> ## 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. # English language model text encoder > The English language encoder is an embedding use that takes in plain English text and returns a 768-dimensional vector encoding of that text. This model powers this semantic search. The API truncates incoming text to approximately 256 words before the model encodes it and returns a vector. An example usage pattern is to encode all the texts and descriptions in a website and then use this encoder on query text, supporting natural language queries such as "1990s children’s fiction". Each API request includes one batch containing up to 32 text strings. ## OpenAPI ````yaml /api-reference/saas/machine-learning-platform-predict.json post /ai/prediction/embedding/text-encoder 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/embedding/text-encoder: post: tags: - Get predictions summary: English language model text encoder description: >- The English language encoder is an embedding use that takes in plain English text and returns a 768-dimensional vector encoding of that text. This model powers this semantic search. The API truncates incoming text to approximately 256 words before the model encodes it and returns a vector. An example usage pattern is to encode all the texts and descriptions in a website and then use this encoder on query text, supporting natural language queries such as "1990s children’s fiction". Each API request includes one batch containing up to 32 text strings. operationId: post-ai-prediction-embedding-text-encoder 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/BatchEmbeddingEncoders' example: batch: - text: city streets - text: city gateways useCaseConfig: dataType: query responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/EmbeddingEncodersResponse' example: predictions: - tokensUsed: inputTokens: 4 vector: - 0.018902658 - -0.03418664 - 0.051422413 - -0.0033997858 - -0.04509421 - 0.054535553 - 0.016070662 - 0.083665654 - 0.050305944 - -0.05140388 - -0.042930763 - -0.006804204 - -0.055920716 - -0.038177665 - 0.0381545 - -0.026262563 - -0.008579668 - 0.016968817 - -0.00981948 - -0.006802901 - -0.004904239 - 0.03174755 - 0.011839171 - 0.001910968 - 0.005824978 - 0.027468137 - -0.0068615335 - 0.024790684 - -0.019892018 - 0.0013098817 - 0.030737631 - 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Up to 32 inputs per request are allowed. maxItems: 32 items: type: object properties: text: type: string description: The content the model analyzes. useCaseConfig: $ref: '#/components/schemas/UseCaseConfigEmbedding' modelConfig: $ref: '#/components/schemas/ModelConfigEmbedding' EmbeddingEncodersResponse: type: object x-examples: {} properties: predictions: type: array items: type: object properties: vector: $ref: '#/components/schemas/JsonNode' UseCaseConfigEmbedding: title: UseCaseConfigKeywordEmbedding type: object properties: dataType: type: string description: >- This optional parameter enables model-specific handling in the Prediction API to help improve model accuracy. Use the most applicable fields based on available dataTypes and the dataType value that best aligns with the text sent to the Prediction API. The two string values to use for embedding models are: * `"dataType": "query"` for the query. For query-to-query pairing, best practice is to use `dataType=query` on both API calls. * `"dataType": "passage"` for fields searched at query time. For example, if questions and answers from a FAQ are indexed, the value for questions is `"dataType": "query"` and the value for the answers is `"dataType": "passage"`. default: query example: passage ModelConfigEmbedding: title: ModelConfigEmbedding type: object description: Provides fields and values specific to pre-trained embedding models. properties: lowercaseInput: type: boolean default: false description: >- This parameter is optional, and controls how query response 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). This field is used for pre-trained models and instances in a similarity query when you don't want the case to affect the vectors returned in the query. Default value is `false`. example: true JsonNode: type: array description: The list of model predictions for the input batch. example: - -0.0011799398344010115 - 0.07051781564950943 - -0.06832550466060638 - 0.020428132265806198 - 0.11977626383304596 items: {} ````