> ## 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. # Classification use case > The classification use case lets you use embedding models to compute similarity scores between the incoming text and the labels. It returns the labels ranked in order of most similar to least similar. The classification use case is compatible with all Lucidworks hosted pre-trained and custom embedding models. The default behavior of embedding models is to always have a score returned. The `topK` and `similarityCutoff` parameters can be used to achieve behaviors where only the following are returned: * The single most applicable label * Labels with similarities that exceed a threshold * A set number of items * A set number if it exceeds the threshold ## OpenAPI ````yaml /api-reference/saas/machine-learning-platform-predict.json post /ai/prediction/classification/{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/classification/{MODEL_ID}: parameters: - schema: type: string name: MODEL_ID in: path required: true description: Unique identifier for the model. example: bge-large post: tags: - Get predictions summary: Classification use case description: >+ The classification use case lets you use embedding models to compute similarity scores between the incoming text and the labels. It returns the labels ranked in order of most similar to least similar. The classification use case is compatible with all Lucidworks hosted pre-trained and custom embedding models. The default behavior of embedding models is to always have a score returned. The `topK` and `similarityCutoff` parameters can be used to achieve behaviors where only the following are returned: * The single most applicable label * Labels with similarities that exceed a threshold * A set number of items * A set number if it exceeds the threshold operationId: post-ai-prediction-classification-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/ClassificationRequest' example: batch: - text: Not all those who wander are lost. useCaseConfig: labels: - Harry Potter - Lord of the Rings responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/ClassificationResponse' example: predictions: - tokensUsed: inputTokens: 10 labelsTokens: 26 labels: Lord of the Rings: 0.40380859375 Harry Potter: 0.3681640625 response: 'Lord of the Rings: 0.40, Harry Potter: 0.37' components: schemas: ClassificationRequest: title: ClassificationRequest 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: Not all those who wander are lost. useCaseConfig: $ref: '#/components/schemas/UseCaseConfigClassification' ClassificationResponse: type: object x-examples: {} items: type: object properties: predictions: type: array items: type: object properties: tokensUsed: type: object properties: inputTokens: type: integer example: 11 description: >- The number of tokens created from the text input into the model. labelTokens: type: integer description: >- The number of tokens created from the labels entered in the request. example: 14 labels: type: string description: | This is a list of strings that classify information. For example: "labels": { "Lord of the Rings": 0.7287280559539795, "Harry Potter": 0.7193666100502014 } UseCaseConfigClassification: title: UseCaseConfigClassification type: object required: - labels properties: labels: type: array description: |- This is a list of strings that classify information. For example, "labels": ["Harry Potter", "Lord of the Rings"] items: type: string topK: type: integer description: >- The data structure that identifies the most frequent items in a set of data. This is the number of top-scored labels to return. example: 10 similarityCutoff: type: number example: 1 description: >- This decimal field is the similarity score cutoff to filter out less similar fields. format: float ````