best: score 0.5541 (recall 0.5759, ndcg 0.4670)

- DENSE_PREFETCH_K 80 → 120
- RERANK_LIMIT 25 → 35
- add question.text as extra dense when differs from search_text

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Lotus_it-academy-hackathon-solution

@@ -0,0 +1 @@
+Subproject commit 3b9ff54ccf1540f8259a438b52d5bb05553e9dcb

index/main.py

@@ -1,19 +1,17 @@
 import asyncio
 import logging
 import os
-from concurrent.futures import ThreadPoolExecutor
-from contextlib import asynccontextmanager
 from functools import lru_cache
 from typing import Any
 
 from fastapi import FastAPI, Request
 from fastapi.exceptions import RequestValidationError
-from fastapi.responses import JSONResponse, ORJSONResponse
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel
 
 HOST = os.getenv("HOST", "0.0.0.0")
 PORT = int(os.getenv("PORT", "8000"))
-UVICORN_WORKERS = 4
+UVICORN_WORKERS = 8
 
 logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
 logger = logging.getLogger("index-service")
@@ -80,24 +78,6 @@ OVERLAP_SIZE = 128
 SPARSE_MODEL_NAME = "Qdrant/bm25"
 FASTEMBED_CACHE_PATH = "/models/fastembed"
 
-_thread_pool = ThreadPoolExecutor(max_workers=4)
-
-
-@lru_cache(maxsize=1)
-def get_sparse_model():
-    from fastembed import SparseTextEmbedding
-
-    logger.info("Loading sparse model %s from cache %s", SPARSE_MODEL_NAME, FASTEMBED_CACHE_PATH)
-    return SparseTextEmbedding(model_name=SPARSE_MODEL_NAME)
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    # Preload BM25 model on startup to avoid cold-start latency
-    await asyncio.to_thread(get_sparse_model)
-    logger.info("BM25 model preloaded")
-    yield
-
 
 def render_message(message: Message) -> str:
     parts_list: list[str] = []
@@ -206,12 +186,7 @@ def build_chunks(
     return result
 
 
-app = FastAPI(
-    title="Index Service",
-    version="0.1.0",
-    lifespan=lifespan,
-    default_response_class=ORJSONResponse,
-)
+app = FastAPI(title="Index Service", version="0.1.0")
 
 
 @app.get("/health")
@@ -230,6 +205,14 @@ async def index(payload: IndexAPIRequest) -> IndexAPIResponse:
     )
 
 
+@lru_cache(maxsize=1)
+def get_sparse_model():
+    from fastembed import SparseTextEmbedding
+
+    logger.info("Loading sparse model %s from cache %s", SPARSE_MODEL_NAME, FASTEMBED_CACHE_PATH)
+    return SparseTextEmbedding(model_name=SPARSE_MODEL_NAME)
+
+
 def embed_sparse_texts(texts: list[str]) -> list[dict]:
     model = get_sparse_model()
     vectors = []
@@ -245,9 +228,7 @@ def embed_sparse_texts(texts: list[str]) -> list[dict]:
 
 @app.post("/sparse_embedding")
 async def sparse_embedding(payload: SparseEmbeddingRequest) -> dict[str, Any]:
-    vectors = await asyncio.get_event_loop().run_in_executor(
-        _thread_pool, embed_sparse_texts, payload.texts
-    )
+    vectors = await asyncio.to_thread(embed_sparse_texts, payload.texts)
     return {"vectors": vectors}
 
 

index/requirements.txt

@@ -2,4 +2,3 @@ fastapi==0.135.1
 uvicorn[standard]==0.42.0
 pydantic==2.12.5
 fastembed==0.7.4
-orjson==3.10.18

search/config.py

@@ -19,10 +19,10 @@ QDRANT_SPARSE_VECTOR_NAME = os.getenv("QDRANT_SPARSE_VECTOR_NAME", "sparse")
 OPEN_API_LOGIN = os.getenv("OPEN_API_LOGIN")
 OPEN_API_PASSWORD = os.getenv("OPEN_API_PASSWORD")
 
-DENSE_PREFETCH_K = 50
-SPARSE_PREFETCH_K = 100
-RETRIEVE_K = 80
-RERANK_LIMIT = 60
+DENSE_PREFETCH_K = 80
+SPARSE_PREFETCH_K = 200
+RETRIEVE_K = 150
+RERANK_LIMIT = 15
 TOP_K = 50
 
 HTTP_TIMEOUT = 30.0

search/main.py

@@ -9,7 +9,7 @@ import httpx
 from fastembed import SparseTextEmbedding
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.exceptions import RequestValidationError
-from fastapi.responses import JSONResponse, ORJSONResponse
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field
 from qdrant_client import AsyncQdrantClient, models
 
@@ -148,13 +148,7 @@ def get_sparse_model() -> SparseTextEmbedding:
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    # Preload BM25 and set up HTTP client with connection pooling
-    await asyncio.to_thread(get_sparse_model)
-    logger.info("BM25 model preloaded")
-    app.state.http = httpx.AsyncClient(
-        timeout=30.0,
-        limits=httpx.Limits(max_connections=100, max_keepalive_connections=20),
-    )
+    app.state.http = httpx.AsyncClient()
     app.state.qdrant = AsyncQdrantClient(
         url=QDRANT_URL,
         api_key=API_KEY,
@@ -166,17 +160,13 @@ async def lifespan(app: FastAPI):
         await app.state.qdrant.close()
 
 
-app = FastAPI(
-    title="Search Service",
-    version="0.1.0",
-    lifespan=lifespan,
-    default_response_class=ORJSONResponse,
-)
+app = FastAPI(title="Search Service", version="0.1.0", lifespan=lifespan)
 
-DENSE_PREFETCH_K = 80
+DENSE_PREFETCH_K = 120
 SPARSE_PREFETCH_K = 200
 RETRIEVE_K = 150
-RERANK_LIMIT = 15
+RERANK_LIMIT = 35
+KEYWORD_BOOST_EXTRA = 10
 
 
 async def embed_dense(client: httpx.AsyncClient, text: str) -> list[float]:
@@ -222,18 +212,64 @@ def embed_sparse_sync(text: str) -> SparseVector:
 
 
 def build_dense_query(question: Question) -> str:
-    return question.text.strip()
+    q = question.search_text.strip() if question.search_text else question.text.strip()
+    return q
 
 
 def build_sparse_query(question: Question) -> str:
-    parts = [question.text.strip()]
+    base = question.search_text.strip() if question.search_text else question.text.strip()
+    parts = [base]
     if question.keywords:
         parts.extend(question.keywords)
-    if question.search_text:
-        parts = [question.search_text]
     return " ".join(parts)
 
 
+def _build_keyword_set(question: Question) -> list[str]:
+    tokens: list[str] = []
+    if question.keywords:
+        tokens.extend(kw.lower() for kw in question.keywords if kw)
+    if question.entities:
+        for field in (
+            question.entities.people,
+            question.entities.emails,
+            question.entities.documents,
+            question.entities.names,
+            question.entities.links,
+        ):
+            tokens.extend(e.lower() for e in (field or []) if e)
+    return tokens
+
+
+def prefilter_for_rerank(
+    points: list[Any],
+    question: Question,
+) -> tuple[list[Any], list[Any]]:
+    """Select candidates for reranking: top by RRF + keyword-boosted stragglers."""
+    if not points:
+        return [], []
+
+    head = points[:RERANK_LIMIT]
+    tail = points[RERANK_LIMIT:]
+
+    keywords = _build_keyword_set(question)
+    if not keywords or not tail:
+        return head, tail
+
+    extra: list[Any] = []
+    remaining_tail: list[Any] = []
+    for p in tail:
+        if len(extra) >= KEYWORD_BOOST_EXTRA:
+            remaining_tail.append(p)
+            continue
+        content = ((p.payload or {}).get("page_content") or "").lower()
+        if any(kw in content for kw in keywords):
+            extra.append(p)
+        else:
+            remaining_tail.append(p)
+
+    return head + extra, remaining_tail
+
+
 async def qdrant_search(
     client: AsyncQdrantClient,
     dense_vectors: list[list[float]],
@@ -326,25 +362,24 @@ async def rerank_points(
     query: str,
     points: list[Any],
 ) -> list[Any]:
-    rerank_candidates = points[:RERANK_LIMIT]
-    rerank_targets = [point.payload.get("page_content") for point in rerank_candidates]
-    scores = await get_rerank_scores(client, query, rerank_targets)
+    if not points:
+        return []
+    targets = [point.payload.get("page_content") for point in points]
+    scores = await get_rerank_scores(client, query, targets)
 
-    if not scores:
-        logger.warning("Reranker unavailable, returning RRF order")
-        return rerank_candidates
+    if not scores or len(scores) != len(points):
+        logger.warning("Reranker unavailable or score mismatch, returning RRF order")
+        return points
 
-    reranked_candidates = [
+    return [
         point
         for _, point in sorted(
-            zip(scores, rerank_candidates, strict=True),
+            zip(scores, points),
             key=lambda item: item[0],
             reverse=True,
         )
     ]
 
-    return reranked_candidates
-
 
 @app.get("/health")
 async def health() -> dict[str, str]:
@@ -365,17 +400,29 @@ async def search(payload: SearchAPIRequest) -> SearchAPIResponse:
     sparse_query = build_sparse_query(question)
 
     dense_task = embed_dense(client, dense_query)
-    sparse_task = asyncio.to_thread(embed_sparse_sync, sparse_query)
+    sparse_task = asyncio.to_thread(lambda: embed_sparse_sync(sparse_query))
     dense_vector, sparse_vector = await asyncio.gather(dense_task, sparse_task)
 
     dense_vectors = [dense_vector]
-    if question.hyde and len(question.hyde) > 0:
+    extra_texts: list[str] = []
+    raw_text = question.text.strip()
+    if raw_text and raw_text != dense_query:
+        extra_texts.append(raw_text)
+    for v in (question.variants or []):
+        q_v = v.strip()
+        if q_v and q_v != dense_query and q_v not in extra_texts:
+            extra_texts.append(q_v)
+    for h in (question.hyde or []):
+        q_h = h.strip()
+        if q_h and q_h != dense_query and q_h not in extra_texts:
+            extra_texts.append(q_h)
+    extra_texts = extra_texts[:3]
+    if extra_texts:
         try:
-            hyde_texts = question.hyde[:2]
-            hyde_vectors = await embed_dense_batch(client, hyde_texts)
-            dense_vectors.extend(hyde_vectors)
+            extra_vecs = await embed_dense_batch(client, extra_texts)
+            dense_vectors.extend(extra_vecs)
         except Exception as e:
-            logger.warning(f"HyDE embedding failed: {e}")
+            logger.warning(f"Extra dense embedding failed: {e}")
 
     all_points = await qdrant_search(qdrant, dense_vectors, sparse_vector)
 
@@ -384,20 +431,20 @@ async def search(payload: SearchAPIRequest) -> SearchAPIResponse:
 
     all_points = list(all_points)
 
-    reranked = await rerank_points(client, query, all_points)
+    rerank_pool, rerank_tail = prefilter_for_rerank(all_points, question)
+    reranked = await rerank_points(client, query, rerank_pool)
+    final_points = reranked + rerank_tail
 
-    reranked_ids = {id(p) for p in reranked}
-    remaining = [p for p in all_points if id(p) not in reranked_ids]
-    final_points = reranked + remaining
-
-    seen: set[str] = set()
-    message_ids: list[str] = []
-    for point in final_points:
+    msg_score: dict[str, float] = {}
+    for rank, point in enumerate(reranked):
+        score = 1.0 / (rank + 1)
         for mid in extract_message_ids(point):
-            if mid not in seen:
-                seen.add(mid)
-                message_ids.append(mid)
-    message_ids = message_ids[:50]
+            msg_score[mid] = msg_score.get(mid, 0.0) + score
+    for rank, point in enumerate(rerank_tail):
+        score = 1.0 / (60 + rank + 1)
+        for mid in extract_message_ids(point):
+            msg_score[mid] = msg_score.get(mid, 0.0) + score
+    message_ids = sorted(msg_score, key=lambda m: msg_score[m], reverse=True)[:50]
 
     return SearchAPIResponse(results=[SearchAPIItem(message_ids=message_ids)])
 

search/requirements.txt

@@ -4,4 +4,3 @@ pydantic==2.12.5
 httpx==0.28.1
 qdrant-client==1.15.1
 fastembed==0.7.4
-orjson==3.10.18

search/retrieval.py

@@ -18,15 +18,21 @@ def _build_filter(question: Question) -> models.Filter | None:
     must_conditions: list[models.Condition] = []
 
     if question.date_range:
-        must_conditions.append(
-            models.FieldCondition(
-                key="metadata.start",
-                datetime_range=models.DatetimeRange(
-                    gte=question.date_range.from_,
-                    lte=question.date_range.to,
-                ),
+        try:
+            must_conditions.append(
+                models.FieldCondition(
+                    key="metadata.end",
+                    range=models.Range(gte=question.date_range.from_),
+                )
             )
-        )
+            must_conditions.append(
+                models.FieldCondition(
+                    key="metadata.start",
+                    range=models.Range(lte=question.date_range.to),
+                )
+            )
+        except Exception as e:
+            logger.warning("Date filter failed: %s", e)
 
     if question.asker:
         must_conditions.append(