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# DEPENDENCIES
import time
import asyncio
from typing import Dict
from typing import List
from typing import Optional
from datetime import datetime
from typing import AsyncGenerator
from config.models import PromptType
from config.models import LLMProvider
from config.models import QueryRequest
from config.models import QueryResponse
from config.models import ChunkWithScore
from config.settings import get_settings
from config.logging_config import get_logger
from utils.error_handler import handle_errors
from generation.llm_client import get_llm_client
from utils.error_handler import ResponseGenerationError
from generation.prompt_builder import get_prompt_builder
from retrieval.hybrid_retriever import get_hybrid_retriever
from generation.query_classifier import get_query_classifier
from generation.general_responder import get_general_responder
from generation.citation_formatter import get_citation_formatter
from generation.temperature_controller import get_temperature_controller
# Setup Settings and Logging
settings = get_settings()
logger = get_logger(__name__)
class ResponseGenerator:
"""
Main orchestrator for RAG response generation with LLM-based intelligent query routing
Handles both:
1. Generic/conversational queries (greetings, system info, general knowledge)
2. Document-based RAG queries (retrieval + generation)
Pipeline: Query → LLM Classifier → Route to (General LLM | RAG Pipeline) → Response
"""
def __init__(self, provider: LLMProvider = None, model_name: str = None):
"""
Initialize response generator with LLM-based query routing capabilities
Arguments:
----------
provider { LLMProvider } : LLM provider (Ollama/OpenAI)
model_name { str } : Model name to use
"""
self.logger = logger
self.settings = get_settings()
# Auto-detect provider for HF Spaces
if provider is None:
if (self.settings.IS_HF_SPACE and not self.settings.OLLAMA_ENABLED):
if (self.settings.USE_OPENAI and self.settings.OPENAI_API_KEY):
provider = LLMProvider.OPENAI
model_name = model_name or self.settings.OPENAI_MODEL
logger.info("Auto-detected: Using OpenAI")
else:
raise ValueError("No LLM provider configured for HF Space. Set OPENAI_API_KEY in Space secrets.")
else:
# Local development - use Ollama
provider = LLMProvider.OLLAMA
self.provider = provider
self.model_name = model_name
# Initialize components
self.llm_client = get_llm_client(provider = self.provider,
model_name = self.model_name,
)
# Query routing components (NOW USES LLM FOR CLASSIFICATION)
self.query_classifier = get_query_classifier(provider = self.provider,
model_name = self.model_name,
)
self.general_responder = get_general_responder(provider = self.provider,
model_name = self.model_name,
)
# RAG components
self.hybrid_retriever = get_hybrid_retriever()
self.prompt_builder = get_prompt_builder(model_name = self.model_name)
self.citation_formatter = get_citation_formatter()
self.temperature_controller = get_temperature_controller()
# Statistics
self.generation_count = 0
self.total_generation_time = 0.0
self.general_query_count = 0
self.rag_query_count = 0
self.logger.info(f"Initialized ResponseGenerator with LLM-Based Query Routing: provider={self.provider.value}, model={self.model_name}")
@handle_errors(error_type = ResponseGenerationError, log_error = True, reraise = True)
async def generate_response(self, request: QueryRequest, conversation_history: List[Dict] = None, has_documents: bool = True) -> QueryResponse:
"""
Generate response with LLM-based intelligent query routing
Arguments:
----------
request { QueryRequest } : Query request object
conversation_history { list } : Previous conversation messages
has_documents { bool } : Whether documents are available in the system
Returns:
--------
{ QueryResponse } : Complete query response
"""
start_time = time.time()
self.logger.info(f"Processing query: '{request.query[:100]}...'")
try:
# Classify query using LLM
classification = await self.query_classifier.classify(query = request.query,
has_documents = has_documents,
)
self.logger.info(f"Query classified as: {classification['type']} (confidence: {classification['confidence']:.2f}, LLM-based: {classification.get('is_llm_classified', False)})")
self.logger.debug(f"Classification reason: {classification['reason']}")
# Route based on classification
if (classification['suggested_action'] == 'respond_with_general_llm'):
# Handle as general query
response = await self._handle_general_query(request = request,
classification = classification,
start_time = start_time,
history = conversation_history,
)
self.general_query_count += 1
return response
elif (classification['suggested_action'] == 'respond_with_rag'):
# Handle as RAG query
response = await self._handle_rag_query(request = request,
classification = classification,
start_time = start_time,
)
self.rag_query_count += 1
return response
else:
# Default to RAG if unclear
self.logger.info("Unclear classification - defaulting to RAG...")
try:
response = await self._handle_rag_query(request = request,
classification = classification,
start_time = start_time,
allow_fallback = True,
)
# If no results from RAG, fall back to general
if ((not response.sources) or (len(response.sources) == 0)):
self.logger.info("No RAG results - falling back to general response")
response = await self._handle_general_query(request = request,
classification = classification,
start_time = start_time,
history = conversation_history,
)
self.general_query_count += 1
else:
self.rag_query_count += 1
return response
except Exception as e:
self.logger.warning(f"RAG attempt failed, falling back to general: {e}")
response = await self._handle_general_query(request = request,
classification = classification,
start_time = start_time,
history = conversation_history,
)
self.general_query_count += 1
return response
except Exception as e:
self.logger.error(f"Response generation failed: {repr(e)}", exc_info = True)
raise ResponseGenerationError(f"Response generation failed: {repr(e)}")
async def _handle_general_query(self, request: QueryRequest, classification: Dict, start_time: float, history: List[Dict] = None) -> QueryResponse:
"""
Handle general/conversational queries without RAG
Arguments:
----------
request { QueryRequest } : Original request
classification { dict } : Classification result
start_time { float } : Start timestamp
history { list } : Conversation history
Returns:
--------
{ QueryResponse } : Response without RAG
"""
self.logger.debug("Handling as general query...")
# Use general responder
general_response = await self.general_responder.respond(query = request.query,
conversation_history = history,
)
answer = general_response.get("answer", "I'm here to help! Please let me know how I can assist you.")
total_time = (time.time() - start_time) * 1000
# Create QueryResponse object
response = QueryResponse(query = request.query,
answer = answer,
sources = [], # No sources for general queries
retrieval_time_ms = 0.0,
generation_time_ms = total_time,
total_time_ms = total_time,
tokens_used = general_response.get("tokens_used", {"input": 0, "output": 0, "total": 0}),
model_used = self.model_name,
timestamp = datetime.now(),
)
# Add metadata about query type
if request.include_metrics:
response.metrics = {"query_type" : "general",
"classification" : classification['type'],
"confidence" : classification['confidence'],
"requires_rag" : False,
"conversation_mode" : True,
"llm_classified" : classification.get('is_llm_classified', False),
}
self.logger.info(f"General response generated in {total_time:.0f}ms")
return response
async def _handle_rag_query(self, request: QueryRequest, classification: Dict, start_time: float, allow_fallback: bool = False) -> QueryResponse:
"""
Handle RAG-based queries with document retrieval
Arguments:
----------
request { QueryRequest } : Original request
classification { dict } : Classification result
start_time { float } : Start timestamp
allow_fallback { bool } : Whether to allow fallback to general
Returns:
--------
{ QueryResponse } : RAG response
"""
self.logger.debug("Handling as RAG query...")
try:
# Retrieve relevant context
self.logger.debug("Retrieving context...")
retrieval_start = time.time()
retrieval_result = self.hybrid_retriever.retrieve_with_context(query = request.query,
top_k = request.top_k or self.settings.TOP_K_RETRIEVE,
enable_reranking = request.enable_reranking,
include_citations = request.include_sources,
)
retrieval_time = (time.time() - retrieval_start) * 1000
chunks = retrieval_result["chunks"]
context = retrieval_result["context"]
if not chunks:
self.logger.warning("No relevant context found for query")
if allow_fallback:
# Return empty response to trigger fallback
return QueryResponse(query = request.query,
answer = "",
sources = [],
retrieval_time_ms = retrieval_time,
generation_time_ms = 0.0,
total_time_ms = retrieval_time,
tokens_used = {"input": 0, "output": 0, "total": 0},
model_used = self.model_name,
timestamp = datetime.now(),
)
else:
return self._create_no_results_response(request = request,
retrieval_time_ms = retrieval_time,
)
self.logger.info(f"Retrieved {len(chunks)} chunks in {retrieval_time:.0f}ms")
# Determine prompt type and temperature
self.logger.debug("Determining prompt strategy...")
prompt_type = self._infer_prompt_type(query = request.query)
temperature = self._get_adaptive_temperature(request = request,
query = request.query,
context = context,
retrieval_scores = [chunk.score for chunk in chunks],
prompt_type = prompt_type,
)
self.logger.debug(f"Prompt type: {prompt_type.value}, Temperature: {temperature}")
# Build optimized prompt
self.logger.debug("Building prompt...")
prompt = self.prompt_builder.build_prompt(query = request.query,
context = context,
sources = chunks,
prompt_type = prompt_type,
include_citations = request.include_sources,
max_completion_tokens = request.max_tokens or self.settings.MAX_TOKENS,
)
# Generate LLM response
self.logger.debug("Generating LLM response...")
generation_start = time.time()
messages = [{"role" : "system",
"content" : prompt["system"]
},
{"role" : "user",
"content" : prompt["user"],
}
]
llm_response = await self.llm_client.generate(messages = messages,
temperature = temperature,
top_p = request.top_p or self.settings.TOP_P,
max_tokens = request.max_tokens or self.settings.MAX_TOKENS,
)
generation_time = (time.time() - generation_start) * 1000
answer = llm_response["content"]
self.logger.info(f"Generated response in {generation_time:.0f}ms ({llm_response['usage']['completion_tokens']} tokens)")
# Format citations (if enabled)
if request.include_sources:
self.logger.debug("Formatting citations...")
answer = self._post_process_citations(answer = answer,
sources = chunks,
)
# Create response object
total_time = (time.time() - start_time) * 1000
response = QueryResponse(query = request.query,
answer = answer,
sources = chunks if request.include_sources else [],
retrieval_time_ms = retrieval_time,
generation_time_ms = generation_time,
total_time_ms = total_time,
tokens_used = {"input" : llm_response["usage"]["prompt_tokens"],
"output" : llm_response["usage"]["completion_tokens"],
"total" : llm_response["usage"]["total_tokens"],
},
model_used = self.model_name,
timestamp = datetime.now(),
)
# Add quality metrics if requested
if request.include_metrics:
response.metrics = self._calculate_quality_metrics(query = request.query,
answer = answer,
context = context,
sources = chunks,
)
# Track both: prediction & reality
response.metrics["predicted_type"] = classification.get('type', 'unknown')
response.metrics["predicted_confidence"] = classification.get('confidence', 0.0)
response.metrics["actual_type"] = "rag" # Always rag if we're here
response.metrics["execution_path"] = "rag_pipeline"
response.metrics["has_context"] = len(chunks) > 0
response.metrics["context_chunks"] = len(chunks)
response.metrics["rag_confidence"] = min(1.0, sum(c.score for c in chunks) / len(chunks) if chunks else 0.0)
response.metrics["is_forced_rag"] = classification.get('is_forced_rag', False)
response.metrics["llm_classified"] = classification.get('is_llm_classified', False)
# Add context for evaluation
response.metrics["context_for_evaluation"] = context
# Update statistics
self.generation_count += 1
self.total_generation_time += total_time
self.logger.info(f"RAG response generated successfully in {total_time:.0f}ms")
return response
except Exception as e:
self.logger.error(f"RAG query handling failed: {repr(e)}", exc_info = True)
if allow_fallback:
# Return empty to trigger fallback
return QueryResponse(query = request.query,
answer = "",
sources = [],
retrieval_time_ms = 0.0,
generation_time_ms = 0.0,
total_time_ms = 0.0,
tokens_used = {"input": 0, "output": 0, "total": 0},
model_used = self.model_name,
timestamp = datetime.now(),
)
else:
raise
@handle_errors(error_type = ResponseGenerationError, log_error = True, reraise = True)
async def generate_response_stream(self, request: QueryRequest, has_documents: bool = True) -> AsyncGenerator[str, None]:
"""
Generate streaming RAG response
Arguments:
----------
request { QueryRequest } : Query request object
has_documents { bool } : Whether documents are available
Yields:
-------
{ str } : Response chunks (tokens)
"""
self.logger.info(f"Generating streaming response for query: '{request.query[:100]}...'")
try:
# Classify query first
classification = await self.query_classifier.classify(query = request.query,
has_documents = has_documents,
)
if (classification['suggested_action'] == 'respond_with_general_llm'):
# Stream general response
general_response = await self.general_responder.respond(query = request.query)
yield general_response.get("answer", "")
return
# Otherwise proceed with RAG streaming - Procced with Retrieving context
retrieval_result = self.hybrid_retriever.retrieve_with_context(query = request.query,
top_k = request.top_k or self.settings.TOP_K_RETRIEVE,
enable_reranking = request.enable_reranking,
include_citations = request.include_sources,
)
chunks = retrieval_result["chunks"]
context = retrieval_result["context"]
if not chunks:
yield "I couldn't find relevant information to answer your question."
return
# Determine strategy
prompt_type = self._infer_prompt_type(query = request.query)
temperature = self._get_adaptive_temperature(request = request,
query = request.query,
context = context,
retrieval_scores = [chunk.score for chunk in chunks],
prompt_type = prompt_type,
)
# Build prompt
prompt = self.prompt_builder.build_prompt(query = request.query,
context = context,
sources = chunks,
prompt_type = prompt_type,
include_citations = request.include_sources,
max_completion_tokens = request.max_tokens or self.settings.MAX_TOKENS,
)
# Stream LLM response
messages = [{"role" : "system",
"content" : prompt["system"],
},
{"role" : "user",
"content" : prompt["user"],
},
]
async for chunk_text in self.llm_client.generate_stream(messages = messages,
temperature = temperature,
top_p = request.top_p or self.settings.TOP_P,
max_tokens = request.max_tokens or self.settings.MAX_TOKENS,
):
yield chunk_text
self.logger.info("Streaming response completed")
except Exception as e:
self.logger.error(f"Streaming generation failed: {repr(e)}", exc_info = True)
yield f"\n\n[Error: {str(e)}]"
def _infer_prompt_type(self, query: str) -> PromptType:
"""
Infer appropriate prompt type from query
Arguments:
----------
query { str } : User query
Returns:
--------
{ PromptType } : Inferred prompt type
"""
query_lower = query.lower()
# Summary indicators
if (any(word in query_lower for word in ['summarize', 'summary', 'overview', 'tldr', 'brief'])):
return PromptType.SUMMARY
# Comparison indicators
if (any(word in query_lower for word in ['compare', 'contrast', 'difference', 'versus', 'vs'])):
return PromptType.COMPARISON
# Analytical indicators
if (any(word in query_lower for word in ['analyze', 'analysis', 'evaluate', 'assess', 'examine'])):
return PromptType.ANALYTICAL
# Extraction indicators
if (any(word in query_lower for word in ['extract', 'list', 'find all', 'identify', 'enumerate'])):
return PromptType.EXTRACTION
# Creative indicators
if (any(word in query_lower for word in ['create', 'write', 'compose', 'generate', 'imagine'])):
return PromptType.CREATIVE
# Conversational indicators
if (any(word in query_lower for word in ['tell me about', 'explain', 'discuss', 'talk about'])):
return PromptType.CONVERSATIONAL
# Default to QA
return PromptType.QA
def _get_adaptive_temperature(self, request: QueryRequest, query: str, context: str, retrieval_scores: List[float], prompt_type: PromptType) -> float:
"""
Get adaptive temperature based on query characteristics
Arguments:
----------
request { QueryRequest } : Original request
query { str } : User query
context { str } : Retrieved context
retrieval_scores { list } : Retrieval scores
prompt_type { PromptType } : Inferred prompt type
Returns:
--------
{ float } : Temperature value
"""
# Use request temperature if explicitly provided
if (request.temperature is not None):
self.logger.debug(f"Using request temperature: {request.temperature}")
return request.temperature
# Otherwise, use adaptive temperature controller
temperature = self.temperature_controller.get_temperature(query = query,
context = context,
retrieval_scores = retrieval_scores,
query_type = prompt_type.value,
)
return temperature
def _post_process_citations(self, answer: str, sources: List[ChunkWithScore]) -> str:
"""
Post-process answer to format citations
Arguments:
----------
answer { str } : Generated answer with citation markers
sources { list } : Source chunks
Returns:
--------
{ str } : Answer with formatted citations
"""
try:
# Validate citations
is_valid, invalid = self.citation_formatter.validate_citations(answer, sources)
if not is_valid:
self.logger.warning(f"Invalid citations found: {invalid}")
# Normalize to fix issues
answer = self.citation_formatter.normalize_citations(answer, sources)
# Format citations according to style
formatted_answer = self.citation_formatter.format_citations_in_text(answer, sources)
return formatted_answer
except Exception as e:
self.logger.error(f"Citation post-processing failed: {repr(e)}")
# Return original answer if formatting fails
return answer
def _create_no_results_response(self, request: QueryRequest, retrieval_time_ms: float) -> QueryResponse:
"""
Create response when no results are found
Arguments:
----------
request { QueryRequest } : Original request
retrieval_time_ms { float } : Time spent on retrieval
Returns:
--------
{ QueryResponse } : Response indicating no results
"""
no_results_answer = ("I couldn't find relevant information in the available documents to answer your question. "
"This could mean:\n"
"1. The information is not present in the indexed documents\n"
"2. The question may need to be rephrased for better matching\n"
"3. The relevant documents haven't been uploaded yet\n\n"
"Please try:\n"
"- Rephrasing your question with different keywords\n"
"- Asking a more specific or general question\n"
"- Ensuring the relevant documents are uploaded\n"
)
return QueryResponse(query = request.query,
answer = no_results_answer,
sources = [],
retrieval_time_ms = retrieval_time_ms,
generation_time_ms = 0.0,
total_time_ms = retrieval_time_ms,
tokens_used = {"input": 0, "output": 0, "total": 0},
model_used = self.model_name,
timestamp = datetime.now(),
)
def _calculate_quality_metrics(self, query: str, answer: str, context: str, sources: List[ChunkWithScore]) -> Dict[str, float]:
"""
Calculate quality metrics for the response
Arguments:
----------
query { str } : User query
answer { str } : Generated answer
context { str } : Retrieved context
sources { list } : Source chunks
Returns:
--------
{ dict } : Quality metrics
"""
metrics = dict()
try:
# Answer length metrics
metrics["answer_length"] = len(answer.split())
metrics["answer_char_length"] = len(answer)
# Citation metrics
citation_stats = self.citation_formatter.get_citation_statistics(answer, sources)
metrics["citations_used"] = citation_stats.get("total_citations", 0)
metrics["unique_citations"] = citation_stats.get("unique_citations", 0)
metrics["citation_density"] = citation_stats.get("citation_density", 0.0)
# Context utilization
context_length = len(context.split())
metrics["context_utilization"] = min(1.0, metrics["answer_length"] / max(1, context_length))
# Retrieval quality
if sources:
avg_score = sum(s.score for s in sources) / len(sources)
metrics["avg_retrieval_score"] = avg_score
metrics["top_retrieval_score"] = sources[0].score if sources else 0.0
# Query-answer alignment (simple keyword overlap)
query_words = set(query.lower().split())
answer_words = set(answer.lower().split())
overlap = len(query_words & answer_words)
metrics["query_answer_overlap"] = overlap / max(1, len(query_words))
except Exception as e:
self.logger.warning(f"Failed to calculate some quality metrics: {repr(e)}")
return metrics
async def generate_batch_responses(self, requests: List[QueryRequest], has_documents: bool = True) -> List[QueryResponse]:
"""
Generate responses for multiple queries in batch
Arguments:
----------
requests { list } : List of query requests
has_documents { bool } : Whether documents are available
Returns:
--------
{ list } : List of query responses
"""
self.logger.info(f"Generating batch responses for {len(requests)} queries")
tasks = [self.generate_response(request = request,
has_documents = has_documents) for request in requests]
responses = await asyncio.gather(*tasks, return_exceptions = True)
# Handle exceptions
results = list()
for i, response in enumerate(responses):
if isinstance(response, Exception):
self.logger.error(f"Batch query {i} failed: {repr(response)}")
# Create error response
error_response = self._create_error_response(requests[i], str(response))
results.append(error_response)
else:
results.append(response)
self.logger.info(f"Completed batch generation: {len(results)} responses")
return results
def _create_error_response(self, request: QueryRequest, error_message: str) -> QueryResponse:
"""
Create error response for failed generation
Arguments:
----------
request { QueryRequest } : Original request
error_message { str } : Error message
Returns:
--------
{ QueryResponse } : Error response
"""
return QueryResponse(query = request.query,
answer = f"An error occurred while generating the response: {error_message}",
sources = [],
retrieval_time_ms = 0.0,
generation_time_ms = 0.0,
total_time_ms = 0.0,
tokens_used = {"input": 0, "output": 0, "total": 0},
model_used = self.model_name,
timestamp = datetime.now(),
)
def get_generation_stats(self) -> Dict:
"""
Get generation statistics including query type breakdown
Returns:
--------
{ dict } : Generation statistics
"""
avg_time = (self.total_generation_time / self.generation_count) if self.generation_count > 0 else 0
return {"total_generations" : self.generation_count,
"general_queries" : self.general_query_count,
"rag_queries" : self.rag_query_count,
"total_generation_time" : self.total_generation_time,
"avg_generation_time_ms" : avg_time,
"provider" : self.provider.value,
"model" : self.model_name,
"llm_health" : self.llm_client.check_health(),
"query_routing_enabled" : True,
"llm_based_routing" : True,
}
def reset_stats(self):
"""
Reset generation statistics
"""
self.generation_count = 0
self.general_query_count = 0
self.rag_query_count = 0
self.total_generation_time = 0.0
self.logger.info("Generation statistics reset")
# Global response generator instance
_response_generator = None
def get_response_generator(provider: LLMProvider = None, model_name: str = None) -> ResponseGenerator:
"""
Get global response generator instance (singleton)
Arguments:
----------
provider { LLMProvider } : LLM provider
model_name { str } : Model name
Returns:
--------
{ ResponseGenerator } : ResponseGenerator instance
"""
global _response_generator
if _response_generator is None or (provider and _response_generator.provider != provider):
_response_generator = ResponseGenerator(provider, model_name)
return _response_generator
@handle_errors(error_type = ResponseGenerationError, log_error = True, reraise = False)
async def generate_answer(query: str, top_k: int = 5, temperature: float = None, has_documents: bool = True, **kwargs) -> str:
"""
Convenience function for quick answer generation
Arguments:
----------
query { str } : User query
top_k { int } : Number of chunks to retrieve
temperature { float } : Temperature for generation
has_documents { bool } : Whether documents are available
**kwargs : Additional parameters
Returns:
--------
{ str } : Generated answer
"""
request = QueryRequest(query = query,
top_k = top_k,
temperature = temperature,
**kwargs
)
generator = get_response_generator()
response = await generator.generate_response(request = request,
has_documents = has_documents,
)
return response.answer |