Update Knowledge Base

curl --request PATCH \
  --url https://{api_base_url}/datasets/{dataset_id} \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "name": "<string>",
  "description": "<string>",
  "embedding_model": "<string>",
  "embedding_model_provider": "<string>",
  "partial_member_list": [
    {
      "user_id": "<string>"
    }
  ],
  "external_retrieval_model": {
    "top_k": 123,
    "score_threshold": 123,
    "score_threshold_enabled": true
  },
  "external_knowledge_id": "<string>",
  "external_knowledge_api_id": "<string>"
}
'

import requests

url = "https://{api_base_url}/datasets/{dataset_id}"

payload = {
    "name": "<string>",
    "description": "<string>",
    "embedding_model": "<string>",
    "embedding_model_provider": "<string>",
    "partial_member_list": [{ "user_id": "<string>" }],
    "external_retrieval_model": {
        "top_k": 123,
        "score_threshold": 123,
        "score_threshold_enabled": True
    },
    "external_knowledge_id": "<string>",
    "external_knowledge_api_id": "<string>"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'PATCH',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    name: '<string>',
    description: '<string>',
    embedding_model: '<string>',
    embedding_model_provider: '<string>',
    partial_member_list: [{user_id: '<string>'}],
    external_retrieval_model: {top_k: 123, score_threshold: 123, score_threshold_enabled: true},
    external_knowledge_id: '<string>',
    external_knowledge_api_id: '<string>'
  })
};

fetch('https://{api_base_url}/datasets/{dataset_id}', 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_base_url}/datasets/{dataset_id}",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "PATCH",
  CURLOPT_POSTFIELDS => json_encode([
    'name' => '<string>',
    'description' => '<string>',
    'embedding_model' => '<string>',
    'embedding_model_provider' => '<string>',
    'partial_member_list' => [
        [
                'user_id' => '<string>'
        ]
    ],
    'external_retrieval_model' => [
        'top_k' => 123,
        'score_threshold' => 123,
        'score_threshold_enabled' => true
    ],
    'external_knowledge_id' => '<string>',
    'external_knowledge_api_id' => '<string>'
  ]),
  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_base_url}/datasets/{dataset_id}"

	payload := strings.NewReader("{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\",\n  \"embedding_model\": \"<string>\",\n  \"embedding_model_provider\": \"<string>\",\n  \"partial_member_list\": [\n    {\n      \"user_id\": \"<string>\"\n    }\n  ],\n  \"external_retrieval_model\": {\n    \"top_k\": 123,\n    \"score_threshold\": 123,\n    \"score_threshold_enabled\": true\n  },\n  \"external_knowledge_id\": \"<string>\",\n  \"external_knowledge_api_id\": \"<string>\"\n}")

	req, _ := http.NewRequest("PATCH", 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.patch("https://{api_base_url}/datasets/{dataset_id}")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\",\n  \"embedding_model\": \"<string>\",\n  \"embedding_model_provider\": \"<string>\",\n  \"partial_member_list\": [\n    {\n      \"user_id\": \"<string>\"\n    }\n  ],\n  \"external_retrieval_model\": {\n    \"top_k\": 123,\n    \"score_threshold\": 123,\n    \"score_threshold_enabled\": true\n  },\n  \"external_knowledge_id\": \"<string>\",\n  \"external_knowledge_api_id\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://{api_base_url}/datasets/{dataset_id}")

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

request = Net::HTTP::Patch.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\",\n  \"embedding_model\": \"<string>\",\n  \"embedding_model_provider\": \"<string>\",\n  \"partial_member_list\": [\n    {\n      \"user_id\": \"<string>\"\n    }\n  ],\n  \"external_retrieval_model\": {\n    \"top_k\": 123,\n    \"score_threshold\": 123,\n    \"score_threshold_enabled\": true\n  },\n  \"external_knowledge_id\": \"<string>\",\n  \"external_knowledge_api_id\": \"<string>\"\n}"

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

{
  "id": "c42e2a6e-40b3-4330-96f8-f1e4d768e8c9",
  "name": "Product Documentation",
  "description": "Technical documentation for the product API",
  "provider": "vendor",
  "permission": "only_me",
  "data_source_type": null,
  "indexing_technique": "high_quality",
  "app_count": 0,
  "document_count": 0,
  "word_count": 0,
  "created_by": "ad313dd6-ef04-4dd1-a5b0-c0f0b9e2e7e4",
  "author_name": "admin",
  "created_at": 1741267200,
  "updated_by": "ad313dd6-ef04-4dd1-a5b0-c0f0b9e2e7e4",
  "updated_at": 1741267200,
  "embedding_model": "text-embedding-3-small",
  "embedding_model_provider": "langgenius/openai/openai",
  "embedding_available": true,
  "retrieval_model_dict": {
    "search_method": "semantic_search",
    "reranking_enable": false,
    "reranking_mode": null,
    "reranking_model": {
      "reranking_provider_name": "",
      "reranking_model_name": ""
    },
    "weights": null,
    "top_k": 3,
    "score_threshold_enabled": false,
    "score_threshold": null
  },
  "tags": [],
  "doc_form": "text_model",
  "external_knowledge_info": null,
  "external_retrieval_model": null,
  "doc_metadata": [],
  "built_in_field_enabled": true,
  "pipeline_id": null,
  "runtime_mode": null,
  "chunk_structure": null,
  "icon_info": null,
  "summary_index_setting": null,
  "is_published": false,
  "total_documents": 0,
  "total_available_documents": 0,
  "enable_api": true,
  "is_multimodal": false,
  "maintainer": "admin",
  "partial_member_list": []
}

{
  "status": 400,
  "code": "invalid_param",
  "message": "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
}

{
  "status": 403,
  "code": "forbidden",
  "message": "Dataset api access is not enabled."
}

{
  "status": 404,
  "code": "not_found",
  "message": "Dataset not found."
}

PATCH

datasets

{dataset_id}

Update Knowledge Base

curl --request PATCH \
  --url https://{api_base_url}/datasets/{dataset_id} \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "name": "<string>",
  "description": "<string>",
  "embedding_model": "<string>",
  "embedding_model_provider": "<string>",
  "partial_member_list": [
    {
      "user_id": "<string>"
    }
  ],
  "external_retrieval_model": {
    "top_k": 123,
    "score_threshold": 123,
    "score_threshold_enabled": true
  },
  "external_knowledge_id": "<string>",
  "external_knowledge_api_id": "<string>"
}
'

import requests

url = "https://{api_base_url}/datasets/{dataset_id}"

payload = {
    "name": "<string>",
    "description": "<string>",
    "embedding_model": "<string>",
    "embedding_model_provider": "<string>",
    "partial_member_list": [{ "user_id": "<string>" }],
    "external_retrieval_model": {
        "top_k": 123,
        "score_threshold": 123,
        "score_threshold_enabled": True
    },
    "external_knowledge_id": "<string>",
    "external_knowledge_api_id": "<string>"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'PATCH',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    name: '<string>',
    description: '<string>',
    embedding_model: '<string>',
    embedding_model_provider: '<string>',
    partial_member_list: [{user_id: '<string>'}],
    external_retrieval_model: {top_k: 123, score_threshold: 123, score_threshold_enabled: true},
    external_knowledge_id: '<string>',
    external_knowledge_api_id: '<string>'
  })
};

fetch('https://{api_base_url}/datasets/{dataset_id}', 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_base_url}/datasets/{dataset_id}",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "PATCH",
  CURLOPT_POSTFIELDS => json_encode([
    'name' => '<string>',
    'description' => '<string>',
    'embedding_model' => '<string>',
    'embedding_model_provider' => '<string>',
    'partial_member_list' => [
        [
                'user_id' => '<string>'
        ]
    ],
    'external_retrieval_model' => [
        'top_k' => 123,
        'score_threshold' => 123,
        'score_threshold_enabled' => true
    ],
    'external_knowledge_id' => '<string>',
    'external_knowledge_api_id' => '<string>'
  ]),
  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_base_url}/datasets/{dataset_id}"

	payload := strings.NewReader("{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\",\n  \"embedding_model\": \"<string>\",\n  \"embedding_model_provider\": \"<string>\",\n  \"partial_member_list\": [\n    {\n      \"user_id\": \"<string>\"\n    }\n  ],\n  \"external_retrieval_model\": {\n    \"top_k\": 123,\n    \"score_threshold\": 123,\n    \"score_threshold_enabled\": true\n  },\n  \"external_knowledge_id\": \"<string>\",\n  \"external_knowledge_api_id\": \"<string>\"\n}")

	req, _ := http.NewRequest("PATCH", 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.patch("https://{api_base_url}/datasets/{dataset_id}")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\",\n  \"embedding_model\": \"<string>\",\n  \"embedding_model_provider\": \"<string>\",\n  \"partial_member_list\": [\n    {\n      \"user_id\": \"<string>\"\n    }\n  ],\n  \"external_retrieval_model\": {\n    \"top_k\": 123,\n    \"score_threshold\": 123,\n    \"score_threshold_enabled\": true\n  },\n  \"external_knowledge_id\": \"<string>\",\n  \"external_knowledge_api_id\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://{api_base_url}/datasets/{dataset_id}")

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

request = Net::HTTP::Patch.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\",\n  \"embedding_model\": \"<string>\",\n  \"embedding_model_provider\": \"<string>\",\n  \"partial_member_list\": [\n    {\n      \"user_id\": \"<string>\"\n    }\n  ],\n  \"external_retrieval_model\": {\n    \"top_k\": 123,\n    \"score_threshold\": 123,\n    \"score_threshold_enabled\": true\n  },\n  \"external_knowledge_id\": \"<string>\",\n  \"external_knowledge_api_id\": \"<string>\"\n}"

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

{
  "id": "c42e2a6e-40b3-4330-96f8-f1e4d768e8c9",
  "name": "Product Documentation",
  "description": "Technical documentation for the product API",
  "provider": "vendor",
  "permission": "only_me",
  "data_source_type": null,
  "indexing_technique": "high_quality",
  "app_count": 0,
  "document_count": 0,
  "word_count": 0,
  "created_by": "ad313dd6-ef04-4dd1-a5b0-c0f0b9e2e7e4",
  "author_name": "admin",
  "created_at": 1741267200,
  "updated_by": "ad313dd6-ef04-4dd1-a5b0-c0f0b9e2e7e4",
  "updated_at": 1741267200,
  "embedding_model": "text-embedding-3-small",
  "embedding_model_provider": "langgenius/openai/openai",
  "embedding_available": true,
  "retrieval_model_dict": {
    "search_method": "semantic_search",
    "reranking_enable": false,
    "reranking_mode": null,
    "reranking_model": {
      "reranking_provider_name": "",
      "reranking_model_name": ""
    },
    "weights": null,
    "top_k": 3,
    "score_threshold_enabled": false,
    "score_threshold": null
  },
  "tags": [],
  "doc_form": "text_model",
  "external_knowledge_info": null,
  "external_retrieval_model": null,
  "doc_metadata": [],
  "built_in_field_enabled": true,
  "pipeline_id": null,
  "runtime_mode": null,
  "chunk_structure": null,
  "icon_info": null,
  "summary_index_setting": null,
  "is_published": false,
  "total_documents": 0,
  "total_available_documents": 0,
  "enable_api": true,
  "is_multimodal": false,
  "maintainer": "admin",
  "partial_member_list": []
}

{
  "status": 400,
  "code": "invalid_param",
  "message": "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
}

{
  "status": 403,
  "code": "forbidden",
  "message": "Dataset api access is not enabled."
}

{
  "status": 404,
  "code": "not_found",
  "message": "Dataset not found."
}

Authorizations

Authorization

string

header

required

Every request authenticates with an API key: Authorization: Bearer {API_KEY}. App endpoints take an app API key; knowledge endpoints take a knowledge base API key (Get Started).

Keep keys server-side; never embed them in client code. Requests with a missing or invalid key fail with HTTP 401 (unauthorized).

Path Parameters

dataset_id

string<uuid>

required

Knowledge base ID, from List Knowledge Bases.

Body

application/json

name

string

Name of the knowledge base.

Required string length: 1 - 40

description

string

Description of the knowledge base.

Maximum string length: 400

indexing_technique

enum<string> | null

high_quality uses embedding models for precise search; economy uses keyword-based indexing.

Available options:

high_quality,

economy

permission

enum<string>

Controls who can access this knowledge base. only_me restricts to the creator, all_team_members grants access to the entire workspace, partial_members grants access to specified members.

Available options:

only_me,

all_team_members,

partial_members

embedding_model

string

Embedding model name. Use the model field from Get Available Models with model_type=text-embedding.

embedding_model_provider

string

Embedding model provider identifier, formatted as organization/plugin_name/provider_name (e.g. langgenius/openai/openai). A bare name like openai expands to langgenius/<name>/<name> and works only for langgenius-published plugins.

Get valid values from the provider field of Get Available Models with model_type=text-embedding.

retrieval_model

object

Retrieval model configuration. Controls how chunks are searched and ranked when querying this knowledge base.

Show child attributes

partial_member_list

object[]

List of team members with access when permission is partial_members.

Show child attributes

external_retrieval_model

object

Retrieval settings for external knowledge bases.

Show child attributes

external_knowledge_id

string

ID of the external knowledge base.

external_knowledge_api_id

string

ID of the external knowledge API connection.

Response

Knowledge base updated successfully.

string

Unique identifier of the knowledge base.

name

string

Display name of the knowledge base. Unique within the workspace.

description

string

Optional text describing the purpose or contents of the knowledge base.

provider

string

Provider type. vendor for internally managed, external for external knowledge base connections.

permission

string

Controls who can access this knowledge base. Possible values: only_me, all_team_members, partial_members.

data_source_type

string

Data source type of the documents, null if not yet configured.

indexing_technique

string

high_quality uses embedding models for precise search; economy uses keyword-based indexing.

app_count

integer

Number of applications currently using this knowledge base.

document_count

integer

Total number of documents in the knowledge base.

word_count

integer

Total word count across all documents.

created_by

string

ID of the user who created the knowledge base.

author_name

string

Display name of the creator.

created_at

number

Creation timestamp (Unix epoch in seconds).

updated_by

string

ID of the user who last updated the knowledge base.

updated_at

number

Last update timestamp (Unix epoch in seconds).

embedding_model

string

Name of the embedding model used for indexing.

embedding_model_provider

string

Embedding model provider identifier, formatted as organization/plugin_name/provider_name (e.g. langgenius/openai/openai). Legacy knowledge bases may return the bare short form (e.g. openai).

embedding_available

boolean

Whether the configured embedding model is currently available.

retrieval_model_dict

object

Retrieval configuration for the knowledge base.

Show child attributes

summary_index_setting

object | null

Summary index configuration.

Show child attributes