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101 lines
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101 lines
5.2 KiB
ReStructuredText
Evaluation
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==============
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.. note::
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`Conceptual Guide <https://docs.langchain.com/docs/use-cases/evaluation>`_
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This section of documentation covers how we approach and think about evaluation in LangChain.
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Both evaluation of internal chains/agents, but also how we would recommend people building on top of LangChain approach evaluation.
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The Problem
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-----------
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It can be really hard to evaluate LangChain chains and agents.
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There are two main reasons for this:
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**# 1: Lack of data**
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You generally don't have a ton of data to evaluate your chains/agents over before starting a project.
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This is usually because Large Language Models (the core of most chains/agents) are terrific few-shot and zero shot learners,
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meaning you are almost always able to get started on a particular task (text-to-SQL, question answering, etc) without
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a large dataset of examples.
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This is in stark contrast to traditional machine learning where you had to first collect a bunch of datapoints
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before even getting started using a model.
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**# 2: Lack of metrics**
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Most chains/agents are performing tasks for which there are not very good metrics to evaluate performance.
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For example, one of the most common use cases is generating text of some form.
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Evaluating generated text is much more complicated than evaluating a classification prediction, or a numeric prediction.
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The Solution
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------------
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LangChain attempts to tackle both of those issues.
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What we have so far are initial passes at solutions - we do not think we have a perfect solution.
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So we very much welcome feedback, contributions, integrations, and thoughts on this.
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Here is what we have for each problem so far:
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**# 1: Lack of data**
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We have started `LangChainDatasets <https://huggingface.co/LangChainDatasets>`_ a Community space on Hugging Face.
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We intend this to be a collection of open source datasets for evaluating common chains and agents.
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We have contributed five datasets of our own to start, but we highly intend this to be a community effort.
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In order to contribute a dataset, you simply need to join the community and then you will be able to upload datasets.
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We're also aiming to make it as easy as possible for people to create their own datasets.
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As a first pass at this, we've added a QAGenerationChain, which given a document comes up
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with question-answer pairs that can be used to evaluate question-answering tasks over that document down the line.
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See `this notebook <./evaluation/qa_generation.html>`_ for an example of how to use this chain.
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**# 2: Lack of metrics**
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We have two solutions to the lack of metrics.
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The first solution is to use no metrics, and rather just rely on looking at results by eye to get a sense for how the chain/agent is performing.
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To assist in this, we have developed (and will continue to develop) `tracing <../tracing.html>`_, a UI-based visualizer of your chain and agent runs.
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The second solution we recommend is to use Language Models themselves to evaluate outputs.
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For this we have a few different chains and prompts aimed at tackling this issue.
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The Examples
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------------
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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.
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In addition to the examples we've curated, we also highly welcome contributions here.
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To facilitate that, we've included a `template notebook <./evaluation/benchmarking_template.html>`_ for community members to use to build their own examples.
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The existing examples we have are:
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`Question Answering (State of Union) <./evaluation/qa_benchmarking_sota.html>`_: An notebook showing evaluation of a question-answering task over a State-of-the-Union address.
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`Question Answering (Paul Graham Essay) <./evaluation/qa_benchmarking_pg.html>`_: An notebook showing evaluation of a question-answering task over a Paul Graham essay.
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`SQL Question Answering (Chinook) <./evaluation/sql_qa_benchmarking_chinook.html>`_: An notebook showing evaluation of a question-answering task over a SQL database (the Chinook database).
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`Agent Vectorstore <./evaluation/agent_vectordb_sota_pg.html>`_: An notebook showing evaluation of an agent doing question answering while routing between two different vector databases.
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`Agent Search + Calculator <./evaluation/agent_benchmarking.html>`_: An notebook showing evaluation of an agent doing question answering using a Search engine and a Calculator as tools.
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Other Examples
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------------
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In addition, we also have some more generic resources for evaluation.
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`Question Answering <./evaluation/question_answering.html>`_: An overview of LLMs aimed at evaluating question answering systems in general.
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`Data Augmented Question Answering <./evaluation/data_augmented_question_answering.html>`_: An end-to-end example of evaluating a question answering system focused on a specific document (a RetrievalQAChain to be precise). This example highlights how to use LLMs to come up with question/answer examples to evaluate over, and then highlights how to use LLMs to evaluate performance on those generated examples.
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`Hugging Face Datasets <./evaluation/huggingface_datasets.html>`_: Covers an example of loading and using a dataset from Hugging Face for evaluation.
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.. toctree::
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:maxdepth: 1
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:glob:
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:hidden:
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evaluation/*
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