Deliver fast and accurate semantic search
"Hip modern bar near the beach" doesn’t work with keyword search. Qdrant enables semantic understanding, real-time availability, and hybrid search that’s predictably fast.
# Hybrid venue search with availability
results = client.query_points(
collection_name="venues",
prefetch=[
Prefetch(query=dense_emb, using="dense", limit=100),
Prefetch(query=sparse_emb, using="sparse", limit=100),
],
query=FusionQuery(fusion=Fusion.RRF),
query_filter=Filter(must=[
FieldCondition("available", match=MatchValue(True)),
FieldCondition("city", match=MatchValue("Paris")),
FieldCondition("rating", range=Range(gte=4.0)),
FieldCondition("price_per_night",
range=Range(lte=300.0)),
]),
limit=20,
)
Step 1
Embed - Parse + Embed Document
Step 2
Search - Semantic Search + Strict Filter
Step 3
Rank - Rank + Rerank (Optional)
Step 4
Result - Evidence-based Match
“With a billion+ user-generated, multimodal reviews from 100s of millions of MAUs, you need a way to bring it together.”
Rahul Todkar
Head of Data and AI, Tripadvisor
2-3x
Revenue
1B+
Reviews Indexed
“Since running it in production, it's probably been one of the most frictionless parts of the stack.”
Patrick Lombardo
Staff ML Engineer, Opentable
Fast, Predictable Response Latency
>60K
Searched with Precision Filtering
Why Teams Choose Qdrant
Legacy Search Engines Weren't Built for How Travelers Think
Keyword search, rigid filters, and batch pipelines break under the demands of modern travel platforms. Teams running semantic search, AI assistants, and real-time inventory hit the same walls.
Real-Time Updates Spike Your Latency
Batch inventory updates can spike P95 latency. Availability changes for hotels and restaurants can't wait for reindexing.
Qdrant's real-time payload updates change availability, pricing, and inventory without touching the index.
Structured Filters Can't Capture Intent
Travelers search semantically (e.g. "romantic rooftop dinner.") Even 600+ filterable attributes can still miss unstructured intent.
Qdrant's hybrid search combines semantic understanding with structured filters for amenities, location, and availability.
Why People Migrate to Qdrant
Evaluating Migration?
Our solutions engineers do technical deep-dives with Travel and Hospitality tech teams weekly.
What you can build with Qdrant
From AI concierges to semantic property search, travel and hospitality teams combine Qdrant's retrieval primitives to deliver experiences keyword search never could.
AI-Powered Discovery
"Find me a romantic rooftop restaurant with a view in Rome" returns ranked results that understand ambiance, cuisine, and location intent. RAG over reviews grounds AI responses in real guest experiences.
Semantic Venue Search
"Competition lap pool" or "hip modern bar" aren't filterable fields. Dense embeddings over reviews and descriptions surface properties matching intent.
Personalized Recommendations
Capture dining style, travel patterns, and review history. Surface personalized recommendations, updated with every interaction.
How It Works Under the Hood
Architecture patterns with API examples for travel and hospitality search, recommendations, and multi-tenancy.
Hybrid Search
Guest intent is part structured (dates, location, price) and part unstructured ("romantic," "family-friendly," "hip vibe"). This pattern fuses semantic and keyword retrieval with structured inventory filters in a single query.
Dense Vectors
Semantic understanding of reviews and descriptions
Sparse Vectors (BM25/SPLADE)
For exact terms (e.g. cuisine, chain, amenity names)
# Hybrid venue search with availability
results = client.query_points(
collection_name="venues",
prefetch=[
Prefetch(query=dense_emb, using="dense", limit=100),
Prefetch(query=sparse_emb, using="sparse", limit=100),
],
query=FusionQuery(fusion=Fusion.RRF),
query_filter=Filter(must=[
FieldCondition("available", match=MatchValue(True)),
FieldCondition("city", match=MatchValue("Paris")),
FieldCondition("rating", range=Range(gte=4.0)),
FieldCondition("price_per_night",
range=Range(lte=300.0)),
]),
limit=20,
)
RAG-Powered AI Concierge
Ground AI assistants in real guest reviews and venue data. This is the pattern OpenTable uses for Concierge: vectorize review chunks, retrieve relevant context per question, and generate grounded answers that reflect actual guest experiences.
Hybrid retrieval for reviews
Dense + sparse captures vibe and specifics
Scoped filtering
Retrieve only reviews for what’s being asked about
Real-time payload updates
Quickly Index New Reviews to Avoid Stale Recommendations
# RAG: retrieve review context for AI assistant
review_chunks = client.query_points(
collection_name="review_chunks",
prefetch=[
Prefetch(query=question_emb, using="dense",
limit=50),
Prefetch(query=question_sparse, using="sparse",
limit=50),
],
query=FusionQuery(fusion=Fusion.RRF),
query_filter=Filter(must=[
FieldCondition("venue_id",
match=MatchValue("restaurant_456")),
]),
limit=10,
)
# Feed to LLM with grounded context
context = "\n".join([r.payload["text"]
for r in review_chunks.points])
answer = llm.generate(
f"Based on reviews: {context}\n"
f"Question: {user_question}"
)
Geo-Partitioned Search
Custom shard keys partition inventory by region so queries stay local, with cross-region discovery when users browse globally.
Custom shard keys by region
Geo-hash or country code — queries stay local to the shard
Cross-region discovery
Search across shards when users browse globally
Time-based sharding for seasonal inventory
Easy deletion of expired listings without reindexing
# Geo-partitioned collection
client.create_collection(
"global_venues",
vectors_config=VectorParams(
size=1536, distance="Cosine"),
sharding_method=ShardingMethod.CUSTOM,
)
# Create region shard
client.create_shard_key(
"global_venues",
shard_key="europe_west",
)
# Upsert with region routing
client.upsert(
"global_venues",
points=[PointStruct(
id=1,
vector=venue_embedding,
payload={
"region": "europe_west",
"city": "Barcelona",
"type": "hotel",
"available": True,
},
)],
shard_key_selector="europe_west",
)
# Query scoped to region
client.query_points(
"global_venues",
query=embedding,
shard_key_filter=["europe_west"],
limit=20,
)
Powering Search For
“Qdrant not only delivered on our performance requirements, but also kept costs in check”
Raghav Sonavane
Associate Director ML, Sprinklr
90%
Faster Indexing Time
30%
Lower Retrieval Costs

