Vectara
Bundles contain custom components that support specific third-party integrations with Langflow.
This page describes the components that are available in the Vectara bundle.
Vectara vector store
The Vectara component reads and writes to Vectara vector stores using an instance of Vectara vector store.
About vector store instances
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.
Vectara vector store parameters
You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
Some parameters are hidden by default in the visual editor. You can modify all parameters through the Controls in the component's header menu.
Some parameters are conditional, and they are only available after you set other parameters or select specific options for other parameters. Conditional parameters may not be visible on the Controls pane until you set the required dependencies.
For information about accepted values and functionality, see the Vectara documentation or inspect component code.
| Name | Type | Description |
|---|---|---|
| vectara_customer_id | String | Input parameter. The Vectara customer ID. |
| vectara_corpus_id | String | Input parameter. The Vectara corpus ID. |
| vectara_api_key | SecretString | Input parameter. The Vectara API key. |
| embedding | Embeddings | Input parameter. The embedding function to use (optional). |
| ingest_data | List[Document/Data] | Input parameter. The data to be ingested into the vector store. |
| search_query | String | Input parameter. The query for similarity search. |
| number_of_results | Integer | Input parameter. The number of results to return in search. |
Vectara RAG
This component enables Vectara's full end-to-end RAG capabilities with reranking options.
This component uses a Vectara vector store to execute the vector search and reranking functions, and then outputs an Answer string in Message format.
About vector store instances
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.
