452 lines
19 KiB
Python
452 lines
19 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 .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 get_available_text_providers, get_tenant_api_key
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from .routing_observability import emit_routing_event
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def _normalize_provider(provider: Optional[str]) -> Optional[str]:
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if not provider:
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return None
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provider_aliases = {
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"gemini": "google",
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"google": "google",
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"hf": "huggingface",
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"hf_response_api": "huggingface",
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"huggingface": "huggingface",
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"wavespeed": "huggingface",
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}
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value = str(provider).strip().lower()
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return provider_aliases.get(value, value)
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def _parse_csv_env(value: Optional[str]) -> List[str]:
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if not value:
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return []
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return [v.strip() for v in str(value).split(",") if v.strip()]
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def _resolve_provider_sequence(
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preferred_provider: Optional[str],
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env_provider_raw: str,
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available_providers: List[str],
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) -> List[str]:
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configured = _parse_csv_env(preferred_provider) if preferred_provider else _parse_csv_env(env_provider_raw)
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normalized = [_normalize_provider(p) for p in configured if _normalize_provider(p)]
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if not normalized:
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if "google" in available_providers:
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return ["google"]
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if "huggingface" in available_providers:
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return ["huggingface"]
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return []
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# preserve order and keep only available providers
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sequence = []
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for provider in normalized:
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if provider in available_providers:
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sequence.append(provider)
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# strict mode for single configured provider: no silent remap
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if len(normalized) == 1:
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return sequence
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# multi-provider mode: append any other available providers as tail only if none configured are available
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if not sequence:
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return [p for p in ["huggingface", "google"] if p in available_providers]
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return sequence
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def _map_logical_model_to_provider_model(provider: str, model_name: str) -> str:
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"""Map logical model aliases/full names to provider-specific model IDs."""
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raw = (model_name or "").strip()
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if not raw:
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return raw
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# Full provider path supplied explicitly; use as-is.
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if "/" in raw:
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return raw
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key = raw.lower()
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hf_map = {
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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:groq",
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"llama-8b": "meta-llama/Llama-3.1-8B-Instruct:groq",
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"llama-70b": "meta-llama/Llama-3.1-70B-Instruct:groq",
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}
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wavespeed_map = {
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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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"gpt-oss-20b": "openai/gpt-oss-20b",
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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 provider in {"huggingface", "hf", "hf_response_api"}:
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return hf_map.get(key, raw)
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if provider == "wavespeed":
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return wavespeed_map.get(key, raw)
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return raw
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def _resolve_model_sequence(provider: str, preferred_hf_models: Optional[List[str]] = None) -> List[str]:
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models_env = _parse_csv_env(os.getenv("TEXTGEN_AI_MODELS", ""))
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if provider == "google":
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return ["gemini-2.0-flash-001"]
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if preferred_hf_models:
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return [_map_logical_model_to_provider_model(provider, m) for m in preferred_hf_models if m]
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if not models_env:
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return ["openai/gpt-oss-120b:groq"]
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resolved = [_map_logical_model_to_provider_model(provider, m) for m in models_env if m.strip()]
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return resolved or ["openai/gpt-oss-120b:groq"]
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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: str = "default",
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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"
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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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env_provider_raw = os.getenv('GPT_PROVIDER', '').lower()
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env_provider = _normalize_provider(env_provider_raw)
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preferred_provider_normalized = _normalize_provider(preferred_provider)
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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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available_providers = get_available_text_providers(user_id)
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provider_sequence = _resolve_provider_sequence(preferred_provider, env_provider_raw, available_providers)
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if not provider_sequence:
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logger.error("[llm_text_gen] No configured providers available for tenant.")
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raise RuntimeError("No LLM providers available for tenant.")
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# strict mode if single configured provider; multi-provider fallback if comma-separated providers
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pinned_provider = len(_parse_csv_env(preferred_provider or env_provider_raw)) == 1 and bool(preferred_provider or env_provider_raw)
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gpt_provider = provider_sequence[0]
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model_sequence = _resolve_model_sequence(gpt_provider, preferred_hf_models)
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model = model_sequence[0]
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hf_api_key = get_tenant_api_key(user_id, "huggingface") if gpt_provider == "huggingface" else None
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logger.info(
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"[llm_text_gen] Mode | providers={} | models={} | env_models={} | strict_provider={} | strict_model={}",
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provider_sequence,
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model_sequence,
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_parse_csv_env(os.getenv("TEXTGEN_AI_MODELS", "")),
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pinned_provider,
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len(model_sequence) == 1,
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)
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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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"text_route_selected",
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user_id=user_id,
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flow_type=flow_type,
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provider_selected=gpt_provider,
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model_selected=model,
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env_provider=env_provider_raw or "auto",
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fallback_count=0,
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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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logger.info(
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"[llm_text_gen][subscription_preflight] start | user_id={} | provider={} | tokens_requested={}",
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user_id,
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actual_provider_name or provider_enum.value,
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estimated_total_tokens,
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)
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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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logger.info(
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"[llm_text_gen][subscription_preflight] pass | user_id={} | provider={} | tokens_requested={}",
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user_id,
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actual_provider_name or provider_enum.value,
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estimated_total_tokens,
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)
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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/model sequence
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response_text = None
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errors: List[str] = []
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for provider_idx, provider_name in enumerate(provider_sequence):
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candidate_models = _resolve_model_sequence(provider_name, preferred_hf_models)
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for model_idx, candidate_model in enumerate(candidate_models):
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try:
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emit_routing_event(
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logger,
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"text_route_attempt",
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user_id=user_id,
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flow_type=flow_type,
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provider_selected=provider_name,
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model_selected=candidate_model,
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provider_attempt=provider_idx + 1,
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model_attempt=model_idx + 1,
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)
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if provider_name == "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 provider_name == "huggingface":
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hf_api_key_current = get_tenant_api_key(user_id, "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=candidate_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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api_key=hf_api_key_current,
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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=candidate_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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api_key=hf_api_key_current,
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)
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else:
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raise RuntimeError(f"Unknown provider {provider_name}")
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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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track_agent_usage_sync(
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user_id=user_id,
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model_name=candidate_model,
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prompt=prompt,
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response_text=response_text,
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duration=0.5,
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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 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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err = f"provider={provider_name},model={candidate_model},error={provider_error}"
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errors.append(err)
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logger.error("[llm_text_gen] Attempt failed: {}", err)
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continue
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# strict provider mode: single configured provider should not switch
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if pinned_provider and len(provider_sequence) == 1:
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break
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logger.error("[llm_text_gen] CIRCUIT BREAKER: All configured provider/model attempts failed. {}", errors)
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raise RuntimeError("All configured LLM provider/model attempts failed.")
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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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providers = [_normalize_provider(p) for p in _parse_csv_env(gpt_provider)]
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if not providers:
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providers = [_normalize_provider(gpt_provider)]
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supported_providers = {"google", "huggingface"}
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return all(p in supported_providers for p in providers if p)
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def get_api_key(gpt_provider: str, user_id: Optional[str] = None) -> Optional[str]:
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"""Get API key for the specified provider, preferring tenant-scoped keys."""
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try:
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return get_tenant_api_key(user_id, gpt_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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