238 lines
10 KiB
Python
238 lines
10 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
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from loguru import logger
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from ..api_key_manager import APIKeyManager
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from .openai_provider import openai_chatgpt
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from .gemini_provider import gemini_text_response, gemini_structured_json_response
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from .anthropic_provider import anthropic_text_response
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from .deepseek_provider import deepseek_text_response
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def llm_text_gen(prompt: str, system_prompt: Optional[str] = None, json_struct: Optional[Dict[str, Any]] = None) -> 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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Returns:
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str: Generated text based on the prompt.
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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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# Initialize API key manager and reload keys from .env file
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api_key_manager = APIKeyManager()
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api_key_manager.load_api_keys() # Force reload from .env file
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# Debug: Log loaded API keys
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logger.debug(f"[llm_text_gen] Loaded API keys: {api_key_manager.get_all_keys()}")
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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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# 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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# Try to get provider from environment or config
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try:
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# Check which providers have API keys available
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available_providers = []
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if api_key_manager.get_api_key("openai"):
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available_providers.append("openai")
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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("anthropic"):
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available_providers.append("anthropic")
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if api_key_manager.get_api_key("deepseek"):
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available_providers.append("deepseek")
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# Prefer Google Gemini if available, otherwise use first available
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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 available_providers:
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gpt_provider = available_providers[0]
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if gpt_provider == "openai":
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model = "gpt-4o"
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elif gpt_provider == "anthropic":
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model = "claude-3-5-sonnet-20241022"
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elif gpt_provider == "deepseek":
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model = "deepseek-chat"
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else:
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logger.error("[llm_text_gen] No API keys found. Structured mock responses are disabled.")
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raise RuntimeError("No LLM API keys configured. Configure provider API keys to enable AI responses.")
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logger.debug(f"[llm_text_gen] Using provider: {gpt_provider}, model: {model}")
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except Exception as err:
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logger.warning(f"[llm_text_gen] Error determining provider, using defaults: {err}")
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gpt_provider = "google"
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model = "gemini-2.0-flash-001"
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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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try:
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if gpt_provider == "openai":
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return openai_chatgpt(
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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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n=n,
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fp=fp,
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system_prompt=system_instructions
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)
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elif gpt_provider == "google":
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if json_struct:
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return 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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return 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 == "anthropic":
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return anthropic_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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system_prompt=system_instructions
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)
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elif gpt_provider == "deepseek":
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return deepseek_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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system_prompt=system_instructions
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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.")
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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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# Try to fallback to another provider
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fallback_providers = ["openai", "anthropic", "deepseek"]
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for fallback_provider in fallback_providers:
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if fallback_provider in available_providers and fallback_provider != gpt_provider:
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try:
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logger.info(f"[llm_text_gen] Trying fallback provider: {fallback_provider}")
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if fallback_provider == "openai":
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return openai_chatgpt(
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prompt=prompt,
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model="gpt-4o",
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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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n=n,
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fp=fp,
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system_prompt=system_instructions
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)
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elif fallback_provider == "anthropic":
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return anthropic_text_response(
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prompt=prompt,
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model="claude-3-5-sonnet-20241022",
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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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elif fallback_provider == "deepseek":
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return deepseek_text_response(
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prompt=prompt,
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model="deepseek-chat",
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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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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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continue
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# If all providers fail, raise an error (no mock)
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logger.error("[llm_text_gen] All providers failed. Structured mock responses are disabled.")
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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 = ["openai", "google", "anthropic", "deepseek"]
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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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"openai": "openai",
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"google": "gemini",
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"anthropic": "anthropic",
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"deepseek": "deepseek"
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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 |