Create a namespace.

import { Agentset } from "agentset";

const agentset = new Agentset({ apiKey: 'agentset_xxx' });

const namespace = await agentset.namespaces.create({
  name: "My Knowledge Base",
  slug: "my-knowledge-base",
  // embeddingConfig: {...},
  // vectorStoreConfig: {...},
});
console.log(namespace);

from agentset import Agentset


with Agentset(
    token="AGENTSET_API_KEY",
) as a_client:

    res = a_client.namespaces.create(name="<value>", slug="<value>", embedding_config={
        "provider": "GOOGLE",
        "model": "text-embedding-004",
        "api_key": "<value>",
    }, vector_store_config={
        "provider": "PINECONE",
        "api_key": "<value>",
        "index_host": "https://example.svc.aped-1234-a56b.pinecone.io",
    })

    # Handle response
    print(res)

curl --request POST \
  --url https://api.agentset.ai/v1/namespace \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "name": "<string>",
  "slug": "<string>",
  "embeddingConfig": {
    "provider": "MANAGED_OPENAI",
    "model": "text-embedding-3-large"
  },
  "vectorStoreConfig": {
    "provider": "MANAGED_TURBOPUFFER"
  }
}
'

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    name: '<string>',
    slug: '<string>',
    embeddingConfig: {provider: 'MANAGED_OPENAI', model: 'text-embedding-3-large'},
    vectorStoreConfig: {provider: 'MANAGED_TURBOPUFFER'}
  })
};

fetch('https://api.agentset.ai/v1/namespace', 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.agentset.ai/v1/namespace",
  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([
    'name' => '<string>',
    'slug' => '<string>',
    'embeddingConfig' => [
        'provider' => 'MANAGED_OPENAI',
        'model' => 'text-embedding-3-large'
    ],
    'vectorStoreConfig' => [
        'provider' => 'MANAGED_TURBOPUFFER'
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($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.agentset.ai/v1/namespace"

	payload := strings.NewReader("{\n  \"name\": \"<string>\",\n  \"slug\": \"<string>\",\n  \"embeddingConfig\": {\n    \"provider\": \"MANAGED_OPENAI\",\n    \"model\": \"text-embedding-3-large\"\n  },\n  \"vectorStoreConfig\": {\n    \"provider\": \"MANAGED_TURBOPUFFER\"\n  }\n}")

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

	req.Header.Add("Authorization", "Bearer <token>")
	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.agentset.ai/v1/namespace")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"<string>\",\n  \"slug\": \"<string>\",\n  \"embeddingConfig\": {\n    \"provider\": \"MANAGED_OPENAI\",\n    \"model\": \"text-embedding-3-large\"\n  },\n  \"vectorStoreConfig\": {\n    \"provider\": \"MANAGED_TURBOPUFFER\"\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.agentset.ai/v1/namespace")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"<string>\",\n  \"slug\": \"<string>\",\n  \"embeddingConfig\": {\n    \"provider\": \"MANAGED_OPENAI\",\n    \"model\": \"text-embedding-3-large\"\n  },\n  \"vectorStoreConfig\": {\n    \"provider\": \"MANAGED_TURBOPUFFER\"\n  }\n}"

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

{
  "success": true,
  "data": {
    "id": "<string>",
    "name": "<string>",
    "slug": "<string>",
    "organizationId": "<string>",
    "createdAt": "<string>",
    "embeddingConfig": {
      "provider": "<string>",
      "apiKey": "<string>"
    },
    "vectorStoreConfig": {
      "provider": "<string>"
    }
  }
}

{
  "success": false,
  "error": {
    "code": "bad_request",
    "message": "The requested resource was not found.",
    "doc_url": "https://docs.agentset.ai/api-reference/errors#bad-request"
  }
}

{
  "success": false,
  "error": {
    "code": "unauthorized",
    "message": "The requested resource was not found.",
    "doc_url": "https://docs.agentset.ai/api-reference/errors#unauthorized"
  }
}

{
  "success": false,
  "error": {
    "code": "forbidden",
    "message": "The requested resource was not found.",
    "doc_url": "https://docs.agentset.ai/api-reference/errors#forbidden"
  }
}

{
  "success": false,
  "error": {
    "code": "not_found",
    "message": "The requested resource was not found.",
    "doc_url": "https://docs.agentset.ai/api-reference/errors#not-found"
  }
}

{
  "success": false,
  "error": {
    "code": "conflict",
    "message": "The requested resource was not found.",
    "doc_url": "https://docs.agentset.ai/api-reference/errors#conflict"
  }
}

{
  "success": false,
  "error": {
    "code": "invite_expired",
    "message": "The requested resource was not found.",
    "doc_url": "https://docs.agentset.ai/api-reference/errors#invite-expired"
  }
}

{
  "success": false,
  "error": {
    "code": "unprocessable_entity",
    "message": "The requested resource was not found.",
    "doc_url": "https://docs.agentset.ai/api-reference/errors#unprocessable-entity"
  }
}

{
  "success": false,
  "error": {
    "code": "rate_limit_exceeded",
    "message": "The requested resource was not found.",
    "doc_url": "https://docs.agentset.ai/api-reference/errors#rate-limit_exceeded"
  }
}

{
  "success": false,
  "error": {
    "code": "internal_server_error",
    "message": "The requested resource was not found.",
    "doc_url": "https://docs.agentset.ai/api-reference/errors#internal-server_error"
  }
}