502 lines
18 KiB
Python
502 lines
18 KiB
Python
"""
|
|
Hugging Face Provider Module for ALwrity
|
|
|
|
This module provides functions for interacting with Hugging Face's Inference Providers API
|
|
using the Responses API (beta) which provides a unified interface for model interactions.
|
|
|
|
Key Features:
|
|
- Text response generation with retry logic
|
|
- Structured JSON response generation with schema validation
|
|
- Comprehensive error handling and logging
|
|
- Automatic API key management
|
|
- Support for various Hugging Face models via Inference Providers
|
|
- Explicit fallback model sequences
|
|
- Client caching for performance
|
|
|
|
Best Practices:
|
|
1. Use structured output for complex, multi-field responses
|
|
2. Keep schemas simple and flat to avoid truncation
|
|
3. Set appropriate token limits (8192 for complex outputs)
|
|
4. Use low temperature (0.1-0.3) for consistent structured output
|
|
5. Implement proper error handling in calling functions
|
|
6. Use the Responses API for better compatibility
|
|
|
|
Usage Examples:
|
|
# Text response
|
|
result = huggingface_text_response(prompt, temperature=0.7, max_tokens=2048)
|
|
|
|
# Structured JSON response
|
|
schema = {
|
|
"type": "object",
|
|
"properties": {
|
|
"tasks": {
|
|
"type": "array",
|
|
"items": {"type": "object", "properties": {...}}
|
|
}
|
|
}
|
|
}
|
|
result = huggingface_structured_json_response(prompt, schema, temperature=0.2, max_tokens=8192)
|
|
|
|
Dependencies:
|
|
- openai (for Hugging Face Responses API)
|
|
- tenacity (for retry logic)
|
|
- logging (for debugging)
|
|
- json (for fallback parsing)
|
|
|
|
Author: ALwrity Team
|
|
Version: 1.0
|
|
Last Updated: January 2025
|
|
"""
|
|
|
|
import os
|
|
import json
|
|
import re
|
|
from functools import lru_cache
|
|
from typing import Optional, Dict, Any, List
|
|
|
|
from loguru import logger
|
|
from utils.logger_utils import get_service_logger
|
|
from .routing_policy import PREMIUM_DEFAULT_MODEL, SIF_LOW_COST_MODEL_DEFAULTS
|
|
|
|
# Use service-specific logger to avoid conflicts
|
|
logger = get_service_logger("huggingface_provider")
|
|
|
|
from tenacity import (
|
|
retry,
|
|
retry_if_exception,
|
|
stop_after_attempt,
|
|
wait_random_exponential,
|
|
)
|
|
|
|
try:
|
|
from openai import OpenAI
|
|
from openai import NotFoundError
|
|
OPENAI_AVAILABLE = True
|
|
except ImportError:
|
|
OPENAI_AVAILABLE = False
|
|
NotFoundError = Exception
|
|
logger.warn("OpenAI library not available. Install with: pip install openai")
|
|
|
|
HF_FALLBACK_MODELS = [
|
|
PREMIUM_DEFAULT_MODEL,
|
|
"moonshotai/Kimi-K2-Instruct-0905:groq",
|
|
"meta-llama/Llama-3.1-8B-Instruct:groq",
|
|
SIF_LOW_COST_MODEL_DEFAULTS[0],
|
|
]
|
|
|
|
|
|
def _should_retry_hf_error(exc: Exception) -> bool:
|
|
"""Determine if an error should trigger a retry based on error type and message."""
|
|
if isinstance(exc, NotFoundError):
|
|
return False # Don't retry model not found errors
|
|
|
|
msg = str(exc).lower()
|
|
# Don't retry authentication errors
|
|
if any(keyword in msg for keyword in ["unauthorized", "forbidden", "401", "403", "invalid api key"]):
|
|
return False
|
|
# Don't retry billing/quota errors
|
|
if any(keyword in msg for keyword in ["insufficient", "quota", "billing", "payment", "credits", "balance"]):
|
|
return False
|
|
# Retry rate limiting and server errors
|
|
if any(keyword in msg for keyword in ["rate limit", "429", "500", "502", "503", "504", "timeout"]):
|
|
return True
|
|
# Default to retry for unknown errors
|
|
return True
|
|
|
|
|
|
def _classify_hf_error(exc: Exception) -> str:
|
|
"""Classify Hugging Face errors for better error reporting."""
|
|
msg = str(exc).lower()
|
|
if any(keyword in msg for keyword in ["insufficient", "quota", "billing", "payment", "credits", "balance"]):
|
|
return "billing_or_quota"
|
|
if any(keyword in msg for keyword in ["unauthorized", "forbidden", "401", "403"]):
|
|
return "auth_or_permission"
|
|
if "not found" in msg or "404" in msg:
|
|
return "model_not_found"
|
|
if any(keyword in msg for keyword in ["rate limit", "429"]):
|
|
return "rate_limit"
|
|
if any(keyword in msg for keyword in ["timeout", "500", "502", "503", "504"]):
|
|
return "server_error"
|
|
return "unknown"
|
|
|
|
|
|
def _error_details(exc: Exception) -> Dict[str, str]:
|
|
"""Extract error details for logging."""
|
|
return {
|
|
"type": type(exc).__name__,
|
|
"message": str(exc),
|
|
"repr": repr(exc),
|
|
}
|
|
|
|
|
|
def _candidate_model_variants(model: str):
|
|
"""Yield model ids to try for a single logical model preference."""
|
|
if not model:
|
|
return
|
|
|
|
# Try configured model first (supports provider suffixes like ":groq")
|
|
yield model
|
|
|
|
# Fallback to base repo id when provider suffix is not recognized by the router
|
|
if ":" in model:
|
|
base_model = model.split(":", 1)[0]
|
|
if base_model:
|
|
yield base_model
|
|
|
|
|
|
def _fallback_model_sequence(model: str, fallback_models: Optional[List[str]] = None):
|
|
"""Generate a sequence of models to try as fallbacks."""
|
|
sequence = [model] + (fallback_models or HF_FALLBACK_MODELS)
|
|
seen = set()
|
|
for preferred_model in sequence:
|
|
for candidate in _candidate_model_variants(preferred_model):
|
|
if candidate and candidate not in seen:
|
|
seen.add(candidate)
|
|
yield candidate
|
|
|
|
|
|
def get_huggingface_api_key(explicit_api_key: Optional[str] = None) -> str:
|
|
"""Get Hugging Face API key with proper error handling."""
|
|
api_key = explicit_api_key or os.getenv('HF_TOKEN')
|
|
if not api_key:
|
|
error_msg = "HF_TOKEN environment variable is not set. Please set it in your .env file."
|
|
logger.error(error_msg)
|
|
raise ValueError(error_msg)
|
|
|
|
# Validate API key format (basic check)
|
|
if not api_key.startswith('hf_'):
|
|
error_msg = "HF_TOKEN appears to be invalid. It should start with 'hf_'."
|
|
logger.error(error_msg)
|
|
raise ValueError(error_msg)
|
|
|
|
return api_key
|
|
|
|
|
|
@lru_cache(maxsize=16)
|
|
def _get_hf_client(api_key: str):
|
|
"""Get cached Hugging Face client for better performance."""
|
|
return OpenAI(base_url="https://router.huggingface.co/v1", api_key=api_key)
|
|
|
|
|
|
@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 = PREMIUM_DEFAULT_MODEL,
|
|
fallback_models: Optional[List[str]] = None,
|
|
temperature: float = 0.7,
|
|
max_tokens: int = 2048,
|
|
top_p: float = 0.9,
|
|
system_prompt: Optional[str] = None,
|
|
api_key: Optional[str] = None,
|
|
) -> str:
|
|
"""
|
|
Generate text response using Hugging Face Inference Providers API.
|
|
|
|
This function uses the Hugging Face Responses API which provides a unified interface
|
|
for model interactions with built-in retry logic and error handling.
|
|
|
|
Args:
|
|
prompt (str): The input prompt for the AI model
|
|
model (str): Hugging Face model identifier (default: PREMIUM_DEFAULT_MODEL)
|
|
fallback_models (list, optional): Custom fallback models to try
|
|
temperature (float): Controls randomness (0.0-1.0)
|
|
max_tokens (int): Maximum tokens in response
|
|
top_p (float): Nucleus sampling parameter (0.0-1.0)
|
|
system_prompt (str, optional): System instruction for the model
|
|
api_key (str, optional): Explicit API key override
|
|
|
|
Returns:
|
|
str: Generated text response
|
|
|
|
Raises:
|
|
Exception: If API key is missing or API call fails
|
|
|
|
Best Practices:
|
|
- Use appropriate temperature for your use case (0.7 for creative, 0.1-0.3 for factual)
|
|
- Set max_tokens based on expected response length
|
|
- Use system_prompt to guide model behavior
|
|
- Handle errors gracefully in calling functions
|
|
"""
|
|
try:
|
|
if not OPENAI_AVAILABLE:
|
|
raise ImportError("OpenAI library not available. Install with: pip install openai")
|
|
|
|
# Get API key with proper error handling
|
|
hf_api_key = get_huggingface_api_key(api_key)
|
|
logger.info(f"🔑 Hugging Face API key loaded: {bool(hf_api_key)} (length: {len(hf_api_key) if hf_api_key else 0})")
|
|
|
|
# Initialize Hugging Face client
|
|
client = _get_hf_client(hf_api_key)
|
|
logger.info("✅ Hugging Face client initialized for text response")
|
|
|
|
# Prepare input for the API
|
|
messages = []
|
|
|
|
# Add system prompt if provided
|
|
if system_prompt:
|
|
messages.append({
|
|
"role": "system",
|
|
"content": system_prompt
|
|
})
|
|
|
|
# Add user prompt
|
|
messages.append({
|
|
"role": "user",
|
|
"content": prompt
|
|
})
|
|
|
|
# Add debugging for API call
|
|
logger.info(
|
|
"Hugging Face text call | model={} | prompt_len={} | temp={} | top_p={} | max_tokens={}",
|
|
model,
|
|
len(prompt) if isinstance(prompt, str) else '<non-str>',
|
|
temperature,
|
|
top_p,
|
|
max_tokens,
|
|
)
|
|
|
|
logger.info("🚀 Making Hugging Face API call (chat completion)...")
|
|
|
|
response = None
|
|
last_error = None
|
|
for candidate_model in _fallback_model_sequence(model, fallback_models):
|
|
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 fallback model used: {}", candidate_model)
|
|
break
|
|
except NotFoundError as nf_err:
|
|
last_error = nf_err
|
|
logger.warning("HF text model not found: {}", candidate_model)
|
|
continue
|
|
except Exception as call_err:
|
|
last_error = call_err
|
|
logger.warning("HF text call failed for model {}: {}", candidate_model, _error_details(call_err))
|
|
continue
|
|
|
|
if response is None:
|
|
raise last_error or RuntimeError("All fallback models failed")
|
|
|
|
# Extract text from response
|
|
generated_text = response.choices[0].message.content or ""
|
|
|
|
# Clean up the response
|
|
generated_text = re.sub(r'```[a-zA-Z]*\n?', '', generated_text)
|
|
generated_text = re.sub(r'```\n?', '', generated_text)
|
|
generated_text = generated_text.strip()
|
|
|
|
logger.info(f"✅ Hugging Face text response generated successfully (length: {len(generated_text)})")
|
|
return generated_text
|
|
|
|
except Exception as exc:
|
|
details = _error_details(exc)
|
|
logger.error(
|
|
"❌ Hugging Face text generation failed | error_class={} | type={} | message={} | repr={}",
|
|
_classify_hf_error(exc),
|
|
details["type"],
|
|
details["message"],
|
|
details["repr"],
|
|
)
|
|
raise Exception(f"Hugging Face text generation failed: {exc}") from exc
|
|
|
|
|
|
@retry(
|
|
retry=retry_if_exception(_should_retry_hf_error),
|
|
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 = PREMIUM_DEFAULT_MODEL,
|
|
fallback_models: Optional[List[str]] = None,
|
|
temperature: float = 0.7,
|
|
max_tokens: int = 8192,
|
|
system_prompt: Optional[str] = None,
|
|
api_key: Optional[str] = None,
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Generate structured JSON response using Hugging Face Inference Providers API.
|
|
|
|
This function uses the Hugging Face Responses API with structured output support
|
|
to generate JSON responses that match a provided schema.
|
|
|
|
Args:
|
|
prompt (str): The input prompt for the AI model
|
|
schema (dict): JSON schema defining the expected output structure
|
|
model (str): Hugging Face model identifier (default: PREMIUM_DEFAULT_MODEL)
|
|
fallback_models (list, optional): Custom fallback models to try
|
|
temperature (float): Controls randomness (0.0-1.0). Use 0.1-0.3 for structured output
|
|
max_tokens (int): Maximum tokens in response. Use 8192 for complex outputs
|
|
system_prompt (str, optional): System instruction for the model
|
|
api_key (str, optional): Explicit API key override
|
|
|
|
Returns:
|
|
dict: Parsed JSON response matching the provided schema
|
|
|
|
Raises:
|
|
Exception: If API key is missing or API call fails
|
|
|
|
Best Practices:
|
|
- Keep schemas simple and flat to avoid truncation
|
|
- Use low temperature (0.1-0.3) for consistent structured output
|
|
- Set max_tokens to 8192 for complex multi-field responses
|
|
- Avoid deeply nested schemas with many required fields
|
|
- Test with smaller outputs first, then scale up
|
|
"""
|
|
try:
|
|
if not OPENAI_AVAILABLE:
|
|
raise ImportError("OpenAI library not available. Install with: pip install openai")
|
|
|
|
# Get API key with proper error handling
|
|
hf_api_key = get_huggingface_api_key(api_key)
|
|
logger.info(f"🔑 Hugging Face API key loaded: {bool(hf_api_key)} (length: {len(hf_api_key) if hf_api_key else 0})")
|
|
|
|
# Initialize OpenAI client with Hugging Face base URL
|
|
client = _get_hf_client(hf_api_key)
|
|
logger.info("✅ Hugging Face client initialized for structured JSON response")
|
|
|
|
# Prepare input for the API
|
|
messages = []
|
|
|
|
# Add system prompt if provided
|
|
if system_prompt:
|
|
messages.append({
|
|
"role": "system",
|
|
"content": system_prompt
|
|
})
|
|
|
|
# Add user prompt with JSON instruction
|
|
json_instruction = "Please respond with valid JSON that matches the provided schema."
|
|
messages.append({
|
|
"role": "user",
|
|
"content": f"{prompt}\n\n{json_instruction}"
|
|
})
|
|
|
|
# Add debugging for API call
|
|
logger.info(
|
|
"Hugging Face structured call | model={} | prompt_len={} | schema_kind={} | temp={} | max_tokens={}",
|
|
model,
|
|
len(prompt) if isinstance(prompt, str) else '<non-str>',
|
|
type(schema).__name__,
|
|
temperature,
|
|
max_tokens,
|
|
)
|
|
|
|
logger.info("🚀 Making Hugging Face structured API call...")
|
|
|
|
# Add JSON schema to prompt for guidance
|
|
json_schema_str = json.dumps(schema, indent=2)
|
|
messages[-1]["content"] += f"\n\nJSON Schema:\n{json_schema_str}"
|
|
|
|
response = None
|
|
last_error = None
|
|
|
|
for candidate_model in _fallback_model_sequence(model, fallback_models):
|
|
try:
|
|
response = client.chat.completions.create(
|
|
model=candidate_model,
|
|
messages=messages,
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
response_format={"type": "json_object"}
|
|
)
|
|
if candidate_model != model:
|
|
logger.warning("HF structured fallback model used: {}", candidate_model)
|
|
break
|
|
except Exception as err:
|
|
last_error = err
|
|
if isinstance(err, NotFoundError):
|
|
logger.warning("HF structured model not found: {}", candidate_model)
|
|
continue
|
|
|
|
msg = str(err).lower()
|
|
if "422" in msg or "not supported" in msg:
|
|
try:
|
|
response = client.chat.completions.create(
|
|
model=candidate_model,
|
|
messages=messages,
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
)
|
|
if candidate_model != model:
|
|
logger.warning("HF structured fallback(no response_format) model: {}", candidate_model)
|
|
break
|
|
except Exception as second_err:
|
|
last_error = second_err
|
|
continue
|
|
|
|
if response is None:
|
|
raise last_error or RuntimeError("All fallback models failed")
|
|
|
|
response_text = (response.choices[0].message.content or "").strip()
|
|
|
|
# Clean up response text if needed
|
|
if response_text.startswith("```json"):
|
|
response_text = response_text[7:]
|
|
if response_text.endswith("```"):
|
|
response_text = response_text[:-3]
|
|
response_text = response_text.strip()
|
|
|
|
try:
|
|
parsed_json = json.loads(response_text)
|
|
logger.info("✅ Hugging Face structured JSON response parsed successfully")
|
|
return parsed_json
|
|
except json.JSONDecodeError:
|
|
json_match = re.search(r"\{.*\}", response_text, re.DOTALL)
|
|
if json_match:
|
|
return json.loads(json_match.group())
|
|
return {"error": "Failed to parse JSON response", "raw_response": response_text}
|
|
|
|
except Exception as exc:
|
|
details = _error_details(exc)
|
|
logger.error(
|
|
"❌ Hugging Face structured JSON generation failed | error_class={} | type={} | message={} | repr={}",
|
|
_classify_hf_error(exc),
|
|
details["type"],
|
|
details["message"],
|
|
details["repr"],
|
|
)
|
|
raise Exception(f"Hugging Face structured JSON generation failed: {exc}") from exc
|
|
|
|
|
|
def get_available_models() -> list:
|
|
"""
|
|
Get list of available Hugging Face models for text generation.
|
|
|
|
Returns:
|
|
list: List of available model identifiers
|
|
"""
|
|
return [
|
|
PREMIUM_DEFAULT_MODEL,
|
|
"moonshotai/Kimi-K2-Instruct-0905:groq",
|
|
"Qwen/Qwen2.5-VL-7B-Instruct",
|
|
"meta-llama/Llama-3.1-8B-Instruct:groq",
|
|
"microsoft/Phi-3-medium-4k-instruct:groq",
|
|
SIF_LOW_COST_MODEL_DEFAULTS[0]
|
|
]
|
|
|
|
|
|
def validate_model(model: str) -> bool:
|
|
"""
|
|
Validate if a model identifier is supported.
|
|
|
|
Args:
|
|
model (str): Model identifier to validate
|
|
|
|
Returns:
|
|
bool: True if model is supported, False otherwise
|
|
"""
|
|
available_models = get_available_models()
|
|
return model in available_models
|