LangChain v0.1.0 Today we’re excited to announce the release of langchain 0.1.0, our first stable version. It is fully backwards compatible, comes in both
LangChain's First Birthday It’s LangChain’s first birthday! It’s been a really exciting year! We worked with thousands of developers building LLM applications and tooling. We
You.com x LangChain Editor's Note: the following is a guest blog post from our friends at You.com. We've seen a lot of interesting
Test Run Comparisons One pattern I noticed is that great AI researchers are willing to manually inspect lots of data. And more than that, they build infrastructure that
Building LLM-Powered Web Apps with Client-Side Technology The initial version of this blog post was a talk for Google’s internal WebML Summit 2023, which you can check out here. It’s
Introducing LangServe, the best way to deploy your LangChains We think the LangChain Expression Language (LCEL) is the quickest way to prototype the brains of your LLM application. The next exciting step is to
Building (and Breaking) WebLangChain Important Links: * Hosted WebLangChain * Open-source code for WebLangChain Introduction One of the big shortcomings of LLMs is that they can only answer questions about data
Fine-tune your LLMs with LangSmith and Lilac In taking your LLM from prototype into production, many have turned to fine-tuning models to get more consistent and high-quality behavior in their applications. Services
Announcing our Student Hacker in Residence Program, Fall '23 Semester Today, we're opening up applications for our inaugural student hacker in residence program. We're looking for 3-5 students to work alongside
Announcing LangChain Hub Today, we're excited to launch LangChain Hub–a home for uploading, browsing, pulling, and managing your prompts. (Soon, we'll be adding
Chat Loaders: Fine-tune a ChatModel in your Voice Summary We are adding a new integration type, ChatLoaders, to make it easier to fine-tune models on your own unique writing style. These utilities help
Using LangSmith to Support Fine-tuning Summary We created a guide for fine-tuning and evaluating LLMs using LangSmith for dataset management and evaluation. We did this both with an open source
