> ## 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. # Passthrough prompt > The prompt request for the passthrough use case and specified generative AI (GenAI) `modelId` (model name) sends text to return results in formats supported by generative AI (GenAI) models. ## OpenAPI ````yaml /api-reference/saas/machine-learning-platform-prompt-passthrough.json post /ai/prompt/passthrough/{MODEL_ID} openapi: 3.0.1 info: title: Lucidworks AI Generative AI Prompt API version: v0 description: >- The Lucidworks AI Generative AI Prompting Preview API returns Prediction API `passthrough` use case prompts before being sent to any generative AI (GenAI) model. This is used to help debug `passthrough` use case prompts to ensure the input to the GenAI model is valid, and within the model's processing limits. The `prompt` endpoint requires an authentication token with scope `machinelearning.predict`. NOTE: Before the prompt is passed to the GenAI model, it may be formatted, truncated, expanded, or modified in other ways to meet that model's requirements so the API call is successful. 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: Passthrough prompt paths: /ai/prompt/passthrough/{MODEL_ID}: parameters: - schema: type: string example: llama-3-8b-instruct name: MODEL_ID in: path required: true description: The generative AI (GenAI) model name. post: tags: - Passthrough prompt summary: Passthrough prompt description: >- The prompt request for the passthrough use case and specified generative AI (GenAI) `modelId` (model name) sends text to return results in formats supported by generative AI (GenAI) models. operationId: post-ai-prompt-passthrough-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/PassthroughRequest' example: batch: - text: >- [{"role": "system", "content": "You are a helpful utility program instructed to accomplish a product classififcation task. Please, classify the provided product name into one of the following categories:\nGROCERIES, FURNITURE, UNKNOWN"}, {"role": "user", "content": "chocolate milk"}, {"role": "assistant", "content": "GROCERIES"}, {"role": "user", "content": "chocolate table"}, {"role": "assistant", "content": "FURNITURE"}, {"role": "user", "content": "stone baked pizza"}] useCaseConfig: dataType: json_prompt responses: '200': description: OK content: application/json: schema: anyOf: - $ref: '#/components/schemas/Llama3ModelResponse' - $ref: '#/components/schemas/OpenAIModelResponse' - $ref: '#/components/schemas/AnthropicModelResponse' example: predictions: - tokensUsed: promptTokens: 95 prompt: >+ <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful utility program instructed to accomplish a product classififcation task. Please, classify the provided product name into one of the following categories: GROCERIES, FURNITURE, UNKNOWN<|eot_id|><|start_header_id|>user<|end_header_id|> chocolate milk<|eot_id|><|start_header_id|>assistant<|end_header_id|> GROCERIES<|eot_id|><|start_header_id|>user<|end_header_id|> chocolate table<|eot_id|><|start_header_id|>assistant<|end_header_id|> FURNITURE<|eot_id|><|start_header_id|>user<|end_header_id|> stone baked pizza<|eot_id|><|start_header_id|>assistant<|end_header_id|> components: schemas: PassthroughRequest: title: PassthroughRequest type: object x-examples: {} properties: batch: type: array description: 'The content sent in the request. ' items: type: object properties: text: type: string description: The content sent in the request. example: >- [{\"role\": \"system\", \"content\": \"You are a helpful utility program instructed to accomplish a product classififcation task. Please, classify the provided product name into one of the following categories:\\nGROCERIES, FURNITURE, UNKNOWN\"}, {\"role\": \"user\", \"content\": \"chocolate milk\"}, {\"role\": \"assistant\", \"content\": \"GROCERIES\"}, {\"role\": \"user\", \"content\": \"chocolate table\"}, {\"role\": \"assistant\", \"content\": \"FURNITURE\"}, {\"role\": \"user\", \"content\": \"stone baked pizza\"}] useCaseConfig: $ref: '#/components/schemas/UseCaseConfigPassthrough' modelConfig: $ref: '#/components/schemas/ModelConfig' Llama3ModelResponse: type: object properties: tokensUsed: $ref: '#/components/schemas/Token' prompt: type: string description: The Llama3 model response from the request, formatted for the model. example: >- <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful utility program instructed to accomplish a product classififcation task. Please, classify the provided product name into one of the following categories:\nGROCERIES, FURNITURE, UNKNOWN<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nchocolate milk<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nGROCERIES<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nchocolate table<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nFURNITURE<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nstone baked pizza<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n title: 'Response: Llama3 Model' description: '' OpenAIModelResponse: type: object properties: tokensUsed: $ref: '#/components/schemas/Token' messages: type: array description: >- The OpenAI model response from the request. Multiple key:value pairs are created in a format supported by the model. items: type: object properties: role: type: string example: system description: >- The account role derived from the request text. For example, system, user, or assistant. content: type: string description: >- The sections of content derived from the request, formatted for the model. example: >- You are a helpful utility program instructed to accomplish a product classififcation task. Please, classify the provided product name into one of the following categories:\nGROCERIES, FURNITURE, UNKNOWN description: Open AI Model Response title: 'Response: OpenAI Model' AnthropicModelResponse: type: object properties: tokensUsed: $ref: '#/components/schemas/Token' messages: type: array description: >- The Anthropic model response from the request. Multiple key:value pairs are created in a format supported by the model. items: type: object properties: role: type: string example: system description: >- The account role derived from the request text. For example, system, user, or assistant. content: type: string description: >- The sections of content derived from the request, formatted for the model. example: >- You are a helpful utility program instructed to accomplish a product classififcation task. Please, classify the provided product name into one of the following categories:\nGROCERIES, FURNITURE, UNKNOWN description: Anthropic Model Response title: 'Response: Anthropic Model' UseCaseConfigPassthrough: title: UseCaseConfigPassthrough type: object properties: useSystemPrompt: type: boolean description: >- This optional parameter contains a default value of true. If set to false, the `batch.text` value serves as the prompt for the model. The prompt must be in a specific format the model can comprehend. dataType: type: string description: >- This optional parameter enables model-specific handling in the 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 API. The values for `dataType` in the Passthrough use case are: * `"dataType": "text"` - This value is equivalent to `"useSystemPrompt": true` and is a pre-defined, generic prompt. * `"dataType": "raw_prompt"` - This value is equivalent to `"useSystemPrompt": false` and is passed directly to the model or third-party API. * `"dataType": "json_prompt"` - This value follows the generics that allow three roles: - `system` - `user` - Only the last user message is truncated. - If the API does not support system prompts, the user role is substituted for the system role. - `assistant` - If the last message role is `assistant`, it is used as a pre-fill for generation and is the first generated token the model uses. The pre-fill is prepended to the model output, which makes models less verbose and helps enforce specific outputs such as YAML. - The Google Vertex AI does not support generation pre-fills, so an exception error is generated. - This follows the HuggingFace template contraints at https://huggingface.co/docs/transformers/main/en/chat_templating. - Additional `json_prompt` information: - Consecutive messages for the same role are merged. - You can paste the information for a hosted model into the `json_prompt` value and change the model name in the stage. example: json_prompt description: >- NOTE: If both `useSystemPrompt` and `dataType` are present, the value in `dataType` is used. 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: 1 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 that penalizes new tokens based on whether they have already appeared in the text. Required range is [-2, 2]. 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 that penalizes new tokens based on their frequency in the generated text. Required range is [-2, 2]. 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 in the request. 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 the 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 in the request. 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 in the request." 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 in the request. googleRegion: type: string description: >- This parameter is optional, and is only required when a Google Vertex AI model is used in the request. 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]' 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 ````