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"feat:enhance-podcast-topic-ai"

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ajaysi
2026-03-11 19:09:27 +05:30
parent e472861967
commit 01881bb405
51 changed files with 3627 additions and 218 deletions
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316
backend/services/llm_providers/main_text_generation.py
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@@ -22,6 +22,8 @@ def llm_text_gen(
json_struct: Optional[Dict[str, Any]] = None,
user_id: str = None,
preferred_hf_models: Optional[List[str]] = None,
preferred_provider: Optional[str] = None,
flow_type: Optional[str] = None,
) -> str:
"""
Generate text using Language Model (LLM) based on the provided prompt.
@@ -39,12 +41,16 @@ def llm_text_gen(
RuntimeError: If subscription limits are exceeded or user_id is missing.
"""
try:
logger.info("[llm_text_gen] Starting text generation")
resolved_flow_type = flow_type or ("sif_agent" if preferred_hf_models else "premium_tool")
flow_tag = f"flow_type={resolved_flow_type}"
subscription_preflight_completed = False
logger.info(f"[llm_text_gen][{flow_tag}] Starting text generation")
logger.debug(f"[llm_text_gen] Prompt length: {len(prompt)} characters")
# Set default values for LLM parameters
gpt_provider = "google" # Default to Google Gemini
model = "gemini-2.0-flash-001"
gpt_provider = "huggingface" # Default to premium HF route for ALwrity AI tools
model = "openai/gpt-oss-120b:cerebras"
temperature = 0.7
max_tokens = 4000
top_p = 0.9
@@ -55,12 +61,87 @@ def llm_text_gen(
# Check for GPT_PROVIDER environment variable
env_provider = os.getenv('GPT_PROVIDER', '').lower()
if env_provider in ['gemini', 'google']:
gpt_provider = "google"
model = "gemini-2.0-flash-001"
elif env_provider in ['hf_response_api', 'huggingface', 'hf']:
gpt_provider = "huggingface"
model = "mistralai/Mistral-7B-Instruct-v0.3:groq"
provider_list = [p.strip() for p in env_provider.split(',') if p.strip()]
# Determine if we're in strict mode (single provider) or fallback mode (multiple providers)
strict_provider_mode = len(provider_list) == 1
if provider_list:
# Use first provider as primary
primary_provider = provider_list[0]
if primary_provider in ['gemini', 'google']:
gpt_provider = "google"
model = "gemini-2.0-flash-001"
elif primary_provider in ['hf_response_api', 'huggingface', 'hf']:
gpt_provider = "huggingface"
model = "openai/gpt-oss-120b:cerebras"
elif primary_provider == 'wavespeed':
gpt_provider = "wavespeed"
model = "openai/gpt-oss-120b"
else:
# Auto-detect mode
strict_provider_mode = False # Auto-detect allows fallbacks
gpt_provider = None
model = None
# Explicit per-call provider override (used by tool-specific flows like podcast maker)
if preferred_provider:
preferred_providers = [p.strip() for p in preferred_provider.split(',') if p.strip()]
# If explicit provider is set, it's strict mode (no cross-provider fallbacks)
strict_provider_mode = len(preferred_providers) == 1
primary_provider = preferred_providers[0]
if primary_provider in ['gemini', 'google']:
gpt_provider = "google"
model = "gemini-2.0-flash-001"
elif primary_provider in ['hf_response_api', 'huggingface', 'hf']:
gpt_provider = "huggingface"
model = "openai/gpt-oss-120b:cerebras"
elif primary_provider == 'wavespeed':
gpt_provider = "wavespeed"
model = "openai/gpt-oss-120b"
# Handle TEXTGEN_AI_MODELS for model selection
textgen_models_env = os.getenv('TEXTGEN_AI_MODELS', '').strip()
model_list = [m.strip() for m in textgen_models_env.split(',') if m.strip()] if textgen_models_env else []
strict_model_mode = len(model_list) == 1
# Map model names to actual provider models
if model_list:
if gpt_provider == "huggingface":
# Handle both short names and full model names
model_mapping = {
"gpt-oss": "openai/gpt-oss-120b:cerebras",
"gpt-oss-120b": "openai/gpt-oss-120b:cerebras",
"mistral": "mistralai/Mistral-7B-Instruct-v0.3:cerebras",
"mistral-7b": "mistralai/Mistral-7B-Instruct-v0.3:cerebras",
"llama": "meta-llama/Llama-3.1-8B-Instruct:cerebras",
"llama-8b": "meta-llama/Llama-3.1-8B-Instruct:cerebras",
"llama-70b": "meta-llama/Llama-3.1-70B-Instruct:cerebras"
}
# If model name contains "/", assume it's already a full model name
if "/" in model_list[0]:
model = model_list[0]
else:
model = model_mapping.get(model_list[0], model_list[0])
elif gpt_provider == "wavespeed":
# Handle both short names and full model names
model_mapping = {
"gpt-oss": "openai/gpt-oss-120b",
"gpt-oss-120b": "openai/gpt-oss-120b",
"mistral": "mistralai/Mistral-7B-Instruct-v0.3",
"mistral-7b": "mistralai/Mistral-7B-Instruct-v0.3",
"llama": "meta-llama/Llama-3.1-8B-Instruct",
"llama-8b": "meta-llama/Llama-3.1-8B-Instruct",
"llama-70b": "meta-llama/Llama-3.1-70B-Instruct"
}
# If model name contains "/", assume it's already a full model name
if "/" in model_list[0]:
model = model_list[0]
else:
model = model_mapping.get(model_list[0], model_list[0])
elif gpt_provider == "google":
model = "gemini-2.0-flash-001" # Google has fewer options
# Default blog characteristics
blog_tone = "Professional"
@@ -77,42 +158,89 @@ def llm_text_gen(
available_providers.append("google")
if api_key_manager.get_api_key("hf_token"):
available_providers.append("huggingface")
if api_key_manager.get_api_key("wavespeed"):
available_providers.append("wavespeed")
logger.info(
f"[llm_text_gen][{flow_tag}] Provider preflight: env_provider='{env_provider or 'auto'}', "
f"provider_list={provider_list}, strict_provider_mode={strict_provider_mode}, "
f"available_providers={available_providers}, preferred_provider={preferred_provider or 'none'}"
)
if model_list:
logger.info(
f"[llm_text_gen][{flow_tag}] Model configuration: model_list={model_list}, "
f"strict_model_mode={strict_model_mode}"
)
# If no environment variable set, auto-detect based on available keys
if not env_provider:
# Prefer Google Gemini if available, otherwise use Hugging Face
if "google" in available_providers:
if preferred_provider:
# Respect explicit per-call preference if the provider key exists
if gpt_provider not in available_providers:
logger.warning(
f"[llm_text_gen] Preferred provider {gpt_provider} unavailable, falling back to available providers"
)
if "huggingface" in available_providers:
gpt_provider = "huggingface"
model = "openai/gpt-oss-120b:cerebras"
elif "wavespeed" in available_providers:
gpt_provider = "wavespeed"
model = "openai/gpt-oss-120b"
elif "google" in available_providers:
gpt_provider = "google"
model = "gemini-2.0-flash-001"
else:
logger.error("[llm_text_gen] No API keys found for supported providers.")
raise RuntimeError("No LLM API keys configured. Configure GEMINI_API_KEY or HF_TOKEN to enable AI responses.")
elif preferred_hf_models and "huggingface" in available_providers:
# Low-cost SIF/agent flows pass preferred_hf_models; route directly to HF.
gpt_provider = "huggingface"
model = preferred_hf_models[0]
logger.info(f"[llm_text_gen] Using preferred low-cost HF model: {model}")
elif "google" in available_providers:
gpt_provider = "google"
model = "gemini-2.0-flash-001"
elif "huggingface" in available_providers:
gpt_provider = "huggingface"
model = "mistralai/Mistral-7B-Instruct-v0.3:groq"
model = "openai/gpt-oss-120b:cerebras"
elif "wavespeed" in available_providers:
gpt_provider = "wavespeed"
model = "openai/gpt-oss-120b"
else:
logger.error("[llm_text_gen] No API keys found for supported providers.")
raise RuntimeError("No LLM API keys configured. Configure GEMINI_API_KEY or HF_TOKEN to enable AI responses.")
else:
# Environment variable was set, validate it's supported
if gpt_provider not in available_providers:
logger.warning(f"[llm_text_gen] Provider {gpt_provider} not available, falling back to available providers")
if "google" in available_providers:
gpt_provider = "google"
model = "gemini-2.0-flash-001"
elif "huggingface" in available_providers:
gpt_provider = "huggingface"
model = "mistralai/Mistral-7B-Instruct-v0.3:groq"
if strict_provider_mode:
# Strict mode: fail if specified provider not available
raise RuntimeError(f"Provider {gpt_provider} not available. Available: {available_providers}")
else:
raise RuntimeError("No supported providers available.")
# Fallback mode: try other providers
logger.warning(f"[llm_text_gen] Provider {gpt_provider} not available, falling back to available providers")
if "google" in available_providers:
gpt_provider = "google"
model = "gemini-2.0-flash-001"
elif "huggingface" in available_providers:
gpt_provider = "huggingface"
model = "openai/gpt-oss-120b:cerebras"
elif "wavespeed" in available_providers:
gpt_provider = "wavespeed"
model = "openai/gpt-oss-120b"
else:
raise RuntimeError("No supported providers available.")
if gpt_provider == "huggingface" and preferred_hf_models:
model = preferred_hf_models[0]
logger.info(f"[llm_text_gen] Using preferred low-cost HF model: {model}")
logger.debug(f"[llm_text_gen] Using provider: {gpt_provider}, model: {model}")
logger.info(f"[llm_text_gen][{flow_tag}] Using provider={gpt_provider}, model={model}")
# Map provider name to APIProvider enum (define at function scope for usage tracking)
from models.subscription_models import APIProvider
provider_enum = None
# Store actual provider name for logging (e.g., "huggingface", "gemini")
# Store actual provider name for logging (e.g., "huggingface", "gemini", "wavespeed")
actual_provider_name = None
if gpt_provider == "google":
provider_enum = APIProvider.GEMINI
@@ -120,6 +248,9 @@ def llm_text_gen(
elif gpt_provider == "huggingface":
provider_enum = APIProvider.MISTRAL # HuggingFace maps to Mistral enum for usage tracking
actual_provider_name = "huggingface" # Keep actual provider name for logs
elif gpt_provider == "wavespeed":
provider_enum = APIProvider.WAVESPEED
actual_provider_name = "wavespeed"
if not provider_enum:
raise RuntimeError(f"Unknown provider {gpt_provider} for subscription checking")
@@ -132,6 +263,11 @@ def llm_text_gen(
from services.database import get_session_for_user
from services.subscription import UsageTrackingService, PricingService
from models.subscription_models import UsageSummary
logger.info(
f"[llm_text_gen][{flow_tag}] Starting subscription preflight for user={user_id}, "
f"provider={actual_provider_name}, model={model}"
)
db = get_session_for_user(user_id)
if not db:
@@ -162,6 +298,12 @@ def llm_text_gen(
tokens_requested=estimated_total_tokens,
actual_provider_name=actual_provider_name # Pass actual provider name for correct error messages
)
subscription_preflight_completed = True
logger.info(
f"[llm_text_gen][{flow_tag}] Subscription preflight complete: can_proceed={can_proceed}, "
f"estimated_tokens={estimated_total_tokens}, provider={actual_provider_name}"
)
if not can_proceed:
logger.warning(f"[llm_text_gen] Subscription limit exceeded for user {user_id}: {message}")
@@ -219,6 +361,32 @@ def llm_text_gen(
else:
system_instructions = system_prompt
# HF behavior: fail fast on selected model; no intra-provider model fallback chain.
hf_fallback_models: List[str] = []
# Set up model fallbacks based on strict_model_mode
if not strict_model_mode and model_list and len(model_list) > 1:
# Multi-model mode: create fallback list from TEXTGEN_AI_MODELS
if gpt_provider == "huggingface":
model_mapping = {
"gpt-oss": "openai/gpt-oss-120b:cerebras",
"gpt-oss-120b": "openai/gpt-oss-120b:cerebras",
"mistral": "mistralai/Mistral-7B-Instruct-v0.3:cerebras",
"mistral-7b": "mistralai/Mistral-7B-Instruct-v0.3:cerebras",
"llama": "meta-llama/Llama-3.1-8B-Instruct:cerebras",
"llama-8b": "meta-llama/Llama-3.1-8B-Instruct:cerebras",
"llama-70b": "meta-llama/Llama-3.1-70B-Instruct:cerebras"
}
hf_fallback_models = []
for model_name in model_list[1:]:
if "/" in model_name:
# Full model name, use as-is
hf_fallback_models.append(model_name)
else:
# Short name, map it
mapped_model = model_mapping.get(model_name, model_name)
hf_fallback_models.append(mapped_model)
# Generate response based on provider
response_text = None
actual_provider_used = gpt_provider
@@ -249,6 +417,7 @@ def llm_text_gen(
prompt=prompt,
schema=json_struct,
model=model,
fallback_models=hf_fallback_models,
temperature=temperature,
max_tokens=max_tokens,
system_prompt=system_instructions
@@ -257,6 +426,29 @@ def llm_text_gen(
response_text = huggingface_text_response(
prompt=prompt,
model=model,
fallback_models=hf_fallback_models,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
system_prompt=system_instructions
)
elif gpt_provider == "wavespeed":
from .wavespeed_provider import wavespeed_text_response, wavespeed_structured_json_response
if json_struct:
response_text = wavespeed_structured_json_response(
prompt=prompt,
schema=json_struct,
model=model,
fallback_models=None, # No fallbacks for WaveSpeed initially
temperature=temperature,
max_tokens=max_tokens,
system_prompt=system_instructions
)
else:
response_text = wavespeed_text_response(
prompt=prompt,
model=model,
fallback_models=None, # No fallbacks for WaveSpeed initially
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
@@ -264,11 +456,13 @@ def llm_text_gen(
)
else:
logger.error(f"[llm_text_gen] Unknown provider: {gpt_provider}")
raise RuntimeError("Unknown LLM provider. Supported providers: google, huggingface")
raise RuntimeError("Unknown LLM provider. Supported providers: google, huggingface, wavespeed")
# TRACK USAGE after successful API call
if response_text:
logger.info(f"[llm_text_gen] ✅ API call successful, tracking usage for user {user_id}, provider {provider_enum.value}")
logger.info(
f"[llm_text_gen][{flow_tag}] ✅ API call successful, tracking usage for user {user_id}, provider {provider_enum.value}"
)
try:
from services.intelligence.agents.agent_usage_tracking import track_agent_usage_sync
@@ -293,16 +487,37 @@ def llm_text_gen(
return response_text
except Exception as provider_error:
logger.error(f"[llm_text_gen] Provider {gpt_provider} failed: {str(provider_error)}")
logger.error(
f"[llm_text_gen][{flow_tag}] Provider {gpt_provider} failed: {str(provider_error)} | "
f"subscription_preflight_completed={subscription_preflight_completed} | model={model}"
)
# CIRCUIT BREAKER: Only try ONE fallback to prevent expensive API calls
fallback_providers = ["google", "huggingface"]
# Use provider list from environment if available, otherwise default
if provider_list and len(provider_list) > 1:
# Use the specified fallback providers from GPT_PROVIDER
fallback_providers = provider_list[1:] # Skip the primary (already tried)
else:
# Default fallback order
fallback_providers = ["google", "huggingface", "wavespeed"]
# Filter to available providers and exclude current failed provider
fallback_providers = [p for p in fallback_providers if p in available_providers and p != gpt_provider]
# Skip fallbacks if in strict provider mode
if strict_provider_mode:
logger.info(f"[llm_text_gen][{flow_tag}] Strict provider mode enabled; skipping cross-provider fallback")
fallback_providers = []
if preferred_provider:
# Caller explicitly pinned provider (e.g. podcast premium HF). Avoid cross-provider fallback noise.
logger.info(f"[llm_text_gen][{flow_tag}] preferred_provider is set; skipping cross-provider fallback")
fallback_providers = []
if fallback_providers:
fallback_provider = fallback_providers[0] # Only try the first available
try:
logger.info(f"[llm_text_gen] Trying SINGLE fallback provider: {fallback_provider}")
logger.info(f"[llm_text_gen][{flow_tag}] Trying SINGLE fallback provider: {fallback_provider}")
actual_provider_used = fallback_provider
# Update provider enum for fallback
@@ -313,7 +528,11 @@ def llm_text_gen(
elif fallback_provider == "huggingface":
provider_enum = APIProvider.MISTRAL
actual_provider_name = "huggingface"
fallback_model = "mistralai/Mistral-7B-Instruct-v0.3:groq"
fallback_model = preferred_hf_models[0] if preferred_hf_models else "openai/gpt-oss-120b:cerebras"
elif fallback_provider == "wavespeed":
provider_enum = APIProvider.WAVESPEED
actual_provider_name = "wavespeed"
fallback_model = "openai/gpt-oss-120b"
if fallback_provider == "google":
if json_struct:
@@ -340,7 +559,8 @@ def llm_text_gen(
response_text = huggingface_structured_json_response(
prompt=prompt,
schema=json_struct,
model="mistralai/Mistral-7B-Instruct-v0.3:groq",
model=fallback_model,
fallback_models=hf_fallback_models,
temperature=temperature,
max_tokens=max_tokens,
system_prompt=system_instructions
@@ -348,7 +568,30 @@ def llm_text_gen(
else:
response_text = huggingface_text_response(
prompt=prompt,
model="mistralai/Mistral-7B-Instruct-v0.3:groq",
model=fallback_model,
fallback_models=hf_fallback_models,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
system_prompt=system_instructions
)
elif fallback_provider == "wavespeed":
from .wavespeed_provider import wavespeed_text_response, wavespeed_structured_json_response
if json_struct:
response_text = wavespeed_structured_json_response(
prompt=prompt,
schema=json_struct,
model=fallback_model,
fallback_models=None,
temperature=temperature,
max_tokens=max_tokens,
system_prompt=system_instructions
)
else:
response_text = wavespeed_text_response(
prompt=prompt,
model=fallback_model,
fallback_models=None,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
@@ -357,7 +600,9 @@ def llm_text_gen(
# 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}")
logger.info(
f"[llm_text_gen][{flow_tag}] ✅ Fallback API call successful, tracking usage for user {user_id}, provider {provider_enum.value}"
)
try:
from services.intelligence.agents.agent_usage_tracking import track_agent_usage_sync
@@ -376,19 +621,19 @@ def llm_text_gen(
return response_text
except Exception as fallback_error:
logger.error(f"[llm_text_gen] Fallback provider {fallback_provider} also failed: {str(fallback_error)}")
logger.error(f"[llm_text_gen][{flow_tag}] Fallback provider {fallback_provider} also failed: {str(fallback_error)}")
# CIRCUIT BREAKER: Stop immediately to prevent expensive API calls
logger.error("[llm_text_gen] CIRCUIT BREAKER: Stopping to prevent expensive API calls.")
logger.error(f"[llm_text_gen][{flow_tag}] CIRCUIT BREAKER: Stopping to prevent expensive API calls.")
raise RuntimeError("All LLM providers failed to generate a response.")
except Exception as e:
logger.error(f"[llm_text_gen] Error during text generation: {str(e)}")
logger.error(f"[llm_text_gen][{flow_tag}] Error during text generation: {str(e)}")
raise
def check_gpt_provider(gpt_provider: str) -> bool:
"""Check if the specified GPT provider is supported."""
supported_providers = ["google", "huggingface"]
supported_providers = ["google", "huggingface", "wavespeed"]
return gpt_provider in supported_providers
def get_api_key(gpt_provider: str) -> Optional[str]:
@@ -397,7 +642,8 @@ def get_api_key(gpt_provider: str) -> Optional[str]:
api_key_manager = APIKeyManager()
provider_mapping = {
"google": "gemini",
"huggingface": "hf_token"
"huggingface": "hf_token",
"wavespeed": "wavespeed"
}
mapped_provider = provider_mapping.get(gpt_provider, gpt_provider)