input_interface:
  - "action"
  - "content"


_target_: flow_modules.aiflows.FunSearchFlowModule.SamplerFlow.instantiate_from_default_config
name: SamplerFlowModule
description: A flow that queries an LLM model to generate prompts for the Sampler flow

input_interface_initialized:
  - header
  - code

input_interface_non_initialized:
  - header
  - code

output_interface:
  - "api_output"
  - "from"

backend:
  api_infos: ???
  model_name: gpt-4
  frequency_penalty: 0
  max_tokens: 2000
  n: 1
  presence_penalty: 0
  stream: false
  temperature: 1.0
  top_p: 0.4

previous_messages:
  last_k: 1
  first_k: 1

system_message_prompt_template:
  template: |2-
    
    Role: You are an expert coder and algorithm designer who provides solutions.


    Goal: Write evolve functions that optimize the score of an evaluation function (provided). Aim to achieve higher scores than previous functions. Write a new function that optimizes the evaluation function and scores better than the given functions. Develop non-trivial functions and actual algorithms.


    Resources:

    You will be given code related to evolve and functions.
    You will be given a few functions attempting to optimize the evaluation function.
    The function you're evolving (the last one passed) is the only one you have to write. Make sure to include only nested functions or recursive calls.
    
    
    Naming:

    Evaluation function is named {{evaluate_name}}.
    Evolve functions have a naming structure similar to: {{evolve_name}}_v.


    Important Constraints:

    Only write one evolve function solution: Write only the uncompleted function given to you (i.e., only {{evolve_name}}_v{i}, where {i} is the function with the largest number in the prompt, and i is at most {{artifacts_per_prompt}}). Stop writing once you're done with your first solution.
    Output must be a function that is parsable by the AST library: Write code only within functions. No textual comments, no code blocks (like ```python), or no explanations. Provide only Python code encapsulated in a single function. Your output should be parsable by the AST Python library. Do not include starting or ending comments like "Here's an improved version."
    Follow the naming structure: Keep the function name as it is in the input.
    Keep the same function format: Maintain the same input and output format in your solution.
    Self-contained solution: You can't call other functions (only recursive calls or calls to nested functions).
    Do not use the evaluate function in the solution: Avoid using the evaluate function in your solution.
    
  partial_variables:
    evaluate_name: ???
    evolve_name: ???
    artifacts_per_prompt: ???


init_human_message_prompt_template:
  _target_: aiflows.prompt_template.JinjaPrompt
  template: |2-
    functions for evaluation:
    {{header}}

    function to evolve and to evaluate:
    {{code}}
  input_variables: ["header","code"]
  partial_variables: {}

human_message_prompt_template:
  _target_: aiflows.prompt_template.JinjaPrompt
  template: |2-
    full code:
    {{header}}

    function to evolve and to evaluate:
    {{code}}
  input_variables: ["header","code"]
  partial_variables: {}