DataStax

import Icon from "@site/src/components/icon"; import PartialParams from '@site/docs/_partial-hidden-params.mdx'; import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx'; import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx'; import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';

<Icon name="Blocks" aria-hidden="true" /> Bundles contain custom components that support specific third-party integrations with Langflow.

This page describes the components that are available in the DataStax bundle, including components that read and write to Astra DB databases.

Astra DB

:::warning It is recommended that you create any databases, keyspaces, and collections you need before configuring the Astra DB component.

You can create new databases and collections through this component, but this is only possible in the Langflow visual editor (not at runtime), and you must wait while the database or collection initializes before proceeding with flow configuration. Additionally, not all database and collection configuration options are available through the Astra DB component, such as hybrid search options, PCU groups, vectorize integration management, and multi-region deployments. :::

The Astra DB component reads and writes to Astra DB Serverless databases, using an instance of AstraDBVectorStore to call the Data API and DevOps API.

<details> <summary>About vector store instances</summary> <PartialVectorStoreInstance /> </details>

Astra DB 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.

For information about accepted values and functionality, see the Astra DB Serverless documentation or inspect component code.

Name	Display Name	Info
token	Astra DB Application Token	Input parameter. An Astra application token with permission to access your vector database. Once the connection is verified, additional fields are populated with your existing databases and collections. If you want to create a database through this component, the application token must have Organization Administrator permissions.
environment	Environment	Input parameter. The environment for the Astra DB API endpoint. Typically always `prod`.
database_name	Database	Input parameter. The name of the database that you want this component to connect to. Or, you can select New Database to create a new database, and then wait for the database to initialize before setting the remaining parameters.
endpoint	Astra DB API Endpoint	Input parameter. For multi-region databases, select the API endpoint for your nearest datacenter. To get the list of regions for a multi-region database, see List database regions. This field is automatically populated when you select a database, and it defaults to the primary region's endpoint.
keyspace	Keyspace	Input parameter. The keyspace in your database that contains the collection specified in `collection_name`. Default: `default_keyspace`.
collection_name	Collection	Input parameter. The name of the collection that you want to use with this flow. Or, select New Collection to create a new collection with limited configuration options. To ensure your collection is configured with the correct embedding provider and search capabilities, it is recommended to create the collection in the Astra Portal or with the Data API before configuring this component. For more information, see Manage collections in Astra DB Serverless.
embedding_model	Embedding Model	Input parameter. Attach an embedding model component to generate embeddings. Only available if the specified collection doesn't have a vectorize integration. If a vectorize integration exists, the component automatically uses the collection's integrated model.
ingest_data	Ingest Data	Input parameter. The documents to load into the specified collection. Accepts `JSON` or `Table` input.
search_query	Search Query	Input parameter. The query string for vector search.
cache_vector_store	Cache Vector Store	Input parameter. Whether to cache the vector store in Langflow memory for faster reads. Default: Enabled (`true`).
search_method	Search Method	Input parameter. The search methods to use, either `Hybrid Search` or `Vector Search`. Your collection must be configured to support the chosen option, and the default depends on what your collection supports. All vector-enabled collections in Astra DB Serverless (vector) databases support vector search, but hybrid search requires that you set specific collection settings when creating the collection. These options are only available when creating a collection programmatically. For more information, see Ways to find data in Astra DB Serverless and Create a collection that supports hybrid search.
reranker	Reranker	Input parameter. The re-ranker model to use for hybrid search, depending on the collection configuration. This parameter is only available for collections that support hybrid search. To determine if a collection supports hybrid search, get collection metadata, and then check that `lexical` and `rerank` both have `"enabled": true`.
lexical_terms	Lexical Terms	Input parameter. A space-separated string of keywords for hybrid search, like `features, data, attributes, characteristics`. This parameter is only available if the collection supports hybrid search. For more information, see the Hybrid search example.
number_of_results	Number of Search Results	Input parameter. The number of search results to return. Default: 4.
search_type	Search Type	Input parameter. The search type to use, either `Similarity` (default), `Similarity with score threshold`, and `MMR (Max Marginal Relevance)`.
search_score_threshold	Search Score Threshold	Input parameter. The minimum similarity score threshold for vector search results with the `Similarity with score threshold` search type. Default: 0.
advanced_search_filter	Search Metadata Filter	Input parameter. An optional dictionary of metadata filters to apply in addition to vector or hybrid search.
autodetect_collection	Autodetect Collection	Input parameter. Whether to automatically fetch a list of available collections after providing an application token and API endpoint.
content_field	Content Field	Input parameter. For writes, this parameter specifies the name of the field in the documents that contains text strings for which you want to generate embeddings.
deletion_field	Deletion Based On Field	Input parameter. When provided, documents in the target collection with metadata field values matching the input metadata field value are deleted before new records are loaded. Use this setting for writes with upserts (overwrites).
ignore_invalid_documents	Ignore Invalid Documents	Input parameter. Whether to ignore invalid documents during writes. If disabled (`false`), then an error is raised for invalid documents. Default: Enabled (`true`).
astradb_vectorstore_kwargs	AstraDBVectorStore Parameters	Input parameter. An optional dictionary of additional parameters for the `AstraDBVectorStore` instance.

Astra DB examples

<details> <summary>Example: Vector RAG</summary>

import PartialVectorRagFlow from '@site/docs/_partial-vector-rag-flow.mdx';

<PartialVectorRagFlow /> </details> <details> <summary>Example: Hybrid search</summary>

The Astra DB component supports the Data API's hybrid search feature. Hybrid search performs a vector similarity search and a lexical search, compares the results of both searches, and then returns the most relevant results overall.

To use hybrid search through the Astra DB component, do the following:

Use the Data API to create a collection that supports hybrid search if you don't already have one.

Although you can create a collection through the Astra DB component, you have more control and insight into the collection settings when using the Data API for this operation.
Create a flow based on the Hybrid Search RAG template, which includes an Astra DB component that is pre-configured for hybrid search.

After loading the template, check for Upgrade available alerts on the components. If any components have an upgrade pending, upgrade and reconnect them before continuing.
In the Language Model components, add your OpenAI API key. If you want to use a different provider or model, see Language model components.
Delete the Language Model component that is connected to the Structured Output component's Input Message port, and then connect the Chat Input component to that port.
Configure the Astra DB vector store component:
1. Enter your Astra DB application token.
2. In the Database field, select your database.
3. In the Collection field, select your collection with hybrid search enabled.
Once you select a collection that supports hybrid search, the other parameters automatically update to allow hybrid search options.
Connect the first Parser component's Parsed Text output to the Astra DB component's Lexical Terms input. This input only appears after connecting a collection that support hybrid search with reranking.

Update the Structured Output template:

Click the Structured Output component to expose the component inspection panel.

Find the Format Instructions row, click <Icon name="Expand" aria-hidden="true"/> Expand, and then replace the prompt with the following text:

text

You are a database query planner that takes a user's requests, and then converts to a search against the subject matter in question.
You should convert the query into:
1. A list of keywords to use against a Lucene text analyzer index, no more than 4. Strictly unigrams.
2. A question to use as the basis for a QA embedding engine.
Avoid common keywords associated with the user's subject matter.

Click Finish Editing, and then click Close to save your changes to the component.

Open the Playground, and then enter a natural language question that you would ask about your database.

In this example, your input is sent to both the Astra DB and Structured Output components:
- The input sent directly to the Astra DB component's Search Query port is used as a string for similarity search. An embedding is generated from the query string using the collection's Astra DB vectorize integration.
- The input sent to the Structured Output component is processed by the Structured Output, Language Model, and Parser components to extract space-separated keywords used for the lexical search portion of the hybrid search.
The complete hybrid search query is executed against your database using the Data API's find_and_rerank command. The API's response is output as a Table that is transformed into a text string Message by another Parser component. Finally, the Chat Output component prints the Message response to the Playground.
Optional: Exit the Playground, and then click <Icon name="TextSearch" aria-hidden="true"/> Inspect Output on each individual component to understand how lexical keywords were constructed and view the raw response from the Data API. This is helpful for debugging flows where a certain component isn't receiving input as expected from another component.
- Structured Output component: The output is the JSON object produced by applying the output schema to the LLM's response to the input message and format instructions. The following example is based on the aforementioned instructions for keyword extraction:
```
1. Keywords: features, data, attributes, characteristics
2. Question: What characteristics can be identified in my data?
```
- Parser component: The output is the string of keywords extracted from the structured output JSON, and then used as lexical terms for the hybrid search.
- Astra DB component: The output is the Table containing the results of the hybrid search as returned by the Data API.

</details>

Astra DB output

<PartialVectorSearchResults /> <details> <summary>Vector Store Connection port</summary>

The Astra DB component has an additional Vector Store Connection output. This output can only connect to a VectorStore input port, and it was intended for use with dedicated Graph RAG components.

The only non-legacy component that supports this input is the Graph RAG component, which can be a Graph RAG extension to the Astra DB component. Instead, use the Astra DB Graph component that includes both the vector store connection and Graph RAG functionality.

</details>

Astra DB CQL

The Astra DB CQL component allows agents to query data from CQL tables in Astra DB.

The output is a list of JSON objects containing the query results from the Astra DB CQL table. Each Data object contains the document fields specified by the projection fields. Limited by the number_of_results parameter.

Astra DB CQL parameters

Name	Type	Description
Tool Name	String	Input parameter. The name used to reference the tool in the agent's prompt.
Tool Description	String	Input parameter. A brief description of the tool to guide the model in using it.
Keyspace	String	Input parameter. The name of the keyspace.
Table Name	String	Input parameter. The name of the Astra DB CQL table to query.
Token	SecretString	Input parameter. The authentication token for Astra DB.
API Endpoint	String	Input parameter. The Astra DB API endpoint.
Projection Fields	String	Input parameter. The attributes to return, separated by commas. Default: "*".
Partition Keys	Dict	Input parameter. Required parameters that the model must fill to query the tool.
Clustering Keys	Dict	Input parameter. Optional parameters the model can fill to refine the query. Required parameters should be marked with an exclamation mark, for example, `!customer_id`.
Static Filters	Dict	Input parameter. Attribute-value pairs used to filter query results.
Limit	String	Input parameter. The number of records to return.

Graph RAG

The Graph RAG component uses an instance of GraphRetriever for Graph RAG traversal enabling graph-based document retrieval in an Astra DB vector store. For more information, see the DataStax Graph RAG documentation.

Graph RAG 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.

Name	Display Name	Info
embedding_model	Embedding Model	Input parameter. Specify the embedding model to use. Not required if the connected vector store has a vectorize integration.
vector_store	Vector Store Connection	Input parameter. An instance of `AstraDbVectorStore` inherited from the Astra DB component's Vector Store Connection output.
edge_definition	Edge Definition	Input parameter. Edge definition for the graph traversal.
strategy	Traversal Strategies	Input parameter. The strategy to use for graph traversal. Strategy options are dynamically loaded from available strategies.
search_query	Search Query	Input parameter. The query to search for in the vector store.
graphrag_strategy_kwargs	Strategy Parameters	Input parameter. Optional dictionary of additional parameters for the retrieval strategy.
search_results	Search Results or DataFrame	Output parameter. The results of the graph-based document retrieval as a list of `JSON` objects or as a tabular `Table`. You can set the desired output type near the component's output port.

Hyper-Converged Database (HCD)

The Hyper-Converged Database (HCD) component uses your cluster's Data API server to read and write to your HCD vector store. Because the underlying functions call the Data API, which originated from Astra DB, the component uses an instance of AstraDBVectorStore.

<details> <summary>About vector store instances</summary> <PartialVectorStoreInstance /> </details> <PartialVectorSearchResults />

For more information about HCD, see Get started with HCD 1.2 and Get started with the Data API in HCD 1.2.

HCD 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.

Name	Display Name	Info
collection_name	Collection Name	Input parameter. The name of a vector store collection in HCD. For write operations, if the collection doesn't exist, then a new one is created. Required.
username	HCD Username	Input parameter. Username for authenticating to your HCD deployment. Default: `hcd-superuser`. Required.
password	HCD Password	Input parameter. Password for authenticating to your HCD deployment. Required.
api_endpoint	HCD API Endpoint	Input parameter. Your deployment's HCD Data API endpoint, formatted as `http[s]://CLUSTER_HOST:GATEWAY_PORT` where `CLUSTER_HOST` is the IP address of any node in your cluster and `GATEWAY_PORT` is the port number for your API gateway service. For example, `http://192.0.2.250:8181`. Required.
ingest_data	Ingest Data	Input parameter. Records to load into the vector store. Only relevant for writes.
search_input	Search Input	Input parameter. Query string for similarity search. Only relevant to reads.
namespace	Namespace	Input parameter. The namespace in HCD that contains or will contain the collection specified in `collection_name`. Default: `default_namespace`.
ca_certificate	CA Certificate	Input parameter. Optional CA certificate for TLS connections to HCD.
metric	Metric	Input parameter. The metrics to use for similarity search calculations, either `cosine`, `dot_product`, or `euclidean`. This is a collection setting. If calling an existing collection, leave unset to use the collection's metric. If a write operation creates a new collection, specify the desired similarity metric setting.
batch_size	Batch Size	Input parameter. Optional number of records to process in a single batch.
bulk_insert_batch_concurrency	Bulk Insert Batch Concurrency	Input parameter. Optional concurrency level for bulk write operations.
bulk_insert_overwrite_concurrency	Bulk Insert Overwrite Concurrency	Input parameter. Optional concurrency level for bulk write operations that allow upserts (overwriting existing records).
bulk_delete_concurrency	Bulk Delete Concurrency	Input parameter. Optional concurrency level for bulk delete operations.
setup_mode	Setup Mode	Input parameter. Configuration mode for setting up the vector store, either `Sync` (default), `Async`, or `Off`.
pre_delete_collection	Pre Delete Collection	Input parameter. Whether to delete the collection before creating a new one.
metadata_indexing_include	Metadata Indexing Include	Input parameter. A list of metadata fields to index if you want to enable selective indexing only when creating a collection. Doesn't apply to existing collections. Only one `_indexing_` parameter can be set per collection. If all `_indexing_` parameters are unset, then all fields are indexed (default indexing).
metadata_indexing_exclude	Metadata Indexing Exclude	Input parameter. A list of metadata fields to exclude from indexing if you want to enable selective indexing only when creating a collection. Doesn't apply to existing collections. Only one `_indexing_` parameter can be set per collection. If all `_indexing_` parameters are unset, then all fields are indexed (default indexing).
collection_indexing_policy	Collection Indexing Policy	Input parameter. A dictionary to define the indexing policy if you want to enable selective indexing only when creating a collection. Doesn't apply to existing collections. Only one `_indexing_` parameter can be set per collection. If all `_indexing_` parameters are unset, then all fields are indexed (default indexing). The `collection_indexing_policy` dictionary is used when you need to set indexing on subfields or a complex indexing definition that isn't compatible as a list.
embedding	Embedding or Astra Vectorize	Input parameter. The embedding model to use by attaching an Embedding Model component. This component doesn't support additional vectorize authentication headers, so it isn't possible to use a vectorize integration with this component, even if you have enabled one on an existing HCD collection.
number_of_results	Number of Results	Input parameter. Number of search results to return. Default: 4. Only relevant to reads.
search_type	Search Type	Input parameter. Search type to use, either `Similarity` (default), `Similarity with score threshold`, or `MMR (Max Marginal Relevance)`. Only relevant to reads.
search_score_threshold	Search Score Threshold	Input parameter. Minimum similarity score threshold for search results if the `search_type` is `Similarity with score threshold`. Default: 0.
search_filter	Search Metadata Filter	Input parameter. Optional dictionary of metadata filters to apply in addition to vector search.

Other DataStax components

The following components are also included in the DataStax bundle.

Astra DB Chat Memory

The Astra DB Chat Memory component retrieves and stores chat messages using an Astra DB database.

Chat memories are passed between memory storage components as the Memory data type. Specifically, the component creates an instance of AstraDBChatMessageHistory, which is a LangChain chat message history class that uses Astra DB for storage.

:::warning The Astra DB Chat Memory component isn't recommended for most memory storage because memories tend to be long JSON objects or strings, often exceeding the maximum size of a document or object supported by Astra DB.

However, Langflow's Agent component includes built-in chat memory that is enabled by default. Your agentic flows don't need an external database to store chat memory. For more information, see Memory management options. :::

For more information about using external chat memory in flows, see the Message History component.

Astra DB Chat Memory parameters

Name	Type	Description
collection_name	String	Input parameter. The name of the Astra DB collection for storing messages. Required.
token	SecretString	Input parameter. The authentication token for Astra DB access. Required.
api_endpoint	SecretString	Input parameter. The API endpoint URL for the Astra DB service. Required.
namespace	String	Input parameter. The optional namespace within Astra DB for the collection.
session_id	MessageText	Input parameter. The unique identifier for the chat session. Uses the current session ID if not provided.

Legacy DataStax components

import PartialLegacy from '@site/docs/_partial-legacy.mdx';

The following DataStax components are in legacy status:

<details> <summary>Astra DB Tool</summary>

Replace the Astra DB Tool component with the Astra DB component.

The Astra DB Tool component enables searching data in Astra DB collections, including hybrid search, vector search, and regular filter-based search. Specialized searches require that the collection is pre-configured with the required parameters.

Outputs a list of JSON objects containing the query results from Astra DB. Each JSON object contains the document fields specified by the projection attributes. Limited by the number_of_results parameter and the upper limit of the Astra DB Data API, depending on the type of search.

You can use the component to execute queries directly as isolated steps in a flow, or you can connect it as a tool for an agent to allow the agent to query data from Astra DB collections as needed to respond to user queries.

The values for Collection Name, Astra DB Application Token, and Astra DB API Endpoint are found in your Astra DB deployment. For more information, see the Astra DB Serverless documentation.

Name	Type	Description
Tool Name	String	Input parameter. The name used to reference the tool in the agent's prompt.
Tool Description	String	Input parameter. A brief description of the tool. This helps the model decide when to use it.
Keyspace Name	String	Input parameter. The name of the keyspace in Astra DB. Default: `default_keyspace`
Collection Name	String	Input parameter. The name of the Astra DB collection to query.
Token	SecretString	Input parameter. The authentication token for accessing Astra DB.
API Endpoint	String	Input parameter. The Astra DB API endpoint.
Projection Fields	String	Input parameter. Comma-separated list of attributes to return from matching documents. The default is the default projection, `*`, which returns all attributes except reserved fields like `$vector`.
Tool Parameters	Dict	Input parameter. Astra DB Data API `find` filters that become tools for an agent. These Filters may be used in a search, if the agent selects them.
Static Filters	Dict	Input parameter. Attribute-value pairs used to filter query results. Equivalent to Astra DB Data API `find` filters. Static Filters are included with every query. Use Static Filters without semantic search to perform a regular filter search.
Number of Results	Int	Input parameter. The maximum number of documents to return.
Semantic Search	Boolean	Input parameter. Whether to run a similarity search by generating a vector embedding from the chat input and following the Semantic Search Instruction. Default: `false`. If `true`, you must attach an embedding model component or have vectorize pre-enabled on your collection.
Use Astra DB Vectorize	Boolean	Input parameter. Whether to use the Astra DB vectorize feature for embedding generation when running a semantic search. Default: `false`. If `true`, you must have vectorize pre-enabled on your collection.
Embedding Model	Embedding	Input parameter. A port to attach an embedding model component to generate a vector from input text for semantic search. This can be used when Semantic Search is `true`, with or without vectorize. Be sure to use a model that aligns with the dimensions of the embeddings already present in the collection.
Semantic Search Instruction	String	Input parameter. The query to use for similarity search. Default: `"Find documents similar to the query."`. This instruction is used to guide the model in performing semantic search.

</details> <details> <summary>Astra DB Graph</summary>

Replace the Astra DB Graph component with the Graph RAG component.

The Astra DB Graph component uses AstraDBGraphVectorStore, an instance of LangChain graph vector store, for graph traversal and graph-based document retrieval in an Astra DB collection. It also supports writing to the vector store. For more information, see Build a Graph RAG system with LangChain and GraphRetriever.

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.

For information about accepted values and functionality, see the Astra DB Serverless documentation or inspect component code.

Name	Display Name	Info
token	Astra DB Application Token	Input parameter. An Astra application token with permission to access your vector database. Once the connection is verified, additional fields are populated with your existing databases and collections. If you want to create a database through this component, the application token must have Organization Administrator permissions.
api_endpoint	API Endpoint	Input parameter. Your database's API endpoint.
keyspace	Keyspace	Input parameter. The keyspace in your database that contains the collection specified in `collection_name`. Default: `default_keyspace`.
collection_name	Collection	Input parameter. The name of the collection that you want to use with this flow. For write operations, if a matching collection doesn't exist, a new one is created.
metadata_incoming_links_key	Metadata Incoming Links Key	Input parameter. The metadata key for the incoming links in the vector store.
ingest_data	Ingest Data	Input parameter. Records to load into the vector store. Only relevant for writes.
search_input	Search Query	Input parameter. Query string for similarity search. Only relevant to reads.
cache_vector_store	Cache Vector Store	Input parameter. Whether to cache the vector store in Langflow memory for faster reads. Default: Enabled (`true`).
embedding_model	Embedding Model	Input parameter. Attach an embedding model component to generate embeddings. If the collection has a vectorize integration, don't attach an embedding model component.
metric	Metric	Input parameter. The metrics to use for similarity search calculations, either `cosine` (default), `dot_product`, or `euclidean`. This is a collection setting.
batch_size	Batch Size	Input parameter. Optional number of records to process in a single batch.
bulk_insert_batch_concurrency	Bulk Insert Batch Concurrency	Input parameter. Optional concurrency level for bulk write operations.
bulk_insert_overwrite_concurrency	Bulk Insert Overwrite Concurrency	Input parameter. Optional concurrency level for bulk write operations that allow upserts (overwriting existing records).
bulk_delete_concurrency	Bulk Delete Concurrency	Input parameter. Optional concurrency level for bulk delete operations.
setup_mode	Setup Mode	Input parameter. Configuration mode for setting up the vector store, either `Sync` (default) or `Off`.
pre_delete_collection	Pre Delete Collection	Input parameter. Whether to delete the collection before creating a new one. Default: Disabled (`false`).
metadata_indexing_include	Metadata Indexing Include	Input parameter. A list of metadata fields to index if you want to enable selective indexing only when creating a collection. Doesn't apply to existing collections. Only one `_indexing_` parameter can be set per collection. If all `_indexing_` parameters are unset, then all fields are indexed (default indexing).
metadata_indexing_exclude	Metadata Indexing Exclude	Input parameter. A list of metadata fields to exclude from indexing if you want to enable selective indexing only when creating a collection. Doesn't apply to existing collections. Only one `_indexing_` parameter can be set per collection. If all `_indexing_` parameters are unset, then all fields are indexed (default indexing).
collection_indexing_policy	Collection Indexing Policy	Input parameter. A dictionary to define the indexing policy if you want to enable selective indexing only when creating a collection. Doesn't apply to existing collections. Only one `_indexing_` parameter can be set per collection. If all `_indexing_` parameters are unset, then all fields are indexed (default indexing). The `collection_indexing_policy` dictionary is used when you need to set indexing on subfields or a complex indexing definition that isn't compatible as a list.
number_of_results	Number of Results	Input parameter. Number of search results to return. Default: 4. Only relevant to reads.
search_type	Search Type	Input parameter. Search type to use, either `Similarity`, `Similarity with score threshold`, or `MMR (Max Marginal Relevance)`, `Graph Traversal`, or `MMR (Max Marginal Relevance) Graph Traversal` (default). Only relevant to reads.
search_score_threshold	Search Score Threshold	Input parameter. Minimum similarity score threshold for search results if the `search_type` is `Similarity with score threshold`. Default: 0.
search_filter	Search Metadata Filter	Input parameter. Optional dictionary of metadata filters to apply in addition to vector search.

</details> <details> <summary>Environment variable components</summary>

The following DataStax components were used to load and retrieve environment variables in a flow:

Dotenv: Loads environment variables from a .env file
Get Environment Variable: Retrieves the value of an environment variable

These components are legacy. Use Langflow's built-in environment variable support or global variables instead.

</details> <details> <summary>Astra Vectorize</summary>

This component was deprecated in Langflow version 1.1.2. Replace it with the Astra DB component.

The Astra DB Vectorize component was used to generate embeddings with Astra DB's vectorize feature in conjunction with an Astra DB component.

The vectorize functionality is now built into the Astra DB component. You no longer need a separate component for vectorize embedding generation.

</details>

Astra DB

Astra DB parameters

Astra DB examples

Astra DB output

Astra DB CQL

Astra DB CQL parameters

Graph RAG

Graph RAG parameters

Hyper-Converged Database (HCD)

HCD parameters

Other DataStax components

Astra DB Chat Memory

Astra DB Chat Memory parameters

Legacy DataStax components

See also