curl --request POST \
  --url https://api.trieve.ai/api/organization/update_dataset_configs \
  --header 'Authorization: <api-key>' \
  --header 'Content-Type: application/json' \
  --header 'TR-Organization: <tr-organization>' \
  --data '
{
  "organization_id": "00000000-0000-0000-0000-000000000000",
  "server_configuration": {
    "AIMON_RERANKER_TASK_DEFINITION": "Your task is to grade the relevance of context document(s) against the specified user query.",
    "BM25_AVG_LEN": 256,
    "BM25_B": 0.75,
    "BM25_ENABLED": true,
    "BM25_K": 0.75,
    "EMBEDDING_BASE_URL": "https://api.openai.com/v1",
    "EMBEDDING_MODEL_NAME": "text-embedding-3-small",
    "EMBEDDING_QUERY_PREFIX": "",
    "EMBEDDING_SIZE": 1536,
    "FREQUENCY_PENALTY": 0,
    "FULLTEXT_ENABLED": true,
    "INDEXED_ONLY": false,
    "LLM_BASE_URL": "https://api.openai.com/v1",
    "LLM_DEFAULT_MODEL": "gpt-3.5-turbo-1106",
    "LOCKED": false,
    "MAX_LIMIT": 10000,
    "MESSAGE_TO_QUERY_PROMPT": "Write a 1-2 sentence semantic search query along the lines of a hypothetical response to: \n\n",
    "N_RETRIEVALS_TO_INCLUDE": 8,
    "PRESENCE_PENALTY": 0,
    "QDRANT_ONLY": false,
    "RAG_PROMPT": "Use the following retrieved documents to respond briefly and accurately:",
    "SEMANTIC_ENABLED": true,
    "STOP_TOKENS": [
      "\n\n",
      "\n"
    ],
    "SYSTEM_PROMPT": "You are a helpful assistant",
    "TEMPERATURE": 0.5,
    "USE_MESSAGE_TO_QUERY_PROMPT": false
  }
}
'

import requests

url = "https://api.trieve.ai/api/organization/update_dataset_configs"

payload = {
    "organization_id": "00000000-0000-0000-0000-000000000000",
    "server_configuration": {
        "AIMON_RERANKER_TASK_DEFINITION": "Your task is to grade the relevance of context document(s) against the specified user query.",
        "BM25_AVG_LEN": 256,
        "BM25_B": 0.75,
        "BM25_ENABLED": True,
        "BM25_K": 0.75,
        "EMBEDDING_BASE_URL": "https://api.openai.com/v1",
        "EMBEDDING_MODEL_NAME": "text-embedding-3-small",
        "EMBEDDING_QUERY_PREFIX": "",
        "EMBEDDING_SIZE": 1536,
        "FREQUENCY_PENALTY": 0,
        "FULLTEXT_ENABLED": True,
        "INDEXED_ONLY": False,
        "LLM_BASE_URL": "https://api.openai.com/v1",
        "LLM_DEFAULT_MODEL": "gpt-3.5-turbo-1106",
        "LOCKED": False,
        "MAX_LIMIT": 10000,
        "MESSAGE_TO_QUERY_PROMPT": "Write a 1-2 sentence semantic search query along the lines of a hypothetical response to: 

",
        "N_RETRIEVALS_TO_INCLUDE": 8,
        "PRESENCE_PENALTY": 0,
        "QDRANT_ONLY": False,
        "RAG_PROMPT": "Use the following retrieved documents to respond briefly and accurately:",
        "SEMANTIC_ENABLED": True,
        "STOP_TOKENS": ["

", "
"],
        "SYSTEM_PROMPT": "You are a helpful assistant",
        "TEMPERATURE": 0.5,
        "USE_MESSAGE_TO_QUERY_PROMPT": False
    }
}
headers = {
    "TR-Organization": "<tr-organization>",
    "Authorization": "<api-key>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {
    'TR-Organization': '<tr-organization>',
    Authorization: '<api-key>',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    organization_id: '00000000-0000-0000-0000-000000000000',
    server_configuration: {
      AIMON_RERANKER_TASK_DEFINITION: 'Your task is to grade the relevance of context document(s) against the specified user query.',
      BM25_AVG_LEN: 256,
      BM25_B: 0.75,
      BM25_ENABLED: true,
      BM25_K: 0.75,
      EMBEDDING_BASE_URL: 'https://api.openai.com/v1',
      EMBEDDING_MODEL_NAME: 'text-embedding-3-small',
      EMBEDDING_QUERY_PREFIX: '',
      EMBEDDING_SIZE: 1536,
      FREQUENCY_PENALTY: 0,
      FULLTEXT_ENABLED: true,
      INDEXED_ONLY: false,
      LLM_BASE_URL: 'https://api.openai.com/v1',
      LLM_DEFAULT_MODEL: 'gpt-3.5-turbo-1106',
      LOCKED: false,
      MAX_LIMIT: 10000,
      MESSAGE_TO_QUERY_PROMPT: 'Write a 1-2 sentence semantic search query along the lines of a hypothetical response to: \n\n',
      N_RETRIEVALS_TO_INCLUDE: 8,
      PRESENCE_PENALTY: 0,
      QDRANT_ONLY: false,
      RAG_PROMPT: 'Use the following retrieved documents to respond briefly and accurately:',
      SEMANTIC_ENABLED: true,
      STOP_TOKENS: ['\n\n', '\n'],
      SYSTEM_PROMPT: 'You are a helpful assistant',
      TEMPERATURE: 0.5,
      USE_MESSAGE_TO_QUERY_PROMPT: false
    }
  })
};

fetch('https://api.trieve.ai/api/organization/update_dataset_configs', 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.trieve.ai/api/organization/update_dataset_configs",
  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([
    'organization_id' => '00000000-0000-0000-0000-000000000000',
    'server_configuration' => [
        'AIMON_RERANKER_TASK_DEFINITION' => 'Your task is to grade the relevance of context document(s) against the specified user query.',
        'BM25_AVG_LEN' => 256,
        'BM25_B' => 0.75,
        'BM25_ENABLED' => true,
        'BM25_K' => 0.75,
        'EMBEDDING_BASE_URL' => 'https://api.openai.com/v1',
        'EMBEDDING_MODEL_NAME' => 'text-embedding-3-small',
        'EMBEDDING_QUERY_PREFIX' => '',
        'EMBEDDING_SIZE' => 1536,
        'FREQUENCY_PENALTY' => 0,
        'FULLTEXT_ENABLED' => true,
        'INDEXED_ONLY' => false,
        'LLM_BASE_URL' => 'https://api.openai.com/v1',
        'LLM_DEFAULT_MODEL' => 'gpt-3.5-turbo-1106',
        'LOCKED' => false,
        'MAX_LIMIT' => 10000,
        'MESSAGE_TO_QUERY_PROMPT' => 'Write a 1-2 sentence semantic search query along the lines of a hypothetical response to: 

',
        'N_RETRIEVALS_TO_INCLUDE' => 8,
        'PRESENCE_PENALTY' => 0,
        'QDRANT_ONLY' => false,
        'RAG_PROMPT' => 'Use the following retrieved documents to respond briefly and accurately:',
        'SEMANTIC_ENABLED' => true,
        'STOP_TOKENS' => [
                '

',
                '
'
        ],
        'SYSTEM_PROMPT' => 'You are a helpful assistant',
        'TEMPERATURE' => 0.5,
        'USE_MESSAGE_TO_QUERY_PROMPT' => false
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: <api-key>",
    "Content-Type: application/json",
    "TR-Organization: <tr-organization>"
  ],
]);

$response = curl_exec($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.trieve.ai/api/organization/update_dataset_configs"

	payload := strings.NewReader("{\n  \"organization_id\": \"00000000-0000-0000-0000-000000000000\",\n  \"server_configuration\": {\n    \"AIMON_RERANKER_TASK_DEFINITION\": \"Your task is to grade the relevance of context document(s) against the specified user query.\",\n    \"BM25_AVG_LEN\": 256,\n    \"BM25_B\": 0.75,\n    \"BM25_ENABLED\": true,\n    \"BM25_K\": 0.75,\n    \"EMBEDDING_BASE_URL\": \"https://api.openai.com/v1\",\n    \"EMBEDDING_MODEL_NAME\": \"text-embedding-3-small\",\n    \"EMBEDDING_QUERY_PREFIX\": \"\",\n    \"EMBEDDING_SIZE\": 1536,\n    \"FREQUENCY_PENALTY\": 0,\n    \"FULLTEXT_ENABLED\": true,\n    \"INDEXED_ONLY\": false,\n    \"LLM_BASE_URL\": \"https://api.openai.com/v1\",\n    \"LLM_DEFAULT_MODEL\": \"gpt-3.5-turbo-1106\",\n    \"LOCKED\": false,\n    \"MAX_LIMIT\": 10000,\n    \"MESSAGE_TO_QUERY_PROMPT\": \"Write a 1-2 sentence semantic search query along the lines of a hypothetical response to: \\n\\n\",\n    \"N_RETRIEVALS_TO_INCLUDE\": 8,\n    \"PRESENCE_PENALTY\": 0,\n    \"QDRANT_ONLY\": false,\n    \"RAG_PROMPT\": \"Use the following retrieved documents to respond briefly and accurately:\",\n    \"SEMANTIC_ENABLED\": true,\n    \"STOP_TOKENS\": [\n      \"\\n\\n\",\n      \"\\n\"\n    ],\n    \"SYSTEM_PROMPT\": \"You are a helpful assistant\",\n    \"TEMPERATURE\": 0.5,\n    \"USE_MESSAGE_TO_QUERY_PROMPT\": false\n  }\n}")

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

	req.Header.Add("TR-Organization", "<tr-organization>")
	req.Header.Add("Authorization", "<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.trieve.ai/api/organization/update_dataset_configs")
  .header("TR-Organization", "<tr-organization>")
  .header("Authorization", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"organization_id\": \"00000000-0000-0000-0000-000000000000\",\n  \"server_configuration\": {\n    \"AIMON_RERANKER_TASK_DEFINITION\": \"Your task is to grade the relevance of context document(s) against the specified user query.\",\n    \"BM25_AVG_LEN\": 256,\n    \"BM25_B\": 0.75,\n    \"BM25_ENABLED\": true,\n    \"BM25_K\": 0.75,\n    \"EMBEDDING_BASE_URL\": \"https://api.openai.com/v1\",\n    \"EMBEDDING_MODEL_NAME\": \"text-embedding-3-small\",\n    \"EMBEDDING_QUERY_PREFIX\": \"\",\n    \"EMBEDDING_SIZE\": 1536,\n    \"FREQUENCY_PENALTY\": 0,\n    \"FULLTEXT_ENABLED\": true,\n    \"INDEXED_ONLY\": false,\n    \"LLM_BASE_URL\": \"https://api.openai.com/v1\",\n    \"LLM_DEFAULT_MODEL\": \"gpt-3.5-turbo-1106\",\n    \"LOCKED\": false,\n    \"MAX_LIMIT\": 10000,\n    \"MESSAGE_TO_QUERY_PROMPT\": \"Write a 1-2 sentence semantic search query along the lines of a hypothetical response to: \\n\\n\",\n    \"N_RETRIEVALS_TO_INCLUDE\": 8,\n    \"PRESENCE_PENALTY\": 0,\n    \"QDRANT_ONLY\": false,\n    \"RAG_PROMPT\": \"Use the following retrieved documents to respond briefly and accurately:\",\n    \"SEMANTIC_ENABLED\": true,\n    \"STOP_TOKENS\": [\n      \"\\n\\n\",\n      \"\\n\"\n    ],\n    \"SYSTEM_PROMPT\": \"You are a helpful assistant\",\n    \"TEMPERATURE\": 0.5,\n    \"USE_MESSAGE_TO_QUERY_PROMPT\": false\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.trieve.ai/api/organization/update_dataset_configs")

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

request = Net::HTTP::Post.new(url)
request["TR-Organization"] = '<tr-organization>'
request["Authorization"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"organization_id\": \"00000000-0000-0000-0000-000000000000\",\n  \"server_configuration\": {\n    \"AIMON_RERANKER_TASK_DEFINITION\": \"Your task is to grade the relevance of context document(s) against the specified user query.\",\n    \"BM25_AVG_LEN\": 256,\n    \"BM25_B\": 0.75,\n    \"BM25_ENABLED\": true,\n    \"BM25_K\": 0.75,\n    \"EMBEDDING_BASE_URL\": \"https://api.openai.com/v1\",\n    \"EMBEDDING_MODEL_NAME\": \"text-embedding-3-small\",\n    \"EMBEDDING_QUERY_PREFIX\": \"\",\n    \"EMBEDDING_SIZE\": 1536,\n    \"FREQUENCY_PENALTY\": 0,\n    \"FULLTEXT_ENABLED\": true,\n    \"INDEXED_ONLY\": false,\n    \"LLM_BASE_URL\": \"https://api.openai.com/v1\",\n    \"LLM_DEFAULT_MODEL\": \"gpt-3.5-turbo-1106\",\n    \"LOCKED\": false,\n    \"MAX_LIMIT\": 10000,\n    \"MESSAGE_TO_QUERY_PROMPT\": \"Write a 1-2 sentence semantic search query along the lines of a hypothetical response to: \\n\\n\",\n    \"N_RETRIEVALS_TO_INCLUDE\": 8,\n    \"PRESENCE_PENALTY\": 0,\n    \"QDRANT_ONLY\": false,\n    \"RAG_PROMPT\": \"Use the following retrieved documents to respond briefly and accurately:\",\n    \"SEMANTIC_ENABLED\": true,\n    \"STOP_TOKENS\": [\n      \"\\n\\n\",\n      \"\\n\"\n    ],\n    \"SYSTEM_PROMPT\": \"You are a helpful assistant\",\n    \"TEMPERATURE\": 0.5,\n    \"USE_MESSAGE_TO_QUERY_PROMPT\": false\n  }\n}"

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

{
  "message": "Bad Request"
}