made MetricResult json serialisable for pydantic

Author: jjmachan

nbs/metric/result.ipynb

@@ -291,6 +291,18 @@
     "        return value\n",
     "    return MetricResult(result=value)\n",
     "\n",
+    "@patch\n",
+    "def __json__(self: MetricResult):\n",
+    "    \"\"\"Return data for JSON serialization.\n",
+    "    \n",
+    "    This method is used by json.dumps and other JSON serializers \n",
+    "    to convert MetricResult to a JSON-compatible format.\n",
+    "    \"\"\"\n",
+    "    return {\n",
+    "        \"result\": self._result,\n",
+    "        \"reason\": self.reason,\n",
+    "    }\n",
+    "\n",
     "# Add Pydantic compatibility methods\n",
     "@patch(cls_method=True)\n",
     "def __get_pydantic_core_schema__(\n",
@@ -299,7 +311,22 @@
     "    _handler: GetCoreSchemaHandler\n",
     ") -> core_schema.CoreSchema:\n",
     "    \"\"\"Generate a Pydantic core schema for MetricResult.\"\"\"\n",
-    "    return core_schema.with_info_plain_validator_function(cls.validate)\n",
+    "    # Create a schema that handles both validation and serialization\n",
+    "    return core_schema.union_schema([\n",
+    "        # First schema: handles validation of MetricResult instances\n",
+    "        core_schema.is_instance_schema(MetricResult),\n",
+    "        \n",
+    "        # Second schema: handles validation of other values and conversion to MetricResult\n",
+    "        core_schema.chain_schema([\n",
+    "            core_schema.any_schema(),\n",
+    "            core_schema.no_info_plain_validator_function(\n",
+    "                lambda value: MetricResult(result=value) if not isinstance(value, MetricResult) else value\n",
+    "            ),\n",
+    "        ]),\n",
+    "    ], serialization=core_schema.plain_serializer_function_ser_schema(\n",
+    "        # This function handles serialization\n",
+    "        lambda instance: instance.__json__()\n",
+    "    ))\n",
     "\n",
     "\n",
     "@patch\n",
@@ -369,13 +396,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9d32b10f",
+   "id": "27f9bc1c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'test'"
+       "'{\"response\":\"test\",\"grade\":{\"result\":1,\"reason\":\"test\"},\"faithfulness\":{\"result\":1,\"reason\":\"test\"}}'"
       ]
      },
      "execution_count": null,
@@ -384,16 +411,10 @@
     }
    ],
    "source": [
-    "m.model_dump()[\"faithfulness\"].reason"
+    "mt = TestModel(response=\"test\", grade=MetricResult(result=1, reason=\"test\"), faithfulness=MetricResult(result=1, reason=\"test\"))\n",
+    "\n",
+    "mt.model_dump_json()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bde70d56",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

ragas_experimental/_modidx.py

@@ -349,6 +349,8 @@
                                                                                                              'ragas_experimental/metric/result.py'),
                                                   'ragas_experimental.metric.result.MetricResult.__iter__': ( 'metric/result.html#metricresult.__iter__',
                                                                                                               'ragas_experimental/metric/result.py'),
+                                                  'ragas_experimental.metric.result.MetricResult.__json__': ( 'metric/result.html#metricresult.__json__',
+                                                                                                              'ragas_experimental/metric/result.py'),
                                                   'ragas_experimental.metric.result.MetricResult.__le__': ( 'metric/result.html#metricresult.__le__',
                                                                                                             'ragas_experimental/metric/result.py'),
                                                   'ragas_experimental.metric.result.MetricResult.__len__': ( 'metric/result.html#metricresult.__len__',

ragas_experimental/metric/result.py

@@ -188,6 +188,18 @@ def validate(cls: MetricResult, value: t.Any, info: ValidationInfo):
         return value
     return MetricResult(result=value)
 
+@patch
+def __json__(self: MetricResult):
+    """Return data for JSON serialization.
+    
+    This method is used by json.dumps and other JSON serializers 
+    to convert MetricResult to a JSON-compatible format.
+    """
+    return {
+        "result": self._result,
+        "reason": self.reason,
+    }
+
 # Add Pydantic compatibility methods
 @patch(cls_method=True)
 def __get_pydantic_core_schema__(
@@ -196,7 +208,22 @@ def __get_pydantic_core_schema__(
     _handler: GetCoreSchemaHandler
 ) -> core_schema.CoreSchema:
     """Generate a Pydantic core schema for MetricResult."""
-    return core_schema.with_info_plain_validator_function(cls.validate)
+    # Create a schema that handles both validation and serialization
+    return core_schema.union_schema([
+        # First schema: handles validation of MetricResult instances
+        core_schema.is_instance_schema(MetricResult),
+        
+        # Second schema: handles validation of other values and conversion to MetricResult
+        core_schema.chain_schema([
+            core_schema.any_schema(),
+            core_schema.no_info_plain_validator_function(
+                lambda value: MetricResult(result=value) if not isinstance(value, MetricResult) else value
+            ),
+        ]),
+    ], serialization=core_schema.plain_serializer_function_ser_schema(
+        # This function handles serialization
+        lambda instance: instance.__json__()
+    ))
 
 
 @patch