Agentic AI Configuration¶
- pydantic model ibm_watsonx_gov.config.agentic_ai_configuration.AgenticAIConfiguration¶
Bases:
GenAIConfigurationDefines the AgenticAIConfiguration class.
The configuration interface for Agentic AI tools and applications. This is used to specify the fields mapping details in the data and other configuration parameters needed for evaluation.
Examples
- Create configuration with default parameters
configuration = AgenticAIConfiguration()
- Create configuration with parameters
configuration = AgenticAIConfiguration(input_fields=["input"], output_fields=["output"])
- Create configuration with dict parameters
config = {"input_fields": ["input"], "output_fields": ["output"], "context_fields": ["contexts"], "reference_fields": ["reference"]} configuration = AgenticAIConfiguration(**config)
Show JSON schema
{ "title": "AgenticAIConfiguration", "description": "Defines the AgenticAIConfiguration class.\n\nThe configuration interface for Agentic AI tools and applications.\nThis is used to specify the fields mapping details in the data and other configuration parameters needed for evaluation.\n\nExamples:\n 1. Create configuration with default parameters\n .. code-block:: python\n\n configuration = AgenticAIConfiguration()\n\n 2. Create configuration with parameters\n .. code-block:: python\n\n configuration = AgenticAIConfiguration(input_fields=[\"input\"], \n output_fields=[\"output\"])\n\n 2. Create configuration with dict parameters\n .. code-block:: python\n\n config = {\"input_fields\": [\"input\"],\n \"output_fields\": [\"output\"],\n \"context_fields\": [\"contexts\"],\n \"reference_fields\": [\"reference\"]}\n configuration = AgenticAIConfiguration(**config)", "type": "object", "properties": { "record_id_field": { "default": "record_id", "description": "The record identifier field name.", "examples": [ "record_id" ], "title": "Record id field", "type": "string" }, "record_timestamp_field": { "default": "record_timestamp", "description": "The record timestamp field name.", "examples": [ "record_timestamp" ], "title": "Record timestamp field", "type": "string" }, "task_type": { "anyOf": [ { "$ref": "#/$defs/TaskType" }, { "type": "null" } ], "default": null, "description": "The generative task type. Default value is None.", "examples": [ "retrieval_augmented_generation" ], "title": "Task Type" }, "input_fields": { "default": [ "input_text" ], "description": "The list of model input fields in the data. Default value is ['input_text'].", "examples": [ [ "question" ] ], "items": { "type": "string" }, "title": "Input Fields", "type": "array" }, "context_fields": { "default": [ "context" ], "description": "The list of context fields in the input fields. Default value is ['context'].", "examples": [ [ "context1", "context2" ] ], "items": { "type": "string" }, "title": "Context Fields", "type": "array" }, "output_fields": { "default": [ "generated_text" ], "description": "The list of model output fields in the data. Default value is ['generated_text'].", "examples": [ [ "output" ] ], "items": { "type": "string" }, "title": "Output Fields", "type": "array" }, "reference_fields": { "default": [ "ground_truth" ], "description": "The list of reference fields in the data. Default value is ['ground_truth'].", "examples": [ [ "reference" ] ], "items": { "type": "string" }, "title": "Reference Fields", "type": "array" }, "locale": { "anyOf": [ { "$ref": "#/$defs/Locale" }, { "type": "null" } ], "default": null, "description": "The language locale of the input, output and reference fields in the data.", "title": "Locale" }, "tools": { "default": [], "description": "The list of tools used by the LLM.", "examples": [ [ "function1", "function2" ] ], "items": { "type": "object" }, "title": "Tools", "type": "array" }, "tool_calls_field": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "tool_calls", "description": "The tool calls field in the input fields. Default value is 'tool_calls'.", "examples": [ "tool_calls" ], "title": "Tool Calls Field" }, "available_tools_field": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "available_tools", "description": "The tool inventory field in the data. Default value is 'available_tools'.", "examples": [ "available_tools" ], "title": "Available Tools Field" }, "llm_judge": { "anyOf": [ { "$ref": "#/$defs/LLMJudge" }, { "type": "null" } ], "default": null, "description": "LLM as Judge Model details.", "title": "LLM Judge" }, "prompt_field": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "model_prompt", "description": "The prompt field in the input fields. Default value is 'model_prompt'.", "examples": [ "model_prompt" ], "title": "Model Prompt Field" }, "message_id_field": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "message_id", "description": "The message identifier field name. Default value is 'message_id'.", "examples": [ "message_id" ], "title": "Message id field" }, "conversation_id_field": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "conversation_id", "description": "The conversation identifier field name. Default value is 'conversation_id'.", "examples": [ "conversation_id" ], "title": "Conversation id field" } }, "$defs": { "AWSBedrockCredentials": { "description": "Defines the AWSBedrockCredentials class for accessing AWS Bedrock using environment variables or manual input.\n\nExamples:\n 1. Create credentials manually:\n .. code-block:: python\n\n credentials = AWSBedrockCredentials(\n aws_access_key_id=\"your-access-key-id\",\n aws_secret_access_key=\"your-secret-access-key\",\n aws_region_name=\"us-east-1\",\n aws_session_token=\"optional-session-token\"\n )\n\n 2. Create credentials from environment:\n .. code-block:: python\n\n os.environ[\"AWS_ACCESS_KEY_ID\"] = \"your-access-key-id\"\n os.environ[\"AWS_DEFAULT_REGION\"] = \"us-east-1\"\n os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"your-secret-access-key\"\n\n credentials = AWSBedrockCredentials.create_from_env()", "properties": { "aws_access_key_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The AWS access key id. This attribute value will be read from AWS_ACCESS_KEY_ID environment variable when creating AWSBedrockCredentials from environment.", "title": "AWS Access Key ID" }, "aws_secret_access_key": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The AWS secret access key. This attribute value will be read from AWS_SECRET_ACCESS_KEY environment variable when creating AWSBedrockCredentials from environment.", "title": "AWS Secret Access Key" }, "aws_region_name": { "default": "us-east-1", "description": "AWS region. This attribute value will be read from AWS_DEFAULT_REGION environment variable when creating AWSBedrockCredentials from environment.", "examples": [ "us-east-1", "eu-west-1" ], "title": "AWS Region", "type": "string" }, "aws_session_token": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Optional AWS session token for temporary credentials.", "title": "AWS Session Token" } }, "required": [ "aws_access_key_id", "aws_secret_access_key", "aws_session_token" ], "title": "AWSBedrockCredentials", "type": "object" }, "AWSBedrockFoundationModel": { "description": " The Amazon Bedrock foundation model details.\n\n Examples:\n 1. Create AWS Bedrock foundation model by passing credentials manually:\n .. code-block:: python\n\n bedrock_model = AWSBedrockFoundationModel(\n model_id=\"anthropic.claude-v2\",\n provider=AWSBedrockModelProvider(\n credentials=AWSBedrockCredentials(\n aws_access_key_id=\"your-access-key-id\",\n aws_secret_access_key=\"your-secret-access-key\",\n aws_region_name=\"us-east-1\",\n aws_session_token=\"optional-session-token\"\n )\n ),\n parameters={\n \"temperature\": 0.7,\n \"top_p\": 0.9,\n \"max_tokens\": 200,\n \"stop_sequences\": [\"\n\"],\n \"system\": \"You are a concise assistant.\",\n \"reasoning_effort\": \"high\",\n \"tool_choice\": \"auto\"\n }\n )\n\n 2. Create AWS Bedrock foundation model using environment variables:\n os.environ[\"AWS_ACCESS_KEY_ID\"] = \"your-access-key-id\"\n os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"your-secret-access-key\"\n os.environ[\"AWS_DEFAULT_REGION\"] = \"us-east-1\"\n\n .. code-block:: python\n\n bedrock_model = AWSBedrockFoundationModel(\n model_id=\"anthropic.claude-v2\"\n )\n ", "properties": { "model_id": { "description": "The AWS Bedrock model name. It must be a valid AWS Bedrock model identifier.", "examples": [ "anthropic.claude-v2" ], "title": "Model ID", "type": "string" }, "provider": { "$ref": "#/$defs/AWSBedrockModelProvider", "description": "The AWS Bedrock provider details.", "title": "Provider" }, "parameters": { "anyOf": [ { "type": "object" }, { "type": "null" } ], "description": "The model parameters to be used when invoking the model. The parameters may include temperature, top_p, max_tokens, etc..", "title": "Parameters" } }, "required": [ "model_id" ], "title": "AWSBedrockFoundationModel", "type": "object" }, "AWSBedrockModelProvider": { "description": "Represents a model provider using Amazon Bedrock.\n\nExamples:\n 1. Create provider using credentials object:\n .. code-block:: python\n\n provider = AWSBedrockModelProvider(\n credentials=AWSBedrockCredentials(\n aws_access_key_id=\"your-access-key-id\",\n aws_secret_access_key=\"your-secret-access-key\",\n aws_region_name=\"us-east-1\",\n aws_session_token=\"optional-session-token\"\n )\n )\n\n 2. Create provider using environment variables:\n .. code-block:: python\n\n os.environ['AWS_ACCESS_KEY_ID'] = \"your-access-key-id\"\n os.environ['AWS_SECRET_ACCESS_KEY'] = \"your-secret-access-key\"\n os.environ['AWS_SESSION_TOKEN'] = \"optional-session-token\" # Optional\n os.environ['AWS_DEFAULT_REGION'] = \"us-east-1\"\n provider = AWSBedrockModelProvider()", "properties": { "type": { "$ref": "#/$defs/ModelProviderType", "default": "aws_bedrock", "description": "The type of model provider." }, "credentials": { "anyOf": [ { "$ref": "#/$defs/AWSBedrockCredentials" }, { "type": "null" } ], "default": null, "description": "AWS Bedrock credentials." } }, "title": "AWSBedrockModelProvider", "type": "object" }, "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": "#/$defs/AzureOpenAICredentials" }, { "type": "null" } ], "default": null, "description": "Azure OpenAI credentials." } }, "title": "AzureOpenAIModelProvider", "type": "object" }, "CustomFoundationModel": { "description": "Defines the CustomFoundationModel class.\n\nThis class extends the base `FoundationModel` to support custom inference logic through a user-defined scoring function.\nIt is intended for use cases where the model is externally hosted and not in the list of supported frameworks.\nExamples:\n 1. Define a custom scoring function and create a model:\n .. code-block:: python\n\n import pandas as pd\n\n def scoring_fn(data: pd.DataFrame):\n predictions_list = []\n # Custom logic to call an external LLM\n return pd.DataFrame({\"generated_text\": predictions_list}) \n\n model = CustomFoundationModel(\n scoring_fn=scoring_fn\n )", "properties": { "model_name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The name of the foundation model.", "title": "Model Name" }, "provider": { "$ref": "#/$defs/ModelProvider", "description": "The provider of the model." } }, "title": "CustomFoundationModel", "type": "object" }, "GoogleAIStudioCredentials": { "description": "Defines the GoogleAIStudioCredentials class for accessing Google AI Studio using an API key.\n\nExamples:\n 1. Create credentials manually:\n .. code-block:: python\n\n google_credentials = GoogleAIStudioCredentials(api_key=\"your-api-key\")\n\n 2. Create credentials from environment:\n .. code-block:: python\n\n os.environ[\"GOOGLE_API_KEY\"] = \"your-api-key\"\n google_credentials = GoogleAIStudioCredentials.create_from_env()", "properties": { "api_key": { "description": "The Google AI Studio key. This attribute can be read from GOOGLE_API_KEY environment variable when creating GoogleAIStudioCredentials from environment.", "title": "Api Key", "type": "string" } }, "required": [ "api_key" ], "title": "GoogleAIStudioCredentials", "type": "object" }, "GoogleAIStudioFoundationModel": { "description": "Represents a foundation model served via Google AI Studio.\n\nExamples:\n 1. Create Google AI Studio foundation model by passing the credentials during object creation.\n .. code-block:: python\n\n model = GoogleAIStudioFoundationModel(\n model_id=\"gemini-1.5-pro-002\",\n provider=GoogleAIStudioModelProvider(\n credentials=GoogleAIStudioCredentials(api_key=\"your_api_key\")\n )\n )\n 2. Create Google AI Studio foundation model by setting the credentials in environment variables:\n * ``GOOGLE_API_KEY`` OR ``GEMINI_API_KEY`` is used to set the Credentials path for Vertex AI.\n .. code-block:: python\n\n model = GoogleAIStudioFoundationModel(\n model_id=\"gemini/gpt-4o-mini\",\n )", "properties": { "model_name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The name of the foundation model.", "title": "Model Name" }, "provider": { "$ref": "#/$defs/GoogleAIStudioModelProvider", "description": "Google AI Studio provider.", "title": "Provider" }, "model_id": { "description": "Model name for Google AI Studio. Must be a valid Google AI model identifier or a fully-qualified publisher path", "examples": [ "gemini-1.5-pro-002" ], "title": "Model id", "type": "string" } }, "required": [ "model_id" ], "title": "GoogleAIStudioFoundationModel", "type": "object" }, "GoogleAIStudioModelProvider": { "description": "Represents a model provider using Google AI Studio.\n\nExamples:\n 1. Create provider using credentials object:\n .. code-block:: python\n\n provider = GoogleAIStudioModelProvider(\n credentials=GoogleAIStudioCredentials(api_key=\"api-key\")\n )\n\n 2. Create provider using environment variables:\n .. code-block:: python\n\n os.environ['GOOGLE_API_KEY'] = \"your_api_key\"\n\n provider = GoogleAIStudioModelProvider()", "properties": { "type": { "$ref": "#/$defs/ModelProviderType", "default": "google_ai_studio", "description": "The type of model provider." }, "credentials": { "anyOf": [ { "$ref": "#/$defs/GoogleAIStudioCredentials" }, { "type": "null" } ], "default": null, "description": "Google AI Studio credentials." } }, "title": "GoogleAIStudioModelProvider", "type": "object" }, "LLMJudge": { "description": "Defines the LLMJudge.\n\nThe LLMJudge class contains the details of the llm judge model to be used for computing the metric.\n\nExamples:\n 1. Create LLMJudge using watsonx.ai foundation model:\n .. code-block:: python\n\n wx_ai_foundation_model = WxAIFoundationModel(\n model_id=\"ibm/granite-3-3-8b-instruct\",\n project_id=PROJECT_ID,\n provider=WxAIModelProvider(\n credentials=WxAICredentials(api_key=wx_apikey)\n )\n )\n llm_judge = LLMJudge(model=wx_ai_foundation_model)", "properties": { "model": { "anyOf": [ { "$ref": "#/$defs/WxAIFoundationModel" }, { "$ref": "#/$defs/OpenAIFoundationModel" }, { "$ref": "#/$defs/AzureOpenAIFoundationModel" }, { "$ref": "#/$defs/PortKeyGateway" }, { "$ref": "#/$defs/RITSFoundationModel" }, { "$ref": "#/$defs/VertexAIFoundationModel" }, { "$ref": "#/$defs/GoogleAIStudioFoundationModel" }, { "$ref": "#/$defs/AWSBedrockFoundationModel" }, { "$ref": "#/$defs/CustomFoundationModel" } ], "description": "The foundation model to be used as judge", "title": "Model" } }, "required": [ "model" ], "title": "LLMJudge", "type": "object" }, "Locale": { "properties": { "input": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" }, { "type": "null" } ], "default": null, "title": "Input" }, "output": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "default": null, "title": "Output" }, "reference": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" }, { "type": "null" } ], "default": null, "title": "Reference" } }, "title": "Locale", "type": "object" }, "ModelProvider": { "properties": { "type": { "$ref": "#/$defs/ModelProviderType", "description": "The type of model provider." } }, "required": [ "type" ], "title": "ModelProvider", "type": "object" }, "ModelProviderType": { "description": "Supported model provider types for Generative AI", "enum": [ "ibm_watsonx.ai", "azure_openai", "rits", "openai", "vertex_ai", "google_ai_studio", "aws_bedrock", "custom", "portkey" ], "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" }, "PortKeyCredentials": { "description": "Defines the PortKeyCredentials class to specify the PortKey Gateway details.\n\nExamples:\n 1. Create PortKeyCredentials with default parameters.\n .. code-block:: python\n\n portkey_credentials = PortKeyCredentials(api_key=api_key,\n url=portkey_url,\n provider_api_key=provider_api_key,\n provider=provider_name)\n\n 2. Create PortKeyCredentials by reading from environment variables.\n .. code-block:: python\n\n os.environ[\"PORTKEY_API_KEY\"] = \"...\"\n os.environ[\"PORTKEY_URL\"] = \"...\"\n os.environ[\"PORTKEY_PROVIDER_API_KEY\"] = \"...\"\n os.environ[\"PORTKEY_PROVIDER_NAME\"] = \"...\"\n portkey_credentials = PortKeyCredentials.create_from_env()", "properties": { "url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "PortKey url. This attribute can be read from `PORTKEY_URL` environment variable.", "title": "Url" }, "api_key": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "API key for PortKey. This attribute can be read from `PORTKEY_API_KEY` environment variable.", "title": "Api Key" }, "provider_api_key": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "API key for the provider. This attribute can be read from `PORTKEY_PROVIDER_API_KEY` environment variable.", "title": "Provider Api Key" }, "provider": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The provider name. This attribute can be read from `PORTKEY_PROVIDER_NAME` environment variable.", "title": "Provider" } }, "required": [ "url", "api_key", "provider_api_key", "provider" ], "title": "PortKeyCredentials", "type": "object" }, "PortKeyGateway": { "description": "The PortKey gateway details\n\nExamples:\n 1. Create PortKeyGateway by passing the credentials during object creation. Note that the url is optional and will be set to the default value for PortKey. To change the default value, the url should be passed to ``PortKeyCredentials`` object.\n .. code-block:: python\n\n port_key_gateway = PortKeyGateway(\n model_id=\"gpt-4o-mini\",\n provider=PortKeyModelProvider(\n credentials=PortKeyCredentials(\n api_key=api_key,\n url=openai_url,\n provider_api_key=provider_api_key,\n provider_name=provider_name\n )\n )\n )\n\n 2. Create PortKeyGateway by setting the credentials in environment variables:\n * ``PORTKEY_API_KEY`` is used to set the api key for PortKey.\n * ``PORTKEY_URL`` is used to set the url for PortKey.\n * ``PORTKEY_PROVIDER_API_KEY`` is used to set the provider api key for PortKey.\n * ``PORTKEY_PROVIDER_NAME`` is used to set the provider name for PortKey\n\n .. code-block:: python\n\n port_key_gateway = PortKeyGateway(\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": "#/$defs/PortKeyModelProvider", "description": "PortKey Provider" }, "model_id": { "description": "Model name from the Provider", "title": "Model Id", "type": "string" } }, "required": [ "model_id" ], "title": "PortKeyGateway", "type": "object" }, "PortKeyModelProvider": { "properties": { "type": { "$ref": "#/$defs/ModelProviderType", "default": "portkey", "description": "The type of model provider." }, "credentials": { "anyOf": [ { "$ref": "#/$defs/PortKeyCredentials" }, { "type": "null" } ], "default": null, "description": "PortKey credentials." } }, "title": "PortKeyModelProvider", "type": "object" }, "RITSCredentials": { "properties": { "hostname": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "https://inference-3scale-apicast-production.apps.rits.fmaas.res.ibm.com", "description": "The rits hostname", "title": "Hostname" }, "api_key": { "title": "Api Key", "type": "string" } }, "required": [ "api_key" ], "title": "RITSCredentials", "type": "object" }, "RITSFoundationModel": { "properties": { "model_name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The name of the foundation model.", "title": "Model Name" }, "provider": { "$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" }, "VertexAICredentials": { "description": "Defines the VertexAICredentials class for accessing Vertex AI using service account credentials.\n\nExamples:\n 1. Create credentials manually:\n .. code-block:: python\n\n vertex_credentials = VertexAICredentials(\n credentials_path=\"path/to/service_account.json\",\n project_id=\"my-gcp-project\",\n location=\"us-central1\"\n )\n\n 2. Create credentials from environment:\n .. code-block:: python\n\n os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"path/to/service_account.json\"\n os.environ[\"GOOGLE_CLOUD_PROJECT\"] = \"my-gcp-project\"\n os.environ[\"GOOGLE_CLOUD_LOCATION\"] = \"us-central1\"\n\n vertex_ai_credentials = VertexAICredentials.create_from_env()", "properties": { "credentials_path": { "description": "Path to service-account JSON. This attribute can be read from GOOGLE_APPLICATION_CREDENTIALS environment variable when creating VertexAICredentials from environment.", "title": "Credentials Path", "type": "string" }, "project_id": { "description": "The Google Cloud project id. This attribute can be read from GOOGLE_CLOUD_PROJECT or GCLOUD_PROJECT environment variable when creating VertexAICredentials from environment.", "title": "Project ID", "type": "string" }, "location": { "default": "us-central1", "description": "Vertex AI region. This attribute can be read from GOOGLE_CLOUD_LOCATION environment variable when creating VertexAICredentials from environment. By default us-central1 location is used.", "examples": [ "us-central1", "europe-west4" ], "title": "Location", "type": "string" } }, "required": [ "credentials_path", "project_id" ], "title": "VertexAICredentials", "type": "object" }, "VertexAIFoundationModel": { "description": "Represents a foundation model served via Vertex AI.\n\nExamples:\n 1. Create Vertex AI foundation model by passing the credentials during object creation.\n .. code-block:: python\n\n model = VertexAIFoundationModel(\n model_id=\"gemini-1.5-pro-002\",\n provider=VertexAIModelProvider(\n credentials=VertexAICredentials(\n project_id=\"your-project\",\n location=\"us-central1\", # This is optional field, by default us-central1 location is selected\n credentials_path=\"/path/to/service_account.json\"\n )\n )\n )\n 2. Create Vertex AI foundation model by setting the credentials in environment variables:\n * ``GOOGLE_APPLICATION_CREDENTIALS`` is used to set the Credentials path for Vertex AI.\n * ``GOOGLE_CLOUD_PROJECT`` is used to set the Project id for Vertex AI.\n * ``GOOGLE_CLOUD_LOCATION`` is uses to set the Location for Vertex AI. By default us-central1 location is used when GOOGLE_CLOUD_LOCATION is not provided .\n\n .. code-block:: python\n\n os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"path/to/service_account.json\"\n os.environ[\"GOOGLE_CLOUD_PROJECT\"] = \"my-gcp-project\"\n os.environ[\"GOOGLE_CLOUD_LOCATION\"] = \"us-central1\"\n\n model = VertexAIFoundationModel(\n model_id=\"gemini/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": "#/$defs/VertexAIModelProvider", "description": "Vertex AI provider.", "title": "Provider" }, "model_id": { "description": "Model name for Vertex AI. Must be a valid Vertex AI model identifier or a fully-qualified publisher path", "examples": [ "gemini-1.5-pro-002" ], "title": "Model id", "type": "string" } }, "required": [ "model_id" ], "title": "VertexAIFoundationModel", "type": "object" }, "VertexAIModelProvider": { "description": "Represents a model provider using Vertex AI.\n\nExamples:\n 1. Create provider using credentials object:\n .. code-block:: python\n\n provider = VertexAIModelProvider(\n credentials=VertexAICredentials(\n credentials_path=\"path/to/key.json\",\n project_id=\"your-project\",\n location=\"us-central1\" \n )\n )\n\n 2. Create provider using environment variables:\n .. code-block:: python\n\n os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = \"/path/to/service_account.json\"\n os.environ['GOOGLE_CLOUD_PROJECT'] = \"your-project\"\n os.environ['GOOGLE_CLOUD_LOCATION'] = \"us-central1\" # This is optional field, by default us-central1 location is selected\n\n provider = VertexAIModelProvider()", "properties": { "type": { "$ref": "#/$defs/ModelProviderType", "default": "vertex_ai", "description": "The type of model provider." }, "credentials": { "anyOf": [ { "$ref": "#/$defs/VertexAICredentials" }, { "type": "null" } ], "default": null, "description": "Vertex AI credentials." } }, "title": "VertexAIModelProvider", "type": "object" }, "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=\"ibm/granite-3-3-8b-instruct\",\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=\"ibm/granite-3-3-8b-instruct\",\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=\"ibm/granite-3-3-8b-instruct\",\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=\"ibm/granite-3-3-8b-instruct\",\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" } } }
- Config:
arbitrary_types_allowed: bool = True
- Fields:
- Validators:
- field conversation_id_field: Annotated[str | None, FieldInfo(annotation=NoneType, required=False, default='conversation_id', title='Conversation id field', description="The conversation identifier field name. Default value is 'conversation_id'.", examples=['conversation_id'])] = 'conversation_id'¶
The conversation identifier field name. Default value is ‘conversation_id’.
- Validated by:
- field message_id_field: Annotated[str | None, FieldInfo(annotation=NoneType, required=False, default='message_id', title='Message id field', description="The message identifier field name. Default value is 'message_id'.", examples=['message_id'])] = 'message_id'¶
The message identifier field name. Default value is ‘message_id’.
- Validated by:
- classmethod create_configuration(*, app_config: Self | None, method_config: Self | None, defaults: list[EvaluatorFields], add_record_fields: bool = True) Self¶
Creates a configuration object based on the provided parameters.
- Parameters:
app_config (Optional[Self]) – The application configuration.
method_config (Optional[Self]) – The method configuration.
defaults (list[EvaluatorFields]) – The default fields to include in the configuration.
add_record_fields (bool, optional) – Whether to add record fields to the configuration. Defaults to True.
- Returns:
The created configuration object.
- Return type:
Self
- pydantic model ibm_watsonx_gov.config.agentic_ai_configuration.OTLPCollectorConfiguration¶
Bases:
BaseModelDefines the OTLPCollectorConfiguration class. It contains the configuration settings for the OpenTelemetry Protocol collector.
Examples
- Create OTLPCollectorConfiguration with default parameters
oltp_config = OTLPCollectorConfiguration()
- Create OTLPCollectorConfiguration by providing server endpoint details.
oltp_config = OTLPCollectorConfiguration(app_name="app", endpoint="https://hostname/ml/v1/traces", timeout=10, headers={"Authorization": "Bearer token"})
Show JSON schema
{ "title": "OTLPCollectorConfiguration", "description": "Defines the OTLPCollectorConfiguration class.\nIt contains the configuration settings for the OpenTelemetry Protocol collector.\n\nExamples:\n 1. Create OTLPCollectorConfiguration with default parameters\n .. code-block:: python\n\n oltp_config = OTLPCollectorConfiguration()\n\n 1. Create OTLPCollectorConfiguration by providing server endpoint details.\n .. code-block:: python\n\n oltp_config = OTLPCollectorConfiguration(app_name=\"app\",\n endpoint=\"https://hostname/ml/v1/traces\",\n timeout=10,\n headers={\"Authorization\": \"Bearer token\"})", "type": "object", "properties": { "app_name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Application name for tracing.", "title": "App Name" }, "endpoint": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "http://localhost:4318/v1/traces", "description": "The OTLP collector endpoint URL for sending trace data. Default value is 'http://localhost:4318/v1/traces'", "title": "OTLP Endpoint" }, "insecure": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": false, "description": "Whether to disable TLS for the exporter (i.e., use an insecure connection). Default is False.", "title": "Insecure Connection" }, "is_grpc": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": false, "description": "If True, use gRPC for exporting traces instead of HTTP. Default is False.", "title": "Use gRPC" }, "timeout": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": 100, "description": "Timeout in milliseconds for sending telemetry data to the collector. Default is 100ms.", "title": "Timeout" }, "headers": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "null" } ], "description": "Headers needed to call the server.", "title": "Headers" } }, "required": [ "app_name" ] }
- Fields:
- field app_name: Annotated[str | None, FieldInfo(annotation=NoneType, required=True, title='App Name', description='Application name for tracing.')] [Required]¶
Application name for tracing.
- field endpoint: Annotated[str | None, FieldInfo(annotation=NoneType, required=False, default='http://localhost:4318/v1/traces', title='OTLP Endpoint', description="The OTLP collector endpoint URL for sending trace data. Default value is 'http://localhost:4318/v1/traces'")] = 'http://localhost:4318/v1/traces'¶
The OTLP collector endpoint URL for sending trace data. Default value is ‘http://localhost:4318/v1/traces’
- field headers: Annotated[dict[str, str] | None, FieldInfo(annotation=NoneType, required=False, default_factory=dict, title='Headers', description='Headers needed to call the server.')] [Optional]¶
Headers needed to call the server.
- field insecure: Annotated[bool | None, FieldInfo(annotation=NoneType, required=False, default=False, title='Insecure Connection', description='Whether to disable TLS for the exporter (i.e., use an insecure connection). Default is False.')] = False¶
Whether to disable TLS for the exporter (i.e., use an insecure connection). Default is False.
- field is_grpc: Annotated[bool | None, FieldInfo(annotation=NoneType, required=False, default=False, title='Use gRPC', description='If True, use gRPC for exporting traces instead of HTTP. Default is False.')] = False¶
If True, use gRPC for exporting traces instead of HTTP. Default is False.
- field timeout: Annotated[int | None, FieldInfo(annotation=NoneType, required=False, default=100, title='Timeout', description='Timeout in milliseconds for sending telemetry data to the collector. Default is 100ms.')] = 100¶
Timeout in milliseconds for sending telemetry data to the collector. Default is 100ms.
- pydantic model ibm_watsonx_gov.config.agentic_ai_configuration.TracingConfiguration¶
Bases:
BaseModelDefines the tracing configuration class. Tracing configuration is required if the the evaluations are needed to be tracked in an experiment or if the agentic application traces should be sent to a Open Telemetry Collector. One of project_id or space_id is required. If the otlp_collector_config is provided, the traces are logged to Open Telemetry Collector, otherwise the traces are logged to file on disk. If its required to log the traces to both collector and local file, provide the otlp_collector_config and set the flag log_traces_to_file to True.
Examples
- Create Tracing configuration to track the results in an experiment
tracing_config = TracingConfiguration(project_id="...") agentic_evaluator = AgenticEvaluator(tracing_configuration=tracing_config) agentic_evaluator.track_experiment(name="my_experiment") ...
- Create Tracing configuration to send traces to collector
oltp_collector_config = OTLPCollectorConfiguration(endpoint="http://hostname:4318/v1/traces") tracing_config = TracingConfiguration(space_id="...", resource_attributes={ "wx-deployment-id": deployment_id, "wx-instance-id": "wml-instance-id1", "wx-ai-service-id": "ai-service-id1"}, otlp_collector_config=oltp_collector_config) agentic_evaluator = AgenticEvaluator(tracing_configuration=tracing_config) ...
Show JSON schema
{ "title": "TracingConfiguration", "description": "Defines the tracing configuration class. \nTracing configuration is required if the the evaluations are needed to be tracked in an experiment or if the agentic application traces should be sent to a Open Telemetry Collector.\nOne of project_id or space_id is required.\nIf the otlp_collector_config is provided, the traces are logged to Open Telemetry Collector, otherwise the traces are logged to file on disk.\nIf its required to log the traces to both collector and local file, provide the otlp_collector_config and set the flag log_traces_to_file to True.\n\nExamples:\n 1. Create Tracing configuration to track the results in an experiment\n .. code-block:: python\n\n tracing_config = TracingConfiguration(project_id=\"...\")\n agentic_evaluator = AgenticEvaluator(tracing_configuration=tracing_config)\n agentic_evaluator.track_experiment(name=\"my_experiment\")\n ...\n\n 2. Create Tracing configuration to send traces to collector\n .. code-block:: python\n\n oltp_collector_config = OTLPCollectorConfiguration(endpoint=\"http://hostname:4318/v1/traces\")\n tracing_config = TracingConfiguration(space_id=\"...\",\n resource_attributes={\n \"wx-deployment-id\": deployment_id,\n \"wx-instance-id\": \"wml-instance-id1\",\n \"wx-ai-service-id\": \"ai-service-id1\"},\n otlp_collector_config=oltp_collector_config)\n agentic_evaluator = AgenticEvaluator(tracing_configuration=tracing_config)\n ...", "type": "object", "properties": { "project_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The project id.", "title": "Project ID" }, "space_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The space id.", "title": "Space ID" }, "resource_attributes": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "null" } ], "description": "The resource attributes set in all the spans.", "title": "Resource Attributes" }, "otlp_collector_config": { "anyOf": [ { "$ref": "#/$defs/OTLPCollectorConfiguration" }, { "type": "null" } ], "default": null, "description": "OTLP Collector configuration.", "title": "OTLP Collector Config" }, "log_traces_to_file": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": false, "description": "The flag to enable logging of traces to a file. If set to True, the traces are logged to a file. Use the flag when its needed to log the traces to file and to be sent to the server simultaneously.", "title": "Log Traces to file" } }, "$defs": { "OTLPCollectorConfiguration": { "description": "Defines the OTLPCollectorConfiguration class.\nIt contains the configuration settings for the OpenTelemetry Protocol collector.\n\nExamples:\n 1. Create OTLPCollectorConfiguration with default parameters\n .. code-block:: python\n\n oltp_config = OTLPCollectorConfiguration()\n\n 1. Create OTLPCollectorConfiguration by providing server endpoint details.\n .. code-block:: python\n\n oltp_config = OTLPCollectorConfiguration(app_name=\"app\",\n endpoint=\"https://hostname/ml/v1/traces\",\n timeout=10,\n headers={\"Authorization\": \"Bearer token\"})", "properties": { "app_name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Application name for tracing.", "title": "App Name" }, "endpoint": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "http://localhost:4318/v1/traces", "description": "The OTLP collector endpoint URL for sending trace data. Default value is 'http://localhost:4318/v1/traces'", "title": "OTLP Endpoint" }, "insecure": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": false, "description": "Whether to disable TLS for the exporter (i.e., use an insecure connection). Default is False.", "title": "Insecure Connection" }, "is_grpc": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": false, "description": "If True, use gRPC for exporting traces instead of HTTP. Default is False.", "title": "Use gRPC" }, "timeout": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": 100, "description": "Timeout in milliseconds for sending telemetry data to the collector. Default is 100ms.", "title": "Timeout" }, "headers": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "null" } ], "description": "Headers needed to call the server.", "title": "Headers" } }, "required": [ "app_name" ], "title": "OTLPCollectorConfiguration", "type": "object" } } }
- Fields:
- Validators:
validate_fields»all fields
- field log_traces_to_file: Annotated[bool | None, FieldInfo(annotation=NoneType, required=False, default=False, title='Log Traces to file', description='The flag to enable logging of traces to a file. If set to True, the traces are logged to a file. Use the flag when its needed to log the traces to file and to be sent to the server simultaneously.')] = False¶
The flag to enable logging of traces to a file. If set to True, the traces are logged to a file. Use the flag when its needed to log the traces to file and to be sent to the server simultaneously.
- Validated by:
- field otlp_collector_config: Annotated[OTLPCollectorConfiguration | None, FieldInfo(annotation=NoneType, required=False, default=None, title='OTLP Collector Config', description='OTLP Collector configuration.')] = None¶
OTLP Collector configuration.
- Validated by:
- field project_id: Annotated[str | None, FieldInfo(annotation=NoneType, required=False, default=None, title='Project ID', description='The project id.')] = None¶
The project id.
- Validated by:
- field resource_attributes: Annotated[dict[str, str] | None, FieldInfo(annotation=NoneType, required=False, default_factory=dict, title='Resource Attributes', description='The resource attributes set in all the spans.')] [Optional]¶
The resource attributes set in all the spans.
- Validated by:
- field space_id: Annotated[str | None, FieldInfo(annotation=NoneType, required=False, default=None, title='Space ID', description='The space id.')] = None¶
The space id.
- Validated by:
- classmethod create_from_env()¶
- validator validate_fields » all fields¶
- property enable_local_traces: bool¶
- property enable_server_traces: bool¶