LangChain Changelog

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June 2024

🛝 Custom models in LangSmith Playground

Once you’ve deployed a model server, use it in the LangSmith Playground by selecting either ChatCustomModel or the CustomModel provider for chat-style model...

🥷 PII Masking in LangSmith

LangSmith SDK versions now have enhanced PII masking capabilities. You can easily create anonymizers by specifying a list of regular expressions or providing...

📁 Workspaces in LangSmith for improved collaboration & organization

LangSmith now offers Workspaces to separate resources for different teams or different environments. Admins can add users to Workspaces, granting them...

🗺️ Variable mapping for online evaluator prompts

In LangSmith, you can now use any structured prompt from the LangChain Hub with customizable variables. You can personalize your inputs based on recent runs...

🛝 Enter the playground from scratch instead of from a trace or a prompt

The Playground is now its own tab in the sidebar of LangSmith. To create a new prompt , simply craft a prompt in the empty playground and click "Save As" to...

💬 Chat LangChain improvements

Users can now view and continue previous chats in Chat LangChain , our chatbot for LangChain Python docs and API reference. These improvements were made...

🧬 Build generative UI applications with LangChain in JavaScript/TypeScript and Python

Using streaming agent events and tool calls to pick pre-built components, you can now use generative UI to improve your chatbot with interactable components....

May 2024

💬 Manage private prompts without a handle

We’ve improved our SDK and LangSmith Prompts UI to make navigating prompts simpler. Now, only public prompts require a handle on creation. New accounts won’t...

🔧 Off-the-shelf online evaluator prompts to catch bad retrieval and hallucinations for RAG

LangSmith’s online evaluator (LLM-as-judge) automatically runs on production traces with a customizable prompt and automation rules. Our new off-the-shelf...

🔁 Repetitions to build confidence in your experiment results

You can now run multiple repetitions of your experiment in LangSmith. This helps smooth out noise from variability introduced by your application or from...

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