Tool Call Relevance Metric¶
- pydantic model ibm_watsonx_gov.metrics.tool_call_relevance.tool_call_relevance_metric.ToolCallRelevanceMetric¶
Bases:
GenAIMetricToolCallRelevanceMetric assesses whether this function call correctly implements the user’s immediate request as the appropriate next step in the conversation. Compares against all available functions in the tool inventory to determine if the selection aligns with user intent and context.
The ToolCallRelevanceMetric will be computed using llm_as_judge.
Examples
- Create ToolCallRelevanceMetric by passing the basic configuration.
config = GenAIConfiguration(tools = [get_weather,fetch_stock_price]) evaluator = MetricsEvaluator(configuration=config) df = pd.read_csv("") llm_judge = LLMJudge( model=WxAIFoundationModel( model_id="meta-llama/llama-3-3-70b-instruct", project_id=os.getenv("WATSONX_PROJECT_ID"), ) ) metrics = [ToolCallRelevanceMetric(llm_judge=llm_judge)] result = evaluator.evaluate(data=df, metrics=metrics)
- Create ToolCallRelevanceMetric by passing custom tool calls field in configuration.
config = GenAIConfiguration(tools = [get_weather,fetch_stock_price], tool_calls_field="tools_used") evaluator = MetricsEvaluator(configuration=config) df = pd.read_csv("") llm_judge = LLMJudge( model=WxAIFoundationModel( model_id="meta-llama/llama-3-3-70b-instruct", project_id=os.getenv("WATSONX_PROJECT_ID"), ) ) metrics = [ToolCallRelevanceMetric(llm_judge=llm_judge)] result = evaluator.evaluate(data=df, metrics=metrics)
- Create ToolCallRelevanceMetric with a custom threshold.
llm_judge = LLMJudge( model=WxAIFoundationModel( model_id="meta-llama/llama-3-3-70b-instruct", project_id=os.getenv("WATSONX_PROJECT_ID"), ) ) threshold = MetricThreshold(type="upper_limit", value=0.8) metric = ToolCallRelevanceMetric(llm_judge=llm_judge, threshold=threshold)
Show JSON schema
{ "title": "ToolCallRelevanceMetric", "description": "ToolCallRelevanceMetric assesses whether this function call correctly implements \nthe user's immediate request as the appropriate next step in the conversation. \nCompares against all available functions in the tool inventory to determine if \nthe selection aligns with user intent and context.\n\nThe ToolCallRelevanceMetric will be computed using llm_as_judge.\n\nExamples:\n 1. Create ToolCallRelevanceMetric by passing the basic configuration.\n .. code-block:: python\n\n config = GenAIConfiguration(tools = [get_weather,fetch_stock_price])\n evaluator = MetricsEvaluator(configuration=config)\n df = pd.read_csv(\"\")\n llm_judge = LLMJudge(\n model=WxAIFoundationModel(\n model_id=\"meta-llama/llama-3-3-70b-instruct\",\n project_id=os.getenv(\"WATSONX_PROJECT_ID\"),\n )\n )\n metrics = [ToolCallRelevanceMetric(llm_judge=llm_judge)]\n result = evaluator.evaluate(data=df, metrics=metrics)\n\n 2. Create ToolCallRelevanceMetric by passing custom tool calls field in configuration.\n .. code-block:: python\n\n config = GenAIConfiguration(tools = [get_weather,fetch_stock_price],\n tool_calls_field=\"tools_used\")\n evaluator = MetricsEvaluator(configuration=config)\n df = pd.read_csv(\"\")\n llm_judge = LLMJudge(\n model=WxAIFoundationModel(\n model_id=\"meta-llama/llama-3-3-70b-instruct\",\n project_id=os.getenv(\"WATSONX_PROJECT_ID\"),\n )\n )\n metrics = [ToolCallRelevanceMetric(llm_judge=llm_judge)]\n result = evaluator.evaluate(data=df, metrics=metrics)\n\n 3. Create ToolCallRelevanceMetric with a custom threshold.\n .. code-block:: python\n\n llm_judge = LLMJudge(\n model=WxAIFoundationModel(\n model_id=\"meta-llama/llama-3-3-70b-instruct\",\n project_id=os.getenv(\"WATSONX_PROJECT_ID\"),\n )\n )\n threshold = MetricThreshold(type=\"upper_limit\", value=0.8)\n metric = ToolCallRelevanceMetric(llm_judge=llm_judge, threshold=threshold)", "type": "object", "properties": { "name": { "const": "tool_call_relevance", "default": "tool_call_relevance", "description": "The name of metric.", "title": "Metric Name", "type": "string" }, "thresholds": { "default": [ { "type": "lower_limit", "value": 0.8 } ], "description": "Value that defines the violation limit for the metric", "items": { "$ref": "#/$defs/MetricThreshold" }, "title": "Metric threshold", "type": "array" }, "tasks": { "default": [ "retrieval_augmented_generation" ], "description": "The generative task type.", "items": { "$ref": "#/$defs/TaskType" }, "title": "Task Type", "type": "array" }, "group": { "$ref": "#/$defs/MetricGroup", "default": "tool_call_quality" }, "is_reference_free": { "default": true, "description": "Decides whether this metric needs a reference for computation", "title": "Is Reference Free", "type": "boolean" }, "method": { "const": "llm_as_judge", "default": "llm_as_judge", "description": "The method used to compute the metric.", "title": "Computation Method", "type": "string" }, "metric_dependencies": { "default": [], "description": "Metrics that needs to be evaluated first", "items": { "$ref": "#/$defs/GenAIMetric" }, "title": "Metric Dependencies", "type": "array" }, "llm_judge": { "anyOf": [ { "$ref": "#/$defs/LLMJudge" }, { "type": "null" } ], "default": null, "description": "The LLM judge used to compute the metric." }, "metric_mapping_name": { "const": "function_selection_appropriateness", "default": "function_selection_appropriateness", "description": "The mapping name of metric with llmevalkit.", "title": "Metric Mapping Name", "type": "string" } }, "$defs": { "AzureOpenAICredentials": { "properties": { "url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI url. This attribute can be read from `AZURE_OPENAI_HOST` environment variable.", "title": "Url" }, "api_key": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "API key for Azure OpenAI. This attribute can be read from `AZURE_OPENAI_API_KEY` environment variable.", "title": "Api Key" }, "api_version": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The model API version from Azure OpenAI. This attribute can be read from `AZURE_OPENAI_API_VERSION` environment variable.", "title": "Api Version" } }, "required": [ "url", "api_key", "api_version" ], "title": "AzureOpenAICredentials", "type": "object" }, "AzureOpenAIFoundationModel": { "description": "The Azure OpenAI foundation model details\n\nExamples:\n 1. Create Azure OpenAI foundation model by passing the credentials during object creation.\n .. code-block:: python\n\n azure_openai_foundation_model = AzureOpenAIFoundationModel(\n model_id=\"gpt-4o-mini\",\n provider=AzureOpenAIModelProvider(\n credentials=AzureOpenAICredentials(\n api_key=azure_api_key,\n url=azure_host_url,\n api_version=azure_api_model_version,\n )\n )\n )\n\n2. Create Azure OpenAI foundation model by setting the credentials in environment variables:\n * ``AZURE_OPENAI_API_KEY`` is used to set the api key for OpenAI.\n * ``AZURE_OPENAI_HOST`` is used to set the url for Azure OpenAI.\n * ``AZURE_OPENAI_API_VERSION`` is uses to set the the api version for Azure OpenAI.\n\n .. code-block:: python\n\n openai_foundation_model = AzureOpenAIFoundationModel(\n model_id=\"gpt-4o-mini\",\n )", "properties": { "model_name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The name of the foundation model.", "title": "Model Name" }, "provider": { "$ref": "#/$defs/AzureOpenAIModelProvider", "description": "Azure OpenAI provider" }, "model_id": { "description": "Model deployment name from Azure OpenAI", "title": "Model Id", "type": "string" } }, "required": [ "model_id" ], "title": "AzureOpenAIFoundationModel", "type": "object" }, "AzureOpenAIModelProvider": { "properties": { "type": { "$ref": "#/$defs/ModelProviderType", "default": "azure_openai", "description": "The type of model provider." }, "credentials": { "anyOf": [ { "$ref": "#/$defs/AzureOpenAICredentials" }, { "type": "null" } ], "default": null, "description": "Azure OpenAI credentials." } }, "title": "AzureOpenAIModelProvider", "type": "object" }, "GenAIMetric": { "description": "Defines the Generative AI metric interface", "properties": { "name": { "description": "The name of the metric", "title": "Metric Name", "type": "string" }, "thresholds": { "default": [], "description": "The list of thresholds", "items": { "$ref": "#/$defs/MetricThreshold" }, "title": "Thresholds", "type": "array" }, "tasks": { "description": "The task types this metric is associated with.", "items": { "$ref": "#/$defs/TaskType" }, "title": "Tasks", "type": "array" }, "group": { "anyOf": [ { "$ref": "#/$defs/MetricGroup" }, { "type": "null" } ], "default": null, "description": "The metric group this metric belongs to." }, "is_reference_free": { "default": true, "description": "Decides whether this metric needs a reference for computation", "title": "Is Reference Free", "type": "boolean" }, "method": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The method used to compute the metric.", "title": "Method" }, "metric_dependencies": { "default": [], "description": "Metrics that needs to be evaluated first", "items": { "$ref": "#/$defs/GenAIMetric" }, "title": "Metric Dependencies", "type": "array" } }, "required": [ "name", "tasks" ], "title": "GenAIMetric", "type": "object" }, "LLMJudge": { "description": "Defines the LLMJudge.\n\nThe LLMJudge class contains the details of the llm judge model to be used for computing the metric.\n\nExamples:\n 1. Create LLMJudge using watsonx.ai foundation model:\n .. code-block:: python\n\n wx_ai_foundation_model = WxAIFoundationModel(\n model_id=\"google/flan-ul2\",\n project_id=PROJECT_ID,\n provider=WxAIModelProvider(\n credentials=WxAICredentials(api_key=wx_apikey)\n )\n )\n llm_judge = LLMJudge(model=wx_ai_foundation_model)", "properties": { "model": { "anyOf": [ { "$ref": "#/$defs/WxAIFoundationModel" }, { "$ref": "#/$defs/OpenAIFoundationModel" }, { "$ref": "#/$defs/AzureOpenAIFoundationModel" }, { "$ref": "#/$defs/RITSFoundationModel" } ], "description": "The foundation model to be used as judge", "title": "Model" } }, "required": [ "model" ], "title": "LLMJudge", "type": "object" }, "MetricGroup": { "enum": [ "retrieval_quality", "answer_quality", "content_safety", "performance", "usage", "tool_call_quality", "readability" ], "title": "MetricGroup", "type": "string" }, "MetricThreshold": { "description": "The class that defines the threshold for a metric.", "properties": { "type": { "description": "Threshold type. One of 'lower_limit', 'upper_limit'", "enum": [ "lower_limit", "upper_limit" ], "title": "Type", "type": "string" }, "value": { "default": 0, "description": "The value of metric threshold", "title": "Threshold value", "type": "number" } }, "required": [ "type" ], "title": "MetricThreshold", "type": "object" }, "ModelProviderType": { "description": "Supported model provider types for Generative AI", "enum": [ "ibm_watsonx.ai", "azure_openai", "rits", "openai", "custom" ], "title": "ModelProviderType", "type": "string" }, "OpenAICredentials": { "description": "Defines the OpenAICredentials class to specify the OpenAI server details.\n\nExamples:\n 1. Create OpenAICredentials with default parameters. By default Dallas region is used.\n .. code-block:: python\n\n openai_credentials = OpenAICredentials(api_key=api_key,\n url=openai_url)\n\n 2. Create OpenAICredentials by reading from environment variables.\n .. code-block:: python\n\n os.environ[\"OPENAI_API_KEY\"] = \"...\"\n os.environ[\"OPENAI_URL\"] = \"...\"\n openai_credentials = OpenAICredentials.create_from_env()", "properties": { "url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Url" }, "api_key": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Api Key" } }, "required": [ "url", "api_key" ], "title": "OpenAICredentials", "type": "object" }, "OpenAIFoundationModel": { "description": "The OpenAI foundation model details\n\nExamples:\n 1. Create OpenAI foundation model by passing the credentials during object creation. Note that the url is optional and will be set to the default value for OpenAI. To change the default value, the url should be passed to ``OpenAICredentials`` object.\n .. code-block:: python\n\n openai_foundation_model = OpenAIFoundationModel(\n model_id=\"gpt-4o-mini\",\n provider=OpenAIModelProvider(\n credentials=OpenAICredentials(\n api_key=api_key,\n url=openai_url,\n )\n )\n )\n\n 2. Create OpenAI foundation model by setting the credentials in environment variables:\n * ``OPENAI_API_KEY`` is used to set the api key for OpenAI.\n * ``OPENAI_URL`` is used to set the url for OpenAI\n\n .. code-block:: python\n\n openai_foundation_model = OpenAIFoundationModel(\n model_id=\"gpt-4o-mini\",\n )", "properties": { "model_name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The name of the foundation model.", "title": "Model Name" }, "provider": { "$ref": "#/$defs/OpenAIModelProvider", "description": "OpenAI provider" }, "model_id": { "description": "Model name from OpenAI", "title": "Model Id", "type": "string" } }, "required": [ "model_id" ], "title": "OpenAIFoundationModel", "type": "object" }, "OpenAIModelProvider": { "properties": { "type": { "$ref": "#/$defs/ModelProviderType", "default": "openai", "description": "The type of model provider." }, "credentials": { "anyOf": [ { "$ref": "#/$defs/OpenAICredentials" }, { "type": "null" } ], "default": null, "description": "OpenAI credentials. This can also be set by using `OPENAI_API_KEY` environment variable." } }, "title": "OpenAIModelProvider", "type": "object" }, "RITSCredentials": { "properties": { "hostname": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "https://inference-3scale-apicast-production.apps.rits.fmaas.res.ibm.com", "description": "The rits hostname", "title": "Hostname" }, "api_key": { "title": "Api Key", "type": "string" } }, "required": [ "api_key" ], "title": "RITSCredentials", "type": "object" }, "RITSFoundationModel": { "properties": { "model_name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The name of the foundation model.", "title": "Model Name" }, "provider": { "$ref": "#/$defs/RITSModelProvider", "description": "The provider of the model." } }, "title": "RITSFoundationModel", "type": "object" }, "RITSModelProvider": { "properties": { "type": { "$ref": "#/$defs/ModelProviderType", "default": "rits", "description": "The type of model provider." }, "credentials": { "anyOf": [ { "$ref": "#/$defs/RITSCredentials" }, { "type": "null" } ], "default": null, "description": "RITS credentials." } }, "title": "RITSModelProvider", "type": "object" }, "TaskType": { "description": "Supported task types for generative AI models", "enum": [ "question_answering", "classification", "summarization", "generation", "extraction", "retrieval_augmented_generation" ], "title": "TaskType", "type": "string" }, "WxAICredentials": { "description": "Defines the WxAICredentials class to specify the watsonx.ai server details.\n\nExamples:\n 1. Create WxAICredentials with default parameters. By default Dallas region is used.\n .. code-block:: python\n\n wxai_credentials = WxAICredentials(api_key=\"...\")\n\n 2. Create WxAICredentials by specifying region url.\n .. code-block:: python\n\n wxai_credentials = WxAICredentials(api_key=\"...\",\n url=\"https://au-syd.ml.cloud.ibm.com\")\n\n 3. Create WxAICredentials by reading from environment variables.\n .. code-block:: python\n\n os.environ[\"WATSONX_APIKEY\"] = \"...\"\n # [Optional] Specify watsonx region specific url. Default is https://us-south.ml.cloud.ibm.com .\n os.environ[\"WATSONX_URL\"] = \"https://eu-gb.ml.cloud.ibm.com\"\n wxai_credentials = WxAICredentials.create_from_env()\n\n 4. Create WxAICredentials for on-prem.\n .. code-block:: python\n\n wxai_credentials = WxAICredentials(url=\"https://<hostname>\",\n username=\"...\"\n api_key=\"...\",\n version=\"5.2\")\n\n 5. Create WxAICredentials by reading from environment variables for on-prem.\n .. code-block:: python\n\n os.environ[\"WATSONX_URL\"] = \"https://<hostname>\"\n os.environ[\"WATSONX_VERSION\"] = \"5.2\"\n os.environ[\"WATSONX_USERNAME\"] = \"...\"\n os.environ[\"WATSONX_APIKEY\"] = \"...\"\n # Only one of api_key or password is needed\n #os.environ[\"WATSONX_PASSWORD\"] = \"...\"\n wxai_credentials = WxAICredentials.create_from_env()", "properties": { "url": { "default": "https://us-south.ml.cloud.ibm.com", "description": "The url for watsonx ai service", "examples": [ "https://us-south.ml.cloud.ibm.com", "https://eu-de.ml.cloud.ibm.com", "https://eu-gb.ml.cloud.ibm.com", "https://jp-tok.ml.cloud.ibm.com", "https://au-syd.ml.cloud.ibm.com" ], "title": "watsonx.ai url", "type": "string" }, "api_key": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The user api key. Required for using watsonx as a service and one of api_key or password is required for using watsonx on-prem software.", "strip_whitespace": true, "title": "Api Key" }, "version": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The watsonx on-prem software version. Required for using watsonx on-prem software.", "title": "Version" }, "username": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The user name. Required for using watsonx on-prem software.", "title": "User name" }, "password": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The user password. One of api_key or password is required for using watsonx on-prem software.", "title": "Password" }, "instance_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "openshift", "description": "The watsonx.ai instance id. Default value is openshift.", "title": "Instance id" } }, "title": "WxAICredentials", "type": "object" }, "WxAIFoundationModel": { "description": "The IBM watsonx.ai foundation model details\n\nTo initialize the foundation model, you can either pass in the credentials directly or set the environment.\nYou can follow these examples to create the provider.\n\nExamples:\n 1. Create foundation model by specifying the credentials during object creation:\n .. code-block:: python\n\n # Specify the credentials during object creation\n wx_ai_foundation_model = WxAIFoundationModel(\n model_id=\"google/flan-ul2\",\n project_id=<PROJECT_ID>,\n provider=WxAIModelProvider(\n credentials=WxAICredentials(\n url=wx_url, # This is optional field, by default US-Dallas region is selected\n api_key=wx_apikey,\n )\n )\n )\n\n 2. Create foundation model by setting the credentials environment variables:\n * The api key can be set using one of the environment variables ``WXAI_API_KEY``, ``WATSONX_APIKEY``, or ``WXG_API_KEY``. These will be read in the order of precedence.\n * The url is optional and will be set to US-Dallas region by default. It can be set using one of the environment variables ``WXAI_URL``, ``WATSONX_URL``, or ``WXG_URL``. These will be read in the order of precedence.\n\n .. code-block:: python\n\n wx_ai_foundation_model = WxAIFoundationModel(\n model_id=\"google/flan-ul2\",\n project_id=<PROJECT_ID>,\n )\n\n 3. Create foundation model by specifying watsonx.governance software credentials during object creation:\n .. code-block:: python\n\n wx_ai_foundation_model = WxAIFoundationModel(\n model_id=\"google/flan-ul2\",\n project_id=project_id,\n provider=WxAIModelProvider(\n credentials=WxAICredentials(\n url=wx_url,\n api_key=wx_apikey,\n username=wx_username,\n version=wx_version,\n )\n )\n )\n\n 4. Create foundation model by setting watsonx.governance software credentials environment variables:\n * The api key can be set using one of the environment variables ``WXAI_API_KEY``, ``WATSONX_APIKEY``, or ``WXG_API_KEY``. These will be read in the order of precedence.\n * The url can be set using one of these environment variable ``WXAI_URL``, ``WATSONX_URL``, or ``WXG_URL``. These will be read in the order of precedence.\n * The username can be set using one of these environment variable ``WXAI_USERNAME``, ``WATSONX_USERNAME``, or ``WXG_USERNAME``. These will be read in the order of precedence.\n * The version of watsonx.governance software can be set using one of these environment variable ``WXAI_VERSION``, ``WATSONX_VERSION``, or ``WXG_VERSION``. These will be read in the order of precedence.\n\n .. code-block:: python\n\n wx_ai_foundation_model = WxAIFoundationModel(\n model_id=\"google/flan-ul2\",\n project_id=project_id,\n )", "properties": { "model_name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The name of the foundation model.", "title": "Model Name" }, "provider": { "$ref": "#/$defs/WxAIModelProvider", "description": "The provider of the model." }, "model_id": { "description": "The unique identifier for the watsonx.ai model.", "title": "Model Id", "type": "string" }, "project_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The project ID associated with the model.", "title": "Project Id" }, "space_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The space ID associated with the model.", "title": "Space Id" } }, "required": [ "model_id" ], "title": "WxAIFoundationModel", "type": "object" }, "WxAIModelProvider": { "description": "This class represents a model provider configuration for IBM watsonx.ai. It includes the provider type and\ncredentials required to authenticate and interact with the watsonx.ai platform. If credentials are not explicitly\nprovided, it attempts to load them from environment variables.\n\nExamples:\n 1. Create provider using credentials object:\n .. code-block:: python\n\n credentials = WxAICredentials(\n url=\"https://us-south.ml.cloud.ibm.com\",\n api_key=\"your-api-key\"\n )\n provider = WxAIModelProvider(credentials=credentials)\n\n 2. Create provider using environment variables:\n .. code-block:: python\n\n import os\n\n os.environ['WATSONX_URL'] = \"https://us-south.ml.cloud.ibm.com\"\n os.environ['WATSONX_APIKEY'] = \"your-api-key\"\n\n provider = WxAIModelProvider()", "properties": { "type": { "$ref": "#/$defs/ModelProviderType", "default": "ibm_watsonx.ai", "description": "The type of model provider." }, "credentials": { "anyOf": [ { "$ref": "#/$defs/WxAICredentials" }, { "type": "null" } ], "default": null, "description": "The credentials used to authenticate with watsonx.ai. If not provided, they will be loaded from environment variables." } }, "title": "WxAIModelProvider", "type": "object" } } }
- Fields:
group (Annotated[ibm_watsonx_gov.entities.enums.MetricGroup, FieldInfo(annotation=NoneType, required=False, default=tasks (Annotated[list[ibm_watsonx_gov.entities.enums.TaskType], FieldInfo(annotation=NoneType, required=False, default=[
- field group: TOOL_CALL_QUALITY: 'tool_call_quality'>, frozen=True)] = MetricGroup.TOOL_CALL_QUALITY¶
- field llm_judge: Annotated[LLMJudge | None, FieldInfo(annotation=NoneType, required=False, default=None, description='The LLM judge used to compute the metric.')] = None¶
The LLM judge used to compute the metric.
- field method: Annotated[Literal['llm_as_judge'], FieldInfo(annotation=NoneType, required=False, default='llm_as_judge', title='Computation Method', description='The method used to compute the metric.')] = 'llm_as_judge'¶
The method used to compute the metric.
- field metric_mapping_name: Annotated[Literal['function_selection_appropriateness'], FieldInfo(annotation=NoneType, required=False, default='function_selection_appropriateness', title='Metric Mapping Name', description='The mapping name of metric with llmevalkit.')] = 'function_selection_appropriateness'¶
The mapping name of metric with llmevalkit.
- field name: Annotated[Literal['tool_call_relevance'], FieldInfo(annotation=NoneType, required=False, default='tool_call_relevance', title='Metric Name', description='The name of metric.')] = 'tool_call_relevance'¶
The name of metric.
- field tasks: ')] = [TaskType.RAG]¶
The generative task type.
- field thresholds: Annotated[list[MetricThreshold], FieldInfo(annotation=NoneType, required=False, default=[MetricThreshold(type='lower_limit', value=0.8)], title='Metric threshold', description='Value that defines the violation limit for the metric')] = [MetricThreshold(type='lower_limit', value=0.8)]¶
Value that defines the violation limit for the metric
- evaluate(data: DataFrame | dict, configuration: GenAIConfiguration | AgenticAIConfiguration, **kwargs)¶
- async evaluate_async(data: DataFrame | dict, configuration: GenAIConfiguration | AgenticAIConfiguration, **kwargs) AggregateMetricResult¶
Evaluate the data for ToolCallRelevanceMetric :param data: Data to be evaluated :type data: pd.DataFrame | dict :param configuration: Metrics configuration :type configuration: GenAIConfiguration | AgenticAIConfiguration :param **kwargs: Additional keyword arguments
- Returns:
The computed metrics
- Return type:
- model_post_init(context: Any, /) None¶
We need to both initialize private attributes and call the user-defined model_post_init method.