> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.vellum.ai/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.vellum.ai/_mcp/server.

# Retrieve Document Index

GET https://api.vellum.ai/v1/document-indexes/{id}

Used to retrieve a Document Index given its ID or name.

Reference: https://docs.vellum.ai/developers/client-sdk/document-indexes/retrieve

## Authentication

- `X-API-KEY` header (required) — API Key authentication via header

## Request

### Path parameters

- `id` (string, required) — Either the Document Index's ID or its unique name

### Query parameters

- `mask_indexing_config` (boolean, optional) — Whether to mask the indexing configuration in the response

## Response

### 200

- `id` (string, required)
- `created` (string, required)
- `label` (string, required) — A human-readable label for the document index
- `name` (string, required) — A name that uniquely identifies this index within its workspace
- `indexing_config` (DocumentIndexIndexingConfig, required)
- `status` (enum, optional) — The current status of the document index * `ACTIVE` - Active * `ARCHIVED` - Archived * `PENDING_DELETION` - Pending Deletion
  - Allowed values: `ACTIVE`, `ARCHIVED`, `PENDING_DELETION`

## Types

### DocumentIndexIndexingConfig

- `vectorizer` (IndexingConfigVectorizer, required)
- `chunking` (DocumentIndexChunking, optional, nullable)

### IndexingConfigVectorizer

### DocumentIndexChunking

### OpenAIVectorizerTextEmbedding3Small

OpenAI vectorizer for text-embedding-3-small.

- `config` (OpenAIVectorizerConfig, required) — Configuration for using an OpenAI vectorizer.
- `model_name` (enum, required)
  - Allowed values: `text-embedding-3-small`

### OpenAIVectorizerTextEmbedding3Large

OpenAI vectorizer for text-embedding-3-large.

- `config` (OpenAIVectorizerConfig, required) — Configuration for using an OpenAI vectorizer.
- `model_name` (enum, required)
  - Allowed values: `text-embedding-3-large`

### OpenAIVectorizerTextEmbeddingAda002

OpenAI vectorizer for text-embedding-ada-002.

- `config` (OpenAIVectorizerConfig, required) — Configuration for using an OpenAI vectorizer.
- `model_name` (enum, required)
  - Allowed values: `text-embedding-ada-002`

### BasicVectorizerIntfloatMultilingualE5Large

Basic vectorizer for intfloat/multilingual-e5-large.

- `model_name` (enum, required)
  - Allowed values: `intfloat/multilingual-e5-large`
- `config` (map from string to any, optional, nullable)

### BasicVectorizerSentenceTransformersMultiQaMpnetBaseCosV1

Basic vectorizer for sentence-transformers/multi-qa-mpnet-base-cos-v1.

- `model_name` (enum, required)
  - Allowed values: `sentence-transformers/multi-qa-mpnet-base-cos-v1`
- `config` (map from string to any, optional, nullable)

### BasicVectorizerSentenceTransformersMultiQaMpnetBaseDotV1

Basic vectorizer for sentence-transformers/multi-qa-mpnet-base-dot-v1.

- `model_name` (enum, required)
  - Allowed values: `sentence-transformers/multi-qa-mpnet-base-dot-v1`
- `config` (map from string to any, optional, nullable)

### HkunlpInstructorXlVectorizer

Vectorizer for hkunlp/instructor-xl.

- `model_name` (enum, required)
  - Allowed values: `hkunlp/instructor-xl`
- `config` (InstructorVectorizerConfig, required) — Configuration for using an Instructor vectorizer.

### GoogleVertexAIVectorizerTextEmbedding004

- `model_name` (enum, required)
  - Allowed values: `text-embedding-004`
- `config` (GoogleVertexAIVectorizerConfig, required)

### GoogleVertexAIVectorizerTextMultilingualEmbedding002

- `model_name` (enum, required)
  - Allowed values: `text-multilingual-embedding-002`
- `config` (GoogleVertexAIVectorizerConfig, required)

### GoogleVertexAIVectorizerGeminiEmbedding001

- `model_name` (enum, required)
  - Allowed values: `gemini-embedding-001`
- `config` (GoogleVertexAIVectorizerConfig, required)

### FastEmbedVectorizerBAAIBgeSmallEnV15

FastEmbed vectorizer for BAAI/bge-small-en-v1.5.

- `model_name` (enum, required)
  - Allowed values: `BAAI/bge-small-en-v1.5`

### PrivateVectorizer

Serializer for private vectorizer.

- `model_name` (enum, required)
  - Allowed values: `private-vectorizer`

### ReductoChunking

Reducto chunking

- `chunker_name` (enum, required)
  - Allowed values: `reducto-chunker`
- `chunker_config` (ReductoChunkerConfig, optional) — Configuration for Reducto chunking

### SentenceChunking

Sentence chunking

- `chunker_name` (enum, required)
  - Allowed values: `sentence-chunker`
- `chunker_config` (SentenceChunkerConfig, optional) — Configuration for sentence chunking

### TokenOverlappingWindowChunking

Token overlapping window chunking

- `chunker_name` (enum, required)
  - Allowed values: `token-overlapping-window-chunker`
- `chunker_config` (TokenOverlappingWindowChunkerConfig, optional) — Configuration for token overlapping window chunking

### DelimiterChunking

- `chunker_name` (enum, required)
  - Allowed values: `delimiter-chunker`
- `chunker_config` (DelimiterChunkerConfig, optional)

### OpenAIVectorizerConfig

Configuration for using an OpenAI vectorizer.

- `add_openai_api_key` (boolean, optional) — * `True` - True

### InstructorVectorizerConfig

Configuration for using an Instructor vectorizer.

- `instruction_domain` (string, required)
- `instruction_query_text_type` (string, required)
- `instruction_document_text_type` (string, required)

### GoogleVertexAIVectorizerConfig

- `project_id` (string, required)
- `region` (string, required)

### ReductoChunkerConfig

Configuration for Reducto chunking

- `character_limit` (integer, optional, default: 1000)

### SentenceChunkerConfig

Configuration for sentence chunking

- `character_limit` (integer, optional, default: 1000)
- `min_overlap_ratio` (double, optional, default: 0.5)

### TokenOverlappingWindowChunkerConfig

Configuration for token overlapping window chunking

- `token_limit` (integer, optional, default: 250)
- `overlap_ratio` (double, optional, default: 0.5)

### DelimiterChunkerConfig

- `delimiter` (string, optional, default: \n\n)
- `is_regex` (boolean, optional, default: false)

## Examples

**Response**

```json
{
  "id": "string",
  "created": "2024-01-15T09:30:00Z",
  "label": "string",
  "name": "string",
  "indexing_config": {
    "vectorizer": {
      "config": {
        "add_openai_api_key": true
      },
      "model_name": "text-embedding-3-small"
    },
    "chunking": {
      "chunker_name": "reducto-chunker",
      "chunker_config": {
        "character_limit": 1000
      }
    }
  },
  "status": "ACTIVE"
}
```

**SDK Code**

```python
import requests

url = "https://api.vellum.ai/v1/document-indexes/id"

headers = {"X-API-KEY": "<apiKey>"}

response = requests.get(url, headers=headers)

print(response.json())
```

```typescript
import { VellumClient } from "vellum-ai";

const client = new VellumClient({ apiKey: "YOUR_API_KEY", apiVersion: "YOUR_API_VERSION" });
await client.documentIndexes.retrieve("id");

```

```go
package main

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

func main() {

	url := "https://api.vellum.ai/v1/document-indexes/id"

	req, _ := http.NewRequest("GET", url, nil)

	req.Header.Add("X-API-KEY", "<apiKey>")

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

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

	fmt.Println(res)
	fmt.Println(string(body))

}
```

```ruby
require 'uri'
require 'net/http'

url = URI("https://api.vellum.ai/v1/document-indexes/id")

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

request = Net::HTTP::Get.new(url)
request["X-API-KEY"] = '<apiKey>'

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

```java
import com.mashape.unirest.http.HttpResponse;
import com.mashape.unirest.http.Unirest;

HttpResponse<String> response = Unirest.get("https://api.vellum.ai/v1/document-indexes/id")
  .header("X-API-KEY", "<apiKey>")
  .asString();
```

```php
<?php
require_once('vendor/autoload.php');

$client = new \GuzzleHttp\Client();

$response = $client->request('GET', 'https://api.vellum.ai/v1/document-indexes/id', [
  'headers' => [
    'X-API-KEY' => '<apiKey>',
  ],
]);

echo $response->getBody();
```

```csharp
using RestSharp;

var client = new RestClient("https://api.vellum.ai/v1/document-indexes/id");
var request = new RestRequest(Method.GET);
request.AddHeader("X-API-KEY", "<apiKey>");
IRestResponse response = client.Execute(request);
```

```swift
import Foundation

let headers = ["X-API-KEY": "<apiKey>"]

let request = NSMutableURLRequest(url: NSURL(string: "https://api.vellum.ai/v1/document-indexes/id")! as URL,
                                        cachePolicy: .useProtocolCachePolicy,
                                    timeoutInterval: 10.0)
request.httpMethod = "GET"
request.allHTTPHeaderFields = headers

let session = URLSession.shared
let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in
  if (error != nil) {
    print(error as Any)
  } else {
    let httpResponse = response as? HTTPURLResponse
    print(httpResponse)
  }
})

dataTask.resume()
```