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# algoforge_prime/core/generation_engine.py
from .llm_clients import call_huggingface_api, call_gemini_api, LLMResponse
from ..prompts.system_prompts import get_system_prompt # Relative import from parent
from ..prompts.prompt_templates import format_genesis_user_prompt
def generate_initial_solutions(
problem_description,
initial_hints,
problem_type, # e.g., "Python Algorithm with Tests"
num_solutions_to_generate,
llm_client_config # Dict: {"type": ..., "model_id": ..., "temp": ..., "max_tokens": ...}
):
"""
Generates a list of initial solution strings using the configured LLM.
Returns a list of strings, where each string is either a solution or an error message.
"""
solutions_or_errors = []
# Select system prompt based on problem type, more specific for Python
system_p_key = "genesis_general"
if "python" in problem_type.lower():
system_p_key = "genesis_python"
system_p_genesis = get_system_prompt(system_p_key)
for i in range(num_solutions_to_generate):
user_p_genesis = format_genesis_user_prompt(
problem_description, initial_hints, i + 1, num_solutions_to_generate
)
llm_response_obj = None # type: LLMResponse
if llm_client_config["type"] == "hf":
llm_response_obj = call_huggingface_api(
user_p_genesis, llm_client_config["model_id"],
temperature=llm_client_config["temp"], max_new_tokens=llm_client_config["max_tokens"],
system_prompt_text=system_p_genesis
)
elif llm_client_config["type"] == "google_gemini":
llm_response_obj = call_gemini_api(
user_p_genesis, llm_client_config["model_id"],
temperature=llm_client_config["temp"], max_new_tokens=llm_client_config["max_tokens"],
system_prompt_text=system_p_genesis
)
else:
solutions_or_errors.append(f"ERROR (Genesis Attempt {i+1}): Unknown LLM client type '{llm_client_config['type']}'")
continue
if llm_response_obj.success:
solutions_or_errors.append(llm_response_obj.text)
else:
solutions_or_errors.append(f"ERROR (Genesis Attempt {i+1} with {llm_response_obj.model_id_used}): {llm_response_obj.error}")
return solutions_or_errors |