> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/openai/openai-python/llms.txt
> Use this file to discover all available pages before exploring further.

# Vector Store Files

Create a vector store file by attaching a [File](/api-reference/files) to a vector store.

## Path Parameters

<ParamField path="vector_store_id" type="string" required>
  The ID of the vector store.
</ParamField>

## Request Body

<ParamField body="file_id" type="string" required>
  A [File](/api-reference/files) ID that the vector store should use. Useful for tools like `file_search` that can access files.
</ParamField>

<ParamField body="chunking_strategy" type="object">
  The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy.

  <Expandable title="Auto strategy">
    ```json theme={null}
    {
      "type": "auto"
    }
    ```
  </Expandable>

  <Expandable title="Static strategy">
    ```json theme={null}
    {
      "type": "static",
      "static": {
        "max_chunk_size_tokens": 800,
        "chunk_overlap_tokens": 400
      }
    }
    ```
  </Expandable>
</ParamField>

<ParamField body="attributes" type="object">
  Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format.

  Keys are strings with a maximum length of 64 characters. Values can be strings (max 512 characters), booleans, or numbers.
</ParamField>

## Response

Returns a `VectorStoreFile` object.

<ResponseField name="id" type="string">
  The identifier of the vector store file.
</ResponseField>

<ResponseField name="object" type="string">
  The object type, always `vector_store.file`.
</ResponseField>

<ResponseField name="vector_store_id" type="string">
  The ID of the vector store that the file is attached to.
</ResponseField>

<ResponseField name="status" type="string">
  The status of the vector store file. One of:

  * `in_progress`
  * `completed`
  * `cancelled`
  * `failed`
</ResponseField>

<ResponseField name="created_at" type="integer">
  The Unix timestamp (in seconds) for when the vector store file was created.
</ResponseField>

## List Vector Store Files

`GET https://api.openai.com/v1/vector_stores/{vector_store_id}/files`

Returns a list of vector store files.

### Query Parameters

<ParamField query="limit" type="integer" default="20">
  A limit on the number of objects to be returned. Limit can range between 1 and 100.
</ParamField>

<ParamField query="order" type="string" default="desc">
  Sort order by the `created_at` timestamp. `asc` for ascending order and `desc` for descending order.
</ParamField>

<ParamField query="after" type="string">
  A cursor for use in pagination.
</ParamField>

<ParamField query="before" type="string">
  A cursor for use in pagination.
</ParamField>

<ParamField query="filter" type="string">
  Filter by file status. One of `in_progress`, `completed`, `failed`, `cancelled`.
</ParamField>

## Retrieve Vector Store File

`GET https://api.openai.com/v1/vector_stores/{vector_store_id}/files/{file_id}`

Retrieves a vector store file.

## Delete Vector Store File

`DELETE https://api.openai.com/v1/vector_stores/{vector_store_id}/files/{file_id}`

Delete a vector store file. This will remove the file from the vector store but the file itself will not be deleted. To delete the file, use the delete file endpoint.

<RequestExample>
  ```python Python theme={null}
  from openai import OpenAI
  client = OpenAI()

  # Add file to vector store
  vector_store_file = client.beta.vector_stores.files.create(
      vector_store_id="vs_abc123",
      file_id="file-abc123"
  )

  print(vector_store_file.id, vector_store_file.status)

  # List files in vector store
  files = client.beta.vector_stores.files.list(
      vector_store_id="vs_abc123"
  )

  for file in files:
      print(file.id, file.status)

  # Upload and attach file in one step
  vector_store_file = client.beta.vector_stores.files.upload(
      vector_store_id="vs_abc123",
      file=open("mydata.pdf", "rb")
  )
  ```

  ```javascript Node.js theme={null}
  import OpenAI from "openai";
  import fs from "fs";
  const openai = new OpenAI();

  // Add file to vector store
  const vectorStoreFile = await openai.beta.vectorStores.files.create(
    "vs_abc123",
    {
      file_id: "file-abc123"
    }
  );

  console.log(vectorStoreFile.id, vectorStoreFile.status);

  // List files in vector store
  const files = await openai.beta.vectorStores.files.list(
    "vs_abc123"
  );

  for (const file of files.data) {
    console.log(file.id, file.status);
  }

  // Upload and attach file in one step
  const uploadedFile = await openai.beta.vectorStores.files.upload(
    "vs_abc123",
    fs.createReadStream("mydata.pdf")
  );
  ```

  ```bash cURL theme={null}
  # Add file to vector store
  curl https://api.openai.com/v1/vector_stores/vs_abc123/files \\
    -H "Authorization: Bearer $OPENAI_API_KEY" \\
    -H "Content-Type: application/json" \\
    -H "OpenAI-Beta: assistants=v2" \\
    -d '{
      "file_id": "file-abc123"
    }'

  # List files
  curl https://api.openai.com/v1/vector_stores/vs_abc123/files \\
    -H "Authorization: Bearer $OPENAI_API_KEY" \\
    -H "OpenAI-Beta: assistants=v2"
  ```
</RequestExample>

<ResponseExample>
  ```json theme={null}
  {
    "id": "file-abc123",
    "object": "vector_store.file",
    "usage_bytes": 1234,
    "created_at": 1699061776,
    "vector_store_id": "vs_abc123",
    "status": "in_progress",
    "last_error": null,
    "chunking_strategy": {
      "type": "auto"
    }
  }
  ```
</ResponseExample>