AI-Powered Semantic Search
Search that understands meaning, not just keywords. Vector embeddings, hybrid retrieval, and intelligent re-ranking for enterprise data.
Keyword search fails when meaning matters
Traditional keyword search matches strings. Semantic search matches intent — understanding that “work from home policy” and “remote work guidelines” mean the same thing.
All employees must submit PTO requests…
Use exact keywords when filing tickets…
Configure keyword match thresholds…
Matched “keyword” and “work” literally — missed the intent entirely
Employees may work from home up to 3 days per week with manager approval.
Core hours are 10 AM–3 PM; start and end times are flexible.
Teams can choose their in-office days collaboratively.
Understood “work from home” = remote work, flexible schedule, hybrid workplace
How semantic search works
From raw query to ranked results — five stages that turn natural language into precise, meaning-aware retrieval.
Query Embedding
Convert query to a vector
Vector Search
Find nearest neighbors
Hybrid Retrieval
Combine BM25 + dense vectors
Re-Ranking
Cross-encoder precision scoring
Result Presentation
Ranked answers with context
The user's natural-language query is passed through an embedding model that maps it to a high-dimensional vector. This vector captures semantic meaning — "work from home" and "remote policy" produce nearby vectors even though they share no words.
Embedding model landscape
The embedding model determines how well your search understands meaning. Each model trades off dimensions, speed, multilingual coverage, and benchmark accuracy.
OpenAI · large
Strong general-purpose accuracy on MTEB-class benchmarks; supports variable output dimensions (Matryoshka-style truncation).
Cohere · Embed
Multilingual leader with 100+ languages; built-in input-type classification (search_query vs search_document).
BAAI · BGE (large)
Open-source; fine-tunable on private data. Ideal for on-premise deployments with no external API calls.
Microsoft · E5 (large)
Instruction-tuned; strong zero-shot transfer across domains. Pairs well with Azure ecosystem.
Jina AI · embeddings
Late-interaction architecture; strong long-document retrieval up to 8K-token context.
Vector database comparison
Where your embeddings live matters. The right vector database depends on scale, existing infrastructure, and operational requirements.
Pinecone
Fully managedServerless auto-scale to billions
Teams wanting zero-ops vector infra with enterprise SLAs.
Weaviate
Open-source / CloudHorizontal sharding with replication
Hybrid search (vector + BM25) in a single engine with GraphQL API.
Qdrant
Open-source / CloudDistributed with consensus
Performance-critical workloads needing fine-grained filtering and payload indexing.
pgvector
Postgres extensionScales with Postgres (pgBouncer, Citus)
Teams already on Postgres who want vectors alongside relational data.
Chroma
Open-source / EmbeddedSingle-node to client-server
Prototyping and developer experience; Python-native API with LangChain integration.
Milvus
Open-source / Cloud (Zilliz)Disaggregated storage + compute
Multi-billion-vector deployments with GPU-accelerated indexing.
Enterprise search patterns
Production semantic search requires more than vector similarity. These patterns solve the hard problems of multi-tenancy, security, federation, and freshness.
Multi-Tenant Search
Isolate each tenant's index partition using namespace prefixes or metadata filters. Queries never cross tenant boundaries, even when the underlying vector store is shared.
Access-Controlled Results
Every indexed chunk carries ACL metadata (roles, groups, permissions). At query time, results are filtered to only return documents the authenticated user is authorized to see.
Federated Search
Query multiple indexes simultaneously — internal wikis, CRM notes, support tickets, code repositories — and merge results with cross-source re-ranking.
Real-Time Indexing
Stream new and updated documents into the vector index within seconds of creation. CDC (Change Data Capture) pipelines keep the search index perpetually in sync with source systems.
Multi-Tenant Search — Implementation Detail
Tenant IDs are injected at indexing time and enforced as pre-filters on every query. Combined with row-level security on metadata, this ensures complete data isolation without separate infrastructure per tenant.
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