你好，@usha2405!我在这里帮助你解决任何bug、问题或贡献。让我们一起解决你面临的问题。
要解决在Langflow中看不到向量搜索命令并在尝试导入文件时遇到错误页面的问题，请确保 VectaraSearchComponent 正确定义并集成。以下是 VectaraSearchComponent 类的相关代码部分：

class VectaraSearchComponent(VectaraVectorStoreComponent, LCVectorStoreComponent):
    display_name: str = "Vectara Search"
    description: str = "Search a Vectara Vector Store for similar documents."
    documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara"
    icon = "Vectara"

    field_config = {
        "search_type": {
            "display_name": "Search Type",
            "options": ["Similarity", "MMR"],
        },
        "input_value": {"display_name": "Input"},
        "vectara_customer_id": {
            "display_name": "Vectara Customer ID",
        },
        "vectara_corpus_id": {
            "display_name": "Vectara Corpus ID",
        },
        "vectara_api_key": {
            "display_name": "Vectara API Key",
            "password": True,
        },
        "files_url": {
            "display_name": "Files Url",
            "info": "Make vectara object using url of files (optional)",
        },
        "number_of_results": {
            "display_name": "Number of Results",
            "info": "Number of results to return.",
            "advanced": True,
        },
    }

    def build(  # type: ignore[override]
        self,
        input_value: Text,
        search_type: str,
        vectara_customer_id: str,
        vectara_corpus_id: str,
        vectara_api_key: str,
        number_of_results: int = 4,
    ) -> List[Data]:
        source = "Langflow"
        vector_store = Vectara(
            vectara_customer_id=vectara_customer_id,
            vectara_corpus_id=vectara_corpus_id,
            vectara_api_key=vectara_api_key,
            source=source,
        )

        if not vector_store:
            raise ValueError("Failed to create Vectara Vector Store")

        return self.search_with_vector_store(
            vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results
        )

确保这个类在你的项目中正确放置，并且所有依赖项都已正确安装。 VectaraSearchComponent 正常运行所需的特定依赖项如下：

1. langchain_community.vectorstores.Vectara
2. langflow.base.vectorstores.model.LCVectorStoreComponent
3. langflow.helpers.data.docs_to_data
4. langflow.io.HandleInput
5. langflow.io.IntInput
6. langflow.io.MessageTextInput
7. langflow.io.SecretStrInput
8. langflow.io.StrInput
9. langflow.schema.Data
10. loguru.logger1(https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/components/vectorsearch/VectaraSearch.py)2(https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/components/vectorstores/Vectara.py)。
    此外，请确保你的 Langflow 安装是最新的。你可以使用以下命令更新 Langflow 及其依赖项：

pip install --upgrade langflow
pip install --upgrade langchain_community.vectorstores.vectara

如果问题仍然存在，可能与你的设置或配置的其他部分有关。

• 要继续与 @dosu 交流，请提及 @dosu。*