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c4f5f25 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 | """
Optimized query builder for OpenSearch to improve search performance.
"""
import logging
from datetime import datetime, timedelta
from typing import Any
logger = logging.getLogger(__name__)
class OptimizedQueryBuilder:
"""Builds optimized OpenSearch queries for better performance."""
@staticmethod
def build_bm25_query(
query_text: str,
*,
top_k: int = 10,
filters: dict[str, Any] | None = None,
min_score: float = 0.5,
boost_recent: bool = True
) -> dict[str, Any]:
"""Build optimized BM25 query with performance enhancements."""
# Use function score for better relevance and performance
query = {
"size": top_k,
"min_score": min_score,
"query": {
"function_score": {
"query": {
"bool": {
"must": [
{
"multi_match": {
"query": query_text,
"fields": [
"chunk_text^3",
"title^2",
"section_title^1.5",
"abstract^1"
],
"type": "best_fields",
"fuzziness": "AUTO",
"prefix_length": 2,
"max_expansions": 50
}
}
]
}
},
"functions": [],
"score_mode": "multiply",
"boost_mode": "replace"
}
},
# Optimize for performance
"_source": ["_id", "chunk_text", "title", "section_title", "abstract", "metadata"],
"sort": ["_score"],
"track_total_hits": False # Disable total hit counting for better performance
}
# Add recency boost if enabled
if boost_recent:
query["query"]["function_score"]["functions"].append({
"gauss": {
"metadata.publication_date": {
"origin": "now",
"scale": "365d",
"offset": "30d",
"decay": 0.5
}
},
"weight": 1.2
})
# Add filters
if filters:
query["query"]["function_score"]["query"]["bool"]["filter"] = (
OptimizedQueryBuilder._build_filters(filters)
)
return query
@staticmethod
def build_vector_query(
query_vector: list[float],
*,
top_k: int = 10,
filters: dict[str, Any] | None = None,
min_score: float = 0.7,
num_candidates: int = 100 # Larger candidate set for better recall
) -> dict[str, Any]:
"""Build optimized vector KNN query."""
query = {
"size": top_k,
"min_score": min_score,
"query": {
"knn": {
"embedding": {
"vector": query_vector,
"k": top_k,
"num_candidates": num_candidates
}
}
},
"_source": ["_id", "chunk_text", "title", "section_title", "abstract", "metadata"],
"track_total_hits": False
}
# Add filters for KNN (must be in filter context)
if filters:
query["query"] = {
"bool": {
"must": [query["query"]],
"filter": OptimizedQueryBuilder._build_filters(filters)
}
}
return query
@staticmethod
def build_hybrid_query(
query_text: str,
query_vector: list[float],
*,
top_k: int = 10,
filters: dict[str, Any] | None = None,
rrf_window_size: int = 50,
rrf_rank_constant: int = 60
) -> dict[str, Any]:
"""Build optimized hybrid query using RRF (Reciprocal Rank Fusion)."""
# Build separate queries for BM25 and vector
bm25_query = OptimizedQueryBuilder.build_bm25_query(
query_text, top_k=rrf_window_size, filters=filters, min_score=0.1
)
vector_query = OptimizedQueryBuilder.build_vector_query(
query_vector, top_k=rrf_window_size, filters=filters, min_score=0.1
)
# Combine using RRF
query = {
"size": top_k,
"query": {
"rrf": {
"queries": [bm25_query["query"], vector_query["query"]],
"rank_constant": rrf_rank_constant
}
},
"_source": ["_id", "chunk_text", "title", "section_title", "abstract", "metadata"],
"track_total_hits": False
}
return query
@staticmethod
def build_aggregation_query(
query_text: str,
agg_field: str,
*,
size: int = 10,
filters: dict[str, Any] | None = None
) -> dict[str, Any]:
"""Build query with aggregations for analytics."""
query = {
"size": 0, # We only want aggregations
"query": {
"multi_match": {
"query": query_text,
"fields": ["chunk_text", "title", "abstract"]
}
},
"aggs": {
"top_values": {
"terms": {
"field": f"{agg_field}.keyword",
"size": size,
"min_doc_count": 1
}
}
}
}
if filters:
query["query"] = {
"bool": {
"must": [query["query"]],
"filter": OptimizedQueryBuilder._build_filters(filters)
}
}
return query
@staticmethod
def _build_filters(filters: dict[str, Any]) -> list[dict[str, Any]]:
"""Build optimized filter clauses."""
filter_clauses = []
for field, value in filters.items():
if isinstance(value, list):
# Multiple values - use terms query
filter_clauses.append({
"terms": {f"{field}.keyword": value}
})
elif isinstance(value, dict):
# Range query
if "gte" in value or "lte" in value or "gt" in value or "lt" in value:
range_filter = {"range": {field: {}}}
for op, val in value.items():
if op in ["gte", "lte", "gt", "lt"]:
range_filter["range"][field][op] = val
filter_clauses.append(range_filter)
else:
# Nested query
filter_clauses.append({
"nested": {
"path": field,
"query": {
"bool": {
"must": [
{"term": {f"{field}.{k}.keyword": v}}
for k, v in value.items()
]
}
}
}
})
else:
# Single value - use term query
filter_clauses.append({
"term": {f"{field}.keyword": value}
})
return filter_clauses
@staticmethod
def build_suggestion_query(
text: str,
*,
field: str = "chunk_text",
size: int = 5
) -> dict[str, Any]:
"""Build query for spell-check suggestions."""
return {
"suggest": {
"text": text,
"simple_phrase": {
"phrase": {
"field": field,
"size": size,
"gram_size": 3,
"direct_generator": [{
"field": field,
"suggest_mode": "missing"
}],
"highlight": {
"pre_tag": "<em>",
"post_tag": "</em>"
}
}
}
}
}
@staticmethod
def build_more_like_this_query(
doc_id: str,
*,
top_k: int = 10,
min_term_freq: int = 1,
max_query_terms: int = 25,
min_doc_freq: int = 2
) -> dict[str, Any]:
"""Build More Like This query."""
return {
"size": top_k,
"query": {
"more_like_this": {
"fields": ["chunk_text", "title", "abstract"],
"like": [{"_index": "medical_chunks", "_id": doc_id}],
"min_term_freq": min_term_freq,
"max_query_terms": max_query_terms,
"min_doc_freq": min_doc_freq
}
},
"_source": ["_id", "chunk_text", "title", "section_title", "abstract", "metadata"],
"track_total_hits": False
}
class QueryCache:
"""Simple query result cache for frequently executed queries."""
def __init__(self, max_size: int = 1000, ttl_seconds: int = 300):
self.cache: dict[str, dict[str, Any]] = {}
self.max_size = max_size
self.ttl_seconds = ttl_seconds
def get(self, query_hash: str) -> list[dict[str, Any]] | None:
"""Get cached results if not expired."""
if query_hash in self.cache:
entry = self.cache[query_hash]
if datetime.now() - entry["timestamp"] < timedelta(seconds=self.ttl_seconds):
return entry["results"]
else:
del self.cache[query_hash]
return None
def set(self, query_hash: str, results: list[dict[str, Any]]) -> None:
"""Cache query results."""
# Remove oldest entries if cache is full
if len(self.cache) >= self.max_size:
oldest_key = min(self.cache.keys(),
key=lambda k: self.cache[k]["timestamp"])
del self.cache[oldest_key]
self.cache[query_hash] = {
"results": results,
"timestamp": datetime.now()
}
def clear(self) -> None:
"""Clear the cache."""
self.cache.clear()
def get_stats(self) -> dict[str, Any]:
"""Get cache statistics."""
return {
"size": len(self.cache),
"max_size": self.max_size,
"ttl_seconds": self.ttl_seconds
}
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