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https://github.com/hwchase17/langchain.git
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Wfh/json edit dist (#12361)
Compare predicted json to reference. First canonicalize (sort keys, rm whitespace separators), then return normalized string edit distance. Not a silver bullet but maybe an easy way to capture structure differences in a less flakey way --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This commit is contained in:
William FH
GitHub
294
docs/docs/guides/evaluation/string/json.ipynb
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294
docs/docs/guides/evaluation/string/json.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "465cfbef-5bba-4b3b-b02d-fe2eba39db17",
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"metadata": {},
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"source": [
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"# Evaluating Structured Output: JSON Evaluators\n",
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"\n",
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"Evaluating [extraction](https://python.langchain.com/docs/use_cases/extraction) and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following JSON validators provide provide functionality to check your model's output in a consistent way.\n",
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"\n",
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"## JsonValidityEvaluator\n",
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"\n",
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"The `JsonValidityEvaluator` is designed to check the validity of a JSON string prediction.\n",
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"\n",
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"### Overview:\n",
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"- **Requires Input?**: No\n",
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"- **Requires Reference?**: No"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "02e5f7dd-82fe-48f9-a251-b2052e17e61c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'score': 1}\n"
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]
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}
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],
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"source": [
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"from langchain.evaluation import JsonValidityEvaluator, load_evaluator\n",
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"\n",
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"evaluator = JsonValidityEvaluator()\n",
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"# Equivalently\n",
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"# evaluator = load_evaluator(\"json_validity\")\n",
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"prediction = '{\"name\": \"John\", \"age\": 30, \"city\": \"New York\"}'\n",
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"\n",
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"result = evaluator.evaluate_strings(prediction=prediction)\n",
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"print(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9a9607c6-edab-4c26-86c4-22b226e18aa9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'score': 0, 'reasoning': 'Expecting property name enclosed in double quotes: line 1 column 48 (char 47)'}\n"
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]
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}
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],
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"source": [
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"prediction = '{\"name\": \"John\", \"age\": 30, \"city\": \"New York\",}'\n",
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"result = evaluator.evaluate_strings(prediction=prediction)\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8ac18a83-30d8-4c11-abf2-7a36e4cb829f",
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"metadata": {},
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"source": [
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"## JsonEqualityEvaluator\n",
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"\n",
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"The `JsonEqualityEvaluator` assesses whether a JSON prediction matches a given reference after both are parsed.\n",
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"\n",
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"### Overview:\n",
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"- **Requires Input?**: No\n",
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"- **Requires Reference?**: Yes\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "ab97111e-cba9-4273-825f-d5d4278a953c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'score': True}\n"
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]
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}
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],
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"source": [
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"from langchain.evaluation import JsonEqualityEvaluator\n",
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"\n",
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"evaluator = JsonEqualityEvaluator()\n",
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"# Equivalently\n",
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"# evaluator = load_evaluator(\"json_equality\")\n",
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"result = evaluator.evaluate_strings(prediction='{\"a\": 1}', reference='{\"a\": 1}')\n",
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"print(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "655ba486-09b6-47ce-947d-b2bd8b6f6364",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'score': False}\n"
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]
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}
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],
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"source": [
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"result = evaluator.evaluate_strings(prediction='{\"a\": 1}', reference='{\"a\": 2}')\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1ac7e541-b7fe-46b6-bc3a-e94fe316227e",
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"metadata": {},
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"source": [
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"The evaluator also by default lets you provide a dictionary directly"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "36e70ba3-4e62-483c-893a-5f328b7f303d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'score': False}\n"
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]
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}
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],
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"source": [
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"result = evaluator.evaluate_strings(prediction={\"a\": 1}, reference={\"a\": 2})\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "921d33f0-b3c2-4e9e-820c-9ec30bc5bb20",
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"metadata": {},
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"source": [
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"## JsonEditDistanceEvaluator\n",
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"\n",
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"The `JsonEditDistanceEvaluator` computes a normalized Damerau-Levenshtein distance between two \"canonicalized\" JSON strings.\n",
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"\n",
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"### Overview:\n",
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"- **Requires Input?**: No\n",
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"- **Requires Reference?**: Yes\n",
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"- **Distance Function**: Damerau-Levenshtein (by default)\n",
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"\n",
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"_Note: Ensure that `rapidfuzz` is installed or provide an alternative `string_distance` function to avoid an ImportError._"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "da9ec3a3-675f-4420-8ec7-cde48d8c2918",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'score': 0.07692307692307693}\n"
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]
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}
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],
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"source": [
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"from langchain.evaluation import JsonEditDistanceEvaluator\n",
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"\n",
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"evaluator = JsonEditDistanceEvaluator()\n",
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"# Equivalently\n",
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"# evaluator = load_evaluator(\"json_edit_distance\")\n",
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"\n",
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"result = evaluator.evaluate_strings(\n",
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" prediction='{\"a\": 1, \"b\": 2}', reference='{\"a\": 1, \"b\": 3}'\n",
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")\n",
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"print(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "537ed58c-6a9c-402f-8f7f-07b1119a9ae0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'score': 0.0}\n"
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]
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}
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],
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"source": [
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"# The values are canonicalized prior to comparison\n",
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"result = evaluator.evaluate_strings(\n",
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" prediction=\"\"\"\n",
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" {\n",
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" \"b\": 3,\n",
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" \"a\": 1\n",
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" }\"\"\",\n",
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" reference='{\"a\": 1, \"b\": 3}',\n",
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")\n",
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"print(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "7a8f3ec5-1cde-4b0e-80cd-ac0ac290d375",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'score': 0.18181818181818182}\n"
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]
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}
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],
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"source": [
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"# Lists maintain their order, however\n",
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"result = evaluator.evaluate_strings(\n",
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" prediction='{\"a\": [1, 2]}', reference='{\"a\": [2, 1]}'\n",
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")\n",
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"print(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "52abec79-58ed-4ab6-9fb1-7deb1f5146cc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'score': 0.14285714285714285}\n"
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]
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}
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],
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"source": [
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"# You can also pass in objects directly\n",
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"result = evaluator.evaluate_strings(prediction={\"a\": 1}, reference={\"a\": 2})\n",
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"print(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "85afcf33-d2f4-406e-9d8f-15dc0a4772f2",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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