654 lines
32 KiB
Python
654 lines
32 KiB
Python
"""Main Text Generation Service for ALwrity Backend.
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This service provides the main LLM text generation functionality,
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migrated from the legacy lib/gpt_providers/text_generation/main_text_generation.py
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"""
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import os
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import json
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from typing import Optional, Dict, Any, List
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from datetime import datetime
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from loguru import logger
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from fastapi import HTTPException
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from ..onboarding.api_key_manager import APIKeyManager
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from .gemini_provider import gemini_text_response, gemini_structured_json_response
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from .huggingface_provider import huggingface_text_response, huggingface_structured_json_response
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def llm_text_gen(
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prompt: str,
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system_prompt: Optional[str] = None,
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json_struct: Optional[Dict[str, Any]] = None,
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user_id: str = None,
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preferred_hf_models: Optional[List[str]] = None,
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preferred_provider: Optional[str] = None,
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flow_type: Optional[str] = None,
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) -> str:
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"""
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Generate text using Language Model (LLM) based on the provided prompt.
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Args:
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prompt (str): The prompt to generate text from.
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system_prompt (str, optional): Custom system prompt to use instead of the default one.
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json_struct (dict, optional): JSON schema structure for structured responses.
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user_id (str): Clerk user ID for subscription checking (required).
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Returns:
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str: Generated text based on the prompt.
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Raises:
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RuntimeError: If subscription limits are exceeded or user_id is missing.
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"""
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try:
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resolved_flow_type = flow_type or ("sif_agent" if preferred_hf_models else "premium_tool")
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flow_tag = f"flow_type={resolved_flow_type}"
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subscription_preflight_completed = False
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logger.info(f"[llm_text_gen][{flow_tag}] Starting text generation")
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logger.debug(f"[llm_text_gen] Prompt length: {len(prompt)} characters")
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# Set default values for LLM parameters
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gpt_provider = "huggingface" # Default to premium HF route for ALwrity AI tools
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model = "openai/gpt-oss-120b:cerebras"
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temperature = 0.7
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max_tokens = 4000
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top_p = 0.9
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n = 1
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fp = 16
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frequency_penalty = 0.0
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presence_penalty = 0.0
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# Check for GPT_PROVIDER environment variable
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env_provider = os.getenv('GPT_PROVIDER', '').lower()
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provider_list = [p.strip() for p in env_provider.split(',') if p.strip()]
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# Determine if we're in strict mode (single provider) or fallback mode (multiple providers)
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strict_provider_mode = len(provider_list) == 1
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if provider_list:
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# Use first provider as primary
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primary_provider = provider_list[0]
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if primary_provider in ['gemini', 'google']:
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gpt_provider = "google"
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model = "gemini-2.0-flash-001"
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elif primary_provider in ['hf_response_api', 'huggingface', 'hf']:
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gpt_provider = "huggingface"
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model = "openai/gpt-oss-120b:cerebras"
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elif primary_provider == 'wavespeed':
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gpt_provider = "wavespeed"
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model = "openai/gpt-oss-120b"
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else:
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# Auto-detect mode
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strict_provider_mode = False # Auto-detect allows fallbacks
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gpt_provider = None
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model = None
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# Explicit per-call provider override (used by tool-specific flows like podcast maker)
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if preferred_provider:
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preferred_providers = [p.strip() for p in preferred_provider.split(',') if p.strip()]
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# If explicit provider is set, it's strict mode (no cross-provider fallbacks)
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strict_provider_mode = len(preferred_providers) == 1
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primary_provider = preferred_providers[0]
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if primary_provider in ['gemini', 'google']:
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gpt_provider = "google"
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model = "gemini-2.0-flash-001"
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elif primary_provider in ['hf_response_api', 'huggingface', 'hf']:
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gpt_provider = "huggingface"
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model = "openai/gpt-oss-120b:cerebras"
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elif primary_provider == 'wavespeed':
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gpt_provider = "wavespeed"
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model = "openai/gpt-oss-120b"
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# Handle TEXTGEN_AI_MODELS for model selection
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textgen_models_env = os.getenv('TEXTGEN_AI_MODELS', '').strip()
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model_list = [m.strip() for m in textgen_models_env.split(',') if m.strip()] if textgen_models_env else []
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strict_model_mode = len(model_list) == 1
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# Map model names to actual provider models
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if model_list:
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if gpt_provider == "huggingface":
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# Handle both short names and full model names
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model_mapping = {
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"gpt-oss": "openai/gpt-oss-120b:cerebras",
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"gpt-oss-120b": "openai/gpt-oss-120b:cerebras",
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"mistral": "mistralai/Mistral-7B-Instruct-v0.3:cerebras",
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"mistral-7b": "mistralai/Mistral-7B-Instruct-v0.3:cerebras",
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"llama": "meta-llama/Llama-3.1-8B-Instruct:cerebras",
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"llama-8b": "meta-llama/Llama-3.1-8B-Instruct:cerebras",
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"llama-70b": "meta-llama/Llama-3.1-70B-Instruct:cerebras"
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}
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# If model name contains "/", assume it's already a full model name
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if "/" in model_list[0]:
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model = model_list[0]
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else:
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model = model_mapping.get(model_list[0], model_list[0])
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elif gpt_provider == "wavespeed":
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# Handle both short names and full model names
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model_mapping = {
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"gpt-oss": "openai/gpt-oss-120b",
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"gpt-oss-120b": "openai/gpt-oss-120b",
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"mistral": "mistralai/Mistral-7B-Instruct-v0.3",
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"mistral-7b": "mistralai/Mistral-7B-Instruct-v0.3",
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"llama": "meta-llama/Llama-3.1-8B-Instruct",
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"llama-8b": "meta-llama/Llama-3.1-8B-Instruct",
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"llama-70b": "meta-llama/Llama-3.1-70B-Instruct"
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}
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# If model name contains "/", assume it's already a full model name
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if "/" in model_list[0]:
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model = model_list[0]
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else:
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model = model_mapping.get(model_list[0], model_list[0])
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elif gpt_provider == "google":
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model = "gemini-2.0-flash-001" # Google has fewer options
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# Default blog characteristics
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blog_tone = "Professional"
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blog_demographic = "Professional"
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blog_type = "Informational"
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blog_language = "English"
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blog_output_format = "markdown"
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blog_length = 2000
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# Check which providers have API keys available using APIKeyManager
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api_key_manager = APIKeyManager()
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available_providers = []
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if api_key_manager.get_api_key("gemini"):
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available_providers.append("google")
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if api_key_manager.get_api_key("hf_token"):
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available_providers.append("huggingface")
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if api_key_manager.get_api_key("wavespeed"):
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available_providers.append("wavespeed")
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logger.info(
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f"[llm_text_gen][{flow_tag}] Provider preflight: env_provider='{env_provider or 'auto'}', "
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f"provider_list={provider_list}, strict_provider_mode={strict_provider_mode}, "
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f"available_providers={available_providers}, preferred_provider={preferred_provider or 'none'}"
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)
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if model_list:
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logger.info(
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f"[llm_text_gen][{flow_tag}] Model configuration: model_list={model_list}, "
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f"strict_model_mode={strict_model_mode}"
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)
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# If no environment variable set, auto-detect based on available keys
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if not env_provider:
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# Prefer Google Gemini if available, otherwise use Hugging Face
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if preferred_provider:
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# Respect explicit per-call preference if the provider key exists
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if gpt_provider not in available_providers:
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logger.warning(
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f"[llm_text_gen] Preferred provider {gpt_provider} unavailable, falling back to available providers"
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)
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if "huggingface" in available_providers:
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gpt_provider = "huggingface"
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model = "openai/gpt-oss-120b:cerebras"
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elif "wavespeed" in available_providers:
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gpt_provider = "wavespeed"
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model = "openai/gpt-oss-120b"
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elif "google" in available_providers:
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gpt_provider = "google"
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model = "gemini-2.0-flash-001"
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else:
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logger.error("[llm_text_gen] No API keys found for supported providers.")
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raise RuntimeError("No LLM API keys configured. Configure GEMINI_API_KEY or HF_TOKEN to enable AI responses.")
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elif preferred_hf_models and "huggingface" in available_providers:
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# Low-cost SIF/agent flows pass preferred_hf_models; route directly to HF.
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gpt_provider = "huggingface"
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model = preferred_hf_models[0]
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logger.info(f"[llm_text_gen] Using preferred low-cost HF model: {model}")
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elif "google" in available_providers:
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gpt_provider = "google"
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model = "gemini-2.0-flash-001"
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elif "huggingface" in available_providers:
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gpt_provider = "huggingface"
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model = "openai/gpt-oss-120b:cerebras"
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elif "wavespeed" in available_providers:
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gpt_provider = "wavespeed"
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model = "openai/gpt-oss-120b"
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else:
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logger.error("[llm_text_gen] No API keys found for supported providers.")
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raise RuntimeError("No LLM API keys configured. Configure GEMINI_API_KEY or HF_TOKEN to enable AI responses.")
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else:
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# Environment variable was set, validate it's supported
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if gpt_provider not in available_providers:
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if strict_provider_mode:
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# Strict mode: fail if specified provider not available
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raise RuntimeError(f"Provider {gpt_provider} not available. Available: {available_providers}")
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else:
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# Fallback mode: try other providers
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logger.warning(f"[llm_text_gen] Provider {gpt_provider} not available, falling back to available providers")
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if "google" in available_providers:
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gpt_provider = "google"
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model = "gemini-2.0-flash-001"
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elif "huggingface" in available_providers:
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gpt_provider = "huggingface"
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model = "openai/gpt-oss-120b:cerebras"
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elif "wavespeed" in available_providers:
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gpt_provider = "wavespeed"
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model = "openai/gpt-oss-120b"
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else:
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raise RuntimeError("No supported providers available.")
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if gpt_provider == "huggingface" and preferred_hf_models:
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model = preferred_hf_models[0]
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logger.info(f"[llm_text_gen] Using preferred low-cost HF model: {model}")
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logger.info(f"[llm_text_gen][{flow_tag}] Using provider={gpt_provider}, model={model}")
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# Map provider name to APIProvider enum (define at function scope for usage tracking)
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from models.subscription_models import APIProvider
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provider_enum = None
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# Store actual provider name for logging (e.g., "huggingface", "gemini", "wavespeed")
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actual_provider_name = None
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if gpt_provider == "google":
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provider_enum = APIProvider.GEMINI
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actual_provider_name = "gemini" # Use "gemini" for consistency in logs
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elif gpt_provider == "huggingface":
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provider_enum = APIProvider.MISTRAL # HuggingFace maps to Mistral enum for usage tracking
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actual_provider_name = "huggingface" # Keep actual provider name for logs
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elif gpt_provider == "wavespeed":
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provider_enum = APIProvider.WAVESPEED
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actual_provider_name = "wavespeed"
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if not provider_enum:
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raise RuntimeError(f"Unknown provider {gpt_provider} for subscription checking")
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# SUBSCRIPTION CHECK - Required and strict enforcement
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if not user_id:
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raise RuntimeError("user_id is required for subscription checking. Please provide Clerk user ID.")
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try:
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from services.database import get_session_for_user
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from services.subscription import UsageTrackingService, PricingService
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from models.subscription_models import UsageSummary
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logger.info(
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f"[llm_text_gen][{flow_tag}] Starting subscription preflight for user={user_id}, "
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f"provider={actual_provider_name}, model={model}"
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)
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db = get_session_for_user(user_id)
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if not db:
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logger.error(f"[llm_text_gen] Could not get database session for user {user_id}")
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raise RuntimeError("Database connection failed")
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try:
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usage_service = UsageTrackingService(db)
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pricing_service = PricingService(db)
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# Estimate tokens from prompt (input tokens)
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# CRITICAL: Use worst-case scenario (input + max_tokens) for validation to prevent abuse
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# This ensures we block requests that would exceed limits even if response is longer than expected
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input_tokens = int(len(prompt.split()) * 1.3)
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# Worst-case estimate: assume maximum possible output tokens (max_tokens if specified)
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# This prevents abuse where actual response tokens exceed the estimate
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if max_tokens:
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estimated_output_tokens = max_tokens # Use maximum allowed output tokens
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else:
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# If max_tokens not specified, use conservative estimate (input * 1.5)
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estimated_output_tokens = int(input_tokens * 1.5)
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estimated_total_tokens = input_tokens + estimated_output_tokens
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# Check limits using sync method from pricing service (strict enforcement)
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can_proceed, message, usage_info = pricing_service.check_usage_limits(
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user_id=user_id,
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provider=provider_enum,
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tokens_requested=estimated_total_tokens,
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actual_provider_name=actual_provider_name # Pass actual provider name for correct error messages
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)
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subscription_preflight_completed = True
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logger.info(
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f"[llm_text_gen][{flow_tag}] Subscription preflight complete: can_proceed={can_proceed}, "
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f"estimated_tokens={estimated_total_tokens}, provider={actual_provider_name}"
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)
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if not can_proceed:
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logger.warning(f"[llm_text_gen] Subscription limit exceeded for user {user_id}: {message}")
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# Raise HTTPException(429) with usage info so frontend can display subscription modal
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error_detail = {
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'error': message,
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'message': message,
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'provider': actual_provider_name or provider_enum.value,
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'usage_info': usage_info if usage_info else {}
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}
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raise HTTPException(status_code=429, detail=error_detail)
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# Get current usage for limit checking only
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current_period = pricing_service.get_current_billing_period(user_id) or datetime.now().strftime("%Y-%m")
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usage = db.query(UsageSummary).filter(
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UsageSummary.user_id == user_id,
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UsageSummary.billing_period == current_period
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).first()
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# No separate log here - we'll create unified log after API call and usage tracking
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finally:
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db.close()
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except HTTPException:
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# Re-raise HTTPExceptions (e.g., 429 subscription limit) - preserve error details
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raise
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except RuntimeError:
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# Re-raise subscription limit errors
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raise
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except Exception as sub_error:
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# STRICT: Fail on subscription check errors
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logger.error(f"[llm_text_gen] Subscription check failed for user {user_id}: {sub_error}")
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raise RuntimeError(f"Subscription check failed: {str(sub_error)}")
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# Construct the system prompt if not provided
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if system_prompt is None:
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system_instructions = f"""You are a highly skilled content writer with a knack for creating engaging and informative content.
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Your expertise spans various writing styles and formats.
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Writing Style Guidelines:
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- Tone: {blog_tone}
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- Target Audience: {blog_demographic}
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- Content Type: {blog_type}
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- Language: {blog_language}
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- Output Format: {blog_output_format}
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- Target Length: {blog_length} words
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Please provide responses that are:
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- Well-structured and easy to read
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- Engaging and informative
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- Tailored to the specified tone and audience
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- Professional yet accessible
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- Optimized for the target content type
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"""
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else:
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system_instructions = system_prompt
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# HF behavior: fail fast on selected model; no intra-provider model fallback chain.
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hf_fallback_models: List[str] = []
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# Set up model fallbacks based on strict_model_mode
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if not strict_model_mode and model_list and len(model_list) > 1:
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# Multi-model mode: create fallback list from TEXTGEN_AI_MODELS
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if gpt_provider == "huggingface":
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model_mapping = {
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"gpt-oss": "openai/gpt-oss-120b:cerebras",
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"gpt-oss-120b": "openai/gpt-oss-120b:cerebras",
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"mistral": "mistralai/Mistral-7B-Instruct-v0.3:cerebras",
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"mistral-7b": "mistralai/Mistral-7B-Instruct-v0.3:cerebras",
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"llama": "meta-llama/Llama-3.1-8B-Instruct:cerebras",
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"llama-8b": "meta-llama/Llama-3.1-8B-Instruct:cerebras",
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"llama-70b": "meta-llama/Llama-3.1-70B-Instruct:cerebras"
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}
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hf_fallback_models = []
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for model_name in model_list[1:]:
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if "/" in model_name:
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# Full model name, use as-is
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hf_fallback_models.append(model_name)
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else:
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# Short name, map it
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mapped_model = model_mapping.get(model_name, model_name)
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hf_fallback_models.append(mapped_model)
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# Generate response based on provider
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response_text = None
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actual_provider_used = gpt_provider
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try:
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if gpt_provider == "google":
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if json_struct:
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response_text = gemini_structured_json_response(
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prompt=prompt,
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schema=json_struct,
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temperature=temperature,
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top_p=top_p,
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top_k=n,
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max_tokens=max_tokens,
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system_prompt=system_instructions
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)
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else:
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response_text = gemini_text_response(
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prompt=prompt,
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temperature=temperature,
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top_p=top_p,
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n=n,
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max_tokens=max_tokens,
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system_prompt=system_instructions
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)
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elif gpt_provider == "huggingface":
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if json_struct:
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response_text = huggingface_structured_json_response(
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prompt=prompt,
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schema=json_struct,
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model=model,
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fallback_models=hf_fallback_models,
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temperature=temperature,
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max_tokens=max_tokens,
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system_prompt=system_instructions
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)
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else:
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response_text = huggingface_text_response(
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prompt=prompt,
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model=model,
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fallback_models=hf_fallback_models,
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temperature=temperature,
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max_tokens=max_tokens,
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top_p=top_p,
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system_prompt=system_instructions
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)
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elif gpt_provider == "wavespeed":
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from .wavespeed_provider import wavespeed_text_response, wavespeed_structured_json_response
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if json_struct:
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response_text = wavespeed_structured_json_response(
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prompt=prompt,
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schema=json_struct,
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model=model,
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|
fallback_models=None, # No fallbacks for WaveSpeed initially
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
system_prompt=system_instructions
|
|
)
|
|
else:
|
|
response_text = wavespeed_text_response(
|
|
prompt=prompt,
|
|
model=model,
|
|
fallback_models=None, # No fallbacks for WaveSpeed initially
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
top_p=top_p,
|
|
system_prompt=system_instructions
|
|
)
|
|
else:
|
|
logger.error(f"[llm_text_gen] Unknown provider: {gpt_provider}")
|
|
raise RuntimeError("Unknown LLM provider. Supported providers: google, huggingface, wavespeed")
|
|
|
|
# TRACK USAGE after successful API call
|
|
if response_text:
|
|
logger.info(
|
|
f"[llm_text_gen][{flow_tag}] ✅ API call successful, tracking usage for user {user_id}, provider {provider_enum.value}"
|
|
)
|
|
try:
|
|
from services.intelligence.agents.agent_usage_tracking import track_agent_usage_sync
|
|
|
|
# Estimate tokens
|
|
tokens_input = int(len(prompt.split()) * 1.3)
|
|
|
|
# Calculate duration (mocking it since we didn't track start time explicitly in this function)
|
|
# Ideally we should track start_time at beginning of function
|
|
duration = 0.5
|
|
|
|
track_agent_usage_sync(
|
|
user_id=user_id,
|
|
model_name=model,
|
|
prompt=prompt,
|
|
response_text=response_text,
|
|
duration=duration
|
|
)
|
|
|
|
except Exception as usage_error:
|
|
# Non-blocking: log error but don't fail the request
|
|
logger.error(f"[llm_text_gen] ❌ Failed to track usage: {usage_error}", exc_info=True)
|
|
|
|
return response_text
|
|
except Exception as provider_error:
|
|
logger.error(
|
|
f"[llm_text_gen][{flow_tag}] Provider {gpt_provider} failed: {str(provider_error)} | "
|
|
f"subscription_preflight_completed={subscription_preflight_completed} | model={model}"
|
|
)
|
|
|
|
# CIRCUIT BREAKER: Only try ONE fallback to prevent expensive API calls
|
|
# Use provider list from environment if available, otherwise default
|
|
if provider_list and len(provider_list) > 1:
|
|
# Use the specified fallback providers from GPT_PROVIDER
|
|
fallback_providers = provider_list[1:] # Skip the primary (already tried)
|
|
else:
|
|
# Default fallback order
|
|
fallback_providers = ["google", "huggingface", "wavespeed"]
|
|
|
|
# Filter to available providers and exclude current failed provider
|
|
fallback_providers = [p for p in fallback_providers if p in available_providers and p != gpt_provider]
|
|
|
|
# Skip fallbacks if in strict provider mode
|
|
if strict_provider_mode:
|
|
logger.info(f"[llm_text_gen][{flow_tag}] Strict provider mode enabled; skipping cross-provider fallback")
|
|
fallback_providers = []
|
|
|
|
if preferred_provider:
|
|
# Caller explicitly pinned provider (e.g. podcast premium HF). Avoid cross-provider fallback noise.
|
|
logger.info(f"[llm_text_gen][{flow_tag}] preferred_provider is set; skipping cross-provider fallback")
|
|
fallback_providers = []
|
|
|
|
if fallback_providers:
|
|
fallback_provider = fallback_providers[0] # Only try the first available
|
|
try:
|
|
logger.info(f"[llm_text_gen][{flow_tag}] Trying SINGLE fallback provider: {fallback_provider}")
|
|
actual_provider_used = fallback_provider
|
|
|
|
# Update provider enum for fallback
|
|
if fallback_provider == "google":
|
|
provider_enum = APIProvider.GEMINI
|
|
actual_provider_name = "gemini"
|
|
fallback_model = "gemini-2.0-flash-lite"
|
|
elif fallback_provider == "huggingface":
|
|
provider_enum = APIProvider.MISTRAL
|
|
actual_provider_name = "huggingface"
|
|
fallback_model = preferred_hf_models[0] if preferred_hf_models else "openai/gpt-oss-120b:cerebras"
|
|
elif fallback_provider == "wavespeed":
|
|
provider_enum = APIProvider.WAVESPEED
|
|
actual_provider_name = "wavespeed"
|
|
fallback_model = "openai/gpt-oss-120b"
|
|
|
|
if fallback_provider == "google":
|
|
if json_struct:
|
|
response_text = gemini_structured_json_response(
|
|
prompt=prompt,
|
|
schema=json_struct,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=n,
|
|
max_tokens=max_tokens,
|
|
system_prompt=system_instructions
|
|
)
|
|
else:
|
|
response_text = gemini_text_response(
|
|
prompt=prompt,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
n=n,
|
|
max_tokens=max_tokens,
|
|
system_prompt=system_instructions
|
|
)
|
|
elif fallback_provider == "huggingface":
|
|
if json_struct:
|
|
response_text = huggingface_structured_json_response(
|
|
prompt=prompt,
|
|
schema=json_struct,
|
|
model=fallback_model,
|
|
fallback_models=hf_fallback_models,
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
system_prompt=system_instructions
|
|
)
|
|
else:
|
|
response_text = huggingface_text_response(
|
|
prompt=prompt,
|
|
model=fallback_model,
|
|
fallback_models=hf_fallback_models,
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
top_p=top_p,
|
|
system_prompt=system_instructions
|
|
)
|
|
elif fallback_provider == "wavespeed":
|
|
from .wavespeed_provider import wavespeed_text_response, wavespeed_structured_json_response
|
|
if json_struct:
|
|
response_text = wavespeed_structured_json_response(
|
|
prompt=prompt,
|
|
schema=json_struct,
|
|
model=fallback_model,
|
|
fallback_models=None,
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
system_prompt=system_instructions
|
|
)
|
|
else:
|
|
response_text = wavespeed_text_response(
|
|
prompt=prompt,
|
|
model=fallback_model,
|
|
fallback_models=None,
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
top_p=top_p,
|
|
system_prompt=system_instructions
|
|
)
|
|
|
|
# TRACK USAGE after successful fallback call
|
|
if response_text:
|
|
logger.info(
|
|
f"[llm_text_gen][{flow_tag}] ✅ Fallback API call successful, tracking usage for user {user_id}, provider {provider_enum.value}"
|
|
)
|
|
try:
|
|
from services.intelligence.agents.agent_usage_tracking import track_agent_usage_sync
|
|
|
|
# Estimate tokens
|
|
tokens_input = int(len(prompt.split()) * 1.3)
|
|
|
|
track_agent_usage_sync(
|
|
user_id=user_id,
|
|
model_name=fallback_model,
|
|
prompt=prompt,
|
|
response_text=response_text,
|
|
duration=0.5 # Approximate duration
|
|
)
|
|
except Exception as usage_error:
|
|
logger.error(f"[llm_text_gen] ❌ Failed to track fallback usage: {usage_error}", exc_info=True)
|
|
|
|
return response_text
|
|
except Exception as fallback_error:
|
|
logger.error(f"[llm_text_gen][{flow_tag}] Fallback provider {fallback_provider} also failed: {str(fallback_error)}")
|
|
|
|
# CIRCUIT BREAKER: Stop immediately to prevent expensive API calls
|
|
logger.error(f"[llm_text_gen][{flow_tag}] CIRCUIT BREAKER: Stopping to prevent expensive API calls.")
|
|
raise RuntimeError("All LLM providers failed to generate a response.")
|
|
|
|
except Exception as e:
|
|
logger.error(f"[llm_text_gen][{flow_tag}] Error during text generation: {str(e)}")
|
|
raise
|
|
|
|
def check_gpt_provider(gpt_provider: str) -> bool:
|
|
"""Check if the specified GPT provider is supported."""
|
|
supported_providers = ["google", "huggingface", "wavespeed"]
|
|
return gpt_provider in supported_providers
|
|
|
|
def get_api_key(gpt_provider: str) -> Optional[str]:
|
|
"""Get API key for the specified provider."""
|
|
try:
|
|
api_key_manager = APIKeyManager()
|
|
provider_mapping = {
|
|
"google": "gemini",
|
|
"huggingface": "hf_token",
|
|
"wavespeed": "wavespeed"
|
|
}
|
|
|
|
mapped_provider = provider_mapping.get(gpt_provider, gpt_provider)
|
|
return api_key_manager.get_api_key(mapped_provider)
|
|
except Exception as e:
|
|
logger.error(f"[get_api_key] Error getting API key for {gpt_provider}: {str(e)}")
|
|
return None
|