> ## 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. # Part Number Detection use case > The Part Number Detection use case of the LWAI Prediction API detects part numbers in order to help with query routing and classification, and uses the embedding model to ingest text and return a JSON response of `true` if the input is a part number, or `false` if the input is not a part number. ## OpenAPI ````yaml /api-reference/saas/machine-learning-platform-predict.json post /ai/prediction/part-number-detection/{CUSTOM_DEPLOYMENT_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/part-number-detection/{CUSTOM_DEPLOYMENT_ID}: post: tags: - Get predictions summary: Part Number Detection use case description: >- The Part Number Detection use case of the LWAI Prediction API detects part numbers in order to help with query routing and classification, and uses the embedding model to ingest text and return a JSON response of `true` if the input is a part number, or `false` if the input is not a part number. operationId: post-ai-prediction-part-number-detection-customDeploymentId parameters: - in: path name: CUSTOM_DEPLOYMENT_ID required: true schema: type: string description: >- Unique deployment ID for the model. This use case is only supported with a custom-trained embedding model trained with the Part Number Classification data schema option. example: part-number-model-v1 - in: header name: Authorization required: true schema: type: string description: >- Bearer token used for authentication with machinelearning.predict scope. requestBody: content: application/json: schema: $ref: '#/components/schemas/PartNumberRequest' example: batch: - text: 54956gf-98796v - text: bright pink sprinkles responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/PartNumberResponse' example: predictions: - tokensUsed: inputTokens: 6 labelsTokens: 2 labels: 'true': 0.6388378143310547 response: 'true: 0.64' - tokensUsed: inputTokens: 4 labelsTokens: 2 labels: 'false': 0.6262129545211792 response: 'false: 0.63' components: schemas: PartNumberRequest: title: PartNumberRequest type: object x-examples: Example 1: value: batch: - text: 54956gf-98796v - text: bright pink sprinkles 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 required: - text properties: text: type: string description: >- The query text to analyze for part number identification. This text is freeform content contained in the document. If you have content in multiple fields that would be valuable, such as title and body, please concatenate the relevant fields to a single field before inputting to the parameter. example: 54956gf-98796v modelConfig: $ref: '#/components/schemas/ModelConfigPartNumber' required: - batch PartNumberResponse: type: object properties: predictions: type: array items: $ref: '#/components/schemas/PartNumberResponseTokens' x-examples: Example 1: value: predictions: - tokensUsed: inputTokens: 6 labelsTokens: 2 labels: 'true': 0.6388378143310547 response: 'true: 0.64' - tokensUsed: inputTokens: 4 labelsTokens: 2 labels: 'false': 0.6262129545211792 response: 'false: 0.63' ModelConfigPartNumber: title: ModelConfigPartNumber type: object description: Provides fields and values specific to part number detection prediction. properties: vectorQuantizatonMethod: type: integer description: >- Quantization is implemented by converting float vectors into integer vectors, allowing for byte vector search using 8-bit integers. The methods are named min-max and max-scale. The min-max method creates tensors of embeddings and converts them to uint8 by normalizing them to the range [0, 255]. The max-scale method finds the maximum absolute value along each embedding, normalizes the embeddings by scaling them to a range of -127 to 127, and returns the quantized embeddings as an 8-bit integer tensor. lowercaseInput: type: boolean default: false description: >- This parameter is optional, and controls how submitted text becomes vectors. If `lowercaseInput` is set to `true`, inputs are set to lowercase before encoding, so variation in embeddings is not introduced (which makes similarity search more stable and predictable). Default value is `false`. example: true PartNumberResponseTokens: type: object properties: response: type: string description: >- The prediction result in the format `label: confidence` where label is either `true` or `false`, and confidence is a decimal value between 0 and 1. example: 'true: 0.64' labels: type: object description: >- Object containing the predicted label (`true` or `false`) as a key with its confidence score as the value. properties: 'true': type: number description: Confidence score if predicted as a part number. 'false': type: number description: Confidence score if predicted as not a part number. tokensUsed: type: object description: Token usage information for this prediction. properties: inputTokens: type: integer description: Number of input tokens used. labelsTokens: type: integer description: Number of label tokens used. ````