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Ukweli — RAG Query Endpoint
POST /query — The primary retrieval-augmented generation endpoint.
Orchestrates the full pipeline per Architecture Section 4.2.
"""
from __future__ import annotations
import logging
import time
import uuid
from fastapi import APIRouter, Depends
from sqlalchemy.ext.asyncio import AsyncSession
from app.api.auth import AuthContext, require_auth
from app.db.session import get_db_session
from app.dependencies import get_citation_formatter, get_llm_gateway, get_retrieval_orchestrator
from app.models.database import QueryLog
from app.models.enums import Confidence
from app.models.schemas import (
QueryRequest,
QueryResponse,
RetrievalMetadata,
)
from app.services.citation.formatter import CitationFormatter
from app.services.llm.gateway import LLMGateway
from app.services.llm.guardrails import check_query_safety
from app.services.llm.prompts import build_rag_prompt
from app.services.retrieval.orchestrator import RetrievalOrchestrator
logger = logging.getLogger("ukweli.api.query")
router = APIRouter(tags=["RAG"])
@router.post("/query", response_model=QueryResponse)
async def query_rag(
request: QueryRequest,
db: AsyncSession = Depends(get_db_session),
auth: AuthContext = Depends(require_auth),
retriever: RetrievalOrchestrator = Depends(get_retrieval_orchestrator),
llm: LLMGateway = Depends(get_llm_gateway),
citation_fmt: CitationFormatter = Depends(get_citation_formatter),
) -> QueryResponse:
"""
Full RAG pipeline:
1. Validate auth + rate limit (multi-tier)
2. Safety guardrails on query
3. Hybrid retrieval (vector + keyword → RRF → reranker)
4. Build LLM prompt with retrieved context
5. Generate answer via HuggingFace Inference API
6. Format citations with page/paragraph precision
7. Log to audit trail
8. Return response
"""
start_time = time.monotonic()
query_id = uuid.uuid4()
# --- Step 1: Safety check ---
safety_result = check_query_safety(request.query)
if safety_result.blocked:
latency_ms = int((time.monotonic() - start_time) * 1000)
return QueryResponse(
query_id=query_id,
answer=safety_result.message,
citations=[],
retrieval_metadata=RetrievalMetadata(
chunks_considered=0, latency_ms=latency_ms, graph_entities_used=[]
),
confidence=Confidence.INSUFFICIENT_CONTEXT,
suggested_followups=[],
)
# --- Step 2: Retrieve context ---
retrieval_result = await retriever.retrieve(
query=request.query,
language=request.language,
filters=request.filters,
tier=auth.tier,
)
# --- Step 3: Build prompt and generate ---
prompt_messages = build_rag_prompt(
query=request.query,
context_chunks=retrieval_result.context_blocks,
language=request.language,
mode=request.mode.value,
)
llm_response = await llm.generate(messages=prompt_messages)
# --- Step 4: Format citations ---
formatted = citation_fmt.format_response(
raw_answer=llm_response.text,
retrieved_chunks=retrieval_result.context_blocks,
)
latency_ms = int((time.monotonic() - start_time) * 1000)
# --- Step 5: Determine confidence ---
confidence = _assess_confidence(
num_chunks=len(retrieval_result.context_blocks),
top_score=retrieval_result.top_score,
)
# --- Step 6: Log to audit trail ---
query_log = QueryLog(
id=query_id,
query_text=request.query,
language=request.language,
mode=request.mode.value,
user_tier=auth.tier,
user_id=auth.user.id if auth.user else None,
api_key_id=auth.api_key.id if auth.api_key else None,
fingerprint=auth.fingerprint,
filters=request.filters.model_dump() if request.filters else None,
answer=formatted.answer,
citations=[c.model_dump() for c in formatted.citations] if formatted.citations else None,
chunks_considered=retrieval_result.total_candidates,
latency_ms=latency_ms,
confidence=confidence.value,
llm_model_used=llm_response.model_used,
llm_tokens_used=llm_response.tokens_used,
conversation_id=request.conversation_id,
)
db.add(query_log)
await db.flush()
logger.info(
"Query processed: id=%s, tier=%s, latency=%dms, chunks=%d, confidence=%s",
query_id,
auth.tier,
latency_ms,
retrieval_result.total_candidates,
confidence.value,
)
return QueryResponse(
query_id=query_id,
answer=formatted.answer,
citations=formatted.citations,
retrieval_metadata=RetrievalMetadata(
chunks_considered=retrieval_result.total_candidates,
latency_ms=latency_ms,
graph_entities_used=retrieval_result.entities_used,
),
confidence=confidence,
suggested_followups=formatted.suggested_followups,
)
def _assess_confidence(num_chunks: int, top_score: float) -> Confidence:
"""
Heuristic confidence assessment based on retrieval quality.
- HIGH: 3+ chunks with top score > 0.8
- MEDIUM: 1+ chunks with top score > 0.5
- LOW: chunks found but low scores
- INSUFFICIENT: no chunks found
"""
if num_chunks == 0:
return Confidence.INSUFFICIENT_CONTEXT
if num_chunks >= 3 and top_score > 0.8:
return Confidence.HIGH
if num_chunks >= 1 and top_score > 0.5:
return Confidence.MEDIUM
return Confidence.LOW
|