知识库文档状态显示错误，无法被索引（Knowledge base document status display error, unable to be indexed）

[WeiMwww]

按照如图完成知识库设置，我的Rerank 模型合Embedding 模型都正常。但最后知识文档状态显示为“错误”，提示如图为“Vector database connection error”。我也查看了我的日志，显示都正常，不知如何解决。期待你的回答（Complete the knowledge base setup as shown in the figure, and both my Rerank model and Embedding model are working properly. But in the end, the status of the knowledge document is displayed as "Error", with a prompt of "Vector database connection error" as shown in the figure. I have also checked my log and it shows that everything is normal. I don't know how to solve it. Looking forward to your answer）

[dosubot[bot]]

@WeiMwww 你好！我是 Dosu，一个可以帮助你解决 bug、回答问题并成为贡献者的机器人。我会在等待人类维护者的时候帮助你。

要解决“Vector database connection error”并成功索引您的知识库文档，您可以使用 api/commands.py 文件中提供的 migrate_knowledge_vector_database 函数。此函数处理将向量数据库数据迁移到目标向量数据库。

以下是迁移知识向量数据库的相关代码片段：

def migrate_knowledge_vector_database():
    """
    Migrate vector database datas to target vector database .
    """
    click.echo(click.style('Start migrate vector db.', fg='green'))
    create_count = 0
    skipped_count = 0
    total_count = 0
    config = current_app.config
    vector_type = config.get('VECTOR_STORE')
    page = 1
    while True:
        try:
            datasets = db.session.query(Dataset).filter(Dataset.indexing_technique == 'high_quality') \
                .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50)
        except NotFound:
            break

        page += 1
        for dataset in datasets:
            total_count = total_count + 1
            click.echo(f'Processing the {total_count} dataset {dataset.id}. '
                       + f'{create_count} created, {skipped_count} skipped.')
            try:
                click.echo('Create dataset vdb index: {}'.format(dataset.id))
                if dataset.index_struct_dict:
                    if dataset.index_struct_dict['type'] == vector_type:
                        skipped_count = skipped_count + 1
                        continue
                collection_name = ''
                if vector_type == VectorType.WEAVIATE:
                    dataset_id = dataset.id
                    collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                    index_struct_dict = {
                        "type": VectorType.WEAVIATE,
                        "vector_store": {"class_prefix": collection_name}
                    }
                    dataset.index_struct = json.dumps(index_struct_dict)
                elif vector_type == VectorType.QDRANT:
                    if dataset.collection_binding_id:
                        dataset_collection_binding = db.session.query(DatasetCollectionBinding). \
                            filter(DatasetCollectionBinding.id == dataset.collection_binding_id). \
                            one_or_none()
                        if dataset_collection_binding:
                            collection_name = dataset_collection_binding.collection_name
                        else:
                            raise ValueError('Dataset Collection Bindings is not exist!')
                    else:
                        dataset_id = dataset.id
                        collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                    index_struct_dict = {
                        "type": VectorType.QDRANT,
                        "vector_store": {"class_prefix": collection_name}
                    }
                    dataset.index_struct = json.dumps(index_struct_dict)
                db.session.add(dataset)
                db.session.commit()
                click.echo(f'Successfully migrated dataset {dataset.id}.')
                create_count += 1
            except Exception as e:
                db.session.rollback()
                click.echo(
                    click.style('Create dataset index error: {} {}'.format(e.__class__.__name__, str(e)),
                                fg='red'))
                continue

    click.echo(
        click.style(f'Congratulations! Create {create_count} dataset indexes, and skipped {skipped_count} datasets.',
                    fg='green'))

要运行此函数，您可以使用 vdb_migrate 命令并设置适当的范围：

@click.command('vdb-migrate', help='migrate vector db.')
@click.option('--scope', default='all', prompt=False, help='The scope of vector database to migrate, Default is All.')
def vdb_migrate(scope: str):
    if scope in ['knowledge', 'all']:
        migrate_knowledge_vector_database()
    if scope in ['annotation', 'all']:
        migrate_annotation_vector_database()

您可以执行以下命令，将范围设置为 knowledge 以迁移知识向量数据库：

vdb_migrate --scope knowledge

如果您使用的是 Weaviate 作为向量数据库，请确保 Weaviate 客户端已正确配置以处理连接错误。以下是初始化 Weaviate 客户端的示例：

class WeaviateVector(BaseVector):

    def __init__(self, collection_name: str, config: WeaviateConfig, attributes: list):
        super().__init__(collection_name)
        self._client = self._init_client(config)
        self._attributes = attributes

    def _init_client(self, config: WeaviateConfig) -> weaviate.Client:
        auth_config = weaviate.auth.AuthApiKey(api_key=config.api_key)

        weaviate.connect.connection.has_grpc = False

        try:
            client = weaviate.Client(
                url=config.endpoint,
                auth_client_secret=auth_config,
                timeout_config=(5, 60),
                startup_period=None
            )
        except requests.exceptions.ConnectionError:
            raise ConnectionError("Vector database connection error")

        client.batch.configure(
            batch_size=config.batch_size,
            dynamic=True,
            timeout_retries=3,
        )

        return client

此外，请确保您的环境变量已正确设置以进行向量数据库配置。以下是一些可能需要配置的相关环境变量：

#### Vector Database Configuration

* VECTOR\_STORE
  * **Available enumeration types include：**
    * `weaviate`
    * `qdrant`
    * `milvus`
    * `zilliz` (share the same configuration as `milvus`)
    * `pinecone` (not yet open)
*   WEAVIATE\_ENDPOINT

    Weaviate endpoint address, such as: `http://weaviate:8080`.
*   WEAVIATE\_API\_KEY

    The api-key credential used to connect to Weaviate.
*   WEAVIATE\_BATCH\_SIZE

    The number of index Objects created in batches in Weaviate, default is 100.

    Refer to this document: [https://weaviate.io/developers/weaviate/manage-data/import#how-to-set-batch-parameters](https://weaviate.io/developers/weaviate/manage-data/import#how-to-set-batch-parameters)
*   WEAVIATE\_GRPC\_ENABLED

    Whether to use the gRPC method to interact with Weaviate, performance will greatly increase when enabled, may not be usable locally, default is true.
*   QDRANT\_URL

    Qdrant endpoint address, such as: `https://your-qdrant-cluster-url.qdrant.tech/`
*   QDRANT\_API\_KEY

    The api-key credential used to connect to Qdrant.
*   PINECONE\_API\_KEY

    The api-key credential used to connect to Pinecone.
*   PINECONE\_ENVIRONMENT

    The environment where Pinecone is located, such as: `us-east4-gcp`
*   MILVUS\_HOST

    Milvus host configuration.
*   MILVUS\_PORT

    Milvus port configuration.
*   MILVUS\_USER

    Milvus user configuration, default is empty.
*   MILVUS\_PASSWORD

    Milvus password configuration, default is empty.
*   MILVUS\_SECURE

    Whether Milvus uses SSL connection, default is false.

这些步骤应有助于解决“Vector database connection error”并成功索引您的知识库文档^[1][2][3].

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[night666e]

这个环境变量如何配置

[WThirteen]

如何你用的是docker进行部署，这个似乎不需要配置。

[WeiMwww]

您好，您的邮件已收到，我会尽快处理，谢谢！Hello, Your email is received and will be handled as soon as possible，Thank you！

[night666e]

有用吗

[WeiMwww]

您好，您的邮件已收到，我会尽快处理，谢谢！Hello, Your email is received and will be handled as soon as possible，Thank you！

[GaoXiong666]

我是在docker安装dify后发现没有weaviate容器，看到docker-compose.yaml里有配置，在目录里执行一下docker compose up -d weaviate就好了
I found that there was no weaviate container after installing dify on Docker. I saw that there was a configuration in docker-compose.yaml, so I executed 'docker compose up - d weaviate' in the directory to fix it