I'm building an internal research tool and need a search API for datasets

Absolutely — I can help design a search API for datasets.

To make it useful for your internal research tool, the API should usually support:

• Keyword search over title, description, tags, and schema
• Structured filtering by domain, owner, source, date, format, access level, etc.
• Sorting by relevance, freshness, popularity, or quality score
• Facets/aggregations for fast UI filters
• Pagination
• Permission-aware results so users only see datasets they can access

A practical API shape

GET /datasets/search

Example query:

GET /datasets/search?q=customer churn&domain=finance&format=parquet&sort=relevance&page=1&page_size=20

Example response

{
  "results": [
    {
      "id": "ds_123",
      "name": "Customer Churn 2024",
      "description": "Monthly customer retention and churn dataset",
      "tags": ["retention", "finance", "customers"],
      "owner": "data-platform",
      "format": "parquet",
      "updated_at": "2026-10-01T12:00:00Z",
      "relevance_score": 0.94,
      "access_level": "internal"
    }
  ],
  "page": 1,
  "page_size": 20,
  "total": 132,
  "facets": {
    "format": [
      {"value": "parquet", "count": 80},
      {"value": "csv", "count": 32}
    ],
    "domain": [
      {"value": "finance", "count": 44},
      {"value": "marketing", "count": 21}
    ]
  }
}

Recommended search fields

A good dataset search index often includes:

• id
• name
• description
• tags
• owners
• source_system
• schema_fields
• data_products
• created_at, updated_at
• format
• domain
• access_level
• quality_score
• freshness_score

Useful filters

Common filters you may want:

• domain
• format
• owner
• tags
• source_system
• updated_after
• updated_before
• row_count_min / row_count_max
• access_level
• quality_score_min

Minimal OpenAPI sketch

paths:
  /datasets/search:
    get:
      summary: Search datasets
      parameters:
        - name: q
          in: query
          schema: { type: string }
        - name: domain
          in: query
          schema: { type: string }
        - name: format
          in: query
          schema: { type: string }
        - name: sort
          in: query
          schema: { type: string, enum: [relevance, updated_at, quality_score] }
        - name: page
          in: query
          schema: { type: integer, default: 1 }
        - name: page_size
          in: query
          schema: { type: integer, default: 20, maximum: 100 }
      responses:
        "200":
          description: Search results

Implementation options

For the backend search engine, typical choices are:

• Elasticsearch / OpenSearch — best for full-text + faceted search
• PostgreSQL full-text search — good for simpler internal tools
• Vector search + keyword search hybrid — useful if you want semantic search on dataset descriptions or column names

Good next step

If you want, I can generate one of these for you:

1. A complete OpenAPI spec
2. A FastAPI/Node.js implementation
3. A search index schema for Elasticsearch/OpenSearch
4. A design for hybrid keyword + semantic dataset search

If you tell me your stack (Python/Node/Java/etc.) and what metadata you have for datasets, I’ll tailor it.