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import logging
from typing import List, Dict, Any, Tuple
from src.config import config
from src.retriever import HybridRetriever, Chunk
from src.reranker import Reranker
from src.query_expansion import QueryExpander
from src.hallucination_guard import HallucinationGuard
from src.llm_client import LocalLLMClient
logger = logging.getLogger("VigilantRAG")
logging.basicConfig(level=logging.INFO)
class VigilantRAGEngine:
def __init__(self):
self.retriever = HybridRetriever()
self.reranker = Reranker()
self.query_expander = QueryExpander()
self.hallucination_guard = HallucinationGuard()
self.llm = LocalLLMClient()
def query(self, user_query: str, config_overrides: Dict[str, Any] = None) -> Dict[str, Any]:
"""
Runs the self-correcting RAG pipeline on a user query.
Returns:
Dictionary containing final answer and detailed step-by-step execution telemetry.
"""
start_time = time.time()
# Apply temporary configuration overrides if any
active_config = config
if config_overrides:
active_config = config.copy(update=config_overrides)
telemetry = {
"original_query": user_query,
"final_query": user_query,
"retrieval_attempts": [],
"query_expansion_triggered": False,
"generation_attempts": [],
"hallucination_blocked_count": 0,
"success": False,
"execution_time_sec": 0.0
}
current_query = user_query
retrieval_success = False
top_chunks: List[Dict[str, Any]] = []
# --- STAGE 1: Retrieval Loop (with Query Expansion) ---
for attempt in range(active_config.max_query_expansion_retries + 1):
attempt_log = {
"attempt": attempt,
"query": current_query,
"dense_hits": 0,
"sparse_hits": 0,
"total_candidates": 0,
"top_score": 0.0,
"chunks": [],
"status": "pending"
}
# 1. Hybrid Search
candidates = self.retriever.search(
current_query,
top_k_dense=active_config.top_k_dense,
top_k_sparse=active_config.top_k_sparse
)
# [SaaS Limit] If limit_document_id is set (Free plan: single doc search restriction)
if config_overrides and config_overrides.get("limit_document_id"):
doc_id_limit = config_overrides["limit_document_id"]
candidates = [c for c in candidates if c["chunk"].doc_id == doc_id_limit]
# Count source types for telemetry
dense_count = sum(1 for c in candidates if "dense" in c["sources"])
sparse_count = sum(1 for c in candidates if "sparse" in c["sources"])
attempt_log["dense_hits"] = dense_count
attempt_log["sparse_hits"] = sparse_count
attempt_log["total_candidates"] = len(candidates)
# 2. Re-ranking (Bypassed if use_reranking is False in Free plan)
if config_overrides and config_overrides.get("use_reranking") is False:
# Bypass Cross-Encoder. Direct sort chunks by dense score
reranked = [
{
"chunk": item["chunk"],
"cross_score": item.get("dense_score", 0.0), # Fallback mockup score
"dense_score": item.get("dense_score", 0.0),
"sparse_score": item.get("sparse_score", 0.0),
"sources": item.get("sources", ["dense"])
}
for item in candidates[:active_config.top_n_final]
]
else:
# Standard Cross-Encoder Reranker execution
reranked = self.reranker.rerank(current_query, candidates, top_n=active_config.top_n_final)
# 3. Evaluate Retrieval Relevance
top_score = reranked[0]["cross_score"] if reranked else -1.0
attempt_log["top_score"] = top_score
if top_score >= active_config.relevance_threshold:
attempt_log["status"] = "relevant"
telemetry["retrieval_attempts"].append(attempt_log)
top_chunks = reranked
retrieval_success = True
break
else:
attempt_log["status"] = "irrelevant"
telemetry["retrieval_attempts"].append(attempt_log)
# If we have retries left, trigger Query Expansion
if attempt < active_config.max_query_expansion_retries:
telemetry["query_expansion_triggered"] = True
logger.info(f"Retrieval top score {top_score:.3f} below threshold {active_config.relevance_threshold}. Triggering query expansion...")
# Expand query using LLM (if model is initialized) or fall back to thesaurus
# To pass the LLM generator dynamically:
def llm_gen(p, temp, sys):
return self.llm.generate(p, temp, sys)
current_query = self.query_expander.expand_llm(current_query, llm_gen)
logger.info(f"Query expanded to: '{current_query}'")
else:
logger.warning("Query expansion retry limit reached. Proceeding with best available chunks.")
top_chunks = reranked
telemetry["final_query"] = current_query
# If absolutely no chunks were found (database is empty)
if not top_chunks:
telemetry["execution_time_sec"] = time.time() - start_time
return {
"answer": "I don't have any documents indexed yet. Please upload or ingest some documents first.",
"telemetry": telemetry
}
# Combine texts from top 5 chunks to serve as context (Premise)
context_text = "\n\n".join([f"Document [{item['chunk'].doc_title}]: {item['chunk'].text}" for item in top_chunks])
# --- STAGE 2: Generation & Hallucination Guard Loop ---
final_answer = ""
current_temp = active_config.temperature_default
# Default strict system prompt
system_prompt = (
"You are a factual assistant. Answer the query ONLY using the provided context. "
"If the answer cannot be found in the context, say 'I cannot find the answer in the provided context'. "
"Do not use external knowledge or make up facts."
)
for attempt in range(active_config.max_hallucination_retries + 1):
gen_log = {
"attempt": attempt,
"temperature": current_temp,
"system_prompt": system_prompt,
"response_draft": "",
"nli_scores": {},
"status": "pending"
}
# Format Prompt
generation_prompt = (
f"Context information is below.\n"
f"---------------------\n"
f"{context_text}\n"
f"---------------------\n"
f"Given the context information above, answer the query.\n"
f"Query: {telemetry['original_query']}\n"
f"Answer:"
)
# 1. Generate Response
draft = self.llm.generate(generation_prompt, temperature=current_temp, system_prompt=system_prompt)
gen_log["response_draft"] = draft
# 2. Audit Response via NLI Guard (Bypassed if use_nli_guard is False in Free plan)
if config_overrides and config_overrides.get("use_nli_guard") is False:
# Bypass DeBERTa NLI checks. Auto-verify the draft response
is_hallucination = False
nli_scores = {"entailment": 1.0, "contradiction": 0.0, "neutral": 0.0}
else:
# Standard DeBERTa validation audit execution
is_hallucination, nli_scores = self.hallucination_guard.evaluate_response(context_text, draft)
gen_log["nli_scores"] = nli_scores
if not is_hallucination:
final_answer = draft
telemetry["success"] = True
gen_log["status"] = "passed"
telemetry["generation_attempts"].append(gen_log)
break
else:
telemetry["hallucination_blocked_count"] += 1
gen_log["status"] = "hallucinated"
telemetry["generation_attempts"].append(gen_log)
# Adjust generation constraints for next try
if attempt < active_config.max_hallucination_retries:
# Switch to a higher temperature to explore alternative wordings,
# and tighten the system prompt to enforce strict context alignment.
current_temp = active_config.temperature_retry
system_prompt = (
"CRITICAL: You generated information not present in the context. "
"You must ONLY use the provided context. If the query cannot be answered exactly "
"using the context, say 'I cannot find the answer in the provided context'. "
"DO NOT make up facts. Answer strictly and factually."
)
else:
logger.warning("Max hallucination retries reached. Returning the most factual fallback statement.")
final_answer = (
"I cannot answer this question with absolute certainty based on the provided documents. "
"My NLI guard detected a potential hallucination in the drafted response."
)
telemetry["execution_time_sec"] = time.time() - start_time
return {
"answer": final_answer,
"telemetry": telemetry
}
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