LangChain Changelog

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June 2024

🔁 Self-improving LLM evaluators in LangSmith

Using an “ LLM-as-a-Judge ” is a popular way to grade outputs from LLM applications. This involves passing the generated output to a separate LLM and asking...

🧷 Store the model and configuration when saving a prompt

When you save a prompt in LangSmith, the model and its configuration will also be stored. You no longer need to reset your preferences in the Playground...

🛝 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...

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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