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Video Intelligence Platform — Query Engine
Handles natural language queries with boolean decomposition,
dual-channel search (visual + caption), and result fusion.
"""
import numpy as np
from typing import List, Dict, Optional, Tuple, Set
from collections import defaultdict
from .index_store import VideoIndex
from .gemini_client import GeminiClient
from .visual_encoders import SigLIPEncoder
class QueryResult:
"""A single search result with timestamp and relevance info."""
def __init__(self, frame_id: int, timestamp_sec: float, score: float,
caption: str = "", detections: List[str] = None,
match_source: str = ""):
self.frame_id = frame_id
self.timestamp_sec = timestamp_sec
self.score = score
self.caption = caption
self.detections = detections or []
self.match_source = match_source # "visual", "caption", "detection", "fused"
@property
def time_str(self) -> str:
"""Format timestamp as HH:MM:SS."""
ts = self.timestamp_sec
hrs = int(ts // 3600)
mins = int((ts % 3600) // 60)
secs = int(ts % 60)
return f"{hrs:02d}:{mins:02d}:{secs:02d}"
def to_dict(self) -> Dict:
return {
"frame_id": self.frame_id,
"timestamp_sec": self.timestamp_sec,
"time_str": self.time_str,
"score": self.score,
"caption": self.caption,
"detections": self.detections,
"match_source": self.match_source,
}
def __repr__(self):
cap = self.caption[:80] if self.caption else ""
return f"[{self.time_str}] score={self.score:.3f} ({self.match_source}) {cap}"
class QueryEngine:
"""
Multi-channel query engine:
1. Visual search: SigLIP2 text→frame embedding similarity
2. Caption search: Gemini embedding text→caption similarity
3. Detection search: SQL structured search on detected objects
4. Fusion: merge results from all channels with score weighting
5. Boolean ops: AND (intersect timestamps), OR (union), NOT (exclude)
"""
def __init__(self, index: VideoIndex, gemini: GeminiClient,
siglip: SigLIPEncoder, top_k: int = 20):
self.index = index
self.gemini = gemini
self.siglip = siglip
self.top_k = top_k
# Channel weights for fusion
self.weights = {
"visual": 0.35,
"caption": 0.35,
"detection": 0.30,
}
def search(self, query: str, top_k: Optional[int] = None) -> List[QueryResult]:
"""
Full search pipeline:
1. Decompose query (detect boolean operators)
2. Search each sub-query across all channels
3. Apply boolean operations
4. Return fused, ranked results
"""
top_k = top_k or self.top_k
# Step 1: Decompose query
decomposed = self.gemini.decompose_query(query)
sub_queries = decomposed.get("sub_queries", [query])
operator = decomposed.get("operator", "SINGLE")
print(f"🔍 Query: '{query}'")
print(f" Decomposed: {sub_queries} [{operator}]")
# Step 2: Search each sub-query
sub_results = []
for sq in sub_queries:
results = self._search_single(sq, top_k=top_k * 2) # Over-fetch for fusion
sub_results.append(results)
# Step 3: Apply boolean operations
if operator == "AND" and len(sub_results) > 1:
final = self._boolean_and(sub_results)
elif operator == "OR" and len(sub_results) > 1:
final = self._boolean_or(sub_results)
else:
final = sub_results[0] if sub_results else []
# Step 4: Sort by score, deduplicate nearby timestamps, limit
final = self._deduplicate_temporal(final, window_sec=3.0)
final.sort(key=lambda r: r.score, reverse=True)
return final[:top_k]
def _search_single(self, query: str, top_k: int = 40) -> List[QueryResult]:
"""Search a single query across all channels and fuse results."""
results_by_frame: Dict[int, Dict] = defaultdict(lambda: {
"scores": {}, "caption": "", "detections": [], "timestamp_sec": 0
})
# Channel 1: Visual search (SigLIP2)
try:
text_emb = self.siglip.embed_texts([query])
if text_emb.size > 0:
visual_hits = self.index.search_visual(text_emb[0], top_k=top_k)
for frame_id, score in visual_hits:
results_by_frame[frame_id]["scores"]["visual"] = score
frame = self.index.get_frame(frame_id)
if frame:
results_by_frame[frame_id]["timestamp_sec"] = frame["timestamp_sec"]
results_by_frame[frame_id]["caption"] = frame.get("caption", "")
except Exception as e:
print(f" ⚠️ Visual search failed: {e}")
# Channel 2: Caption search (Gemini embeddings)
try:
query_emb = self.gemini.embed_query(query)
if query_emb:
caption_hits = self.index.search_captions(
np.array(query_emb), top_k=top_k
)
for frame_id, score in caption_hits:
results_by_frame[frame_id]["scores"]["caption"] = score
frame = self.index.get_frame(frame_id)
if frame:
results_by_frame[frame_id]["timestamp_sec"] = frame["timestamp_sec"]
results_by_frame[frame_id]["caption"] = frame.get("caption", "")
except Exception as e:
print(f" ⚠️ Caption search failed: {e}")
# Channel 3: Detection search (structured SQL)
try:
detection_hits = self.index.search_detections(query)
for det in detection_hits[:top_k]:
fid = det["frame_id"]
# Score based on detection confidence
det_score = det["confidence"]
existing = results_by_frame[fid]["scores"].get("detection", 0)
results_by_frame[fid]["scores"]["detection"] = max(existing, det_score)
results_by_frame[fid]["timestamp_sec"] = det["timestamp_sec"]
results_by_frame[fid]["caption"] = det.get("caption", "")
results_by_frame[fid]["detections"].append(det["label"])
except Exception as e:
print(f" ⚠️ Detection search failed: {e}")
# Fuse scores
fused_results = []
for frame_id, data in results_by_frame.items():
# Weighted score fusion
total_score = 0
total_weight = 0
sources = []
for channel, weight in self.weights.items():
if channel in data["scores"]:
total_score += data["scores"][channel] * weight
total_weight += weight
sources.append(channel)
final_score = total_score / total_weight if total_weight > 0 else 0
fused_results.append(QueryResult(
frame_id=frame_id,
timestamp_sec=data["timestamp_sec"],
score=final_score,
caption=data["caption"],
detections=list(set(data["detections"])),
match_source="+".join(sources),
))
return fused_results
def _boolean_and(self, sub_results: List[List[QueryResult]]) -> List[QueryResult]:
"""
AND operation: find timestamps where ALL sub-queries match.
Uses a temporal window (±5 seconds) for fuzzy timestamp matching.
"""
if not sub_results:
return []
window = 5.0 # seconds tolerance for "same moment"
# Get timestamp sets for each sub-query
def get_timestamp_set(results: List[QueryResult]) -> List[Tuple[float, QueryResult]]:
return [(r.timestamp_sec, r) for r in results]
sets = [get_timestamp_set(sr) for sr in sub_results]
# Find timestamps in first set that have matches in all other sets
merged = []
for ts1, r1 in sets[0]:
all_match = True
combined_score = r1.score
combined_detections = list(r1.detections)
for other_set in sets[1:]:
# Find closest match within window
best_match = None
best_dist = float("inf")
for ts2, r2 in other_set:
dist = abs(ts1 - ts2)
if dist < window and dist < best_dist:
best_dist = dist
best_match = r2
if best_match is None:
all_match = False
break
else:
combined_score = (combined_score + best_match.score) / 2
combined_detections.extend(best_match.detections)
if all_match:
merged.append(QueryResult(
frame_id=r1.frame_id,
timestamp_sec=r1.timestamp_sec,
score=combined_score,
caption=r1.caption,
detections=list(set(combined_detections)),
match_source="fused_AND",
))
return merged
def _boolean_or(self, sub_results: List[List[QueryResult]]) -> List[QueryResult]:
"""OR operation: union of all results."""
seen_frames: Set[int] = set()
merged = []
for result_list in sub_results:
for r in result_list:
if r.frame_id not in seen_frames:
seen_frames.add(r.frame_id)
r.match_source += "_OR"
merged.append(r)
return merged
def _deduplicate_temporal(self, results: List[QueryResult],
window_sec: float = 3.0) -> List[QueryResult]:
"""Remove results that are too close in time (keep highest score)."""
if not results:
return []
results.sort(key=lambda r: r.timestamp_sec)
deduped = [results[0]]
for r in results[1:]:
if abs(r.timestamp_sec - deduped[-1].timestamp_sec) > window_sec:
deduped.append(r)
elif r.score > deduped[-1].score:
deduped[-1] = r
return deduped
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