Update for tags field on Golden and ConversationalGolden

fern/docs/pages/llm-evaluation/core-concepts/test-cases-goldens-datasets.mdx

@@ -208,6 +208,7 @@ class Golden(BaseModel):
     additional_metadata: Optional[Dict] = None
     comments: Optional[str] = None
     custom_column_key_values: Optional[Dict[str, str]] = None
+    tags: Optional[List[str]] = None
 
     # Fields that you should ideally not populate
     actual_output: Optional[str] = None
@@ -240,6 +241,7 @@ class ConversationalGolden(BaseModel):
     additional_metadata: Optional[Dict] = None
     comments: Optional[str] = None
     custom_column_key_values: Optional[Dict[str, str]] = None
+    tags: Optional[List[str]] = None
 
     # Fields that you should ideally not populate
     turns: Optional[Turn] = None
@@ -255,7 +257,7 @@ class ConversationalGolden(BaseModel):
 
 </Tabs>
 
-You'll notice that goldens are more opinionated and contains a `custom_column_key_values` field that you can edit either on the platform or via code.
+You'll notice that goldens are more opinionated and contains `custom_column_key_values` and `tags` fields that you can edit either on the platform or via code.
 
 ## Datasets
 

fern/docs/pages/llm-evaluation/dataset-management/automate-dataset-management.mdx

@@ -207,6 +207,59 @@ const multiturnGolden = new ConversationalGolden({
 
 </Tabs>
 
+## Add Tags
+
+You can include tags when pushing goldens. Tags allow you to categorize and filter goldens within a dataset.
+
+<Tabs>
+
+<Tab title="Python" language="python">
+
+```python main.py
+from deepeval.dataset import Golden, ConversationalGolden
+
+golden = Golden(
+    input="How tall is Mt. Everest?",
+    tags=["geography", "factual"]
+)
+
+multiturn_golden = ConversationalGolden(
+    scenario="User asking for a refund.",
+    tags=["support", "refund"]
+)
+```
+
+</Tab>
+
+<Tab title="Typescript" language="typescript">
+
+```ts index.ts
+import { Golden, ConversationalGolden } from "deepeval";
+
+const golden = new Golden({
+  input: "How tall is Mt. Everest?",
+  tags: ["geography", "factual"],
+});
+
+const multiturnGolden = new ConversationalGolden({
+  scenario: "User asking for a refund.",
+  tags: ["support", "refund"],
+});
+```
+
+</Tab>
+
+<Tab title="curl" language="curl">
+
+<EndpointRequestSnippet
+  endpoint="POST /v1/datasets/{alias}"
+  example="Tags"
+/>
+
+</Tab>
+
+</Tabs>
+
 ## Delete Dataset
 
 Delete a dataset programmatically via the Evals API.

fern/docs/pages/llm-evaluation/dataset-management/using-datasets.mdx

@@ -384,6 +384,63 @@ for (const golden of dataset.goldens as Golden[]) {
 
 </Tabs>
 
+## Using Tags
+
+If your dataset has tags, you can access them via the `tags` field on each golden:
+
+<Tabs>
+
+<Tab title="Python" language="python">
+
+```python main.py
+from deepeval.dataset import EvaluationDataset
+from deepeval.test_case import LLMTestCase
+
+dataset = EvaluationDataset()
+dataset.pull(alias="YOUR-DATASET-ALIAS")
+
+for golden in dataset.goldens:
+    # Access tags
+    tags = golden.tags
+
+    # Filter or use tags in your evaluation logic
+    if "factual" in tags:
+        test_case = LLMTestCase(
+            input=golden.input,
+            actual_output=llm_app(golden.input),
+        )
+        dataset.add_test_case(test_case)
+```
+
+</Tab>
+
+<Tab title="Typescript" language="typescript">
+
+```ts index.ts
+import { EvaluationDataset, Golden, LLMTestCase } from "deepeval";
+
+const dataset = new EvaluationDataset();
+await dataset.pull({ alias: "YOUR-DATASET-ALIAS" });
+
+for (const golden of dataset.goldens as Golden[]) {
+  // Access tags
+  const tags = golden.tags ?? [];
+
+  // Filter or use tags in your evaluation logic
+  if (tags.includes("factual")) {
+    const testCase = new LLMTestCase({
+      input: golden.input,
+      actualOutput: await llmApp(golden.input),
+    });
+    dataset.addTestCase(testCase);
+  }
+}
+```
+
+</Tab>
+
+</Tabs>
+
 ## Using Images
 
 Any (list of) golden text fields (such as input, scenario, etc.) that contains an image will be in the format of `[DEEPEVAL:IMAGE:url]`. The `url` inside the `[DEEPEVAL:IMAGE:url]` format is a public url that can be accessed by anyone.

fern/openapi.yaml

@@ -435,6 +435,14 @@ paths:
                   - input: "What is 2 + 2?"
                     customColumnKeyValues:
                       key: "value"
+              Tags:
+                finalized: true
+                goldens:
+                  - input: "How is the weather like in NYC?"
+                    expectedOutput: "No idea"
+                    tags:
+                      - "weather"
+                      - "geography"
       responses:
         "200":
           description: ""
@@ -3247,6 +3255,11 @@ components:
           type: object
           additionalProperties: true
           description: "This is the custom column key values of the LLM application."
+        tags:
+          type: array
+          items:
+            type: string
+          description: "A list of tags associated with this golden."
       required:
         - input
 
@@ -3286,6 +3299,11 @@ components:
           type: object
           additionalProperties: true
           description: "This is the custom column key values of the LLM application."
+        tags:
+          type: array
+          items:
+            type: string
+          description: "A list of tags associated with this golden."
       required:
         - scenario
 
@@ -3338,6 +3356,11 @@ components:
           type: object
           additionalProperties: true
           description: "Key-value pairs representing custom table column data for this golden. Keys correspond to the custom column keys defined in the dataset."
+        tags:
+          type: array
+          items:
+            type: string
+          description: "A list of tags to associate with this golden."
       required:
         - input
 
@@ -3380,6 +3403,11 @@ components:
           type: object
           additionalProperties: true
           description: "Key-value pairs representing custom table column data for this golden. Keys correspond to the custom column keys defined in the dataset."
+        tags:
+          type: array
+          items:
+            type: string
+          description: "A list of tags to associate with this golden."
       required:
         - scenario