# Upload files to a Vector Store

{% openapi src="<https://2952947711-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FyNqpTh7Mh66N0RnO0k24%2Fuploads%2Fgit-blob-77b6137eeb641262ec8e531c78123c02b825b865%2Frememberizer_openapi.yml?alt=media&token=cbad765b-1613-4222-b591-9ae17a3b7cfa>" path="/vector-stores/{vector-store-id}/documents/upload" method="post" %}
[rememberizer\_openapi.yml](https://2952947711-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FyNqpTh7Mh66N0RnO0k24%2Fuploads%2Fgit-blob-77b6137eeb641262ec8e531c78123c02b825b865%2Frememberizer_openapi.yml?alt=media\&token=cbad765b-1613-4222-b591-9ae17a3b7cfa)
{% endopenapi %}

## Example Requests

{% tabs %}
{% tab title="cURL" %}

```bash
curl -X POST \
  https://api.rememberizer.ai/api/v1/vector-stores/vs_abc123/documents/upload \
  -H "x-api-key: YOUR_API_KEY" \
  -F "files=@/path/to/document1.pdf" \
  -F "files=@/path/to/document2.docx"
```

{% hint style="info" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key, `vs_abc123` with your Vector Store ID, and provide the paths to your local files.
{% endhint %}
{% endtab %}

{% tab title="JavaScript" %}

```javascript
const uploadFiles = async (vectorStoreId, files) => {
  const formData = new FormData();
  
  // Add multiple files to the form data
  for (const file of files) {
    formData.append('files', file);
  }
  
  const response = await fetch(`https://api.rememberizer.ai/api/v1/vector-stores/${vectorStoreId}/documents/upload`, {
    method: 'POST',
    headers: {
      'x-api-key': 'YOUR_API_KEY'
      // Note: Do not set Content-Type header, it will be set automatically with the correct boundary
    },
    body: formData
  });
  
  const data = await response.json();
  console.log(data);
};

// Example usage with file input element
const fileInput = document.getElementById('fileInput');
uploadFiles('vs_abc123', fileInput.files);
```

{% hint style="info" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key and `vs_abc123` with your Vector Store ID.
{% endhint %}
{% endtab %}

{% tab title="Python" %}

```python
import requests

def upload_files(vector_store_id, file_paths):
    headers = {
        "x-api-key": "YOUR_API_KEY"
    }
    
    files = [('files', (file_path.split('/')[-1], open(file_path, 'rb'))) for file_path in file_paths]
    
    response = requests.post(
        f"https://api.rememberizer.ai/api/v1/vector-stores/{vector_store_id}/documents/upload",
        headers=headers,
        files=files
    )
    
    data = response.json()
    print(data)

upload_files('vs_abc123', ['/path/to/document1.pdf', '/path/to/document2.docx'])
```

{% hint style="info" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key, `vs_abc123` with your Vector Store ID, and provide the paths to your local files.
{% endhint %}
{% endtab %}

{% tab title="Ruby" %}

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

def upload_files(vector_store_id, file_paths)
  uri = URI("https://api.rememberizer.ai/api/v1/vector-stores/#{vector_store_id}/documents/upload")
  
  # Create a new HTTP object
  http = Net::HTTP.new(uri.host, uri.port)
  http.use_ssl = true
  
  # Create a multipart-form request
  request = Net::HTTP::Post.new(uri)
  request['x-api-key'] = 'YOUR_API_KEY'
  
  # Create a multipart boundary
  boundary = "RubyFormBoundary#{rand(1000000)}"
  request['Content-Type'] = "multipart/form-data; boundary=#{boundary}"
  
  # Build the request body
  body = []
  file_paths.each do |file_path|
    file_name = File.basename(file_path)
    file_content = File.read(file_path, mode: 'rb')
    
    body << "--#{boundary}\r\n"
    body << "Content-Disposition: form-data; name=\"files\"; filename=\"#{file_name}\"\r\n"
    body << "Content-Type: #{get_content_type(file_name)}\r\n\r\n"
    body << file_content
    body << "\r\n"
  end
  body << "--#{boundary}--\r\n"
  
  request.body = body.join
  
  # Send the request
  response = http.request(request)
  
  # Parse and return the response
  JSON.parse(response.body)
end

# Helper method to determine content type
def get_content_type(filename)
  ext = File.extname(filename).downcase
  case ext
  when '.pdf'  then 'application/pdf'
  when '.doc'  then 'application/msword'
  when '.docx' then 'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
  when '.txt'  then 'text/plain'
  when '.md'   then 'text/markdown'
  when '.json' then 'application/json'
  else 'application/octet-stream'
  end
end

# Example usage
result = upload_files('vs_abc123', ['/path/to/document1.pdf', '/path/to/document2.docx'])
puts result
```

{% hint style="info" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key, `vs_abc123` with your Vector Store ID, and provide the paths to your local files.
{% endhint %}
{% endtab %}
{% endtabs %}

## Path Parameters

| Parameter       | Type   | Description                                                  |
| --------------- | ------ | ------------------------------------------------------------ |
| vector-store-id | string | **Required.** The ID of the vector store to upload files to. |

## Request Body

This endpoint accepts a `multipart/form-data` request with one or more files in the `files` field.

## Response Format

```json
{
  "documents": [
    {
      "id": 1234,
      "name": "document1.pdf",
      "type": "application/pdf",
      "size": 250000,
      "status": "processing",
      "created": "2023-06-15T10:15:00Z",
      "vector_store": "vs_abc123"
    },
    {
      "id": 1235,
      "name": "document2.docx",
      "type": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
      "size": 180000,
      "status": "processing",
      "created": "2023-06-15T10:15:00Z",
      "vector_store": "vs_abc123"
    }
  ],
  "errors": []
}
```

If some files fail to upload, they will be listed in the `errors` array:

```json
{
  "documents": [
    {
      "id": 1234,
      "name": "document1.pdf",
      "type": "application/pdf",
      "size": 250000,
      "status": "processing",
      "created": "2023-06-15T10:15:00Z",
      "vector_store": "vs_abc123"
    }
  ],
  "errors": [
    {
      "file": "document2.docx",
      "error": "File format not supported"
    }
  ]
}
```

## Authentication

This endpoint requires authentication using an API key in the `x-api-key` header.

## Supported File Formats

* PDF (`.pdf`)
* Microsoft Word (`.doc`, `.docx`)
* Microsoft Excel (`.xls`, `.xlsx`)
* Microsoft PowerPoint (`.ppt`, `.pptx`)
* Text files (`.txt`)
* Markdown (`.md`)
* JSON (`.json`)
* HTML (`.html`, `.htm`)

## File Size Limits

* Individual file size limit: 50MB
* Total request size limit: 100MB
* Maximum number of files per request: 20

## Error Responses

| Status Code | Description                                                             |
| ----------- | ----------------------------------------------------------------------- |
| 400         | Bad Request - No files provided or invalid request format               |
| 401         | Unauthorized - Invalid or missing API key                               |
| 404         | Not Found - Vector Store not found                                      |
| 413         | Payload Too Large - Files exceed size limit                             |
| 415         | Unsupported Media Type - File format not supported                      |
| 500         | Internal Server Error                                                   |
| 207         | Multi-Status - Some files were uploaded successfully, but others failed |

## Processing Status

Files are initially accepted with a status of `processing`. You can check the processing status of the documents using the [Get a List of Documents in a Vector Store](https://docs.rememberizer.ai/developer-resources/api-docs/vector-store/get-a-list-of-documents-in-a-vector-store) endpoint. Final status will be one of:

* `done`: Document was successfully processed
* `error`: An error occurred during processing
* `processing`: Document is still being processed

Processing time depends on file size and complexity. Typical processing time is between 30 seconds to 5 minutes per document.

## Batch Operations

For efficiently uploading multiple files to your Vector Store, Rememberizer supports batch operations. This approach helps optimize performance when dealing with large numbers of documents.

### Batch Upload Implementation

{% tabs %}
{% tab title="Python" %}

```python
import os
import requests
import time
import concurrent.futures
from pathlib import Path

def batch_upload_to_vector_store(vector_store_id, folder_path, batch_size=5, file_types=None):
    """
    Upload all files from a directory to a Vector Store in batches
    
    Args:
        vector_store_id: ID of the vector store
        folder_path: Path to folder containing files to upload
        batch_size: Number of files to upload in each batch
        file_types: Optional list of file extensions to filter by (e.g., ['.pdf', '.docx'])
        
    Returns:
        List of upload results
    """
    api_key = "YOUR_API_KEY"
    headers = {"x-api-key": api_key}
    
    # Get list of files in directory
    files = []
    for entry in os.scandir(folder_path):
        if entry.is_file():
            file_path = Path(entry.path)
            # Filter by file extension if specified
            if file_types is None or file_path.suffix.lower() in file_types:
                files.append(file_path)
    
    print(f"Found {len(files)} files to upload")
    results = []
    
    # Process files in batches
    for i in range(0, len(files), batch_size):
        batch = files[i:i+batch_size]
        print(f"Processing batch {i//batch_size + 1}/{(len(files) + batch_size - 1)//batch_size}: {len(batch)} files")
        
        # Upload batch
        upload_files = []
        for file_path in batch:
            upload_files.append(('files', (file_path.name, open(file_path, 'rb'))))
        
        try:
            response = requests.post(
                f"https://api.rememberizer.ai/api/v1/vector-stores/{vector_store_id}/documents/upload",
                headers=headers,
                files=upload_files
            )
            
            # Close all file handles
            for _, (_, file_obj) in upload_files:
                file_obj.close()
            
            if response.status_code in (200, 201, 207):
                batch_result = response.json()
                results.append(batch_result)
                print(f"Successfully uploaded batch - {len(batch_result.get('documents', []))} documents processed")
                
                # Check for errors
                if batch_result.get('errors') and len(batch_result['errors']) > 0:
                    print(f"Errors encountered: {len(batch_result['errors'])}")
                    for error in batch_result['errors']:
                        print(f"- {error['file']}: {error['error']}")
            else:
                print(f"Batch upload failed with status code {response.status_code}: {response.text}")
                results.append({"error": f"Batch failed: {response.text}"})
                
        except Exception as e:
            print(f"Exception during batch upload: {str(e)}")
            results.append({"error": str(e)})
            
            # Close any remaining file handles in case of exception
            for _, (_, file_obj) in upload_files:
                try:
                    file_obj.close()
                except:
                    pass
        
        # Rate limiting - pause between batches
        if i + batch_size < len(files):
            print("Pausing before next batch...")
            time.sleep(2)
    
    return results

# Example usage
results = batch_upload_to_vector_store(
    'vs_abc123',
    '/path/to/documents/folder',
    batch_size=5,
    file_types=['.pdf', '.docx', '.txt']
)
```

{% endtab %}

{% tab title="JavaScript" %}

```javascript
/**
 * Upload files to a Vector Store in batches
 * 
 * @param {string} vectorStoreId - ID of the Vector Store
 * @param {FileList|File[]} files - Files to upload
 * @param {Object} options - Configuration options
 * @returns {Promise<Array>} - List of upload results
 */
async function batchUploadToVectorStore(vectorStoreId, files, options = {}) {
  const {
    batchSize = 5,
    delayBetweenBatches = 2000,
    onProgress = null
  } = options;
  
  const apiKey = 'YOUR_API_KEY';
  const results = [];
  const fileList = Array.from(files);
  const totalBatches = Math.ceil(fileList.length / batchSize);
  
  console.log(`Preparing to upload ${fileList.length} files in ${totalBatches} batches`);
  
  // Process files in batches
  for (let i = 0; i < fileList.length; i += batchSize) {
    const batch = fileList.slice(i, i + batchSize);
    const batchNumber = Math.floor(i / batchSize) + 1;
    
    console.log(`Processing batch ${batchNumber}/${totalBatches}: ${batch.length} files`);
    
    if (onProgress) {
      onProgress({
        currentBatch: batchNumber,
        totalBatches: totalBatches,
        filesInBatch: batch.length,
        totalFiles: fileList.length,
        completedFiles: i
      });
    }
    
    // Create FormData for this batch
    const formData = new FormData();
    batch.forEach(file => {
      formData.append('files', file);
    });
    
    try {
      const response = await fetch(
        `https://api.rememberizer.ai/api/v1/vector-stores/${vectorStoreId}/documents/upload`,
        {
          method: 'POST',
          headers: {
            'x-api-key': apiKey
          },
          body: formData
        }
      );
      
      if (response.ok) {
        const batchResult = await response.json();
        results.push(batchResult);
        
        console.log(`Successfully uploaded batch - ${batchResult.documents?.length || 0} documents processed`);
        
        // Check for errors
        if (batchResult.errors && batchResult.errors.length > 0) {
          console.warn(`Errors encountered: ${batchResult.errors.length}`);
          batchResult.errors.forEach(error => {
            console.warn(`- ${error.file}: ${error.error}`);
          });
        }
      } else {
        console.error(`Batch upload failed with status ${response.status}: ${await response.text()}`);
        results.push({ error: `Batch failed with status: ${response.status}` });
      }
    } catch (error) {
      console.error(`Exception during batch upload: ${error.message}`);
      results.push({ error: error.message });
    }
    
    // Add delay between batches to avoid rate limiting
    if (i + batchSize < fileList.length) {
      console.log(`Pausing for ${delayBetweenBatches}ms before next batch...`);
      await new Promise(resolve => setTimeout(resolve, delayBetweenBatches));
    }
  }
  
  console.log(`Upload complete. Processed ${fileList.length} files.`);
  return results;
}

// Example usage with file input element
document.getElementById('upload-button').addEventListener('click', async () => {
  const fileInput = document.getElementById('file-input');
  const vectorStoreId = 'vs_abc123';
  
  const progressBar = document.getElementById('progress-bar');
  
  try {
    const results = await batchUploadToVectorStore(vectorStoreId, fileInput.files, {
      batchSize: 5,
      onProgress: (progress) => {
        // Update progress UI
        const percentage = Math.round((progress.completedFiles / progress.totalFiles) * 100);
        progressBar.style.width = `${percentage}%`;
        progressBar.textContent = `${percentage}% (Batch ${progress.currentBatch}/${progress.totalBatches})`;
      }
    });
    
    console.log('Complete upload results:', results);
  } catch (error) {
    console.error('Upload failed:', error);
  }
});
```

{% endtab %}

{% tab title="Ruby" %}

```ruby
require 'net/http'
require 'uri'
require 'json'
require 'mime/types'

# Upload files to a Vector Store in batches
#
# @param vector_store_id [String] ID of the Vector Store
# @param folder_path [String] Path to folder containing files to upload
# @param batch_size [Integer] Number of files to upload in each batch
# @param file_types [Array<String>] Optional array of file extensions to filter by
# @param delay_between_batches [Float] Seconds to wait between batches
# @return [Array] List of upload results
def batch_upload_to_vector_store(vector_store_id, folder_path, batch_size: 5, file_types: nil, delay_between_batches: 2.0)
  api_key = 'YOUR_API_KEY'
  results = []
  
  # Get list of files in directory
  files = Dir.entries(folder_path)
    .select { |f| File.file?(File.join(folder_path, f)) }
    .select { |f| file_types.nil? || file_types.include?(File.extname(f).downcase) }
    .map { |f| File.join(folder_path, f) }
  
  puts "Found #{files.count} files to upload"
  total_batches = (files.count.to_f / batch_size).ceil
  
  # Process files in batches
  files.each_slice(batch_size).with_index do |batch, batch_index|
    puts "Processing batch #{batch_index + 1}/#{total_batches}: #{batch.count} files"
    
    # Prepare the HTTP request
    uri = URI("https://api.rememberizer.ai/api/v1/vector-stores/#{vector_store_id}/documents/upload")
    request = Net::HTTP::Post.new(uri)
    request['x-api-key'] = api_key
    
    # Create a multipart form boundary
    boundary = "RubyBoundary#{rand(1000000)}"
    request['Content-Type'] = "multipart/form-data; boundary=#{boundary}"
    
    # Build the request body
    body = []
    batch.each do |file_path|
      file_name = File.basename(file_path)
      mime_type = MIME::Types.type_for(file_path).first&.content_type || 'application/octet-stream'
      
      begin
        file_content = File.binread(file_path)
        
        body << "--#{boundary}\r\n"
        body << "Content-Disposition: form-data; name=\"files\"; filename=\"#{file_name}\"\r\n"
        body << "Content-Type: #{mime_type}\r\n\r\n"
        body << file_content
        body << "\r\n"
      rescue => e
        puts "Error reading file #{file_path}: #{e.message}"
      end
    end
    body << "--#{boundary}--\r\n"
    
    request.body = body.join
    
    # Send the request
    begin
      http = Net::HTTP.new(uri.host, uri.port)
      http.use_ssl = true
      response = http.request(request)
      
      if response.code.to_i == 200 || response.code.to_i == 201 || response.code.to_i == 207
        batch_result = JSON.parse(response.body)
        results << batch_result
        
        puts "Successfully uploaded batch - #{batch_result['documents']&.count || 0} documents processed"
        
        # Check for errors
        if batch_result['errors'] && !batch_result['errors'].empty?
          puts "Errors encountered: #{batch_result['errors'].count}"
          batch_result['errors'].each do |error|
            puts "- #{error['file']}: #{error['error']}"
          end
        end
      else
        puts "Batch upload failed with status code #{response.code}: #{response.body}"
        results << { "error" => "Batch failed: #{response.body}" }
      end
    rescue => e
      puts "Exception during batch upload: #{e.message}"
      results << { "error" => e.message }
    end
    
    # Rate limiting - pause between batches
    if batch_index < total_batches - 1
      puts "Pausing for #{delay_between_batches} seconds before next batch..."
      sleep(delay_between_batches)
    end
  end
  
  puts "Upload complete. Processed #{files.count} files."
  results
end

# Example usage
results = batch_upload_to_vector_store(
  'vs_abc123',
  '/path/to/documents/folder',
  batch_size: 5,
  file_types: ['.pdf', '.docx', '.txt'],
  delay_between_batches: 2.0
)
```

{% endtab %}
{% endtabs %}

### Batch Upload Best Practices

To optimize performance and reliability when uploading large volumes of files:

1. **Manage Batch Size**: Keep batch sizes between 5-10 files for optimal performance. Too many files in a single request increases the risk of timeouts.
2. **Implement Rate Limiting**: Add delays between batches (2-3 seconds recommended) to avoid hitting API rate limits.
3. **Add Error Retry Logic**: For production systems, implement retry logic for failed uploads with exponential backoff.
4. **Validate File Types**: Pre-filter files to ensure they're supported types before attempting upload.
5. **Monitor Batch Progress**: For user-facing applications, provide progress feedback on batch operations.
6. **Handle Partial Success**: The API may return a 207 status code for partial success. Always check individual document statuses.
7. **Clean Up Resources**: Ensure all file handles are properly closed, especially when errors occur.
8. **Parallelize Wisely**: For very large uploads (thousands of files), consider multiple concurrent batch processes targeting different vector stores, then combine results later if needed.
9. **Implement Checksums**: For critical data, verify file integrity before and after upload with checksums.
10. **Log Comprehensive Results**: Maintain detailed logs of all upload operations for troubleshooting.

By following these best practices, you can efficiently manage large-scale document ingestion into your vector stores.