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Overview

The AI Filter Block applies a filter operation over an array of values where the filter condition is evaluated by one of the AI models from the 250+ models LawMe connects to. This powerful block allows you to leverage AI capabilities to perform complex filtering tasks on your data.

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Inputs

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System Prompt
string

The system prompt to send to the model. Optional. Used to provide high-level guidance to the AI model.

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Condition Prompt
string
required

The condition prompt to filter the array. Required. This prompt describes the criteria that each array element will be evaluated against.

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Input
any[]
required

The input array to filter. Required. Can be an array of any data type.

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Outputs

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Indicies
number[]

The index numbers of each element that passed the filter condition.

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Output
any[]

The filtered input array containing only the elements that passed the condition.

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Editor Settings

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AI Model
string
default:"gpt-3.5-turbo"

The AI model to use for evaluating filter conditions. Available models are dynamically populated based on the LLM provider configuration.

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Temperature
number
default:0

The sampling temperature to use. Values between 0-2. Higher values (0.8) make output more random, lower values (0.2) make it more focused and deterministic.

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Top P
number
default:1

Alternative to temperature sampling. Only tokens comprising the top P probability mass are considered. For example, 0.1 means only tokens in the top 10% probability are considered.

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Use Top P
boolean
default:false

Toggle between using top P sampling or temperature sampling.

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Max Tokens
number
default:2048

The maximum number of tokens to generate in each chat completion.

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Stop
string

A sequence where the API will stop generating further tokens.

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Presence Penalty
number

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.

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Frequency Penalty
number

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.

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Seed
number

If specified, the model will attempt to generate deterministic results for repeated requests with the same seed and parameters.

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Advanced Settings

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Custom Max Tokens
number

Overrides the max number of tokens a model can support. Leave blank for preconfigured token limits.

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Cache Responses
boolean
default:false

If enabled, requests with the same parameters and messages will be cached for immediate responses without an API call.

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Use for subgraph partial output
boolean
default:false

If enabled, streaming responses from this node will be shown in Subgraph nodes that call this graph.

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Error Handling

  • The block will retry failed attempts up to 3 times with exponential backoff
  • Token limits are automatically enforced based on the selected model
  • Invalid responses (non TRUE/FALSE) from the model will trigger an error
  • The block includes built-in timeout handling and request cancellation support

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Example Usage

  1. Connect an array of items to the Input port
  2. Add a condition prompt like “Keep only items that are environmentally sustainable products”
  3. Optionally add a system prompt for additional context
  4. Configure the model and parameters in the settings
  5. Connect the Output port to use the filtered results

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Notes

  • The block automatically handles token counting and cost tracking
  • Responses are strictly evaluated as TRUE/FALSE
  • The block supports parallel processing of array elements
  • Built-in caching can improve performance for repeated operations