4o to extract out structured JSONs from images. It does this much better than GPT-4V. We feed it detailed papercards (created by Val Andrei Fajardo) of various research papers (see diagram below), and measure quantitative metrics like failure rates and quality of extracted outputs. GPT-4o was able to extract structured output from every image (0% failure rate), and synthesizes much higher quality answers/insights than 4V. Check out our full cookbook here - also by Val Andrei Fajardo!https://lnkd.in/gptzdcUq

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Announcing our first-ever meetup at our brand-new office in San Francisco! Come hang out with us and hear from us and our friends at Activeloop and Tryolabs about the latest developments in generative AI.https://lnkd.in/d3HXb3wF

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🔥 Introducing GPT-4o + LlamaParse 🔥GPT-4o is the state-of-the-art model for multimodal understanding, meaning it also has state-of-the-art document parsing capabilities.LlamaParse is the platform for enabling LLM-powered parsing - it uses LLMs to extract documents from any file type in a performant, reliable fashion, offering state-of-the-art response quality for advanced document RAG.We’re excited to offer GPT-4o as an explicit option in LlamaParse, which will use GPT-4o for extraction per page into markdown, instead of using our default parsers/models. Why:- GPT-4o is very good at parsing very complex documents into well-formatted markdown. Oftentimes it outperforms our default approaches.- This means that it can turn documents with very complex tables / charts into clean, indexable data for your RAG pipeline - higher response quality, lower hallucinations 📈Tradeoffs / Caveats ⚠️:- It’s expensive 💵: Due to the cost of inference, using GPT-4o is currently $0.60 USD per page (while by default LlamaParse is $0.003 per page). This cost can spike quickly - beware!- You can specify your OpenAI key, in which case the marginal cost per page goes down to 0.3c per page.- This is a beta feature. Given the cost and latency, use this with caution! If you want to give this a shot, signup for an account and check out our UI: https://lnkd.in/gbkxQAQdNotebook: https://lnkd.in/grwUVr-G

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LlamaParse 🤝 Quivr 🧠Quivr (YC W24) (Stan Girard) is a popular open-source platform where you can create personalized AI assistants over your data.We’re excited to partner with Quivr to introduce LlamaParse - parse any complex document (.pdf, .pptx, .md) through our advanced parsing capabilities, ensuring that you get clean data before storing in the agent’s personalized memory. This ensures accurate retrieval and lower hallucinations during conversation.Check out the docs! https://lnkd.in/d7Ftjd5w

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GPT-4o support now available in create-llama! Get 90% of the way through building a chatbot over your data just by answering a few questions.

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