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# llm_router.py - FIXED VERSION
import logging
import asyncio
from typing import Dict
from .models_config import LLM_CONFIG
logger = logging.getLogger(__name__)
class LLMRouter:
def __init__(self, hf_token):
self.hf_token = hf_token
self.health_status = {}
logger.info("LLMRouter initialized")
if hf_token:
logger.info("HF token available")
else:
logger.warning("No HF token provided")
async def route_inference(self, task_type: str, prompt: str, **kwargs):
"""
Smart routing based on task specialization
"""
logger.info(f"Routing inference for task: {task_type}")
model_config = self._select_model(task_type)
logger.info(f"Selected model: {model_config['model_id']}")
# Health check and fallback logic
if not await self._is_model_healthy(model_config["model_id"]):
logger.warning(f"Model unhealthy, using fallback")
model_config = self._get_fallback_model(task_type)
logger.info(f"Fallback model: {model_config['model_id']}")
# FIXED: Ensure task_type is passed to the _call_hf_endpoint method
result = await self._call_hf_endpoint(model_config, prompt, task_type, **kwargs)
logger.info(f"Inference complete for {task_type}")
return result
def _select_model(self, task_type: str) -> dict:
model_map = {
"intent_classification": LLM_CONFIG["models"]["classification_specialist"],
"embedding_generation": LLM_CONFIG["models"]["embedding_specialist"],
"safety_check": LLM_CONFIG["models"]["safety_checker"],
"general_reasoning": LLM_CONFIG["models"]["reasoning_primary"],
"response_synthesis": LLM_CONFIG["models"]["reasoning_primary"]
}
return model_map.get(task_type, LLM_CONFIG["models"]["reasoning_primary"])
async def _is_model_healthy(self, model_id: str) -> bool:
"""
Check if the model is healthy and available
Mark models as healthy by default - actual availability checked at API call time
"""
# Check cached health status
if model_id in self.health_status:
return self.health_status[model_id]
# All models marked healthy initially - real check happens during API call
self.health_status[model_id] = True
return True
def _get_fallback_model(self, task_type: str) -> dict:
"""
Get fallback model configuration for the task type
"""
# Fallback mapping
fallback_map = {
"intent_classification": LLM_CONFIG["models"]["reasoning_primary"],
"embedding_generation": LLM_CONFIG["models"]["embedding_specialist"],
"safety_check": LLM_CONFIG["models"]["reasoning_primary"],
"general_reasoning": LLM_CONFIG["models"]["reasoning_primary"],
"response_synthesis": LLM_CONFIG["models"]["reasoning_primary"]
}
return fallback_map.get(task_type, LLM_CONFIG["models"]["reasoning_primary"])
async def _call_hf_endpoint(self, model_config: dict, prompt: str, task_type: str, **kwargs):
"""
FIXED: Make actual call to Hugging Face Chat Completions API
Uses the correct chat completions protocol with retry logic and exponential backoff
IMPORTANT: task_type parameter is now properly included in the method signature
"""
# Retry configuration
max_retries = kwargs.get('max_retries', 3)
initial_delay = kwargs.get('initial_delay', 1.0) # Start with 1 second
max_delay = kwargs.get('max_delay', 16.0) # Cap at 16 seconds
timeout = kwargs.get('timeout', 30)
try:
import requests
from requests.exceptions import Timeout, RequestException, ConnectionError as RequestsConnectionError
model_id = model_config["model_id"]
# Use the chat completions endpoint
api_url = "https://router.huggingface.co/v1/chat/completions"
logger.info(f"Calling HF Chat Completions API for model: {model_id}")
logger.debug(f"Prompt length: {len(prompt)}")
logger.info("=" * 80)
logger.info("LLM API REQUEST - COMPLETE PROMPT:")
logger.info("=" * 80)
logger.info(f"Model: {model_id}")
# FIXED: task_type is now properly available as a parameter
logger.info(f"Task Type: {task_type}")
logger.info(f"Prompt Length: {len(prompt)} characters")
logger.info("-" * 40)
logger.info("FULL PROMPT CONTENT:")
logger.info("-" * 40)
logger.info(prompt)
logger.info("-" * 40)
logger.info("END OF PROMPT")
logger.info("=" * 80)
# Prepare the request payload
max_tokens = kwargs.get('max_tokens', 512)
temperature = kwargs.get('temperature', 0.7)
payload = {
"model": model_id,
"messages": [
{
"role": "user",
"content": prompt
}
],
"max_tokens": max_tokens,
"temperature": temperature,
"stream": False
}
headers = {
"Authorization": f"Bearer {self.hf_token}",
"Content-Type": "application/json"
}
# Retry logic with exponential backoff
last_exception = None
for attempt in range(max_retries + 1):
try:
if attempt > 0:
# Calculate exponential backoff delay
delay = min(initial_delay * (2 ** (attempt - 1)), max_delay)
logger.warning(f"Retry attempt {attempt}/{max_retries} after {delay:.1f}s delay (exponential backoff)")
await asyncio.sleep(delay)
logger.info(f"Sending request to: {api_url} (attempt {attempt + 1}/{max_retries + 1})")
logger.debug(f"Payload: {payload}")
response = requests.post(api_url, json=payload, headers=headers, timeout=timeout)
if response.status_code == 200:
result = response.json()
logger.debug(f"Raw response: {result}")
if 'choices' in result and len(result['choices']) > 0:
generated_text = result['choices'][0]['message']['content']
if not generated_text or generated_text.strip() == "":
logger.warning(f"Empty or invalid response, using fallback")
return None
if attempt > 0:
logger.info(f"Successfully retrieved response after {attempt} retry attempts")
logger.info(f"HF API returned response (length: {len(generated_text)})")
logger.info("=" * 80)
logger.info("COMPLETE LLM API RESPONSE:")
logger.info("=" * 80)
logger.info(f"Model: {model_id}")
# FIXED: task_type is now properly available
logger.info(f"Task Type: {task_type}")
logger.info(f"Response Length: {len(generated_text)} characters")
logger.info("-" * 40)
logger.info("FULL RESPONSE CONTENT:")
logger.info("-" * 40)
logger.info(generated_text)
logger.info("-" * 40)
logger.info("END OF LLM RESPONSE")
logger.info("=" * 80)
return generated_text
else:
logger.error(f"Unexpected response format: {result}")
return None
elif response.status_code == 503:
# Model is loading - this is retryable
if attempt < max_retries:
logger.warning(f"Model loading (503), will retry (attempt {attempt + 1}/{max_retries + 1})")
last_exception = Exception(f"Model loading (503)")
continue
else:
# After max retries, try fallback model
logger.warning(f"Model loading (503) after {max_retries} retries, trying fallback model")
fallback_config = self._get_fallback_model(task_type)
# FIXED: Ensure task_type is passed in recursive call
return await self._call_hf_endpoint(fallback_config, prompt, task_type, **kwargs)
else:
# Non-retryable HTTP errors
logger.error(f"HF API error: {response.status_code} - {response.text}")
return None
except Timeout as e:
last_exception = e
if attempt < max_retries:
logger.warning(f"Request timeout (attempt {attempt + 1}/{max_retries + 1}): {str(e)}")
continue
else:
logger.error(f"Request timeout after {max_retries} retries: {str(e)}")
# Try fallback model on final timeout
logger.warning("Attempting fallback model due to persistent timeout")
fallback_config = self._get_fallback_model(task_type)
return await self._call_hf_endpoint(fallback_config, prompt, task_type, **kwargs)
except (RequestsConnectionError, RequestException) as e:
last_exception = e
if attempt < max_retries:
logger.warning(f"Connection error (attempt {attempt + 1}/{max_retries + 1}): {str(e)}")
continue
else:
logger.error(f"Connection error after {max_retries} retries: {str(e)}")
# Try fallback model on final connection error
logger.warning("Attempting fallback model due to persistent connection error")
fallback_config = self._get_fallback_model(task_type)
return await self._call_hf_endpoint(fallback_config, prompt, task_type, **kwargs)
# If we exhausted all retries and didn't return
if last_exception:
logger.error(f"Failed after {max_retries} retries. Last error: {last_exception}")
return None
except ImportError:
logger.warning("requests library not available, using mock response")
return f"[Mock] Response to: {prompt[:100]}..."
except Exception as e:
logger.error(f"Error calling HF endpoint: {e}", exc_info=True)
return None
async def get_available_models(self):
"""
Get list of available models for testing
"""
return list(LLM_CONFIG["models"].keys())
async def health_check(self):
"""
Perform health check on all models
"""
health_status = {}
for model_name, model_config in LLM_CONFIG["models"].items():
model_id = model_config["model_id"]
is_healthy = await self._is_model_healthy(model_id)
health_status[model_name] = {
"model_id": model_id,
"healthy": is_healthy
}
return health_status
def prepare_context_for_llm(self, raw_context: Dict, max_tokens: int = 4000) -> str:
"""Smart context windowing for LLM calls"""
try:
from transformers import AutoTokenizer
# Initialize tokenizer lazily
if not hasattr(self, 'tokenizer'):
try:
self.tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
except Exception as e:
logger.warning(f"Could not load tokenizer: {e}, using character count estimation")
self.tokenizer = None
except ImportError:
logger.warning("transformers library not available, using character count estimation")
self.tokenizer = None
# Priority order for context elements
priority_elements = [
('current_query', 1.0),
('recent_interactions', 0.8),
('user_preferences', 0.6),
('session_summary', 0.4),
('historical_context', 0.2)
]
formatted_context = []
total_tokens = 0
for element, priority in priority_elements:
# Map element names to context keys
element_key_map = {
'current_query': raw_context.get('user_input', ''),
'recent_interactions': raw_context.get('interaction_contexts', []),
'user_preferences': raw_context.get('preferences', {}),
'session_summary': raw_context.get('session_context', {}),
'historical_context': raw_context.get('user_context', '')
}
content = element_key_map.get(element, '')
# Convert to string if needed
if isinstance(content, dict):
content = str(content)
elif isinstance(content, list):
content = "\n".join([str(item) for item in content[:10]]) # Limit to 10 items
if not content:
continue
# Estimate tokens
if self.tokenizer:
try:
tokens = len(self.tokenizer.encode(content))
except:
# Fallback to character-based estimation (rough: 1 token ≈ 4 chars)
tokens = len(content) // 4
else:
# Character-based estimation (rough: 1 token ≈ 4 chars)
tokens = len(content) // 4
if total_tokens + tokens <= max_tokens:
formatted_context.append(f"=== {element.upper()} ===\n{content}")
total_tokens += tokens
elif priority > 0.5: # Critical elements - truncate if needed
available = max_tokens - total_tokens
if available > 100: # Only truncate if we have meaningful space
truncated = self._truncate_to_tokens(content, available)
formatted_context.append(f"=== {element.upper()} (TRUNCATED) ===\n{truncated}")
break
return "\n\n".join(formatted_context)
def _truncate_to_tokens(self, content: str, max_tokens: int) -> str:
"""Truncate content to fit within token limit"""
if not self.tokenizer:
# Simple character-based truncation
max_chars = max_tokens * 4
if len(content) <= max_chars:
return content
return content[:max_chars-3] + "..."
try:
# Tokenize and truncate
tokens = self.tokenizer.encode(content)
if len(tokens) <= max_tokens:
return content
truncated_tokens = tokens[:max_tokens-3] # Leave room for "..."
truncated_text = self.tokenizer.decode(truncated_tokens)
return truncated_text + "..."
except Exception as e:
logger.warning(f"Error truncating with tokenizer: {e}, using character truncation")
max_chars = max_tokens * 4
if len(content) <= max_chars:
return content
return content[:max_chars-3] + "..." |