GET
/
api
/
search
/
query
curl --request GET \
  --url https://api.data.adj.news/api/search/query \
  --header 'Authorization: Bearer <token>'
{
  "data": [
    {
      "market_id": "<string>",
      "platform_id": "<string>",
      "ticker": "<string>",
      "adj_ticker": "<string>",
      "market_slug": "<string>",
      "platform": "<string>",
      "market_type": "<string>",
      "question": "<string>",
      "description": "<string>",
      "rules": "<string>",
      "result": "<string>",
      "link": "<string>",
      "status": "<string>",
      "reported_date": "<string>",
      "created_at": "<string>",
      "updated_at": "<string>",
      "end_date": "<string>",
      "resolution_date": "<string>",
      "probability": 123,
      "volume": 123,
      "open_interest": 123,
      "liquidity": 123,
      "category": "<string>",
      "tags": [
        "<string>"
      ],
      "platform_ids": {},
      "status_details": {},
      "settlement_sources": [
        "<string>"
      ],
      "comments_count": 123,
      "has_comments": 123,
      "trades_count": 123,
      "event": "<string>"
    }
  ],
  "meta": {
    "query": "<string>",
    "score": 123
  }
}

This endpoint provides powerful semantic search capabilities that go beyond simple keyword matching. It uses machine learning-based vector embeddings to find markets that are conceptually related to your search query, even if they don’t contain the exact words.

​
How It Works

The search uses Cloudflare Vectorize to find markets semantically related to your query:

  1. Your search query is converted to a vector embedding
  2. This embedding is compared to pre-computed embeddings of all markets
  3. Markets with the most similar embeddings are returned
  4. If vector search fails, it falls back to a sophisticated text search algorithm

​
Query Parameters

  • q (required): The search query text
  • limit (optional): Maximum number of results to return (default: 10)
  • include_context (optional): Whether to include relevance scores and match context (default: false)

​
Response Format

{
  "data": [
    {
      "market_id": "polymarket_will-us-inflation-exceed-3-percent-in-2025",
      "platform": "polymarket",
      "question": "Will US inflation exceed 3% in 2025?",
      // ... other market fields
    },
    // ... more markets
  ],
  "meta": {
    "query": "future inflation rates",
    "score": 0.95  // Only included if include_context=true
  }
}

​
Search Capabilities

This endpoint can handle various types of search queries:

  • Conceptual Searches: “economic impact of election”
  • Question-Based Searches: “Will Trump win in 2024?”
  • Topic Searches: “climate change predictions”
  • Event Searches: “world cup outcomes”

​
Text Search Fallback

If vector search is unavailable or returns insufficient results, the search will automatically fall back to text search, which:

  1. Breaks your query into keywords
  2. Searches for these keywords across questions, descriptions, and rules
  3. Ranks results by relevance and recency

​
Use Cases

  1. Natural Language Search: Allow users to search using everyday language
  2. Discovery Interfaces: Help users find markets related to concepts they’re interested in
  3. Research Tools: Find markets related to specific topics for analysis
  4. Recommendation Systems: Suggest markets based on user interests

​
Example Queries

# Search for markets about presidential elections
curl "https://api.data.adj.news/api/search/query?q=who%20will%20win%20the%20presidential%20election"

# Search with context information included
curl "https://api.data.adj.news/api/search/query?q=climate%20change%20impact&include_context=true"

Authorizations

​
Authorization
string
header
required

Enter your API key as the bearer token

Query Parameters

​
q
string
required

Search query text

​
limit
integer
default:10

Number of results to return

​
include_context
boolean
default:false

Include context and relevance info

Response

200 - application/json
Search results
​
data
object[]
​
meta
object