490 lines
22 KiB
Python
490 lines
22 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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This is a clean version that imports from modular components to avoid merge conflicts.
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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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# Import all functionality from our modular textgen_utils package
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from .textgen_utils import (
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llm_text_gen,
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check_gpt_provider,
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get_api_key,
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_normalize_provider,
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_parse_csv_env,
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_resolve_provider_sequence,
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_map_logical_model_to_provider_model,
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_resolve_model_sequence,
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)
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# Re-export all the main functions for backward compatibility
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__all__ = [
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"llm_text_gen",
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"check_gpt_provider",
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"get_api_key",
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"_normalize_provider",
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"_parse_csv_env",
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"_resolve_provider_sequence",
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"_map_logical_model_to_provider_model",
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"_resolve_model_sequence",
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]
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# Maintain any additional constants or configurations that might be needed
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PREMIUM_HF_MINIMAL_FALLBACK_MODELS = [
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"openai/gpt-oss-120b:groq",
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]
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# Legacy compatibility - any imports that other modules might expect
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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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<<<<<<< HEAD
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from .tenant_provider_config import tenant_provider_config_resolver
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from .routing_policy import (
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PREMIUM_DEFAULT_MODEL,
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SIF_LOW_COST_MODEL_DEFAULTS,
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resolve_text_provider_alias,
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)
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=======
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from ...utils.logger_utils import emit_routing_event
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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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) -> 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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logger.info("[llm_text_gen] 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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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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if env_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 env_provider in ['hf_response_api', 'huggingface', 'hf']:
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gpt_provider = "huggingface"
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model = "mistralai/Mistral-7B-Instruct-v0.3:groq"
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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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preferred_provider = env_provider or None
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flow_type = "text_generation"
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route_intent = "primary"
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fallback_count = 0
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fallback_models_tried = []
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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 "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 = "mistralai/Mistral-7B-Instruct-v0.3:groq"
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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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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 = "mistralai/Mistral-7B-Instruct-v0.3:groq"
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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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fallback_models_tried.append(model)
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logger.debug(f"[llm_text_gen] Using provider: {gpt_provider}, model: {model}")
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emit_routing_event(
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logger,
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flow_type=flow_type,
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route_intent=route_intent,
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provider_selected=gpt_provider,
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model_selected=model,
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preferred_provider=preferred_provider,
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fallback_count=fallback_count,
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fallback_models_tried=fallback_models_tried,
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tenant_user_id=user_id,
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extra={"available_providers": available_providers},
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)
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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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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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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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# 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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# 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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tenant_user_id=user_id
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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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tenant_user_id=user_id
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)
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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("Unknown LLM provider. Supported providers: google, huggingface")
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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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# CIRCUIT BREAKER: Only try ONE fallback to prevent expensive API calls
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fallback_providers = ["google", "huggingface"]
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fallback_providers = [p for p in fallback_providers if p in available_providers and p != gpt_provider]
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if fallback_providers:
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fallback_provider = fallback_providers[0] # Only try the first available
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try:
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logger.info(f"[llm_text_gen] Trying SINGLE fallback provider: {fallback_provider}")
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actual_provider_used = fallback_provider
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fallback_count += 1
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route_intent = "fallback"
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# Update provider enum for fallback
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if fallback_provider == "google":
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provider_enum = APIProvider.GEMINI
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actual_provider_name = "gemini"
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fallback_model = "gemini-2.0-flash-lite"
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fallback_models_tried.append(fallback_model)
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elif fallback_provider == "huggingface":
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provider_enum = APIProvider.MISTRAL
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actual_provider_name = "huggingface"
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fallback_model = "mistralai/Mistral-7B-Instruct-v0.3:groq"
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fallback_models_tried.append(fallback_model)
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emit_routing_event(
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logger,
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flow_type=flow_type,
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route_intent=route_intent,
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provider_selected=fallback_provider,
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model_selected=fallback_model,
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preferred_provider=preferred_provider,
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fallback_count=fallback_count,
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fallback_models_tried=fallback_models_tried,
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tenant_user_id=user_id,
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extra={"available_providers": available_providers},
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)
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if fallback_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 fallback_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="mistralai/Mistral-7B-Instruct-v0.3:groq",
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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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tenant_user_id=user_id
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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="mistralai/Mistral-7B-Instruct-v0.3:groq",
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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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tenant_user_id=user_id
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)
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# TRACK USAGE after successful fallback call
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if response_text:
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logger.info(f"[llm_text_gen] ✅ Fallback 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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track_agent_usage_sync(
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user_id=user_id,
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model_name=fallback_model,
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prompt=prompt,
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response_text=response_text,
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duration=0.5 # Approximate duration
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)
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except Exception as usage_error:
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logger.error(f"[llm_text_gen] ❌ Failed to track fallback usage: {usage_error}", exc_info=True)
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return response_text
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except Exception as fallback_error:
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logger.error(f"[llm_text_gen] Fallback provider {fallback_provider} also failed: {str(fallback_error)}")
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# CIRCUIT BREAKER: Stop immediately to prevent expensive API calls
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logger.error("[llm_text_gen] CIRCUIT BREAKER: Stopping to prevent expensive API calls.")
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raise RuntimeError("All LLM providers failed to generate a response.")
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except Exception as e:
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logger.error(f"[llm_text_gen] Error during text generation: {str(e)}")
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raise
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def check_gpt_provider(gpt_provider: str) -> bool:
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"""Check if the specified GPT provider is supported."""
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supported_providers = ["google", "huggingface"]
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return gpt_provider in supported_providers
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def get_api_key(gpt_provider: str) -> Optional[str]:
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"""Get API key for the specified provider."""
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try:
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api_key_manager = APIKeyManager()
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provider_mapping = {
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"google": "gemini",
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"huggingface": "hf_token"
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}
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mapped_provider = provider_mapping.get(gpt_provider, gpt_provider)
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return api_key_manager.get_api_key(mapped_provider)
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except Exception as e:
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logger.error(f"[get_api_key] Error getting API key for {gpt_provider}: {str(e)}")
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return None
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>>>>>>> pr-421
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