{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "2da95378",
            "metadata": {},
            "source": [
                "# String Distance\n",
                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
                "\n",
                "One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance.  This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
                "\n",
                "This can be accessed using the `string_distance` evaluator, which uses distance metric's from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
                "\n",
                "**Note:** The returned scores are _distances_, meaning lower is typically \"better\".\n",
                "\n",
                "For more information, check out the reference docs for the [StringDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html#langchain.evaluation.string_distance.base.StringDistanceEvalChain) for more info."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "8b47b909-3251-4774-9a7d-e436da4f8979",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "# %pip install rapidfuzz"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "f6790c46",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "from langchain.evaluation import load_evaluator\n",
                "\n",
                "evaluator = load_evaluator(\"string_distance\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "49ad9139",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 0.11555555555555552}"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "evaluator.evaluate_strings(\n",
                "    prediction=\"The job is completely done.\",\n",
                "    reference=\"The job is done\",\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "c06a2296",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 0.0724999999999999}"
                        ]
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# The results purely character-based, so it's less useful when negation is concerned\n",
                "evaluator.evaluate_strings(\n",
                "    prediction=\"The job is done.\",\n",
                "    reference=\"The job isn't done\",\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
            "metadata": {},
            "source": [
                "## Configure the String Distance Metric\n",
                "\n",
                "By default, the `StringDistanceEvalChain` uses  levenshtein distance, but it also supports other string distance algorithms. Configure using the `distance` argument."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "a88bc7d7-62d3-408d-b0e0-43abcecf35c8",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[<StringDistance.DAMERAU_LEVENSHTEIN: 'damerau_levenshtein'>,\n",
                            " <StringDistance.LEVENSHTEIN: 'levenshtein'>,\n",
                            " <StringDistance.JARO: 'jaro'>,\n",
                            " <StringDistance.JARO_WINKLER: 'jaro_winkler'>]"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "from langchain.evaluation import StringDistance\n",
                "\n",
                "list(StringDistance)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "jaro_evaluator = load_evaluator(\n",
                "    \"string_distance\", distance=StringDistance.JARO\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 0.19259259259259254}"
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "jaro_evaluator.evaluate_strings(\n",
                "    prediction=\"The job is completely done.\",\n",
                "    reference=\"The job is done\",\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "7020b046-0ef7-40cc-8778-b928e35f3ce1",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 0.12083333333333324}"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "jaro_evaluator.evaluate_strings(\n",
                "    prediction=\"The job is done.\",\n",
                "    reference=\"The job isn't done\",\n",
                ")"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3 (ipykernel)",
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            "codemirror_mode": {
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