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
[Week of 9/18] LangChain Release Notes New in LangSmith * Org Support in LangChain Hub: share and collaborate on prompts across your team. Easily pull in organizationally-approved prompts into your LangChain code.
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 the LangChain team this
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 other artifacts like chains
[Week of 8/21] LangChain Release Notes New in Retrieval There was a lot happening in the retrieval space these past two weeks, so we wanted to highlight these explicitly! * MultiVector Retriever:
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
Benchmarking Question/Answering Over CSV Data This is a bit of a longer post. It's a deep dive on question-answering over tabular data. We discuss (and use) CSV data in this
GPT Researcher x LangChain Here at LangChain we think that web research is fantastic use case for LLMs. So much so that we wrote a blog on it about
Making Data Ingestion Production Ready: a LangChain-Powered Airbyte Destination A big focus of ours over the past few months has been enabling teams to go from prototype to production. To take apps they developed
Conversational Retrieval Agents TL;DR: There have been several emerging trends in LLM applications over the past few months: RAG, chat interfaces, agents. Our newest functionality - conversational
LangChain Expression Language TL;DR: * We’re excited to announce a new syntax to create chains with composition. This comes along with a new interface that supports batch,