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Fusion 5.9
    Fusion 5.9

    Named entity recognition (NER) use caseLucidworks AI Prediction API

    In the Named entity recognition (NER) use case of the LWAI Prediction API, the LLM ingests text and entities to extract and return a JSON response that contains a list of entities extracted from the text. No options can be configured.

    This use case can be used to extract nouns and proper nouns such as Brand, Date, Company, Places, and Category in order to guide and refine searches.

    Prerequisites

    To use this API, you need:

    • The unique APPLICATION_ID for your Lucidworks AI application. For more information, see credentials to use APIs.

    • A bearer token generated with a scope value of machinelearning.predict. For more information, see Authentication API.

    • The USE_CASE and MODEL_ID fields for the use case request. The path is: /ai/prediction/USE_CASE/MODEL_ID. A list of supported models is returned in the Lucidworks AI Use Case API. For more information about supported models, see Generative AI models.

    Common parameters and fields

    modelConfig

    Some parameters of the /ai/prediction/USE_CASE/MODEL_ID request are common to all of the generative AI (GenAI) use cases, including the modelConfig parameter. If you do not enter values, the following defaults are used.

    "modelConfig":{
  "temperature": 0.7,
  "topP": 1.0,
  "presencePenalty": 0.0,
  "frequencyPenalty": 0.0,
  "maxTokens": 256
}

    Also referred to as hyperparameters, these fields set certain controls on the response of a LLM:

    Field Description

    temperature

    A sampling temperature between 0 and 2. A higher sampling temperature such as 0.8, results in more random (creative) output. A lower value such as 0.2 results in more focused (conservative) output. A lower value does not guarantee the model returns the same response for the same input.

    topP

    A floating-point number between 0 and 1 that controls the cumulative probability of the top tokens to consider, known as the randomness of the LLM’s response. This parameter is also referred to as top probability. Set topP to 1 to consider all tokens. A higher value specifies a higher probability threshold and selects tokens whose cumulative probability is greater than the threshold. The higher the value, the more diverse the output.

    presencePenalty

    A floating-point number between -2.0 and 2.0 that penalizes new tokens based on whether they have already appeared in the text. This increases the model’s use of diverse tokens. A value greater than zero (0) encourages the model to use new tokens. A value less than zero (0) encourages the model to repeat existing tokens.

    frequencyPenalty

    A floating-point number between -2.0 and 2.0 that penalizes new tokens based on their frequency in the generated text. A value greater than zero (0) encourages the model to use new tokens. A value less than zero (0) encourages the model to repeat existing tokens.

    maxTokens

    The maximum number of tokens to generate per output sequence. The value is different for each model. Review individual model specifications when the value exceeds 2048.

    apiKey

    The optional parameter is only required when the specified model is used for prediction. This secret value is specified in the external model. For:

    • OpenAI models, "apiKey" is the value in the model’s "[OPENAI_API_KEY]" field. For more information, see Authentication API keys.

    • Azure OpenAI models, "apiKey" is the value generated by Azure in either the model’s "[KEY1 or KEY2]" field. For requirements to use Azure models, see Generative AI models.

    • Google VertexAI models, "apiKey" is the value in the model’s

      "[BASE64_ENCODED_GOOGLE_SERVICE_ACCOUNT_KEY]" field. For more information, see Create and delete Google service account keys.

    The parameter (for OpenAI, Azure OpenAI, or Google VertexAI models) is only available for the following use cases:

    • Pass-through

    • RAG

    • Standalone query rewriter

    • Summarization

    • Keyword extraction

    • NER

    azureDeployment

    The optional "azureDeployment": "[DEPLOYMENT_NAME]" parameter is the deployment name of the Azure OpenAI model and is only required when a deployed Azure OpenAI model is used for prediction.

    azureEndpoint

    The optional "azureEndpoint": "[ENDPOINT]" parameter is the URL endpoint of the deployed Azure OpenAI model and is only required when a deployed Azure OpenAI model is used for prediction.

    googleProjectId

    The optional "googleProjectId": "[GOOGLE_PROJECT_ID]" parameter is only required when a Google VertexAI model is used for prediction.

    googleRegion

    The optional "googleRegion": "[GOOGLE_PROJECT_REGION_OF_MODEL_ACCESS]" parameter is only required when a Google VertexAI model is used for prediction. The possible region values are:

    • us-central1

    • us-west4

    • northamerica-northeast1

    • us-east4

    • us-west1

    • asia-northeast3

    • asia-southeast1

    • asia-northeast

    Unique values for the named entity recognition ner use case

    The values available in this use case (that may not be available in other use cases) are:

    Parameter Value

    "useCaseConfig"

    "entityTypeMap": {"ENTITY": ["exampleA", "exampleB"], "ENTITY1": ["exampleC", "exampleD"]}

    This parameter provides a map with entity type as a key with a list of example values to search. The entity type is required, but example values are optional and can be empty. Multiple entities with examples can be entered in the request.

    In the LWAI Prediction index stage and the LWAI Prediction query stage, the useCaseConfig entityTypeMap parameter only supports a string. Therefore, the string entered in Fusion is converted to a JSON string, which is required in the Lucidworks AI entityTypeMap variable. For example:

    "entityTypeMap": "{\"ENTITY\": [\"exampleA\", \"exampleB\"], \"ENTITY1\": [\"exampleC\", \"exampleD\"]}

    Example request

    This example does not include modelConfig parameters, but you can submit requests that include parameters described in Common parameters and fields.

    curl --request POST \
  --url https://APPLICATION_ID.applications.lucidworks.com/ai/prediction/ner/MODEL_ID \
 --header 'Authorization: Bearer AUTH_TOKEN' \
  --data '{
	"batch": [
		{
			"text": "Mahatma Gandhi, born on October 2, 1869, in Porbandar, India, led a life that profoundly shaped the course of history. Inspired by his principles of non-violence, truth, and civil disobedience, Gandhi became a pivotal figure in India'\''s struggle for independence from British rule. His journey began as a lawyer in South Africa, where he experienced racial discrimination and injustice, sparking his commitment to social justice. Returning to India, he became the face of the nonviolent resistance movement, employing methods like peaceful protests, fasting, and marches. The iconic Salt March of 1930 exemplified his philosophy as thousands followed him in the defiance of salt taxes imposed by the British. Gandhi'\''s ascetic lifestyle, clad in simple attire and practicing self-sufficiency, endeared him to the masses. Despite facing imprisonment multiple times, he remained steadfast in his pursuit of India'\''s freedom. Tragically, Gandhi was assassinated on January 30, 1948, but his legacy endures globally as a symbol of peace, tolerance, and the transformative power of nonviolent resistance"
		}
	],
	"useCaseConfig": {
		"entityTypeMap":{
					"Location":["India", "South Africa"]
		}
	}
}'

    The following is an example response:

    {
	"predictions": [
		{
			"tokensUsed": {
				"promptTokens": 387,
				"completionTokens": 23,
				"totalTokens": 410
			},
			"entities": {
				"Location": [
					"Porbandar",
					"India",
					"South Africa"
				]
			},
			"response": "{\n\"Location\": [\n\"Porbandar\",\n\"India\"\n]\n}"
		}
	]
}