> ## 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 use case
> The passthrough use case lets you use the service as a proxy to the large language model (LLM). The service sends text (no additional prompts or other information) to the LLM and returns a response.
## OpenAPI
````yaml /api-reference/saas/machine-learning-platform-predict.json post /ai/prediction/passthrough/{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/passthrough/{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: Passthrough use case
description: >-
The passthrough use case lets you use the service as a proxy to the
large language model (LLM). The service sends text (no additional
prompts or other information) to the LLM and returns a response.
operationId: post-ai-prediction-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: who was the first president of the USA?
useCaseConfig:
useSystemPrompt: true
responses:
'200':
description: OK
content:
application/json:
schema:
$ref: '#/components/schemas/PassthroughResponse'
example:
predictions:
- tokensUsed:
promptTokens: 58
completionTokens: 34
totalTokens: 92
response: >-
The first President of the United States of America was
George Washington. He served from April 30, 1789, to March
4, 1797.
components:
schemas:
PassthroughRequest:
title: PassthroughRequest
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: Who was the first President of the United States?
useCaseConfig:
$ref: '#/components/schemas/UseCaseConfigPassthrough'
modelConfig:
$ref: '#/components/schemas/ModelConfig'
PassthroughResponse:
type: object
properties:
predictions:
type: array
items:
$ref: '#/components/schemas/PassthroughResponseTokens'
x-examples: {}
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
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 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: 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]'
PassthroughResponseTokens:
type: object
properties:
response:
type: string
description: The results returned from the request.
example: The first President of the United States was George Washington.
tokensUsed:
$ref: '#/components/schemas/Token'
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
````