langchain/docs/extras/modules/evaluation/string/string_distance.ipynb

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    "# String Distance\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."
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   "source": [
    "# %pip install rapidfuzz"
   ]
  },
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   "execution_count": 2,
   "id": "f6790c46",
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   "source": [
    "from langchain.evaluation import load_evaluator\n",
    "\n",
    "evaluator = load_evaluator(\"string_distance\")"
   ]
  },
  {
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       "{'score': 12}"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "evaluator.evaluate_strings(\n",
    "    prediction=\"The job is completely done.\",\n",
    "    reference=\"The job is done\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "{'score': 4}"
      ]
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   "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",
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   "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."
   ]
  },
  {
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    {
     "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": {
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   },
   "outputs": [],
   "source": [
    "jaro_evaluator = load_evaluator(\n",
    "    \"string_distance\", distance=StringDistance.JARO, requires_reference=True\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": {
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   "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",
    ")"
   ]
  }
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