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- Description: This PR adds a new chain `rl_chain.PickBest` for learned prompt variable injection, detailed description and usage can be found in the example notebook added. It essentially adds a [VowpalWabbit](https://github.com/VowpalWabbit/vowpal_wabbit) layer before the llm call in order to learn or personalize prompt variable selections. Most of the code is to make the API simple and provide lots of defaults and data wrangling that is needed to use Vowpal Wabbit, so that the user of the chain doesn't have to worry about it. - Dependencies: [vowpal-wabbit-next](https://pypi.org/project/vowpal-wabbit-next/), - sentence-transformers (already a dep) - numpy (already a dep) - tagging @ataymano who contributed to this chain - Tag maintainer: @baskaryan - Twitter handle: @olgavrou Added example notebook and unit tests
🦜️🧪 LangChain Experimental
This package holds experimental LangChain code, intended for research and experimental uses.
Warning
Portions of the code in this package may be dangerous if not properly deployed in a sandboxed environment. Please be wary of deploying experimental code to production unless you've taken appropriate precautions and have already discussed it with your security team.
Some of the code here may be marked with security notices. However, given the exploratory and experimental nature of the code in this package, the lack of a security notice on a piece of code does not mean that the code in question does not require additional security considerations in order to be safe to use.