langchain/docs/docs/guides/productionization/evaluation/string/exact_match.ipynb

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            "source": [
                "# Exact Match\n",
                "[![Open In Colab](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/exact_match.ipynb)\n",
                "\n",
                "Probably the simplest ways to evaluate an LLM or runnable's string output against a reference label is by a simple string equivalence.\n",
                "\n",
                "This can be accessed using the `exact_match` evaluator."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "0de44d01-1fea-4701-b941-c4fb74e521e7",
            "metadata": {},
            "outputs": [],
            "source": [
                "from langchain.evaluation import ExactMatchStringEvaluator\n",
                "\n",
                "evaluator = ExactMatchStringEvaluator()"
            ]
        },
        {
            "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(\"exact_match\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
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                            "{'score': 0}"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
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            ],
            "source": [
                "evaluator.evaluate_strings(\n",
                "    prediction=\"1 LLM.\",\n",
                "    reference=\"2 llm\",\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": {},
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                }
            ],
            "source": [
                "evaluator.evaluate_strings(\n",
                "    prediction=\"LangChain\",\n",
                "    reference=\"langchain\",\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
            "metadata": {},
            "source": [
                "## Configure the ExactMatchStringEvaluator\n",
                "\n",
                "You can relax the \"exactness\" when comparing strings."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "evaluator = ExactMatchStringEvaluator(\n",
                "    ignore_case=True,\n",
                "    ignore_numbers=True,\n",
                "    ignore_punctuation=True,\n",
                ")\n",
                "\n",
                "# Alternatively\n",
                "# evaluator = load_evaluator(\"exact_match\", ignore_case=True, ignore_numbers=True, ignore_punctuation=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'score': 1}"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "evaluator.evaluate_strings(\n",
                "    prediction=\"1 LLM.\",\n",
                "    reference=\"2 llm\",\n",
                ")"
            ]
        }
    ],
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