# Module vector_store (0.8.0)

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- [0.8.0 (latest)](https://docs.cloud.google.com/python/docs/reference/langchain-google-bigtable/latest/langchain_google_bigtable.vector_store)
- [0.7.0](https://docs.cloud.google.com/python/docs/reference/langchain-google-bigtable/0.7.0/langchain_google_bigtable.vector_store)
- [0.6.0](https://docs.cloud.google.com/python/docs/reference/langchain-google-bigtable/0.6.0/langchain_google_bigtable.vector_store)
- [0.5.0](https://docs.cloud.google.com/python/docs/reference/langchain-google-bigtable/0.5.0/langchain_google_bigtable.vector_store)
- [0.4.4](https://docs.cloud.google.com/python/docs/reference/langchain-google-bigtable/0.4.4/langchain_google_bigtable.vector_store)
- [0.3.1](https://docs.cloud.google.com/python/docs/reference/langchain-google-bigtable/0.3.1/langchain_google_bigtable.vector_store)
- [0.2.3](https://docs.cloud.google.com/python/docs/reference/langchain-google-bigtable/0.2.3/langchain_google_bigtable.vector_store)
API documentation for `vector_store` module.

## Classes

### [BigtableVectorStore](https://docs.cloud.google.com/python/docs/reference/langchain-google-bigtable/latest/langchain_google_bigtable.vector_store.BigtableVectorStore)

    BigtableVectorStore(instance_id: str, table_id: str, embedding_service: langchain_core.embeddings.embeddings.Embeddings, collection: str, content_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='content', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), embedding_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='embedding', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), project_id: typing.Optional[str] = None, metadata_mappings: typing.Optional[typing.List[langchain_google_bigtable.async_vector_store.VectorMetadataMapping]] = None, metadata_as_json_column: typing.Optional[langchain_google_bigtable.async_vector_store.ColumnConfig] = None, engine: typing.Optional[langchain_google_bigtable.engine.BigtableEngine] = None, app_profile_id: typing.Optional[str] = None, **kwargs: typing.Any)

A vector store implementation using Google Cloud Bigtable.

This class provides the main user-facing interface, conforming to the
`langchain_core.vectorstores.VectorStore` standard, and handles both
synchronous and asynchronous operations by wrapping an async core.

## Modules
Functions

### init_vector_store_table

    init_vector_store_table(
        instance_id: str,
        table_id: str,
        project_id: typing.Optional[str] = None,
        client: typing.Optional[google.cloud.bigtable.client.Client] = None,
        content_column_family: str = "langchain",
        embedding_column_family: str = "langchain",
    ) -> None

Creates a Bigtable table with the necessary column families for the vector store.

It always creates the "md" column family for metadata and allows you to specify
the name of the column family used for content and embeddings.

| **Parameters** ||
|---|---|
| **Name** | **Description** |
| `instance_id` | `str` The ID of the Bigtable instance. |
| `table_id` | `str` The ID of the table to create. |
| `project_id` | `Optional[str]` Your Google Cloud project ID. |
| `client` | `Optional[bigtable.Client]` An optional pre-configured Bigtable admin client. |
| `content_column_family` | `str` The name of the column family to store document content. Defaults to "langchain". |
| `embedding_column_family` | `str` The name of the column family to store embeddings. Defaults to "langchain". |

| **Exceptions** ||
|---|---|
| **Type** | **Description** |
| `ValueError` | If the table already exists. |
| `PermissionDenied` | If the client lacks permission to create the table. |
| `GoogleAPIError` | If any other Google Cloud API error occurs. |