langchain/docs/extras/guides/evaluation/string/regex_match.ipynb

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    "cells": [
        {
            "cell_type": "markdown",
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            "metadata": {},
            "source": [
                "# Regex Match\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/extras/guides/evaluation/string/regex_match.ipynb)\n",
                "\n",
                "To evaluate chain or runnable string predictions against a custom regex, you can use the `regex_match` evaluator."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "0de44d01-1fea-4701-b941-c4fb74e521e7",
            "metadata": {},
            "outputs": [],
            "source": [
                "from langchain.evaluation import RegexMatchStringEvaluator\n",
                "\n",
                "evaluator = RegexMatchStringEvaluator()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "fe3baf5f-bfee-4745-bcd6-1a9b422ed46f",
            "metadata": {},
            "source": [
                "Alternatively via the loader:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "f6790c46",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "from langchain.evaluation import load_evaluator\n",
                "\n",
                "evaluator = load_evaluator(\"regex_match\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "49ad9139",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 1}"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Check for the presence of a YYYY-MM-DD string.\n",
                "evaluator.evaluate_strings(\n",
                "    prediction=\"The delivery will be made on 2024-01-05\",\n",
                "    reference=\".*\\\\b\\\\d{4}-\\\\d{2}-\\\\d{2}\\\\b.*\"\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "1f5e82a3-247e-45a8-85fc-6af53bf7ff82",
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                            "{'score': 0}"
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                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
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            ],
            "source": [
                "# Check for the presence of a MM-DD-YYYY string.\n",
                "evaluator.evaluate_strings(\n",
                "    prediction=\"The delivery will be made on 2024-01-05\",\n",
                "    reference=\".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\"\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "168fcd92-dffb-4345-b097-02d0fedf52fd",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 1}"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Check for the presence of a MM-DD-YYYY string.\n",
                "evaluator.evaluate_strings(\n",
                "    prediction=\"The delivery will be made on 01-05-2024\",\n",
                "    reference=\".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\"\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "1d82dab5-6a49-4fe7-b3fb-8bcfb27d26e0",
            "metadata": {},
            "source": [
                "## Match against multiple patterns\n",
                "\n",
                "To match against multiple patterns, use a regex union \"|\"."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "b87b915e-b7c2-476b-a452-99688a22293a",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 1}"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Check for the presence of a MM-DD-YYYY string or YYYY-MM-DD\n",
                "evaluator.evaluate_strings(\n",
                "    prediction=\"The delivery will be made on 01-05-2024\",\n",
                "    reference=\"|\".join([\".*\\\\b\\\\d{4}-\\\\d{2}-\\\\d{2}\\\\b.*\", \".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\"])\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
            "metadata": {},
            "source": [
                "## Configure the RegexMatchStringEvaluator\n",
                "\n",
                "You can specify any regex flags to use when matching."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "import re\n",
                "\n",
                "evaluator = RegexMatchStringEvaluator(\n",
                "    flags=re.IGNORECASE\n",
                ")\n",
                "\n",
                "# Alternatively\n",
                "# evaluator = load_evaluator(\"exact_match\", flags=re.IGNORECASE)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 1}"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "evaluator.evaluate_strings(\n",
                "    prediction=\"I LOVE testing\",\n",
                "    reference=\"I love testing\",\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "82de8d3e-c829-440e-a582-3fb70cecad3b",
            "metadata": {},
            "outputs": [],
            "source": []
        }
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