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
Introducing Airbyte sources within LangChain Editor's Note: This post was written in collaboration with the Airbyte team. They've made it really easy to connect even more
LangChain 🤝 DemoGPT: New Era for Gen-AI Applications Editor's Note: This post was written in collaboration with the DemoGPT team. We're excited about what they're doing to
Zep x LangSmith: Foundations of LLM app development with LangChain.js and Zep Learn how to build three foundational LLM apps using TypeScript, LangChain.js, and Zep. Editor's Note: This post was written in collaboration with
Langchain x Predibase: The easiest way to fine-tune and productionize OSS LLMs Editor's Note: This post was written in collaboration with the Predibase team. We're really excited about the way their integration with
Qdrant x LangChain: Endgame Performance Editor's Note: This post was written by the Qdrant team and cross-posted from their blog. As more LLM applications move into production, speed,
MultiOn x LangChain: Powering Next-Gen Web Automation & Navigation with AI Editor's Note: This post was written in collaboration with MultiOn. We're really excited about the way they're using Agents
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
Label Studio x LangChain: From Foundation Models to Fine-Tuned Applications Using Label Studio Editor's Note: This post was written by Jimmy Whitaker, Data Scientist in Residence at HumanSignal. Label Studio is an open-source data labeling platform
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
Villagers x LangSmith: Simulating multi-agent social networks with LangSmith Editor's Note: This post was written in collaboration with Kevin Hu, Tae Hyoung Jo, John Kim, and Tejal Patwardhan from the Villagers team.
NeumAI x LangChain: Efficiently maintaining context in sync for AI applications Editors Note: This post was written by the NeumAI team and cross-posted from their blog. Keeping source data relevant and up-to-date efficiently is a challenge
