Posts

Learn how to develop a LangChain agent that has multiple ways of interacting with the Neo4j database Link

In this guide, I share my analysis of the current architectural best practices for data-informed language model applications. This particular subdiscipline is experiencing phenomenal research interest even by the standards of large language models - in this guide, I cite 8 research papers and 4 software projects, with a median initial publication date of November 22nd, 2022. Link

The purpose of this repository is to let people to use lots of open sourced instruction-following fine-tuned LLM models as a Chatbot service. Because different models behave differently, and different models require differently formmated prompts, I made a very simple library Ping Pong for model agnostic conversation and context managements. Link

ML System Design Doc Link

PDFGPT.IO Link

Semantra is a multipurpose tool for semantically searching documents. Query by meaning rather than just by matching text. Link

Text2Diagram: Create schematic architecture diagrams, UML, flowcharts, Gnatt charts, mind maps, Sequence diagrams, User Journey and many more with natural language. Link

A curated (still actively updated) list of practical guide resources of LLMs. It’s based on our survey paper: Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond and efforts from @xinyadu. The survey is partially based on the second half of this Blog. We also build an evolutionary tree of modern Large Language Models (LLMs) to trace the development of language models in recent years and highlights some of the most well-known models.

At present, our core contributors are preparing the 65B version and we expect to empower WizardLM with the ability to perform instruction evolution itself, aiming to evolve your specific data at a low cost. Link

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. While machine learning frameworks and platforms like PyTorch, TensorFlow, and scikit-learn can perform data exploration well, it’s not their primary intent. There are also plenty of data visualization libraries available that can handle exploration like Plotly, matplotlib, D3, Apache ECharts, Bokeh, etc.

In this article, we will be covering what HuggingFace is, how and why it came to exist, and how to best utilize it for your machine learning workflows. We’ll also touch on the different use cases covered by the various HuggingFace packages in the ecosystem. Link

A guide to augmenting your dataset with new and informative features using Upgini Link

Over the past two weeks, there has been a massive increase in using LLMs in an agentic manner. Specifically, projects like AutoGPT, BabyAGI, CAMEL, and Generative Agents have popped up. The LangChain community has now implemented some parts of all of those projects in the LangChain framework. While researching and implementing these projects, we’ve tried to best understand what the differences between them are and what the novel features of each are.

Chat with your CSV files with a memory chatbot🤖 | Made with Langchain🦜 and OpenAI🧠 Link

As an AI enthusiast, I’m always on the lookout for new AI-powered tools and technologies to make my life easier. Recently, I stumbled upon Chart-GPT, a game-changing tool that transforms text into beautiful charts within seconds with the help of OpenAI’s APIs. Link