> ## 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.

# from_documents

Creates a CyborgVectorStore instance from a list of LangChain Document objects.

<Tabs>
  <Tab title="Embedded">
    ```python theme={null}
    @classmethod
    from_documents(
        documents: List[Document],
        embedding: Union[str, Embeddings, SentenceTransformer],
        **kwargs
    ) -> CyborgVectorStore
    ```

    ### Parameters

    | Parameter   | Type                                          | Description                                |
    | ----------- | --------------------------------------------- | ------------------------------------------ |
    | `documents` | `List[Document]`                              | List of LangChain Document objects         |
    | `embedding` | `Union[str, Embeddings, SentenceTransformer]` | Embedding model or model name              |
    | `**kwargs`  | `Any`                                         | Additional arguments passed to from\_texts |

    ### Returns

    `CyborgVectorStore`: Initialized vector store with documents added

    ### Example Usage

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

    # Create documents
    documents = [
        Document(
            page_content="Introduction to natural language processing",
            metadata={"source": "nlp_guide.pdf", "page": 1}
        ),
        Document(
            page_content="Tokenization and text preprocessing",
            metadata={"source": "nlp_guide.pdf", "page": 15}
        ),
        Document(
            page_content="Word embeddings and semantic similarity",
            metadata={"source": "nlp_guide.pdf", "page": 42}
        )
    ]

    # Create store from documents
    store = CyborgVectorStore.from_documents(
        documents=documents,
        embedding="sentence-transformers/all-mpnet-base-v2",
        index_name="nlp_documents",
        index_key=CyborgVectorStore.generate_key(save=True),
        api_key="your-api-key",
        index_location=DBConfig("memory"),
        config_location=DBConfig("memory")
    )
    ```
  </Tab>

  <Tab title="Python SDK">
    ```python theme={null}
    @classmethod
    from_documents(
        documents: List[Document],
        embedding: Union[str, Embeddings, SentenceTransformer],
        **kwargs
    ) -> CyborgVectorStore
    ```

    ### Parameters

    | Parameter   | Type                                          | Description                                                                       |
    | ----------- | --------------------------------------------- | --------------------------------------------------------------------------------- |
    | `documents` | `List[Document]`                              | List of LangChain Document objects                                                |
    | `embedding` | `Union[str, Embeddings, SentenceTransformer]` | Embedding model or model name                                                     |
    | `**kwargs`  | `Any`                                         | Additional arguments passed to from\_texts (includes `base_url`, `api_key`, etc.) |

    ### Returns

    `CyborgVectorStore`: Initialized vector store with documents added

    ### Example Usage

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

    documents = [
        Document(
            page_content="Introduction to natural language processing",
            metadata={"source": "nlp_guide.pdf", "page": 1}
        ),
        Document(
            page_content="Tokenization and text preprocessing",
            metadata={"source": "nlp_guide.pdf", "page": 15}
        )
    ]

    store = CyborgVectorStore.from_documents(
        documents=documents,
        embedding="sentence-transformers/all-mpnet-base-v2",
        index_name="nlp_documents",
        index_key=CyborgVectorStore.generate_key(save=True),
        api_key="your-api-key",
        base_url="http://localhost:8000"
    )
    ```
  </Tab>

  <Tab title="JS/TS">
    ```typescript theme={null}
    static fromDocuments(
        documents: Document[],
        embeddings: EmbeddingsInterface,
        config: CyborgVectorStoreConfig
    ): Promise<CyborgVectorStore>
    ```

    ### Parameters

    | Parameter    | Type                      | Description                                                                  |
    | ------------ | ------------------------- | ---------------------------------------------------------------------------- |
    | `documents`  | `Document[]`              | Array of LangChain Document objects                                          |
    | `embeddings` | `EmbeddingsInterface`     | LangChain embeddings instance                                                |
    | `config`     | `CyborgVectorStoreConfig` | Configuration object with `baseUrl`, `apiKey`, `indexName`, `indexKey`, etc. |

    ### Returns

    `Promise<CyborgVectorStore>`: Initialized vector store with documents added

    ### Example Usage

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

    const documents = [
        new Document({
            pageContent: "Introduction to natural language processing",
            metadata: { source: "nlp_guide.pdf", page: 1 }
        }),
        new Document({
            pageContent: "Tokenization and text preprocessing",
            metadata: { source: "nlp_guide.pdf", page: 15 }
        })
    ];

    const store = await CyborgVectorStore.fromDocuments(
        documents,
        new OpenAIEmbeddings(),
        {
            baseUrl: "http://localhost:8000",
            apiKey: "your-api-key",
            indexName: "nlp_documents",
            indexKey: CyborgVectorStore.generateKey()
        }
    );
    ```
  </Tab>
</Tabs>