ChatGPT unleashed a tidal wave of innovation with large language models (LLMs). More companies than ever before are bringing the power of natural language interaction to their products. The adoption of language model APIs is creating a new stack in its wake. To better understand the applications people are building and the stacks they are using to do so, we spoke with 33 companies across the Sequoia network, from seed stage startups to large public enterprises. We spoke with them two months ago and last week to capture the pace of change. As many founders and builders are in the midst of figuring out their AI strategies themselves, we wanted to share our findings even as this space is rapidly evolving. — Read More
Tag Archives: DevOps
Leveraging FastAPI, OpenAI, and SQLAlchemy for Natural Language SQL Queries
SQL (Structured Query Language) is the standard language for managing and manipulating relational databases. What if we could interact with databases using natural language queries?
In this post we show how you can use SQL to load a dataframe to a database, write a prompt to query it, and connect this to a FastAPI application for deployment and enabling users to interact with the database. — Read More
How open-source LLMs are challenging OpenAI, Google, and Microsoft
In the past few years, it seemed that wealthy tech companies would be able to monopolize the growing market for large language models (LLM). And recent earnings calls from big tech companies suggested they are in control. Microsoft’s announcements, in particular, show that the company has created a billion-dollar business from its AI services, including through Azure OpenAI Services and the workloads OpenAI runs on its cloud infrastructure.
However, a recently leaked internal document from Google indicates that the market share of big tech is not as secure as it seems thanks to advances in open-source LLMs. In short, the document says “We have no moat, and neither does OpenAI.” The dynamics of the market are gradually shifting from “bigger is better” to “cheaper is better,” “more efficient is better,” and “customizable is better.” And while there will always be a market for cloud-based LLM and generative AI products, customers now have open-source options to explore as well. — Read More
Google DeepMind’s game-playing AI just found another way to make code faster
The AI-generated algorithms are already being used by millions of developers.
DeepMind’s run of discoveries in fundamental computer science continues. Last year the company used a version of its game-playing AI AlphaZero to find new ways to speed up the calculation of a crucial piece of math at the heart of many different kinds of code, beating a 50-year-old record.
Now it has pulled the same trick again—twice. Using a new version of AlphaZero called AlphaDev, the UK-based firm (recently renamed Google DeepMind after a merge with its sister company’s AI lab in April) has discovered a way to sort items in a list up to 70% faster than the best existing method.
It has also found a way to speed up a key algorithm used in cryptography by 30%. These algorithms are among the most common building blocks in software. Small speed-ups can make a huge difference, cutting costs and saving energy. — Read More
RedPajama 7B now available, instruct model outperforms all open 7B models on HELM benchmarks
The RedPajama project aims to create a set of leading open-source models and to rigorously understand the ingredients that yield good performance. In April we released the RedPajama base dataset based on the LLaMA paper, which has worked to kindle rapid innovation in open-source AI.
The 5 terabyte dataset has been downloaded thousands of times and used to train over 100 models! — Read More
The Falcon has landed in the Hugging Face ecosystem
Falcon is a new family of state-of-the-art language models created by the Technology Innovation Institute in Abu Dhabi, and released under the Apache 2.0 license. Notably, Falcon-40B is the first “truly open” model with capabilities rivaling many current closed-source models. This is fantastic news for practitioners, enthusiasts, and industry, as it opens the door for many exciting use cases.
In this blog, we will be taking a deep dive into the Falcon models: first discussing what makes them unique and then showcasing how easy it is to build on top of them (inference, quantization, finetuning, and more) with tools from the Hugging Face ecosystem. — Read More
Open-Source LLMs
In February, Meta released its large language model: LLaMA. Unlike OpenAI and its ChatGPT, Meta didn’t just give the world a chat window to play with. Instead, it released the code into the open-source community, and shortly thereafter the model itself was leaked. Researchers and programmers immediately started modifying it, improving it, and getting it to do things no one else anticipated. And their results have been immediate, innovative, and an indication of how the future of this technology is going to play out. Training speeds have hugely increased, and the size of the models themselves has shrunk to the point that you can create and run them on a laptop. The world of AI research has dramatically changed.
This development hasn’t made the same splash as other corporate announcements, but its effects will be much greater. It will wrest power from the large tech corporations, resulting in both much more innovation and a much more challenging regulatory landscape. The large corporations that had controlled these models warn that this free-for-all will lead to potentially dangerous developments, and problematic uses of the open technology have already been documented. But those who are working on the open models counter that a more democratic research environment is better than having this powerful technology controlled by a small number of corporations. — Read More
AI Coding Tools
Coding is arguably the single best application of large language models (LLMs) to date! The market for AI Coding tools is booming, with 100+ tools and competition across Big Tech, established Unicorns and emerging AI-native startups. Read on for an introduction. [1/13] — Read More
Combining Text-to-SQL with Semantic Search for Retrieval Augmented Generation
In this article, we showcase a powerful new query engine ( SQLAutoVectorQueryEngine ) in LlamaIndex that can leverage both a SQL database as well as a vector store to fulfill complex natural language queries over a combination of structured and unstructured data. This query engine can leverage the expressivity of SQL over structured data, and join it with unstructured context from a vector database. We showcase this query engine on a few examples and show that it can handle queries that make use of both structured/unstructured data, or either.
Check out the full guide here: https://gpt-index.readthedocs.io/en/latest/examples/query_engine/SQLAutoVectorQueryEngine.html.
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