Match Persona

curl --request POST \
  --url https://api.upriver.ai/v1/catalogs/{catalog_id}/match \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "persona": {
    "summary": "Tech founders interested in developer tools and automation"
  },
  "settings": {
    "preset": "balanced",
    "priorities": {},
    "exclusions": {
      "categories": [
        "<string>"
      ],
      "brands": [
        "<string>"
      ],
      "sensitive": true
    },
    "inferred_needs": "off",
    "min_evidence": 0.35,
    "price_tier_affinity": true,
    "geo": "<string>",
    "language": "<string>",
    "limit": 10
  }
}
'

import requests

url = "https://api.upriver.ai/v1/catalogs/{catalog_id}/match"

payload = {
    "persona": { "summary": "Tech founders interested in developer tools and automation" },
    "settings": {
        "preset": "balanced",
        "priorities": {},
        "exclusions": {
            "categories": ["<string>"],
            "brands": ["<string>"],
            "sensitive": True
        },
        "inferred_needs": "off",
        "min_evidence": 0.35,
        "price_tier_affinity": True,
        "geo": "<string>",
        "language": "<string>",
        "limit": 10
    }
}
headers = {
    "X-API-Key": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'X-API-Key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    persona: {summary: 'Tech founders interested in developer tools and automation'},
    settings: {
      preset: 'balanced',
      priorities: {},
      exclusions: {categories: ['<string>'], brands: ['<string>'], sensitive: true},
      inferred_needs: 'off',
      min_evidence: 0.35,
      price_tier_affinity: true,
      geo: '<string>',
      language: '<string>',
      limit: 10
    }
  })
};

fetch('https://api.upriver.ai/v1/catalogs/{catalog_id}/match', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.upriver.ai/v1/catalogs/{catalog_id}/match",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'persona' => [
        'summary' => 'Tech founders interested in developer tools and automation'
    ],
    'settings' => [
        'preset' => 'balanced',
        'priorities' => [
                
        ],
        'exclusions' => [
                'categories' => [
                                '<string>'
                ],
                'brands' => [
                                '<string>'
                ],
                'sensitive' => true
        ],
        'inferred_needs' => 'off',
        'min_evidence' => 0.35,
        'price_tier_affinity' => true,
        'geo' => '<string>',
        'language' => '<string>',
        'limit' => 10
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "X-API-Key: <api-key>"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.upriver.ai/v1/catalogs/{catalog_id}/match"

	payload := strings.NewReader("{\n  \"persona\": {\n    \"summary\": \"Tech founders interested in developer tools and automation\"\n  },\n  \"settings\": {\n    \"preset\": \"balanced\",\n    \"priorities\": {},\n    \"exclusions\": {\n      \"categories\": [\n        \"<string>\"\n      ],\n      \"brands\": [\n        \"<string>\"\n      ],\n      \"sensitive\": true\n    },\n    \"inferred_needs\": \"off\",\n    \"min_evidence\": 0.35,\n    \"price_tier_affinity\": true,\n    \"geo\": \"<string>\",\n    \"language\": \"<string>\",\n    \"limit\": 10\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("X-API-Key", "<api-key>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.upriver.ai/v1/catalogs/{catalog_id}/match")
  .header("X-API-Key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"persona\": {\n    \"summary\": \"Tech founders interested in developer tools and automation\"\n  },\n  \"settings\": {\n    \"preset\": \"balanced\",\n    \"priorities\": {},\n    \"exclusions\": {\n      \"categories\": [\n        \"<string>\"\n      ],\n      \"brands\": [\n        \"<string>\"\n      ],\n      \"sensitive\": true\n    },\n    \"inferred_needs\": \"off\",\n    \"min_evidence\": 0.35,\n    \"price_tier_affinity\": true,\n    \"geo\": \"<string>\",\n    \"language\": \"<string>\",\n    \"limit\": 10\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.upriver.ai/v1/catalogs/{catalog_id}/match")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["X-API-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"persona\": {\n    \"summary\": \"Tech founders interested in developer tools and automation\"\n  },\n  \"settings\": {\n    \"preset\": \"balanced\",\n    \"priorities\": {},\n    \"exclusions\": {\n      \"categories\": [\n        \"<string>\"\n      ],\n      \"brands\": [\n        \"<string>\"\n      ],\n      \"sensitive\": true\n    },\n    \"inferred_needs\": \"off\",\n    \"min_evidence\": 0.35,\n    \"price_tier_affinity\": true,\n    \"geo\": \"<string>\",\n    \"language\": \"<string>\",\n    \"limit\": 10\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "interpretation": {
    "mode": "summary",
    "facets": [
      {
        "kind": "identity",
        "text": "<string>",
        "confidence": 0.5,
        "sensitive": true,
        "source_span": "<string>",
        "parent_index": 1
      }
    ],
    "llm_calls": 1,
    "language": "<string>",
    "price_sensitivity": "budget"
  },
  "matches": [
    {
      "ref": "<string>",
      "brand_url": "<string>",
      "score": 0.5,
      "evidence_grade": "grounded",
      "contributions": [
        {
          "edge": "direct",
          "user_facet": {
            "kind": "identity",
            "text": "<string>",
            "confidence": 0.5,
            "sensitive": true,
            "source_span": "<string>",
            "parent_index": 1
          },
          "product_facet": {
            "kind": "identity",
            "text": "<string>",
            "confidence": 0.5,
            "evidence_grade": "grounded"
          },
          "similarity": 123,
          "weight": 123,
          "delta": 123,
          "evidence": [
            {
              "source": "<string>",
              "snippet": "<string>",
              "url": "<string>",
              "similarity": 0.5,
              "judge_probability": 0.5
            }
          ]
        }
      ],
      "reasons": [
        "<string>"
      ],
      "matched_offer": {
        "name": "<string>",
        "description": "<string>",
        "relevance_score": 0.5,
        "url": "<string>",
        "evidence": [
          {
            "source": "<string>",
            "snippet": "<string>",
            "url": "<string>",
            "similarity": 0.5,
            "judge_probability": 0.5
          }
        ]
      },
      "price_tier_fit": "match"
    }
  ],
  "settings_applied": {
    "preset": "balanced",
    "priorities": {},
    "exclusions": {
      "categories": [
        "<string>"
      ],
      "brands": [
        "<string>"
      ],
      "sensitive": true
    },
    "inferred_needs": "off",
    "min_evidence": 0.35,
    "price_tier_affinity": true,
    "geo": "<string>",
    "language": "<string>",
    "limit": 10
  },
  "strategy_version": "<string>",
  "completeness": "complete",
  "brief_fingerprint": "<string>",
  "relevance_status": "disabled",
  "judge_applied": false
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}