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

# add_documents

Adds LangChain Document objects to the vector store.

```python theme={null}
add_documents(
    documents: List[Document],
    ids: Optional[List[str]] = None,
    **kwargs
) -> List[str]
```

### Parameters

| Parameter   | Type                  | Description                                                                 |
| ----------- | --------------------- | --------------------------------------------------------------------------- |
| `documents` | `List[Document]`      | List of LangChain Document objects to add                                   |
| `ids`       | `Optional[List[str]]` | *(Optional)* List of IDs for the documents (auto-generated if not provided) |
| `**kwargs`  | `Any`                 | Additional keyword arguments passed to add\_texts                           |

### Returns

`List[str]`: List of IDs for the added documents

### Example Usage

```python theme={null}
from langchain_core.documents import Document

# Create documents with metadata
documents = [
    Document(
        page_content="Introduction to machine learning",
        metadata={"chapter": 1, "topic": "ML basics"}
    ),
    Document(
        page_content="Deep learning fundamentals",
        metadata={"chapter": 2, "topic": "Neural networks"}
    )
]

# Add documents to the store
ids = store.add_documents(documents)
print(f"Added {len(ids)} documents")
```