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from typing import List, Dict, Any, Optional
# Local imports
from backend.config.settings import settings
from backend.config.logging_config import get_logger
from backend.services.vector_store import vector_store_service
# Setup logging
logger = get_logger("llm_service")
class LLMService:
"""LLM service using ConversationalRetrievalChain for RAG pipeline"""
def __init__(self):
logger.info("🤖 Initializing LLM Service...")
try:
self.llm = self._setup_llm()
self.retriever = self._setup_retriever()
self.memory = self._setup_memory()
self.qa_chain = self._setup_qa_chain()
logger.info("🚀 LLM Service initialized successfully")
except Exception as e:
logger.error(f"❌ LLM Service initialization failed: {str(e)}", exc_info=True)
raise
def _setup_llm(self):
"""Setup LLM based on configuration with conditional imports"""
llm_config = settings.get_llm_config()
provider = llm_config["provider"]
logger.info(f"🔧 Setting up LLM provider: {provider}")
if provider == "openai":
try:
from langchain_openai import ChatOpenAI
logger.info("✅ OpenAI LLM imported successfully")
# Handle special cases for temperature restrictions
temperature = llm_config["temperature"]
model = llm_config["model"]
max_tokens = llm_config.get("max_tokens", 1000)
# GPT-5-nano has temperature restrictions (defaults to 1.0)
if "gpt-5-nano" in model.lower():
temperature = 1.0
logger.info(f"🔧 Using temperature=1.0 for {model} (model restriction)")
# Log token configuration
logger.info(f"🎯 OpenAI config - Model: {model}, Output tokens: {max_tokens}, Temperature: {temperature}")
return ChatOpenAI(
api_key=llm_config["api_key"],
model=model,
temperature=temperature,
max_tokens=max_tokens # This limits OUTPUT tokens only
)
except ImportError as e:
logger.error(f"❌ OpenAI LLM not available: {e}")
raise ImportError("OpenAI provider selected but langchain_openai not installed")
elif provider == "google":
try:
from langchain_google_genai import ChatGoogleGenerativeAI
logger.info("✅ Google LLM imported successfully")
max_output_tokens = llm_config.get("max_tokens", 1000)
model = llm_config["model"]
temperature = llm_config["temperature"]
# Log token configuration
logger.info(f"🎯 Google config - Model: {model}, Output tokens: {max_output_tokens}, Temperature: {temperature}")
return ChatGoogleGenerativeAI(
google_api_key=llm_config["api_key"],
model=model,
temperature=temperature,
max_output_tokens=max_output_tokens # This limits OUTPUT tokens only
)
except ImportError as e:
logger.error(f"❌ Google LLM not available: {e}")
raise ImportError("Google provider selected but langchain_google_genai not installed")
elif provider == "ollama":
try:
from langchain_community.llms import Ollama
logger.info("✅ Ollama LLM imported successfully")
return Ollama(
base_url=llm_config["base_url"],
model=llm_config["model"],
temperature=llm_config["temperature"]
)
except ImportError as e:
logger.error(f"❌ Ollama LLM not available: {e}")
raise ImportError("Ollama provider selected but langchain_community not installed")
elif provider == "huggingface":
try:
# Check if we should use API or local pipeline
use_api = llm_config.get("use_api", False)
if use_api:
# Use HuggingFace Inference API with better error handling
try:
from langchain_huggingface import HuggingFaceEndpoint
logger.info("✅ Using HuggingFace API (no local download)")
return HuggingFaceEndpoint(
repo_id=llm_config["model"],
huggingfacehub_api_token=llm_config["api_token"],
temperature=0.7, # HuggingFace API doesn't support dynamic temperature from config
max_new_tokens=200,
repetition_penalty=1.1,
top_p=0.9
)
except Exception as api_error:
logger.warning(f"⚠️ HuggingFace API failed: {api_error}")
logger.info("🔄 Falling back to HuggingFace Hub API...")
# Fallback to HuggingFaceHub (older but more reliable)
try:
from langchain_community.llms import HuggingFaceHub
return HuggingFaceHub(
repo_id=llm_config["model"],
huggingfacehub_api_token=llm_config["api_token"],
model_kwargs={
"temperature": 0.7, # HuggingFace Hub API has limited temperature control
"max_new_tokens": 200,
"repetition_penalty": 1.1,
"top_p": 0.9,
"do_sample": True
}
)
except Exception as hub_error:
logger.error(f"❌ HuggingFace Hub also failed: {hub_error}")
raise ImportError(f"Both HuggingFace API methods failed: {api_error}, {hub_error}")
else:
# Use local pipeline (downloads model)
from langchain_huggingface import HuggingFacePipeline
from transformers import pipeline
logger.info("✅ Using HuggingFace local pipeline")
# Create HuggingFace pipeline - avoid device_map for CPU-only setups
pipeline_kwargs = {
"task": "text-generation",
"model": llm_config["model"],
"max_length": 512, # Increase max length
"do_sample": True, # Enable sampling for better responses
"temperature": 0.7, # Local pipeline uses default 0.7 for stability
"pad_token_id": 50256, # Set pad token to avoid warnings
"eos_token_id": 50256, # Set end of sequence token
}
# Only add device_map if using GPU
if llm_config.get("use_gpu", False):
pipeline_kwargs["device_map"] = "auto"
else:
# For CPU, use device=0 which maps to CPU
pipeline_kwargs["device"] = "cpu"
hf_pipeline = pipeline(**pipeline_kwargs)
return HuggingFacePipeline(
pipeline=hf_pipeline,
model_kwargs={
"temperature": 0.7, # Local pipeline temperature (limited configurability)
"max_new_tokens": 150, # Reduced for efficiency
"do_sample": True,
"top_p": 0.9,
"repetition_penalty": 1.1,
"early_stopping": True,
"num_beams": 4 # Better quality for instruction following
}
)
except ImportError as e:
logger.error(f"❌ HuggingFace LLM not available: {e}")
raise ImportError("HuggingFace provider selected but required packages not installed")
else:
logger.warning(f"⚠️ Unknown LLM provider '{provider}', falling back to OpenAI")
try:
from langchain_openai import ChatOpenAI
return ChatOpenAI()
except ImportError:
logger.error("❌ No valid LLM provider available")
raise ImportError("No valid LLM provider available")
def _setup_retriever(self):
"""Setup retriever from vector store service"""
return vector_store_service.get_retriever()
def _setup_memory(self):
"""Setup conversation memory"""
try:
from langchain.memory import ConversationBufferMemory
return ConversationBufferMemory(memory_key='chat_history', return_messages=True)
except ImportError as e:
logger.error(f"❌ ConversationBufferMemory not available: {e}")
raise ImportError("langchain memory not available")
def _setup_qa_chain(self):
"""Setup ConversationalRetrievalChain"""
try:
from langchain.chains import ConversationalRetrievalChain
return ConversationalRetrievalChain.from_llm(
llm=self.llm,
retriever=self.retriever,
memory=self.memory,
verbose=settings.LANGCHAIN_DEBUG # Reduce debugging noise
)
except ImportError as e:
logger.error(f"❌ ConversationalRetrievalChain not available: {e}")
raise ImportError("langchain chains not available")
def _preprocess_query(self, question: str) -> str:
"""Preprocess user query to improve vector search accuracy"""
import re
# Convert to lowercase for consistency
processed = question.lower()
# Remove common stop words that don't help with recipe matching
stop_words = ['i', 'want', 'a', 'an', 'the', 'for', 'with', 'can', 'you', 'give', 'me', 'please', 'help']
words = processed.split()
words = [word for word in words if word not in stop_words]
# Remove punctuation except spaces
processed = ' '.join(words)
processed = re.sub(r'[^\w\s]', '', processed)
# Normalize multiple spaces
processed = ' '.join(processed.split())
logger.debug(f"🔧 Query preprocessing: '{question}' → '{processed}'")
return processed
def ask_question(self, user_question: str) -> str:
"""Ask a question using the conversational retrieval chain"""
logger.info(f"❓ Processing: '{user_question[:60]}...'")
try:
# Preprocess query for better matching
processed_query = self._preprocess_query(user_question)
# Get context for token tracking
document_retriever = getattr(self.qa_chain, 'retriever', None)
retrieved_context = ""
if document_retriever:
# Use both queries for comprehensive results
original_docs = document_retriever.invoke(user_question)
processed_docs = document_retriever.invoke(processed_query)
# Deduplicate documents
seen_content = set()
unique_documents = []
for document in original_docs + processed_docs:
if document.page_content not in seen_content:
unique_documents.append(document)
seen_content.add(document.page_content)
retrieved_context = "\n".join([doc.page_content for doc in unique_documents[:8]])
logger.debug(f"📄 Retrieved {len(unique_documents)} unique documents")
# Enhanced question for natural responses
enhanced_question = f"""Based on the available recipe information, please answer this cooking question: "{user_question}"
Respond directly and naturally as if you're sharing your own culinary knowledge. If there's a specific recipe that matches the request, share the complete recipe with ingredients and step-by-step instructions in a friendly, conversational way."""
result = self.qa_chain({"question": enhanced_question})
generated_answer = result["answer"]
self._log_token_usage(user_question, retrieved_context, generated_answer)
logger.info(f"✅ Response generated ({len(generated_answer)} chars)")
return generated_answer
except Exception as error:
logger.error(f"❌ Error in ask_question: {str(error)}")
return f"Sorry, I encountered an error: {str(error)}"
def _count_tokens(self, text: str) -> int:
"""Count tokens in text (rough estimate for debugging)"""
return len(text) // 4 if text else 0
def _log_token_usage(self, question: str, context: str, response: str):
"""Log token usage for monitoring"""
question_tokens = self._count_tokens(question)
context_tokens = self._count_tokens(context)
response_tokens = self._count_tokens(response)
total_input_tokens = question_tokens + context_tokens
logger.info(f"📊 Token Usage - Input:{total_input_tokens} (Q:{question_tokens}+C:{context_tokens}), Output:{response_tokens}")
if context_tokens > 3000:
logger.warning(f"⚠️ Large context detected: {context_tokens} tokens")
return {
"input_tokens": total_input_tokens,
"output_tokens": response_tokens,
"total_tokens": total_input_tokens + response_tokens
}
def clear_memory(self):
"""Clear conversation memory"""
try:
if hasattr(self.memory, 'clear'):
self.memory.clear()
logger.info("✅ Memory cleared")
return True
except Exception as e:
logger.warning(f"⚠️ Could not clear memory: {e}")
return False
def simple_chat_completion(self, user_message: str) -> str:
"""Simple chat completion without RAG - direct LLM response"""
logger.info(f"💭 Simple chat: '{user_message[:50]}...'")
try:
llm_prompt = f"As a knowledgeable cooking expert, share your insights about {user_message}. Provide helpful culinary advice and recommendations:\n\n"
llm_response = self.llm.invoke(llm_prompt) if hasattr(self.llm, 'invoke') else self.llm(llm_prompt)
# Extract content based on response type
if hasattr(llm_response, 'content'):
generated_answer = llm_response.content
elif isinstance(llm_response, str):
generated_answer = llm_response.replace(llm_prompt, "").strip() if llm_prompt in llm_response else llm_response
else:
generated_answer = str(llm_response)
# Validate and clean response
generated_answer = generated_answer.strip()
if not generated_answer or len(generated_answer) < 10:
generated_answer = "I'd be happy to help with recipes! Ask me about specific ingredients or dishes."
# Limit response length
if len(generated_answer) > 300:
answer_sentences = generated_answer.split('. ')
generated_answer = '. '.join(answer_sentences[:2]) + '.' if len(answer_sentences) > 1 else generated_answer[:300]
logger.info(f"✅ Response generated ({len(generated_answer)} chars)")
return generated_answer
except Exception as error:
logger.error(f"❌ Simple chat completion error: {str(error)}")
return f"Sorry, I encountered an error: {str(error)}"
# Create global LLM service instance
llm_service = LLMService()
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