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Evaluation of language models, and by extension applications built on top of language models, is hard. With recent model releases (OpenAI, Anthropic, Google) evaluation is becoming a bigger and bigger issue. People are starting to try to tackle this, with OpenAI releasing OpenAI/evals - focused on evaluating OpenAI models. Correspondingly, we're excited to announce some additions and improvement to how we think about evaluating chains and agents.

The Problem

It can be really hard to evaluate LangChain chains and agents. There are two main reasons for this:

# 1: Lack of data

You generally don't have a ton of data to evaluate your chains/agents over before starting a project. This is usually because Large Language Models (the core of most chains/agents) are terrific few-shot and zero shot learners, meaning you are almost always able to get started on a particular task (text-to-SQL, question answering, etc) without a large dataset of examples. This is in stark contrast to traditional machine learning where you had to first collect a bunch of datapoints before even getting started using a model.

# 2: Lack of metrics

Most chains/agents are performing tasks for which there are not very good metrics to evaluate performance. For example, one of the most common use cases is generating text of some form. Evaluating generated text is much more complicated than evaluating a classification prediction, or a numeric prediction.

The Solution

LangChain attempts to tackle both of those issues. So far, we have initial passes at solutions – by no means perfect solutions. We very much welcome feedback, contributions, integrations, and thoughts on this.

Here is what we have for each problem so far:

# 1: Lack of data

We have started LangChainDatasets a Community space on Hugging Face. We intend this to be a collection of open source datasets for evaluating common chains and agents. We have contributed five datasets of our own to start, but we fully intend this to be a community effort. In order to contribute a dataset, you simply need to join the community. Then, you will be able to upload datasets.

We're also aiming to make it as easy as possible for people to create their own datasets. As a first pass at this, we've added a QAGenerationChain, which, given a document, comes up with question-answer pairs that can be used to evaluate question-answering tasks over that document down the line. See this notebook for an example of how to use this chain.

# 2: Lack of metrics

We have two solutions to the lack of metrics.

The first solution is to use no metrics, and rather just rely on visually inspecting the results get a sense for how the chain/agent is performing. To assist in this, we have developed (and will continue to develop) tracing, a UI-based visualizer of your chain and agent runs, which we first released in late January.

The second solution we recommend is to use language models themselves to evaluate outputs. For this we have a few different chains and prompts aimed at tackling this issue.


We have created a bunch of examples combining the above two solutions to show how we internally evaluate chains and agents when we are developing. In addition to the examples we've curated, we also highly welcome contributions here. To facilitate that, we've included a template notebook for community members to use to build their own examples.

The existing examples we have are:

Question Answering (State of Union): An notebook showing evaluation of a question-answering task over a State-of-the-Union address.

Question Answering (Paul Graham Essay): An notebook showing evaluation of a question-answering task over a Paul Graham essay.

SQL Question Answering (Chinook): An notebook showing evaluation of a question-answering task over a SQL database (the Chinook database).

Agent Vectorstore: An notebook showing evaluation of an agent doing question answering while routing between two different vector databases.

Agent Search + Calculator: An notebook showing evaluation of an agent doing question answering using a Search engine and a Calculator as tools.