Vectara 捆绑包

Vectara 是一个用于构建生成式 AI 搜索和聊天体验的平台。

Vectara 向量存储 (Vectara Vector Store)

使用 Vectara 的向量存储。

关于向量存储实例

Because Langflow is based on LangChain, vector store components use an instance of LangChain vector store to drive the underlying read and write functions. These instances are provider-specific and configured according to the component's parameters, such as the connection string, index name, and schema.

In component code, this is often instantiated as vector_store, but some vector store components use a different name, such as the provider name.

Some LangChain classes don't expose all possible options as component parameters. Depending on the provider, these options might use default values or allow modification through environment variables, if they are supported in Langflow. For information about specific options, see the LangChain API reference and vector store provider's documentation.

If you use a vector store component to query your vector database, it produces search results that you can pass to downstream components in your flow as a list of Data objects or a tabular DataFrame. If both types are supported, you can set the format near the vector store component's output port in the visual editor.

输入参数

名称	类型	描述
客户 ID (Customer ID) (customer_id)	String	输入参数。Vectara 的客户 ID。
语料库 ID (Corpus ID) (corpus_id)	String	输入参数。Vectara 的语料库 ID。
API Key (api_key)	SecretString	输入参数。Vectara 的 API 密钥。
输入内容 (Input Value) (input_value)	Data	输入参数。要建立索引的数据。
搜索查询 (Search Query) (search_query)	String	输入参数。搜索查询语句。

输出参数

名称	类型	描述
搜索结果 (Search Results) (text)	Text	输出参数。文本格式的搜索结果。

Vectara RAG

此组件启用了 Vectara 完整的端到端 RAG 功能，并带有重排序 (reranking) 选项。

此组件使用 Vectara 向量存储来执行向量搜索和重排序功能，然后以 Message 格式输出 Answer 字符串。

关于向量存储实例

Because Langflow is based on LangChain, vector store components use an instance of LangChain vector store to drive the underlying read and write functions. These instances are provider-specific and configured according to the component's parameters, such as the connection string, index name, and schema.

In component code, this is often instantiated as vector_store, but some vector store components use a different name, such as the provider name.

Some LangChain classes don't expose all possible options as component parameters. Depending on the provider, these options might use default values or allow modification through environment variables, if they are supported in Langflow. For information about specific options, see the LangChain API reference and vector store provider's documentation.