596 lines
30 KiB
Python
596 lines
30 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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import time
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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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from .tenant_provider_config import tenant_provider_config_resolver
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HF_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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"gpt-oss-20b": "openai/gpt-oss-20b: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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"openai/gpt-oss-120b:cerebras",
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"moonshotai/Kimi-K2-Instruct-0905:cerebras",
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"meta-llama/Llama-3.1-8B-Instruct:cerebras",
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"mistralai/Mistral-7B-Instruct-v0.3:cerebras",
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]
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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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max_tokens: Optional[int] = None,
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temperature: Optional[float] = 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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preferred_hf_models (list, optional): Preferred HuggingFace models.
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preferred_provider (str, optional): Preferred provider (google, huggingface).
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flow_type (str, optional): Flow type for logging (e.g., 'sif_agent', 'premium_tool').
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max_tokens (int, optional): Max tokens for response. If None, provider default is used.
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temperature (float, optional): Temperature for generation (0.0-1.0). If None, defaults to 0.7.
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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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logger.warning(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 = "google" # Default to Google Gemini
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model = "gemini-2.0-flash-001"
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if temperature is None:
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temperature = 0.7
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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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# Check for TEXTGEN_AI_MODELS environment variable
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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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# Determine provider based on env vars or tenant config
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if provider_list:
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primary_provider = provider_list[0]
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if primary_provider in ['wavespeed', 'wave']:
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gpt_provider = "wavespeed"
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model = os.getenv('WAVESPEED_TEXT_MODEL', 'openai/gpt-oss-120b')
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elif 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 in ['openai', 'gpt']:
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gpt_provider = "openai"
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model = os.getenv('OPENAI_MODEL', 'gpt-4o-mini')
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else:
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logger.warning(f"[llm_text_gen] Unknown GPT_PROVIDER: {primary_provider}, using auto-select")
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gpt_provider = None
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model = None
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elif preferred_provider:
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if preferred_provider in ['wavespeed', 'wave']:
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gpt_provider = "wavespeed"
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model = os.getenv('WAVESPEED_TEXT_MODEL', 'openai/gpt-oss-120b')
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elif preferred_provider in ['openai', 'gpt']:
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gpt_provider = "openai"
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model = os.getenv('OPENAI_MODEL', 'gpt-4o-mini')
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elif preferred_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 preferred_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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else:
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gpt_provider = None
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model = None
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else:
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# Fall back to tenant config
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provider_cfg = tenant_provider_config_resolver.resolve(
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modality="text",
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user_id=user_id,
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)
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selected_provider = (provider_cfg.selected_providers or [None])[0]
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if selected_provider in ["gemini", "google"]:
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gpt_provider = "google"
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model = provider_cfg.model_policy.get("default_model") or "gemini-2.0-flash-001"
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elif selected_provider == "huggingface":
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gpt_provider = "huggingface"
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model = provider_cfg.model_policy.get("default_model") or "openai/gpt-oss-120b:cerebras"
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# Map short model names to full paths for HF
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if model_list and gpt_provider == "huggingface":
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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 = HF_MODEL_MAPPING.get(model_list[0], model_list[0])
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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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# Get strict provider mode from environment
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strict_provider_mode = os.getenv("STRICT_PROVIDER_MODE", "false").lower() in {"1", "true", "yes", "on"}
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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.warning(
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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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f"gpt_provider={gpt_provider}, model={model}"
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)
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if gpt_provider not in available_providers:
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logger.warning(f"[llm_text_gen] Provider {gpt_provider} unavailable for user {user_id}, falling back.")
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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 "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 for tenant or environment defaults.")
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# Ensure downstream provider clients (currently env-based) receive resolved key
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resolved_key = get_api_key(gpt_provider, user_id=user_id)
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if gpt_provider == "google" and resolved_key:
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os.environ["GEMINI_API_KEY"] = resolved_key
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os.environ.setdefault("GOOGLE_API_KEY", resolved_key)
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elif gpt_provider == "huggingface" and resolved_key:
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os.environ["HF_TOKEN"] = resolved_key
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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][{flow_tag}] Using preferred HF model: {model}")
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logger.debug(f"[llm_text_gen] 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")
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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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elif gpt_provider == "openai":
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provider_enum = APIProvider.OPENAI
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actual_provider_name = "openai"
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if not provider_enum:
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# For unknown providers, try to proceed without subscription tracking
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logger.warning(f"[llm_text_gen] Unknown provider {gpt_provider}, proceeding without subscription check")
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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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sub_check_start = time.time()
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logger.warning(f"[llm_text_gen][{flow_tag}] Subscription check START for user {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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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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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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# Log subscription details before making the API call
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if usage:
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total_llm_calls = (usage.gemini_calls or 0) + (usage.openai_calls or 0) + (usage.anthropic_calls or 0) + (usage.mistral_calls or 0) + (usage.wavespeed_calls or 0)
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logger.info(f"[llm_text_gen] Subscription check passed for user {user_id}: provider={actual_provider_name or gpt_provider}, tokens_requested={estimated_total_tokens}, current_usage=${usage.total_cost or 0:.4f}, calls_used={total_llm_calls}")
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else:
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logger.info(f"[llm_text_gen] Subscription check passed for user {user_id}: provider={actual_provider_name or gpt_provider}, tokens_requested={estimated_total_tokens}, new_user_no_usage_record")
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finally:
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sub_check_ms = (time.time() - sub_check_start) * 1000
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logger.warning(f"[llm_text_gen][{flow_tag}] Subscription check took {sub_check_ms:.0f}ms for user {user_id}")
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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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sub_check_ms = (time.time() - sub_check_start) * 1000
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logger.error(f"[llm_text_gen][{flow_tag}] Subscription check FAILED after {sub_check_ms:.0f}ms 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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# 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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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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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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llm_start = time.time()
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if json_struct:
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from services.llm_providers.wavespeed_provider import wavespeed_structured_json_response
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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 or "openai/gpt-oss-120b",
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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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from services.llm_providers.wavespeed_provider import wavespeed_text_response
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response_text = wavespeed_text_response(
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prompt=prompt,
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model=model or "openai/gpt-oss-120b",
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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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llm_ms = (time.time() - llm_start) * 1000
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logger.warning(f"[llm_text_gen][{flow_tag}] LLM API call took {llm_ms:.0f}ms for user {user_id} (wavespeed)")
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else:
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logger.error(f"[llm_text_gen] Unknown provider: {gpt_provider}")
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raise RuntimeError(f"Unknown LLM provider: {gpt_provider}. Supported providers: google, huggingface, wavespeed")
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# TRACK USAGE after successful API call
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if response_text:
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logger.info(f"[llm_text_gen] ✅ API call successful, tracking usage for user {user_id}, provider {provider_enum.value}")
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try:
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from services.intelligence.agents.agent_usage_tracking import track_agent_usage_sync
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# Estimate tokens
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tokens_input = int(len(prompt.split()) * 1.3)
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# Calculate duration (mocking it since we didn't track start time explicitly in this function)
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# Ideally we should track start_time at beginning of function
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duration = 0.5
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track_agent_usage_sync(
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user_id=user_id,
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model_name=model,
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prompt=prompt,
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response_text=response_text,
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duration=duration
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)
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except Exception as usage_error:
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# Non-blocking: log error but don't fail the request
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logger.error(f"[llm_text_gen] ❌ Failed to track usage: {usage_error}", exc_info=True)
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return response_text
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except Exception as provider_error:
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logger.error(f"[llm_text_gen] Provider {gpt_provider} failed: {str(provider_error)}")
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# Surface balance/quota errors immediately without fallback
|
|
error_str = str(provider_error).lower()
|
|
if "insufficient_balance" in error_str or "balance_not_enough" in error_str or ("403" in error_str and "balance" in error_str):
|
|
logger.error(f"[llm_text_gen] Balance/quota error from {gpt_provider}, not attempting fallback")
|
|
raise HTTPException(
|
|
status_code=403,
|
|
detail={
|
|
"error": "insufficient_balance",
|
|
"message": f"Your {gpt_provider.capitalize()} API balance is insufficient. Please top up your account or switch providers.",
|
|
"usage_info": {
|
|
"error_type": "insufficient_balance",
|
|
"provider": gpt_provider,
|
|
"suggestion": f"Set GPT_PROVIDER=google in your environment to use Gemini instead, or add credits to your {gpt_provider.capitalize()} account."
|
|
}
|
|
}
|
|
)
|
|
|
|
# CIRCUIT BREAKER: Only try ONE fallback to prevent expensive API calls
|
|
fallback_providers = ["google", "huggingface"]
|
|
fallback_providers = [p for p in fallback_providers if p in available_providers and p != gpt_provider]
|
|
|
|
if fallback_providers:
|
|
fallback_provider = fallback_providers[0] # Only try the first available
|
|
try:
|
|
logger.info(f"[llm_text_gen] 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 = HF_FALLBACK_MODELS[0]
|
|
|
|
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="mistralai/Mistral-7B-Instruct-v0.3:groq",
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
system_prompt=system_instructions
|
|
)
|
|
else:
|
|
response_text = huggingface_text_response(
|
|
prompt=prompt,
|
|
model="mistralai/Mistral-7B-Instruct-v0.3:groq",
|
|
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] ✅ 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] Fallback provider {fallback_provider} also failed: {str(fallback_error)}")
|
|
|
|
# CIRCUIT BREAKER: Stop immediately to prevent expensive API calls
|
|
logger.error("[llm_text_gen] CIRCUIT BREAKER: All providers failed.")
|
|
|
|
# Provide more helpful error message based on available providers
|
|
if not available_providers:
|
|
raise HTTPException(
|
|
status_code=429,
|
|
detail={
|
|
"error": "No LLM providers configured",
|
|
"message": "No LLM API keys found. Please configure at least one provider (GPT_PROVIDER, GOOGLE_API_KEY, HF_TOKEN, or WAVESPEED_API_KEY).",
|
|
"usage_info": {
|
|
"error_type": "no_providers_configured",
|
|
"operation_type": "text-generation",
|
|
"limit": 0,
|
|
"current_tokens": 0,
|
|
"suggestion": "Set GPT_PROVIDER=wavespeed in environment or configure API keys in the dashboard."
|
|
}
|
|
}
|
|
)
|
|
|
|
raise HTTPException(
|
|
status_code=429,
|
|
detail={
|
|
"error": "All LLM providers failed",
|
|
"message": "All configured LLM providers failed to generate a response. Please check API keys and try again.",
|
|
"usage_info": {
|
|
"error_type": "all_providers_failed",
|
|
"operation_type": "text-generation",
|
|
"available_providers": available_providers,
|
|
"requested_provider": gpt_provider,
|
|
"limit": 0,
|
|
"current_tokens": 0,
|
|
"suggestion": f"Provider {gpt_provider} failed. Available: {', '.join(available_providers)}. Try setting GPT_PROVIDER to one of: {', '.join(available_providers)}"
|
|
}
|
|
}
|
|
)
|
|
|
|
except HTTPException:
|
|
# Re-raise HTTPExceptions (e.g., 429 subscription limit) - preserve error details
|
|
raise
|
|
except Exception as e:
|
|
logger.error(f"[llm_text_gen] 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"]
|
|
return gpt_provider in supported_providers
|
|
|
|
def get_api_key(gpt_provider: str, user_id: Optional[str] = None) -> Optional[str]:
|
|
"""Get API key for the specified provider."""
|
|
try:
|
|
provider_mapping = {
|
|
"google": "gemini",
|
|
"huggingface": "huggingface"
|
|
}
|
|
mapped_provider = provider_mapping.get(gpt_provider, gpt_provider)
|
|
key, _source = tenant_provider_config_resolver.resolve_provider_key(mapped_provider, user_id=user_id)
|
|
return key
|
|
except Exception as e:
|
|
logger.error(f"[get_api_key] Error getting API key for {gpt_provider}: {str(e)}")
|
|
return None
|