> ## 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.

<Tabs>
  <Tab title="Embedded">
    ```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")
    ```
  </Tab>

  <Tab title="Python SDK">
    ```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

    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"}
        )
    ]

    ids = store.add_documents(documents)
    ```
  </Tab>

  <Tab title="JS/TS">
    ```typescript theme={null}
    addDocuments(
        documents: Document[],
        options?: { ids?: string[] }
    ): Promise<string[]>
    ```

    ### Parameters

    | Parameter   | Type         | Description                                           |
    | ----------- | ------------ | ----------------------------------------------------- |
    | `documents` | `Document[]` | Array of LangChain Document objects to add            |
    | `options`   | `object`     | *(Optional)* Options object with optional `ids` array |

    ### Returns

    `Promise<string[]>`: Array of IDs for the added documents

    ### Example Usage

    ```typescript theme={null}
    import { Document } from '@langchain/core/documents';

    const documents = [
        new Document({
            pageContent: "Introduction to machine learning",
            metadata: { chapter: 1, topic: "ML basics" }
        }),
        new Document({
            pageContent: "Deep learning fundamentals",
            metadata: { chapter: 2, topic: "Neural networks" }
        })
    ];

    const ids = await store.addDocuments(documents);
    ```
  </Tab>
</Tabs>

### Async

<Accordion title="Python async variants">
  The Embedded and Python SDK provide async versions of this method prefixed with `a`:

  ```python theme={null}
  # aadd_documents — async variant
  ids = await store.aadd_documents(documents)
  ```
</Accordion>

<Note>
  JS/TS methods are natively async — all signatures above already return `Promise<...>`. No separate async variant is needed.
</Note>