[Week of 8/21] LangChain Release Notes

[Week of 8/21] LangChain Release Notes

3 min read

New in Retrieval

There was a lot happening in the retrieval space these past two weeks, so we wanted to highlight these explicitly!

New in LangSmith

This week, we’re focusing on cookbooks as part of our effort to help more developers build end-to-end applications.

  • Use the run_on_dataset helper to benchmark aggregate metrics and check against a threshold
  • Write individual unit tests to make assertions on every row in a dataset
  • Make user scores more actionable, with optional comments and corrections
  • Evaluate your apps via LLM-based preference scoring
  • Use LangSmith to test your RAG system and make prompt tweaks to improve the chain's performance to improve overall consistency of your LLM applications
  • if there are other recipes you’d like to see, tell us about them @hello@langchain.dev
  • Monitoring Charts: Each project now has a monitor tab that allows you to track important metrics over time including trace count, success rate, and latency. We will be adding more metrics very soon!

New in Open Source

  • Added Fallbacks to the LangChain Expression Language (LCEL): a better way to handle LLM API failures in production-ready LLM applications
  • Caching Embeddings: Embeddings can be stored or temporarily cached to avoid needing to recompute them.
  • ChatLangChain Improvements: We're benchmarking a bunch of retrieval and agent methods for our "chat langchain" app! Interact with the new beta version here.
  • Open Source LLM guide: covers open source LLM SOTA (overview fig below) and ways to run them locally (llama.cpp, http://ollama.ai, gpt4all).
  • MultiVector Retriever: a new retrieval algorithm that enables multiple vector embeddings per document, that can be per-chunk, a summary, hypothetical questions, or more
  • OpenAI Adapter: we added an easy way to switch out our OpenAI calls for the variety of other models that LangChain supports

In case you missed it

Use-cases we love

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