feat: make general purpose metrics more general

docs/howtos/customizations/metrics/cost.ipynb

@@ -29,6 +29,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"your-api-key\""
    ]
   },
@@ -105,8 +106,7 @@
     "metric = AspectCriticWithReference(\n",
     "    name=\"answer_correctness\",\n",
     "    definition=\"is the response correct compared to reference\",\n",
-    ")\n",
-    "\n"
+    ")"
    ]
   },
   {
@@ -126,8 +126,12 @@
     "from ragas import evaluate\n",
     "from ragas.cost import get_token_usage_for_openai\n",
     "\n",
-    "results = evaluate(eval_dataset[:5], metrics=[metric],  llm=gpt4o,\n",
-    "    token_usage_parser=get_token_usage_for_openai,)"
+    "results = evaluate(\n",
+    "    eval_dataset[:5],\n",
+    "    metrics=[metric],\n",
+    "    llm=gpt4o,\n",
+    "    token_usage_parser=get_token_usage_for_openai,\n",
+    ")"
    ]
   },
   {

docs/howtos/customizations/testgenerator/persona_generator.ipynb

@@ -38,9 +38,18 @@
    "source": [
     "from ragas.testset.persona import Persona\n",
     "\n",
-    "persona_new_joinee = Persona(name=\"New Joinee\", role_description=\"Don't know much about the company and is looking for information on how to get started.\")\n",
-    "persona_manager = Persona(name=\"Manager\", role_description=\"Wants to know about the different teams and how they collaborate with each other.\")\n",
-    "persona_senior_manager = Persona(name=\"Senior Manager\", role_description=\"Wants to know about the company vision and how it is executed.\")\n",
+    "persona_new_joinee = Persona(\n",
+    "    name=\"New Joinee\",\n",
+    "    role_description=\"Don't know much about the company and is looking for information on how to get started.\",\n",
+    ")\n",
+    "persona_manager = Persona(\n",
+    "    name=\"Manager\",\n",
+    "    role_description=\"Wants to know about the different teams and how they collaborate with each other.\",\n",
+    ")\n",
+    "persona_senior_manager = Persona(\n",
+    "    name=\"Senior Manager\",\n",
+    "    role_description=\"Wants to know about the company vision and how it is executed.\",\n",
+    ")\n",
     "\n",
     "personas = [persona_new_joinee, persona_manager, persona_senior_manager]\n",
     "personas"
@@ -72,7 +81,7 @@
     "testset_generator = TestsetGenerator(knowledge_graph=kg, persona_list=personas, llm=llm)\n",
     "# Generate the Testset\n",
     "testset = testset_generator.generate(testset_size=10)\n",
-    "testset\n"
+    "testset"
    ]
   },
   {

docs/howtos/integrations/langchain.ipynb

@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "fb5deb25",
    "metadata": {},
    "outputs": [],
@@ -59,10 +59,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "4aa9a986",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jjmachan/.pyenv/versions/ragas/lib/python3.10/site-packages/langchain/indexes/vectorstore.py:128: UserWarning: Using InMemoryVectorStore as the default vectorstore.This memory store won't persist data. You should explicitlyspecify a vectorstore when using VectorstoreIndexCreator\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "ename": "ValidationError",
+     "evalue": "1 validation error for VectorstoreIndexCreator\nembedding\n  Field required [type=missing, input_value={}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.9/v/missing",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain_openai\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ChatOpenAI\n\u001b[1;32m      6\u001b[0m loader \u001b[38;5;241m=\u001b[39m TextLoader(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./nyc_wikipedia/nyc_text.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43mVectorstoreIndexCreator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mfrom_loaders([loader])\n\u001b[1;32m     10\u001b[0m llm \u001b[38;5;241m=\u001b[39m ChatOpenAI(temperature\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     11\u001b[0m qa_chain \u001b[38;5;241m=\u001b[39m RetrievalQA\u001b[38;5;241m.\u001b[39mfrom_chain_type(\n\u001b[1;32m     12\u001b[0m     llm,\n\u001b[1;32m     13\u001b[0m     retriever\u001b[38;5;241m=\u001b[39mindex\u001b[38;5;241m.\u001b[39mvectorstore\u001b[38;5;241m.\u001b[39mas_retriever(),\n\u001b[1;32m     14\u001b[0m     return_source_documents\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m     15\u001b[0m )\n",
+      "File \u001b[0;32m~/.pyenv/versions/ragas/lib/python3.10/site-packages/pydantic/main.py:212\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(self, **data)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m    211\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 212\u001b[0m validated_self \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mself_instance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m validated_self:\n\u001b[1;32m    214\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    215\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA custom validator is returning a value other than `self`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    216\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturning anything other than `self` from a top level model validator isn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt supported when validating via `__init__`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    217\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSee the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    218\u001b[0m         category\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    219\u001b[0m     )\n",
+      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for VectorstoreIndexCreator\nembedding\n  Field required [type=missing, input_value={}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.9/v/missing"
+     ]
+    }
+   ],
    "source": [
     "from langchain_community.document_loaders import TextLoader\n",
     "from langchain.indexes import VectorstoreIndexCreator\n",
@@ -495,7 +516,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,