> ## Documentation Index > Fetch the complete documentation index at: https://doc.lucidworks.com/llms.txt > Use this file to discover all available pages before exploring further. # Named Entity Recognition (NER) use case > In the Named Entity Recognition (NER) use case, 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. ## OpenAPI ````yaml /api-reference/saas/machine-learning-platform-predict.json post /ai/prediction/ner/{MODEL_ID} openapi: 3.0.1 info: title: Lucidworks AI Prediction API version: v0 description: >- The Lucidworks AI Prediction API is used to send synchronous API calls that run predictions from pre-trained models or custom models. The Use Case API returns a list of all supported models. The `prediction` endpoints require an authentication token with scope `machinelearning.predict`. contact: name: Lucidworks url: https://lucidworks.com/ email: support@lucidworks.com termsOfService: https://lucidworks.com/legal/developer-license-agreement/ license: name: Lucidworks url: https://lucidworks.com/legal/developer-license-agreement/ servers: - url: https://APPLICATION_ID.applications.lucidworks.com description: Production security: [] tags: - name: Get predictions description: Submit prediction tasks to Lucidworks AI. paths: /ai/prediction/ner/{MODEL_ID}: parameters: - schema: type: string name: MODEL_ID in: path required: true description: Unique identifier for the model. example: llama-3-8b-instruct post: tags: - Get predictions summary: Named Entity Recognition (NER) use case description: >- In the Named Entity Recognition (NER) use case, 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. operationId: post-ai-prediction-ner-modelId parameters: - in: header name: Authorization schema: type: string required: true description: >- Bearer token used for authentication. Format: `Authorization: Bearer ACCESS_TOKEN`. example: Bearer abc123def456 - schema: type: string example: application/json in: header name: Content-Type description: application/json requestBody: content: application/json: schema: $ref: '#/components/schemas/NerRequest' example: 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 responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/NerResponse' example: predictions: - tokensUsed: promptTokens: 340 completionTokens: 14 totalTokens: 354 entities: Location: - Porbandar - India - South Africa response: |- Location: - Porbandar - India - South Africa components: schemas: NerRequest: title: NerRequest type: object x-examples: {} properties: batch: type: array description: >- The batch of key:value pairs used as inputs in the prediction. Up to 32 inputs per request are allowed. maxItems: 32 items: type: object properties: text: type: string description: The content the model analyzes. example: >- 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: $ref: '#/components/schemas/UseCaseConfigNer' modelConfig: $ref: '#/components/schemas/ModelConfig' NerResponse: type: object x-examples: {} properties: predictions: type: array items: $ref: '#/components/schemas/NerResponseTokens' UseCaseConfigNer: title: UseCaseConfigNer type: object properties: entityTypeMap: type: object properties: entity: type: array description: >- "entity": [exampleA, exampleB], "entity1": [exampleC, exampleD] For example: "Location": ["India", "South Africa"] 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. items: type: string example: example1, example2 ModelConfig: title: ModelConfig type: object description: >- Provides fields and values that specify ranges for tokens. Fields used for specific use cases and models are specified. The default values are used if other values are not specified. properties: temperature: type: number format: float example: 0.8 minimum: 0 maximum: 2 description: >- 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. We recommend staying at or below a temperature of 1.0. Values above 1.0 might return nonsense unless the topP value is lowered to be more deterministic. topP: type: number format: float example: 1 minimum: 0 maximum: 1 description: >- 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. topK: type: integer example: -1 description: >- An integer that controls the number of top tokens to consider. Set topK to -1 to consider all tokens. presencePenalty: type: number format: float minimum: -2 maximum: 2 description: >- 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. This is applicable for all OpenAI and Llama models. example: 2 frequencyPenalty: type: number format: float minimum: -2 maximum: 2 example: 1 description: >- 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. This is applicable for all OpenAI and Llama models. maxTokens: type: integer format: int32 example: 1 description: >- 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: type: string description: >- This optional parameter is only required when using the model for prediction. You can find this value in your model's settings: * **OpenAI**: Copy and paste the API key found in your organization's settings. For more information, see OpenAI Authentication API keys. * **Azure OpenAI**: Copy and paste the API key found in your Azure portal. See Authenticate with API key. * **Anthropic**: Copy and paste the API key found in your Anthropic console or by using the Anthropic API. * **Google Vertex AI**: Copy and paste the base64-encoded service account key JSON found in your Google Cloud console. This service account key must have the Vertex AI user role enabled. For more information, see generate service account key. example: API key specific to use case and model azureDeployment: type: string example: DEPLOYMENT_NAME description: >- This optional parameter is the name of the deployed Azure OpenAI model and is only required when a deployed Azure OpenAI model is used for prediction. azureEndpoint: type: string description: "\t\nThis optional 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." example: https://azure.endpoint.com googleProjectId: type: string example: '[GOOGLE_PROJECT_ID]' description: >- This parameter is optional, and is only required when a Google Vertex AI model is used for prediction. googleRegion: type: string description: >- This parameter is optional, and is only required when a Google Vertex AI model is used for prediction. A value of `global` routes the query to any available region. Other possible region values are: * us-central1 * us-west4 * northamerica-northeast1 * us-east4 * us-west1 * asia-northeast3 * asia-southeast1 * asia-northeast example: '[GOOGLE_PROJECT_REGION_OF_MODEL_ACCESS]' NerResponseTokens: type: object x-examples: {} properties: tokensUsed: $ref: '#/components/schemas/Token' entities: type: object properties: entity: type: array description: "The result of the requested entity type that was a key with a list of example values to search.\nFor example:\n\n{\n\t\t\t\t\"Location\": [\n\t\t\t\t\t\"Porbandar\",\n\t\t\t\t\t\"India\",\n\t\t\t\t\t\"South Africa\"\n\t\t\t\t]\n\t\t\t}" items: type: string response: type: string description: The results returned from the request. example: '{\n\"Location\": [\n\"Porbandar\",\n\"India\"\n]\n}' Token: type: object properties: promptTokens: type: integer format: int32 description: >- The number of tokens generated to prompt the model to continue generating results. example: 148 completionTokens: type: integer format: int32 description: The number of tokens used until the model completes. example: 27 totalTokens: type: integer format: int32 description: The sum of the prompt and completion tokens used in the model. example: 175 ````