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docs: datastax bundles page (#10686)
* init * Apply suggestions from code review Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com> --------- Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com>
This commit is contained in:
Mendon Kissling
@ -194,126 +194,6 @@ The output is a list of [`Data`](/data-types#data) objects containing the query
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| Static Filters | Dict | Input parameter. Attribute-value pairs used to filter query results. |
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| Limit | String | Input parameter. The number of records to return. |
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## Astra DB Tool
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The **Astra DB Tool** component enables searching data in Astra DB collections, including hybrid search, vector search, and regular filter-based search.
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Specialized searches require that the collection is pre-configured with the required parameters.
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Outputs a list of [`Data`](/data-types#data) objects containing the query results from Astra DB. Each `Data` 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.
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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](/agents-tools) to allow the agent to query data from Astra DB collections as needed to respond to user queries.
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For more information, see [Use Langflow agents](/agents).
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### Astra DB Tool parameters
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The following parameters are for the **Astra DB Tool** component overall.
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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](https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html).
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| Name | Type | Description |
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|-------------------|--------|--------|
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| Tool Name | String | Input parameter. The name used to reference the tool in the agent's prompt. |
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| Tool Description | String | Input parameter. A brief description of the tool. This helps the model decide when to use it. |
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| Keyspace Name | String | Input parameter. The name of the keyspace in Astra DB. Default: `default_keyspace` |
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| Collection Name | String | Input parameter. The name of the Astra DB collection to query. |
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| Token | SecretString | Input parameter. The authentication token for accessing Astra DB. |
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| API Endpoint | String | Input parameter. The Astra DB API endpoint. |
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| 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`. |
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| Tool Parameters | Dict | Input parameter. [Astra DB Data API `find` filters](https://docs.datastax.com/en/astra-db-serverless/api-reference/document-methods/find-many.html#parameters) that become tools for an agent. These Filters _may_ be used in a search, if the agent selects them. See [Define tool-specific parameters](#define-tool-specific-parameters). |
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| Static Filters | Dict | Input parameter. Attribute-value pairs used to filter query results. Equivalent to [Astra DB Data API `find` filters](https://docs.datastax.com/en/astra-db-serverless/api-reference/document-methods/find-many.html#parameters). **Static Filters** are included with _every_ query. Use **Static Filters** without semantic search to perform a regular filter search. |
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| Number of Results | Int | Input parameter. The maximum number of documents to return. |
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| 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](/components-embedding-models) or have vectorize pre-enabled on your collection. |
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| 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. |
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| 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. |
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| 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. |
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### Define tool-specific parameters
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:::tip
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**Tool Parameters** are small functions that you create within the **Astra DB Tool** component.
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They give the LLM pre-defined ways to interact with the data in your collection.
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Without these filters, the LLM has no concept of the data in your collection or which attributes are important.
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At runtime, the LLM can decide which filters are relevant to the current query.
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Filters in **Tool Parameters** aren't always applied.
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If you want to enforce filters for _every_ query, use the **Static Filters** parameter.
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You can use both **Tool Parameters** and **Static Filters** to set some required filters and some optional filters.
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:::
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In the **Astra DB Tool** component's **Tool Parameters** field, you can create filters to query documents in your collection.
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When used in **Tool Mode** with an agent, these filters tell the agent which document attributes are most important, which are required in searches, and which operators to use on certain attributes.
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The filters become available as parameters that the LLM can use when calling the tool, with a better understanding of each parameter provided by the **Description** field.
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In the **Tool Parameters** pane, click <Icon name="Plus" aria-hidden="true"/> **Add a new row**, and then edit each cell in the row.
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For example, the following filter allows an LLM to filter by unique `customer_id` values:
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* Name: `customer_id`
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* Attribute Name: Leave empty if the attribute matches the field name in the database.
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* Description: `"The unique identifier of the customer to filter by"`.
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* Is Metadata: Select **False** unless the value is stored in the metadata field.
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* Is Mandatory: Set to **True** to make the filter required.
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* Is Timestamp: For this example, select **False** because the value is an ID, not a timestamp.
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* Operator: `$eq` to look for an exact match.
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The following fields are available for each row in the **Tool Parameters** pane:
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| Parameter | Description |
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|-----------|-------------|
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| Name | The name of the parameter that is exposed to the LLM. It can be the same as the underlying field name or a more descriptive label. The LLM uses this name, along with the description, to infer what value to provide during execution. |
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| Attribute Name | When the parameter name shown to the LLM differs from the actual field or property in the database, use this setting to map the user-facing name to the correct attribute. For example, to apply a range filter to the timestamp field, define two separate parameters, such as `start_date` and `end_date`, that both reference the same timestamp attribute. |
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| Description | Provides instructions to the LLM on how the parameter should be used. Clear and specific guidance helps the LLM provide valid input. For example, if a field such as `specialty` is stored in lowercase, the description should indicate that the input must be lowercase. |
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| Is Metadata | When loading data using LangChain or Langflow, additional attributes may be stored under a metadata object. If the target attribute is stored this way, enable this option. It adjusts the query by generating a filter in the format: `{"metadata.<attribute_name>": "<value>"}` |
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| Is Timestamp | For date or time-based filters, enable this option to automatically convert values to the timestamp format that the Astrapy client expects. This ensures compatibility with the underlying API without requiring manual formatting. |
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| Operator | Defines the filtering logic applied to the attribute. You can use any valid [Data API filter operator](https://docs.datastax.com/en/astra-db-serverless/api-reference/filter-operator-collections.html). For example, to filter a time range on the timestamp attribute, use two parameters: one with the `$gt` operator for "greater than", and another with the `$lt` operator for "less than". |
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## Astra DB Graph
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The **Astra DB Graph** component uses `AstraDBGraphVectorStore`, an instance of [LangChain graph vector store](https://python.langchain.com/api_reference/community/graph_vectorstores.html), for graph traversal and graph-based document retrieval in an Astra DB collection. It also supports writing to the vector store.
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For more information, see [Build a Graph RAG system with LangChain and GraphRetriever](https://docs.datastax.com/en/astra-db-serverless/tutorials/graph-rag.html).
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<PartialVectorSearchResults />
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### Astra DB Graph parameters
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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.
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<PartialParams />
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<PartialConditionalParams />
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For information about accepted values and functionality, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/index.html) or inspect [component code](/concepts-components#component-code).
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| Name | Display Name | Info |
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|------|--------------|------|
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| 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. |
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| api_endpoint | API Endpoint | Input parameter. Your database's API endpoint. |
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| keyspace | Keyspace | Input parameter. The keyspace in your database that contains the collection specified in `collection_name`. Default: `default_keyspace`. |
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| 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. |
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| metadata_incoming_links_key | Metadata Incoming Links Key | Input parameter. The metadata key for the incoming links in the vector store. |
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| ingest_data | Ingest Data | Input parameter. Records to load into the vector store. Only relevant for writes. |
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| search_input | Search Query | Input parameter. Query string for similarity search. Only relevant for reads. |
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| cache_vector_store | Cache Vector Store | Input parameter. Whether to cache the vector store in Langflow memory for faster reads. Default: Enabled (`true`). |
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| embedding_model | Embedding Model | Input parameter. Attach an [embedding model component](/components-embedding-models) to generate embeddings. If the collection has a [vectorize integration](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html), don't attach an embedding model component. |
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| metric | Metric | Input parameter. The metrics to use for similarity search calculations, either `cosine` (default), `dot_product`, or `euclidean`. This is a collection setting. |
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| batch_size | Batch Size | Input parameter. Optional number of records to process in a single batch. |
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| bulk_insert_batch_concurrency | Bulk Insert Batch Concurrency | Input parameter. Optional concurrency level for bulk write operations. |
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| bulk_insert_overwrite_concurrency | Bulk Insert Overwrite Concurrency | Input parameter. Optional concurrency level for bulk write operations that allow upserts (overwriting existing records). |
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| bulk_delete_concurrency | Bulk Delete Concurrency | Input parameter. Optional concurrency level for bulk delete operations. |
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| setup_mode | Setup Mode | Input parameter. Configuration mode for setting up the vector store, either `Sync` (default) or `Off`. |
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| pre_delete_collection | Pre Delete Collection | Input parameter. Whether to delete the collection before creating a new one. Default: Disabled (`false`). |
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| metadata_indexing_include | Metadata Indexing Include | Input parameter. A list of metadata fields to index if you want to enable [selective indexing](https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-indexes.html) *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). |
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| 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). |
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| 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. |
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| number_of_results | Number of Results | Input parameter. Number of search results to return. Default: 4. Only relevant to reads. |
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| 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. |
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| 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. |
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| search_filter | Search Metadata Filter | Input parameter. Optional dictionary of metadata filters to apply in addition to vector search. |
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## Graph RAG
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The **Graph RAG** component uses an instance of [`GraphRetriever`](https://datastax.github.io/graph-rag/reference/langchain_graph_retriever/) for Graph RAG traversal enabling graph-based document retrieval in an Astra DB vector store.
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@ -375,7 +255,7 @@ You can inspect a vector store component's parameters to learn more about the in
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| password | HCD Password | Input parameter. Password for authenticating to your HCD deployment. Required. |
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| 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. |
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| ingest_data | Ingest Data | Input parameter. Records to load into the vector store. Only relevant for writes. |
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| search_input | Search Input | Input parameter. Query string for similarity search. Only relevant for reads. |
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| search_input | Search Input | Input parameter. Query string for similarity search. Only relevant to reads. |
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| namespace | Namespace | Input parameter. The namespace in HCD that contains or will contain the collection specified in `collection_name`. Default: `default_namespace`. |
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| ca_certificate | CA Certificate | Input parameter. Optional CA certificate for TLS connections to HCD. |
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| 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. |
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@ -427,23 +307,6 @@ For more information about using external chat memory in flows, see the [**Messa
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| namespace | String | Input parameter. The optional namespace within Astra DB for the collection. |
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| session_id | MessageText | Input parameter. The unique identifier for the chat session. Uses the current session ID if not provided. |
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### Assistants API
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The following DataStax components are used to create and manage Assistants API functions in a flow:
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* **Astra Assistant Agent**
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* **Create Assistant**
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* **Create Assistant Thread**
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* **Get Assistant Name**
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* **List Assistants**
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* **Run Assistant**
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## Environment variables
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The following DataStax components are used to load and retrieve environment variables in a flow:
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* **Dotenv**
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* **Get Environment Variable**
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## Legacy DataStax components
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@ -453,6 +316,113 @@ import PartialLegacy from '@site/docs/_partial-legacy.mdx';
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The following DataStax components are in legacy status:
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<details>
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<summary>Astra DB Tool</summary>
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Replace the **Astra DB Tool** component with the [**Astra DB** component](#astra-db).
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The **Astra DB Tool** component enables searching data in Astra DB collections, including hybrid search, vector search, and regular filter-based search.
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Specialized searches require that the collection is pre-configured with the required parameters.
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Outputs a list of [`Data`](/data-types#data) objects containing the query results from Astra DB. Each `Data` 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.
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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](/agents-tools) to allow the agent to query data from Astra DB collections as needed to respond to user queries.
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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](https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html).
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| Name | Type | Description |
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|-------------------|--------|--------|
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| Tool Name | String | Input parameter. The name used to reference the tool in the agent's prompt. |
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| Tool Description | String | Input parameter. A brief description of the tool. This helps the model decide when to use it. |
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| Keyspace Name | String | Input parameter. The name of the keyspace in Astra DB. Default: `default_keyspace` |
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| Collection Name | String | Input parameter. The name of the Astra DB collection to query. |
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| Token | SecretString | Input parameter. The authentication token for accessing Astra DB. |
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| API Endpoint | String | Input parameter. The Astra DB API endpoint. |
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| 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`. |
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| Tool Parameters | Dict | Input parameter. [Astra DB Data API `find` filters](https://docs.datastax.com/en/astra-db-serverless/api-reference/document-methods/find-many.html#parameters) that become tools for an agent. These Filters _may_ be used in a search, if the agent selects them. |
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| Static Filters | Dict | Input parameter. Attribute-value pairs used to filter query results. Equivalent to [Astra DB Data API `find` filters](https://docs.datastax.com/en/astra-db-serverless/api-reference/document-methods/find-many.html#parameters). **Static Filters** are included with _every_ query. Use **Static Filters** without semantic search to perform a regular filter search. |
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| Number of Results | Int | Input parameter. The maximum number of documents to return. |
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| 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](/components-embedding-models) or have vectorize pre-enabled on your collection. |
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| 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. |
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| 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. |
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| 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. |
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</details>
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<details>
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<summary>Astra DB Graph</summary>
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Replace the **Astra DB Graph** component with the [**Graph RAG** component](#graph-rag).
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The **Astra DB Graph** component uses `AstraDBGraphVectorStore`, an instance of [LangChain graph vector store](https://python.langchain.com/api_reference/community/graph_vectorstores.html), for graph traversal and graph-based document retrieval in an Astra DB collection. It also supports writing to the vector store.
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For more information, see [Build a Graph RAG system with LangChain and GraphRetriever](https://docs.datastax.com/en/astra-db-serverless/tutorials/graph-rag.html).
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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.
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<PartialParams />
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<PartialConditionalParams />
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For information about accepted values and functionality, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/index.html) or inspect [component code](/concepts-components#component-code).
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| Name | Display Name | Info |
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|------|--------------|------|
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| 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. |
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| api_endpoint | API Endpoint | Input parameter. Your database's API endpoint. |
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| keyspace | Keyspace | Input parameter. The keyspace in your database that contains the collection specified in `collection_name`. Default: `default_keyspace`. |
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| 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. |
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| metadata_incoming_links_key | Metadata Incoming Links Key | Input parameter. The metadata key for the incoming links in the vector store. |
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| ingest_data | Ingest Data | Input parameter. Records to load into the vector store. Only relevant for writes. |
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| search_input | Search Query | Input parameter. Query string for similarity search. Only relevant to reads. |
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| cache_vector_store | Cache Vector Store | Input parameter. Whether to cache the vector store in Langflow memory for faster reads. Default: Enabled (`true`). |
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| embedding_model | Embedding Model | Input parameter. Attach an [embedding model component](/components-embedding-models) to generate embeddings. If the collection has a [vectorize integration](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html), don't attach an embedding model component. |
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| metric | Metric | Input parameter. The metrics to use for similarity search calculations, either `cosine` (default), `dot_product`, or `euclidean`. This is a collection setting. |
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| batch_size | Batch Size | Input parameter. Optional number of records to process in a single batch. |
|
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| bulk_insert_batch_concurrency | Bulk Insert Batch Concurrency | Input parameter. Optional concurrency level for bulk write operations. |
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| bulk_insert_overwrite_concurrency | Bulk Insert Overwrite Concurrency | Input parameter. Optional concurrency level for bulk write operations that allow upserts (overwriting existing records). |
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| bulk_delete_concurrency | Bulk Delete Concurrency | Input parameter. Optional concurrency level for bulk delete operations. |
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| setup_mode | Setup Mode | Input parameter. Configuration mode for setting up the vector store, either `Sync` (default) or `Off`. |
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| pre_delete_collection | Pre Delete Collection | Input parameter. Whether to delete the collection before creating a new one. Default: Disabled (`false`). |
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| metadata_indexing_include | Metadata Indexing Include | Input parameter. A list of metadata fields to index if you want to enable [selective indexing](https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-indexes.html) *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). |
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| 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). |
|
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| 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. |
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| 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>Assistants API components</summary>
|
||||
|
||||
The following DataStax components were used to create and manage Assistants API functions in a flow:
|
||||
|
||||
* **Astra Assistant Agent**
|
||||
* **Create Assistant**
|
||||
* **Create Assistant Thread**
|
||||
* **Get Assistant Name**
|
||||
* **List Assistants**
|
||||
* **Run Assistant**
|
||||
|
||||
These components are legacy and should be replaced with Langflow's native agent components.
|
||||
|
||||
</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>
|
||||
|
||||
|
||||
Reference in New Issue
Block a user