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The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. Link

This notebook showcases basic functionality related to Deep Lake. While Deep Lake can store embeddings, it is capable of storing any type of data. It is a fully fledged serverless data lake with version control, query engine and streaming dataloader to deep learning frameworks. Link

How to Use Deep Lake as a Vector Store in LangChain Link

DocsGPT is a cutting-edge open-source solution that streamlines the process of finding information in project documentation. With its integration of the powerful GPT models, developers can easily ask questions about a project and receive accurate answers. Link

Architecture essays Link

For complicated data extraction you need a robust library. The Kor Library (created by Eugene Yurtsev) is an awesome tool just for this. Link

This is part 4 of our blog series on Generative AI. In the previous blog posts we explained why Ray is a sound platform for Generative AI, we showed how it can push the performance limits, and how you can use Ray for stable diffusion. Link

Build your knowledge bases Link

Steamship is the fastest way to build, ship, and use full-lifecycle language AI. Link

Build a question-answering tool based on financial data with LangChain & Deep Lake’s unified & streamable data store. Prototype with LangChain rapidly with no need to recompute embeddings. Train LLMs faster & cheaper with LangChain & Deep Lake. Link

In this tutorial, we are going to use Langchain + Deep Lake with GPT to analyze the code base of the LangChain itself. Link

Vercel is the platform for frontend developers, providing the speed and reliability innovators need to create at the moment of inspiration. Link LangChain

Modal lets you run or deploy machine learning models, massively parallel compute jobs, task queues, web apps, and much more, without your own infrastructure. 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. Link