__init__.py — langchain Source File

Source Code

"""**Evaluation** chains for grading LLM and Chain outputs.

This module contains off-the-shelf evaluation chains for grading the output of
LangChain primitives such as language models and chains.

**Loading an evaluator**

To load an evaluator, you can use the `load_evaluators <langchain.evaluation.loading.load_evaluators>` or
`load_evaluator <langchain.evaluation.loading.load_evaluator>` functions with the
names of the evaluators to load.

```python
from langchain_classic.evaluation import load_evaluator

evaluator = load_evaluator("qa")
evaluator.evaluate_strings(
    prediction="We sold more than 40,000 units last week",
    input="How many units did we sell last week?",
    reference="We sold 32,378 units",
)
```

The evaluator must be one of `EvaluatorType <langchain.evaluation.schema.EvaluatorType>`.

**Datasets**

To load one of the LangChain HuggingFace datasets, you can use the `load_dataset <langchain.evaluation.loading.load_dataset>` function with the
name of the dataset to load.

```python
from langchain_classic.evaluation import load_dataset

ds = load_dataset("llm-math")
```

**Some common use cases for evaluation include:**

- Grading the accuracy of a response against ground truth answers: `QAEvalChain <langchain.evaluation.qa.eval_chain.QAEvalChain>`
- Comparing the output of two models: `PairwiseStringEvalChain <langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain>` or `LabeledPairwiseStringEvalChain <langchain.evaluation.comparison.eval_chain.LabeledPairwiseStringEvalChain>` when there is additionally a reference label.
- Judging the efficacy of an agent's tool usage: `TrajectoryEvalChain <langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain>`
- Checking whether an output complies with a set of criteria: `CriteriaEvalChain <langchain.evaluation.criteria.eval_chain.CriteriaEvalChain>` or `LabeledCriteriaEvalChain <langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain>` when there is additionally a reference label.
- Computing semantic difference between a prediction and reference: `EmbeddingDistanceEvalChain <langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain>` or between two predictions: `PairwiseEmbeddingDistanceEvalChain <langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain>`
- Measuring the string distance between a prediction and reference `StringDistanceEvalChain <langchain.evaluation.string_distance.base.StringDistanceEvalChain>` or between two predictions `PairwiseStringDistanceEvalChain <langchain.evaluation.string_distance.base.PairwiseStringDistanceEvalChain>`

**Low-level API**

These evaluators implement one of the following interfaces:

- `StringEvaluator <langchain.evaluation.schema.StringEvaluator>`: Evaluate a prediction string against a reference label and/or input context.
- `PairwiseStringEvaluator <langchain.evaluation.schema.PairwiseStringEvaluator>`: Evaluate two prediction strings against each other. Useful for scoring preferences, measuring similarity between two chain or llm agents, or comparing outputs on similar inputs.
- `AgentTrajectoryEvaluator <langchain.evaluation.schema.AgentTrajectoryEvaluator>` Evaluate the full sequence of actions taken by an agent.

These interfaces enable easier composability and usage within a higher level evaluation framework.

"""  # noqa: E501

from langchain_classic.evaluation.agents import TrajectoryEvalChain
from langchain_classic.evaluation.comparison import (
    LabeledPairwiseStringEvalChain,
    PairwiseStringEvalChain,
// ... (78 more lines)