Search and Filters

Omnismith provides a dynamic query engine for searching entity records across templates. You can combine global full-text search with field-level filter conditions and custom sorting.

1. Querying Capabilities

Full-Text Global Search

Enter search terms into the search bar at the top of any entity template view.
Searches across text attributes (Dimensions), list labels, and reference display names.

Filter entity records by specific attribute conditions using flexible comparison operators:

Attribute Type	Available Operators	Example Filter Condition
String	`Equals`, `Contains`, `Starts With`, `Is Empty`	`Hostname contains "prod"`
Number / Metric	`=`, `>`, `<`, `>=`, `<=`, `Between`	`CPU Usage > 80`
List	`In`, `Not In`, `Is Empty`	`Status in [Online, Degraded]`
Date / DateTime	`Before`, `After`, `Between`, `Is Today`	`Created After 2026-01-01`
Reference	`Equals`, `In`, `Is Unlinked`	`Customer = ACME Corp`

2. Step-by-Step Filter Construction

Navigate to Entity Operations → Entities and select your template.
Click + Add Filter next to the search bar.
Select an attribute (e.g. Status), operator (e.g. Equals), and value (e.g. Online).
Add additional filter chips to form compound logical queries (AND conditions).
Click column headers to adjust sort direction (Ascending / Descending).

Multi-filter entity search with active filter chips and sorted results

3. Native Semantic Vector Search (Natural Language Intent)

In addition to traditional keyword matching, Omnismith provides Native Semantic Vector Search across entities:

Search Modes:
- Keyword Mode: Direct lexical matching and field substring queries.
- Semantic Mode: Searches records based on conceptual meaning, synonyms, and natural language symptoms (e.g., searching “thermal throttling and high heat” retrieves records mentioning “fan speed degraded; CPU temperature peaked at 98°C”).
Cosine Proximity Ranking: Results are ranked by semantic similarity score (from 0.0 to 1.0 / percentage match), prioritizing the most contextually relevant entities.
Enterprise Security & Scope: Semantic vector queries enforce active workspace RBAC and row-level access permissions at query time, guaranteeing that unauthorized records are never returned.

Semantic vector search ranking entities by natural language similarity score

Programmatic Semantic Search API (`POST /api/v1/entities/semantic-search`)

External applications, AI agents, and automated pipelines can query the semantic search endpoint by supplying a 768-dimensional query embedding vector:

curl -X POST "https://api.omnismith.io/api/v1/entities/semantic-search" \
  -H "Authorization: Bearer omni_live_secret_key_12345" \
  -H "Content-Type: application/json" \
  -d '{
    "query_vector": [0.042, -0.015, 0.089, ...],
    "template_id": "server_node",
    "limit": 10,
    "threshold": 0.65,
    "attribute_key": "slug"
  }'

[
  {
    "similarity_score": 0.942,
    "entity": {
      "id": "019fe207-38eb-7182-9a5f-70cdaeea34f2",
      "template_slug": "server_node",
      "attribute_values": {
        "hostname": { "value": "prod-worker-04" },
        "issue_description": { "value": "Fan RPM degraded; temperature spike causing throttling" }
      }
    }
  }
]

4. Programmatic Search & Filtering by Slugs

The Search API (POST /api/v1/entities/search/{templateId}) supports filtering directly by attribute_slug:

curl -X POST "https://api.omnismith.io/api/v1/entities/search/server_node" \
  -H "Authorization: Bearer omni_live_secret_key_12345" \
  -H "Content-Type: application/json" \
  -d '{
    "global_search": "prod",
    "filters": [
      {
        "field": "cpu_usage",
        "operator": "gt",
        "value": "80"
      },
      {
        "field": "status",
        "operator": "eq",
        "value": "Online"
      }
    ],
    "attribute_key": "slug"
  }'

Supported Filter Operators:

eq / neq: Exact equality / inequality
gt / lt: Greater than / less than numeric comparison
like / not-like: Substring matching
empty / not-empty: Null / non-null checking