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import time
from collections.abc import AsyncIterator
from typing import Any
from secrag.analytics.planner import execute_plan, plan_numeric
from secrag.cache import SemanticCache, partition_key
from secrag.core.config import Settings, get_settings
from secrag.core.logging import get_logger
from secrag.core.types import (
Answer,
AnswerStatus,
NumericResult,
QueryIntent,
QueryRequest,
QueryResponse,
RouteDecision,
ScoredChunk,
)
from secrag.generation.synthesize import Generator
from secrag.ingest.xbrl import FactStore
from secrag.observability.tracing import Trace, span, start_trace
from secrag.retrieval.embedder import Embedder
from secrag.retrieval.pipeline import HybridRetriever
from secrag.retrieval.store import SearchFilter
from secrag.routing.router import QueryRouter
log = get_logger(__name__)
class QueryEngine:
def __init__(self, settings: Settings | None = None) -> None:
self.settings = settings or get_settings()
self.settings.ensure_dirs()
self.embedder = Embedder(self.settings)
self.retriever = HybridRetriever(self.settings, embedder=self.embedder)
self.generator = Generator(self.settings, embedder=self.embedder)
self.router = QueryRouter(self.settings, embedder=self.embedder)
self.cache = SemanticCache(self.settings, embedder=self.embedder)
self.facts = FactStore.load(self.settings)
def warmup(self) -> None:
self.retriever.warmup()
log.info(
"engine_ready",
corpus=self.retriever.corpus_size,
facts=len(self.facts.df),
router=self.router.is_available,
tickers=self.facts.tickers(),
)
async def aclose(self) -> None:
closer = getattr(self.generator.provider, "aclose", None)
if closer is not None:
await closer()
self.retriever.store.close()
def stats(self) -> dict[str, Any]:
return {
"corpus_chunks": self.retriever.corpus_size,
"bm25_documents": self.retriever.bm25.size,
"xbrl_rows": len(self.facts.df),
"tickers": self.facts.tickers(),
"router_available": self.router.is_available,
"providers": getattr(self.generator.provider, "describe", lambda: [])(),
"cache": self.cache.snapshot(),
"settings": {
"dense_model": self.settings.dense_model,
"sparse_model": self.settings.sparse_model,
"rerank_model": self.settings.rerank_model,
"splade_enabled": self.settings.enable_splade,
"min_groundedness": self.settings.min_groundedness,
},
}
def _filter(self, request: QueryRequest) -> SearchFilter:
return SearchFilter(
tickers=request.companies,
fiscal_years=request.fiscal_years,
sections=request.sections,
)
def _partition(self, request: QueryRequest) -> str:
return partition_key(
request.companies, request.fiscal_years, [s.value for s in request.sections]
)
def _numeric(self, question: str, route: RouteDecision) -> list[NumericResult]:
if route.intent not in (QueryIntent.NUMERIC, QueryIntent.COMPARATIVE):
return []
if self.facts.is_empty:
return []
with span("numeric_plan", intent=route.intent.value):
plan = plan_numeric(question, self.facts)
if plan is None:
log.info("numeric_plan_unresolved", question=question[:80])
return []
return execute_plan(plan, self.facts)
def _retrieve(
self, request: QueryRequest, route: RouteDecision
) -> tuple[list[ScoredChunk], str]:
top_k = request.top_k
if route.intent is QueryIntent.COMPARATIVE:
top_k = min(top_k * 2, 20)
reranker = request.reranker if request.use_reranker else "none"
result = self.retriever.retrieve(
request.question, top_k=top_k, flt=self._filter(request), reranker=reranker
)
return result.chunks, result.reranker
async def answer(self, request: QueryRequest) -> QueryResponse:
started = time.perf_counter()
partition = self._partition(request)
with start_trace(question=request.question[:120]) as trace:
if request.use_cache and (hit := self.cache.get(request.question, partition)):
hit.trace_id = trace.trace_id
hit.latency_ms = round((time.perf_counter() - started) * 1000, 2)
return hit
route = self.router.route(request.question)
numeric = self._numeric(request.question, route)
contexts, reranker = self._retrieve(request, route)
if not contexts and not numeric:
return self._empty_response(request, route, trace, started)
generation = await self.generator.generate(request.question, contexts, numeric)
response = QueryResponse(
question=request.question,
answer=generation.answer,
route=route,
contexts=generation.contexts,
numeric_results=numeric,
trace_id=trace.trace_id,
cached=False,
latency_ms=round((time.perf_counter() - started) * 1000, 2),
)
trace.metadata.update(
{
"intent": route.intent.value,
"reranker": reranker,
"contexts": len(generation.contexts),
"numeric_results": len(numeric),
"groundedness": generation.answer.groundedness,
"status": generation.answer.status.value,
}
)
if request.use_cache and response.answer.status is AnswerStatus.OK:
self.cache.put(request.question, response, partition)
log.info(
"query_answered",
intent=route.intent.value,
status=response.answer.status.value,
groundedness=response.answer.groundedness,
latency_ms=response.latency_ms,
)
return response
def _empty_response(
self, request: QueryRequest, route: RouteDecision, trace: Trace, started: float
) -> QueryResponse:
from secrag.generation.prompts import REFUSAL_NO_CONTEXT
return QueryResponse(
question=request.question,
answer=Answer(
text=REFUSAL_NO_CONTEXT,
status=AnswerStatus.REFUSED_NO_CONTEXT,
refusal_reason="Retrieval returned no passages and no figure could be resolved.",
),
route=route,
trace_id=trace.trace_id,
latency_ms=round((time.perf_counter() - started) * 1000, 2),
)
async def stream(self, request: QueryRequest) -> AsyncIterator[dict[str, Any]]:
started = time.perf_counter()
partition = self._partition(request)
with start_trace(question=request.question[:120]) as trace:
if request.use_cache and (hit := self.cache.get(request.question, partition)):
yield {
"event": "meta",
"data": {
"trace_id": trace.trace_id,
"route": hit.route.model_dump(mode="json") if hit.route else None,
"reranker": "cached",
"contexts": [_context_payload(c) for c in hit.contexts],
"numeric_results": [n.model_dump(mode="json") for n in hit.numeric_results],
"cached": True,
},
}
yield {"event": "token", "data": {"text": hit.answer.text}}
yield {
"event": "done",
"data": {
"status": hit.answer.status.value,
"groundedness": hit.answer.groundedness,
"refusal_reason": hit.answer.refusal_reason,
"citations": [c.model_dump(mode="json") for c in hit.answer.citations],
"cached": True,
"latency_ms": round((time.perf_counter() - started) * 1000, 2),
"trace": trace.to_dict(),
},
}
return
route = self.router.route(request.question)
numeric = self._numeric(request.question, route)
contexts, reranker = self._retrieve(request, route)
yield {
"event": "meta",
"data": {
"trace_id": trace.trace_id,
"route": route.model_dump(mode="json"),
"reranker": reranker,
"contexts": [_context_payload(c) for c in contexts],
"numeric_results": [n.model_dump(mode="json") for n in numeric],
},
}
if not contexts and not numeric:
from secrag.generation.prompts import REFUSAL_NO_CONTEXT
yield {"event": "token", "data": {"text": REFUSAL_NO_CONTEXT}}
yield {
"event": "done",
"data": {
"status": AnswerStatus.REFUSED_NO_CONTEXT.value,
"groundedness": 0.0,
"citations": [],
"cached": False,
"latency_ms": round((time.perf_counter() - started) * 1000, 2),
"trace": trace.to_dict(),
},
}
return
collected: list[str] = []
async for piece in self.generator.stream(request.question, contexts, numeric):
collected.append(piece)
yield {"event": "token", "data": {"text": piece}}
final = self.generator.finalise_streamed(
"".join(collected), contexts, has_verified_figures=bool(numeric)
)
latency_ms = round((time.perf_counter() - started) * 1000, 2)
if request.use_cache and final.answer.status is AnswerStatus.OK:
self.cache.put(
request.question,
QueryResponse(
question=request.question,
answer=final.answer,
route=route,
contexts=final.contexts,
numeric_results=list(numeric),
trace_id=trace.trace_id,
latency_ms=latency_ms,
),
partition,
)
yield {
"event": "done",
"data": {
"status": final.answer.status.value,
"groundedness": final.answer.groundedness,
"refusal_reason": final.answer.refusal_reason,
"citations": [c.model_dump(mode="json") for c in final.answer.citations],
"cached": False,
"latency_ms": latency_ms,
"trace": trace.to_dict(),
},
}
def _context_payload(scored: ScoredChunk) -> dict[str, Any]:
return {
"chunk_id": scored.chunk.chunk_id,
"label": scored.chunk.citation_label(),
"text": scored.chunk.text,
"kind": scored.chunk.kind.value,
"section": scored.chunk.section.value,
"company": scored.chunk.company,
"ticker": scored.chunk.ticker,
"fiscal_year": scored.chunk.fiscal_year,
"source_url": scored.chunk.source_url,
"score": round(scored.score, 6),
"stage": scored.stage,
"rank": scored.rank,
"component_scores": {k: round(v, 6) for k, v in scored.component_scores.items()},
}
def build_engine(settings: Settings | None = None) -> QueryEngine:
return QueryEngine(settings)
|