> ## Documentation Index
> Fetch the complete documentation index at: https://docs.moorcheh.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Get Documents

> Retrieve specific documents by their IDs using the Python SDK

## documents.get

Retrieves specific documents by their IDs from a namespace. This endpoint allows you to fetch documents that have been previously uploaded and indexed.

<Info>
  This method retrieves documents that have been previously uploaded and indexed in the specified namespace. For semantic search and similarity-based retrieval, use the Search API.
</Info>

### Parameters

<ParamField query="namespace_name" type="str" required>
  The name of the namespace containing the documents.
</ParamField>

<ParamField query="ids" type="List[Union[str, int]]" required>
  A list of document IDs to retrieve (max 100 IDs per request).
</ParamField>

**Returns:** `Dict[str, Any]` - A dictionary containing the retrieved documents.

**Raises:** `NamespaceNotFound`, `InvalidInputError`.

### Example

```python Get Documents Example theme={null}
from moorcheh_sdk import MoorchehClient

with MoorchehClient() as client:
    # Get specific documents by ID
    result = client.documents.get(
        namespace_name="my-faq-documents",
        ids=["faq-1", "faq-2", "faq-3"]
    )
    
    for item in result.get('items', []):
        print(f"ID: {item['id']}")
        print(f"Text: {item['text']}")
        print(f"Metadata: {item.get('metadata', {})}")
```

### Response Structure

The response contains:

* `status` (str): "success" or "partial"
* `message` (str): Human-readable message
* `requested_ids` (int): Number of document IDs requested
* `found_items` (int): Number of documents successfully found
* `items` (list): Array of retrieved document objects
* `not_found_ids` (list, optional): IDs that were not found (for partial success)

### Complete Example

```python Complete Example theme={null}
from moorcheh_sdk import MoorchehClient

with MoorchehClient() as client:
    namespace = "my-documents"
    
    # Retrieve multiple documents
    result = client.documents.get(
        namespace_name=namespace,
        ids=["doc-1", "doc-2", "doc-3", "doc-4", "doc-5"]
    )
    
    print(f"Requested: {result.get('requested_ids', 0)}")
    print(f"Found: {result.get('found_items', 0)}")
    
    # Process retrieved documents
    for item in result.get('items', []):
        print(f"\nDocument ID: {item['id']}")
        print(f"Text: {item['text'][:100]}...")  # First 100 chars
        if item.get('metadata'):
            print(f"Metadata: {item['metadata']}")
    
    # Handle partial success
    if result.get('status') == 'partial':
        not_found = result.get('not_found_ids', [])
        if not_found:
            print(f"\nDocuments not found: {not_found}")
```

## Key Features

* **Batch Retrieval**: Retrieve up to 100 documents in a single request
* **Partial Success**: Non-existent document IDs are ignored without causing errors
* **Efficient Processing**: Uses optimized batch retrieval for performance
* **Flexible IDs**: Document IDs can be strings or numbers

## Best Practices

* Use the maximum batch size (100 documents) when possible
* Group related document retrievals to minimize API calls
* Always check the `found_items` count vs `requested_ids`
* Handle partial success responses gracefully
* Cache frequently accessed documents client-side

## Use Cases

* **Document Retrieval**: Fetch specific documents by ID for display or processing
* **Content Management**: Access and manage previously uploaded documents
* **Data Export**: Extract documents for backup or migration purposes
* **Quality Assurance**: Review uploaded content for accuracy and completeness
* **Integration**: Sync document data with external systems and applications

## Related Operations

* [Fetch Text Data](/python-sdk/data/fetch-text-data) - List text and summary chunks in a namespace
* [Upload Text Data](/python-sdk/data/upload-text) - Add new text documents
* [Upload Vector Data](/python-sdk/data/upload-vector) - Add new vector embeddings
* [Delete Data](/python-sdk/data/delete) - Remove specific documents
* [Search](/python-sdk/search/query) - Find documents using semantic search