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"feat:enhance-podcast-topic-ai"

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This commit is contained in:
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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142
backend/services/llm_providers/huggingface_provider.py
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@@ -51,7 +51,7 @@ import sys
from pathlib import Path
import json
import re
from typing import Optional, Dict, Any
from typing import Optional, Dict, Any, List
from dotenv import load_dotenv
@@ -76,6 +76,7 @@ logger = get_service_logger("huggingface_provider")
from tenacity import (
retry,
retry_if_exception,
stop_after_attempt,
wait_random_exponential,
)
@@ -90,10 +91,10 @@ except ImportError:
logger.warn("OpenAI library not available. Install with: pip install openai")
HF_FALLBACK_MODELS = [
"openai/gpt-oss-120b:groq",
"moonshotai/Kimi-K2-Instruct-0905:groq",
"meta-llama/Llama-3.1-8B-Instruct:groq",
"mistralai/Mistral-7B-Instruct-v0.3:groq",
"openai/gpt-oss-120b:cerebras",
"moonshotai/Kimi-K2-Instruct-0905:cerebras",
"meta-llama/Llama-3.1-8B-Instruct:cerebras",
"mistralai/Mistral-7B-Instruct-v0.3:cerebras",
]
@@ -102,7 +103,7 @@ def _candidate_model_variants(model: str):
if not model:
return
# Try configured model first (supports provider suffixes like ":groq")
# Try configured model first (supports provider suffixes like ":cerebras")
yield model
# Fallback to base repo id when provider suffix is not recognized by the router
@@ -112,8 +113,13 @@ def _candidate_model_variants(model: str):
yield base_model
def _fallback_model_sequence(model: str):
sequence = [model] + HF_FALLBACK_MODELS
def _fallback_model_sequence(model: str, fallback_models: Optional[List[str]] = None):
# IMPORTANT: Do not apply implicit global fallback chains.
# Callers must explicitly provide fallback_models when they want multi-model retries.
if fallback_models:
sequence = [model] + fallback_models
else:
sequence = [model]
seen = set()
for preferred_model in sequence:
for candidate in _candidate_model_variants(preferred_model):
@@ -121,6 +127,57 @@ def _fallback_model_sequence(model: str):
seen.add(candidate)
yield candidate
def _is_non_retryable_hf_error(exc: Exception) -> bool:
"""Skip retries for deterministic HF failures (e.g., unknown model ids, billing)."""
msg = str(exc).lower()
status = getattr(exc, "status_code", None)
# Non-retryable errors
if isinstance(exc, NotFoundError) or "not found" in msg or "404" in msg:
return True
if status == 402 or "402" in msg or "depleted" in msg or "credits" in msg:
return True
if status == 401 or "unauthorized" in msg or "401" in msg:
return True
if status == 403 or "forbidden" in msg or "403" in msg:
return True
return False
def _should_retry_hf_error(exc: Exception) -> bool:
return not _is_non_retryable_hf_error(exc)
def _classify_hf_error(exc: Exception) -> str:
"""Classify HF failures for actionable logs."""
msg = str(exc).lower()
if any(token in msg for token in ["insufficient", "balance", "quota", "billing", "payment", "402"]):
return "billing_or_quota"
if "unauthorized" in msg or "forbidden" in msg or "401" in msg or "403" in msg:
return "auth_or_permission"
if "not found" in msg or "404" in msg:
return "model_not_found"
return "unknown"
def _hf_error_details(exc: Exception) -> str:
"""Return compact, actionable exception details for logs."""
status = getattr(exc, "status_code", None)
err_type = type(exc).__name__
message = str(exc)
raw_body = getattr(exc, "body", None)
details = f"type={err_type}"
if status is not None:
details += f", status={status}"
if message:
details += f", message={message}"
if raw_body:
details += f", body={raw_body}"
details += f", repr={repr(exc)}"
return details
def get_huggingface_api_key() -> str:
"""Get Hugging Face API key with proper error handling."""
api_key = os.getenv('HF_TOKEN')
@@ -137,10 +194,15 @@ def get_huggingface_api_key() -> str:
return api_key
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
@retry(
retry=retry_if_exception(_should_retry_hf_error),
wait=wait_random_exponential(min=1, max=60),
stop=stop_after_attempt(6),
)
def huggingface_text_response(
prompt: str,
model: str = "openai/gpt-oss-120b:groq",
model: str = "openai/gpt-oss-120b:cerebras",
fallback_models: Optional[List[str]] = None,
temperature: float = 0.7,
max_tokens: int = 2048,
top_p: float = 0.9,
@@ -175,7 +237,7 @@ def huggingface_text_response(
Example:
result = huggingface_text_response(
prompt="Write a blog post about AI",
model="openai/gpt-oss-120b:groq",
model="openai/gpt-oss-120b:cerebras",
temperature=0.7,
max_tokens=2048,
system_prompt="You are a professional content writer."
@@ -194,7 +256,7 @@ def huggingface_text_response(
# Initialize Hugging Face client
client = OpenAI(
base_url=f"https://router.huggingface.co/hf/v1",
base_url="https://router.huggingface.co/v1",
api_key=api_key,
)
logger.info("✅ Hugging Face client initialized for text response")
@@ -231,27 +293,14 @@ def huggingface_text_response(
import time
time.sleep(1) # 1 second delay between API calls
response = None
last_error = None
for candidate_model in _fallback_model_sequence(model):
try:
response = client.chat.completions.create(
model=candidate_model,
messages=messages,
temperature=temperature,
top_p=top_p,
max_tokens=max_tokens
)
if candidate_model != model:
logger.warning("HF text generation switched to fallback model: {}", candidate_model)
break
except NotFoundError as nf_err:
last_error = nf_err
logger.warning("HF model not found: {}. Trying fallback model.", candidate_model)
continue
if response is None:
raise last_error or Exception("Hugging Face text generation failed: all fallback models failed")
# Call exactly the requested model; no retries, no fallbacks, no variants
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=temperature,
top_p=top_p,
max_tokens=max_tokens
)
# Extract text from response
generated_text = response.choices[0].message.content
@@ -267,14 +316,31 @@ def huggingface_text_response(
return generated_text
except Exception as e:
logger.error(f"❌ Hugging Face text generation failed: {str(e)}")
error_class = _classify_hf_error(e)
error_details = _hf_error_details(e)
logger.error(f"❌ Hugging Face text generation failed: {error_details}")
# Extra diagnostics: try to capture raw response if available
if hasattr(e, 'response') and e.response is not None:
logger.error(f"🔍 HF Error Diagnostics:")
logger.error(f" - Status: {e.response.status_code}")
logger.error(f" - Headers: {dict(e.response.headers)}")
try:
body_json = e.response.json()
logger.error(f" - Body JSON: {json.dumps(body_json, indent=2)}")
except Exception:
logger.error(f" - Body Raw: {e.response.text[:1000]}")
else:
logger.error(f"🔍 No HTTP response attached to exception object.")
raise Exception(f"Hugging Face text generation failed: {str(e)}")
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
def huggingface_structured_json_response(
prompt: str,
schema: Dict[str, Any],
model: str = "openai/gpt-oss-120b:groq",
model: str = "openai/gpt-oss-120b:cerebras",
fallback_models: Optional[List[str]] = None,
temperature: float = 0.7,
max_tokens: int = 8192,
system_prompt: Optional[str] = None
@@ -338,7 +404,7 @@ def huggingface_structured_json_response(
# Initialize OpenAI client with Hugging Face base URL
# Use standard Inference API endpoint
client = OpenAI(
base_url=f"https://router.huggingface.co/hf/v1",
base_url="https://router.huggingface.co/v1",
api_key=api_key,
)
logger.info("✅ Hugging Face client initialized for structured JSON response")
@@ -387,7 +453,7 @@ def huggingface_structured_json_response(
try:
response = None
last_error = None
for candidate_model in _fallback_model_sequence(model):
for candidate_model in _fallback_model_sequence(model, fallback_models):
try:
response = client.chat.completions.create(
model=candidate_model,
@@ -444,7 +510,7 @@ def huggingface_structured_json_response(
logger.info("Retrying without response_format...")
response = None
last_error = None
for candidate_model in _fallback_model_sequence(model):
for candidate_model in _fallback_model_sequence(model, fallback_models):
try:
response = client.chat.completions.create(
model=candidate_model,