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"""
HuggingFace LLM adapter implementation.
This adapter provides integration with HuggingFace Inference API, handling
the specific API format and response structure of HuggingFace models.
Architecture Notes:
- Converts between unified interface and HuggingFace API format
- Handles both chat completion and text generation endpoints
- Supports automatic model selection and fallback
- Maps HuggingFace errors to standard LLMError types
"""
import os
import logging
import time
from typing import Dict, Any, Optional, List, Iterator
from datetime import datetime
from .base_adapter import BaseLLMAdapter, LLMError, ModelNotFoundError, AuthenticationError, RateLimitError
from ..base import GenerationParams
logger = logging.getLogger(__name__)
# Check for HuggingFace Hub availability
try:
from huggingface_hub import InferenceClient
HF_HUB_AVAILABLE = True
except ImportError:
HF_HUB_AVAILABLE = False
logger.warning("huggingface_hub not available. Install with: pip install huggingface-hub")
class HuggingFaceAdapter(BaseLLMAdapter):
"""
Adapter for HuggingFace Inference API integration.
Features:
- Support for both chat completion and text generation
- Automatic model selection and fallback
- OpenAI-compatible chat completion format
- Comprehensive error handling and retry logic
- Multiple model support with automatic fallback
Configuration:
- api_token: HuggingFace API token (required)
- timeout: Request timeout in seconds (default: 30)
- use_chat_completion: Prefer chat completion over text generation
- fallback_models: List of models to try if primary fails
"""
# Models that work well with chat completion format
CHAT_MODELS = [
"microsoft/DialoGPT-medium", # Proven conversational model
"google/gemma-2-2b-it", # Instruction-tuned, good for Q&A
"meta-llama/Llama-3.2-3B-Instruct", # If available with token
"Qwen/Qwen2.5-1.5B-Instruct", # Small, fast, good quality
]
# Fallback models for classic text generation
CLASSIC_MODELS = [
"google/flan-t5-small", # Good for instructions
"deepset/roberta-base-squad2", # Q&A specific
"facebook/bart-base", # Summarization
]
def __init__(self,
model_name: str = "microsoft/DialoGPT-medium",
api_token: Optional[str] = None,
timeout: int = 30,
use_chat_completion: bool = True,
fallback_models: Optional[List[str]] = None,
config: Optional[Dict[str, Any]] = None):
"""
Initialize HuggingFace adapter.
Args:
model_name: HuggingFace model name
api_token: HuggingFace API token
timeout: Request timeout in seconds
use_chat_completion: Prefer chat completion over text generation
fallback_models: List of fallback models to try
config: Additional configuration
"""
if not HF_HUB_AVAILABLE:
raise ImportError("huggingface_hub is required for HuggingFace adapter. Install with: pip install huggingface-hub")
# Get API token from various sources
self.api_token = (
api_token or
os.getenv("HUGGINGFACE_API_TOKEN") or
os.getenv("HF_TOKEN") or
os.getenv("HF_API_TOKEN")
)
if not self.api_token:
raise AuthenticationError("HuggingFace API token required. Set HF_TOKEN environment variable or pass api_token parameter.")
# Merge configuration
adapter_config = {
'api_token': self.api_token,
'timeout': timeout,
'use_chat_completion': use_chat_completion,
'fallback_models': fallback_models or [],
**(config or {})
}
super().__init__(model_name, adapter_config)
self.timeout = adapter_config['timeout']
self.use_chat_completion = adapter_config['use_chat_completion']
self.fallback_models = adapter_config['fallback_models']
# Initialize client
self.client = InferenceClient(token=self.api_token)
# Test connection and determine best model (only if not using dummy token)
if not self.api_token.startswith("dummy_"):
self._test_connection()
else:
logger.info("Using dummy token, skipping connection test")
logger.info(f"Initialized HuggingFace adapter for model '{self.model_name}' (chat_completion: {self.use_chat_completion})")
def _make_request(self, prompt: str, params: GenerationParams) -> Dict[str, Any]:
"""
Make a request to HuggingFace API.
Args:
prompt: The prompt to send
params: Generation parameters
Returns:
HuggingFace API response
Raises:
Various request exceptions
"""
try:
if self.use_chat_completion:
return self._make_chat_completion_request(prompt, params)
else:
return self._make_text_generation_request(prompt, params)
except Exception as e:
# Try fallback models if primary fails
for fallback_model in self.fallback_models:
try:
logger.info(f"Trying fallback model: {fallback_model}")
original_model = self.model_name
self.model_name = fallback_model
if self.use_chat_completion:
result = self._make_chat_completion_request(prompt, params)
else:
result = self._make_text_generation_request(prompt, params)
# Success with fallback
logger.info(f"Successfully used fallback model: {fallback_model}")
return result
except Exception as fallback_error:
logger.warning(f"Fallback model {fallback_model} failed: {fallback_error}")
# Restore original model name
self.model_name = original_model
continue
# All models failed
self._handle_provider_error(e)
def _make_chat_completion_request(self, prompt: str, params: GenerationParams) -> Dict[str, Any]:
"""Make a chat completion request."""
messages = [{"role": "user", "content": prompt}]
try:
response = self.client.chat_completion(
messages=messages,
model=self.model_name,
temperature=params.temperature,
max_tokens=params.max_tokens,
stream=False
)
# Extract content from response
if hasattr(response, 'choices') and response.choices:
content = response.choices[0].message.content
return {
'content': content,
'model': self.model_name,
'usage': getattr(response, 'usage', {}),
'response_type': 'chat_completion'
}
else:
# Handle different response formats
if hasattr(response, 'generated_text'):
content = response.generated_text
else:
content = str(response)
return {
'content': content,
'model': self.model_name,
'usage': {},
'response_type': 'chat_completion'
}
except Exception as e:
logger.error(f"Chat completion failed: {e}")
raise
def _make_text_generation_request(self, prompt: str, params: GenerationParams) -> Dict[str, Any]:
"""Make a text generation request."""
try:
response = self.client.text_generation(
model=self.model_name,
prompt=prompt,
max_new_tokens=params.max_tokens,
temperature=params.temperature,
do_sample=params.temperature > 0,
top_p=params.top_p,
stop_sequences=params.stop_sequences
)
# Handle response format
if isinstance(response, str):
content = response
else:
content = getattr(response, 'generated_text', str(response))
return {
'content': content,
'model': self.model_name,
'usage': {},
'response_type': 'text_generation'
}
except Exception as e:
logger.error(f"Text generation failed: {e}")
raise
def _parse_response(self, response: Dict[str, Any]) -> str:
"""
Parse HuggingFace response to extract generated text.
Args:
response: HuggingFace API response
Returns:
Generated text
"""
content = response.get('content', '')
# Log usage if available
if 'usage' in response and response['usage']:
usage = response['usage']
total_tokens = usage.get('total_tokens', 0)
if total_tokens > 0:
logger.debug(f"HuggingFace used {total_tokens} tokens for generation")
return content
def generate_streaming(self, prompt: str, params: GenerationParams) -> Iterator[str]:
"""
Generate a streaming response from HuggingFace.
Args:
prompt: The prompt to send
params: Generation parameters
Yields:
Generated text chunks
"""
try:
if self.use_chat_completion:
# Try streaming chat completion
messages = [{"role": "user", "content": prompt}]
response = self.client.chat_completion(
messages=messages,
model=self.model_name,
temperature=params.temperature,
max_tokens=params.max_tokens,
stream=True
)
for chunk in response:
if hasattr(chunk, 'choices') and chunk.choices:
delta = chunk.choices[0].delta
if hasattr(delta, 'content') and delta.content:
yield delta.content
else:
# Fallback to non-streaming for text generation
logger.warning("Streaming not supported for text generation, falling back to non-streaming")
yield self.generate(prompt, params)
except Exception as e:
logger.error(f"Streaming generation failed: {e}")
# Fallback to non-streaming
yield self.generate(prompt, params)
def _get_provider_name(self) -> str:
"""Return the provider name."""
return "HuggingFace"
def _validate_model(self) -> bool:
"""Check if the model exists in HuggingFace."""
try:
# Try a simple test request
test_prompt = "Hello"
if self.use_chat_completion:
test_messages = [{"role": "user", "content": test_prompt}]
response = self.client.chat_completion(
messages=test_messages,
model=self.model_name,
max_tokens=10,
temperature=0.1
)
else:
response = self.client.text_generation(
model=self.model_name,
prompt=test_prompt,
max_new_tokens=10
)
return bool(response)
except Exception as e:
logger.warning(f"Model validation failed: {e}")
return False
def _supports_streaming(self) -> bool:
"""HuggingFace supports streaming for chat completion."""
return self.use_chat_completion
def _get_max_tokens(self) -> Optional[int]:
"""Get max tokens for current model."""
# Model-specific limits (approximate)
model_limits = {
'microsoft/DialoGPT-medium': 1024,
'google/gemma-2-2b-it': 8192,
'meta-llama/Llama-3.2-3B-Instruct': 4096,
'Qwen/Qwen2.5-1.5B-Instruct': 32768,
'google/flan-t5-small': 512,
'deepset/roberta-base-squad2': 512,
'facebook/bart-base': 1024,
}
# Check for exact match
if self.model_name in model_limits:
return model_limits[self.model_name]
# Check for partial match
for model_prefix, limit in model_limits.items():
if model_prefix in self.model_name:
return limit
# Default for unknown models
return 1024
def _test_connection(self) -> None:
"""Test the connection and find the best working model."""
logger.info("Testing HuggingFace API connection...")
# Test primary model first
if self._validate_model():
logger.info(f"Primary model {self.model_name} is working")
return
# Try chat models if using chat completion
if self.use_chat_completion:
for model in self.CHAT_MODELS:
if model != self.model_name:
try:
logger.info(f"Testing chat model: {model}")
original_model = self.model_name
self.model_name = model
if self._validate_model():
logger.info(f"Found working chat model: {model}")
return
# Restore original if failed
self.model_name = original_model
except Exception as e:
logger.warning(f"Chat model {model} failed: {e}")
continue
# Try classic models as fallback
logger.info("Trying classic text generation models...")
for model in self.CLASSIC_MODELS:
try:
logger.info(f"Testing classic model: {model}")
original_model = self.model_name
original_chat = self.use_chat_completion
self.model_name = model
self.use_chat_completion = False
if self._validate_model():
logger.info(f"Found working classic model: {model}")
return
# Restore original settings if failed
self.model_name = original_model
self.use_chat_completion = original_chat
except Exception as e:
logger.warning(f"Classic model {model} failed: {e}")
continue
# If we get here, no models worked
raise ModelNotFoundError(f"No working models found. Original model '{self.model_name}' is not accessible.")
def _handle_provider_error(self, error: Exception) -> None:
"""Map HuggingFace-specific errors to standard errors."""
error_msg = str(error).lower()
if 'rate limit' in error_msg or '429' in error_msg:
raise RateLimitError(f"HuggingFace rate limit exceeded: {error}")
elif 'unauthorized' in error_msg or '401' in error_msg or 'token' in error_msg:
raise AuthenticationError(f"HuggingFace authentication failed: {error}")
elif 'not found' in error_msg or '404' in error_msg:
raise ModelNotFoundError(f"HuggingFace model not found: {error}")
elif 'timeout' in error_msg:
raise LLMError(f"HuggingFace request timed out: {error}")
elif 'connection' in error_msg:
raise LLMError(f"Failed to connect to HuggingFace API: {error}")
else:
raise LLMError(f"HuggingFace API error: {error}") |