Vector Stores

Langflow's Vector Store components are used to read and write vector data, including embedding storage, vector search, Graph RAG traversals, and specialized provider-specific search, such as OpenSearch, Elasticsearch, and Vectara.

These components are critical for vector search applications, such as Retrieval Augmented Generation (RAG) chatbots that need to retrieve relevant context from large datasets.

Most of these components connect to a specific vector database provider, but some components support multiple providers or platforms. For example, the Cassandra vector store component can connect to self-managed Apache Cassandra-based clusters as well as Astra DB, which is a managed Cassandra DBaaS.

Other types of storage, like traditional structured databases and chat memory, are handled through other components like the SQL Database component or the Message History component.

Use Vector Store components in a flow

tip

For a tutorial using Vector Store components in a flow, see Create a vector RAG chatbot.

The following steps introduce the use of Vector Store components in a flow, including configuration details, how the components work when you run a flow, why you might need multiple Vector Store components in one flow, and useful supporting components, such as Embedding Model and Parser components.

1. Create a flow with the Vector Store RAG template.

   This template has two subflows. The Load Data subflow loads embeddings and content into a vector database, and the Retriever subflow runs a vector search to retrieve relevant context based on a user's query.

2. Configure the database connection for both Astra DB components, or replace them with another pair of Vector Store components of your choice. Make sure the components connect to the same vector store, and that the component in the Retriever subflow is able to run a similarity search.

   The parameters you set in each Vector Store component depend on the component's role in your flow. In this example, the Load Data subflow writes to the vector store, whereas the Retriever subflow reads from the vector store. Therefore, search-related parameters are only relevant to the Vector Search component in the Retriever subflow.

   For information about specific configuration parameters, see the section of this page for your chosen Vector Store component and Hidden parameters.

3. To configure the embedding model, do one of the following:

   • Use an OpenAI model: In both OpenAI Embeddings components, enter your OpenAI API key. You can use the default model or select a different OpenAI embedding model.

   • Use another provider: Replace the OpenAI Embeddings components with another pair of Embedding Model component of your choice, and then configure the parameters and credentials accordingly.

   • Use Astra DB vectorize: If you are using an Astra DB vector store that has a vectorize integration, you can remove both OpenAI Embeddings components. If you do this, the vectorize integration automatically generates embeddings from the Ingest Data (in the Load Data subflow) and Search Query (in the Retriever subflow).

   tip

   If your vector store already contains embeddings, make sure your Embedding Model components use the same model as your previous embeddings. Mixing embedding models in the same vector store can produce inaccurate search results.

4. Recommended: In the Split Text component, optimize the chunking settings for your embedding model. For example, if your embedding model has a token limit of 512, then the Chunk Size parameter must not exceed that limit.

   Additionally, because the Retriever subflow passes the chat input directly to the Vector Store component for vector search, make sure that your chat input string doesn't exceed your embedding model's limits. For this example, you can enter a query that is within the limits; however, in a production environment, you might need to implement additional checks or preprocessing steps to ensure compliance. For example, use additional components to prepare the chat input before running the vector search, or enforce chat input limits in your application code.

5. In the Language Model component, enter your OpenAI API key, or select a different provider and model to use for the chat portion of the flow.

6. Run the Load Data subflow to populate your vector store. In the File component, select one or more files, and then click Run component on the Vector Store component in the Load Data subflow.

   The Load Data subflow loads files from your local machine, chunks them, generates embeddings for the chunks, and then stores the chunks and their embeddings in the vector database.

   

   The Load Data subflow is separate from the Retriever subflow because you probably won't run it every time you use the chat. You can run the Load Data subflow as needed to preload or update the data in your vector store. Then, your chat interactions only use the components that are necessary for chat.

   If your vector store already contains data that you want to use for vector search, then you don't need to run the Load Data subflow.

7. Open the Playground and start chatting to run the Retriever subflow.

   The Retriever subflow generates an embedding from chat input, runs a vector search to retrieve similar content from your vector store, parses the search results into supplemental context for the LLM, and then uses the LLM to generate a natural language response to your query. The LLM uses the vector search results along with its internal training data and tools, such as basic web search and datetime information, to produce the response.

   

   To avoid passing the entire block of raw search results to the LLM, the Parser component extracts text strings from the search results Data object, and then passes them to the Prompt Template component in Message format. From there, the strings and other template content are compiled into natural language instructions for the LLM.

   You can use other components for this transformation, such as the Data Operations component, depending on how you want to use the search results.

   To view the raw search results, click Inspect output on the Vector Store component after running the Retriever subflow.

Hidden 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.

Many input parameters for Vector Store components are hidden by default in the visual editor. You can toggle parameters through the Controls in each 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. However, all parameters are always listed in a component's code.

For information about a specific component's parameters, see the provider's documentation and the component details.

Search results output

If you use a Vector Store component to query your vector store, 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 component's output port in the visual editor.

The exception to this pattern is the Vectara RAG component, which outputs only an answer string in Message format.

Vector store instances

Because Langflow is based on LangChain, Vector Store components use an instance of LangChain vector store to drive the underlying vector search functions. In the component code, this is often instantiated as vector_store, but some components use a different name, such as the provider name.

For the Cassandra Graph and Astra DB Graph components, vector_store is an instance of LangChain graph vector store.

These instances are provider-specific and configured according to the component's parameters. For example, the Redis component creates an instance of RedisVectorStore based on the component's parameters, such as the connection string, index name, and schema.

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 provider documentation.

Vector Store Connection ports

The Astra DB and OpenSearch components have 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 was meant as a Graph RAG extension to the Astra DB component. Instead, you can use the Astra DB Graph component that includes both the vector store connection and Graph RAG functionality. OpenSearch instances support Graph traversal through built-in RAG functionality and plugins.

Apache Cassandra

The Cassandra and Cassandra Graph components can be used with Cassandra clusters that support vector search, including Astra DB.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Vector search in Cassandra

Cassandra

Use the Cassandra component to read or write to a Cassandra vector store using a CassandraVectorStore instance.

Cassandra parameters

Name	Type	Description
database_ref	String	Input parameter. Contact points for the database or an Astra database ID.
username	String	Input parameter. Username for the database. Leave empty for Astra DB.
token	SecretString	Input parameter. User password for the database or an Astra application token.
keyspace	String	Input parameter. The name of the keyspace containing the vector store specified in Table Name (table_name).
table_name	String	Input parameter. The name of the table or collection that is the vector store.
ttl_seconds	Integer	Input parameter. Time-to-live for added texts, if supported by the cluster. Only relevant for writes.
batch_size	Integer	Input parameter. Amount of records to process in a single batch.
setup_mode	String	Input parameter. Configuration mode for setting up a Cassandra table.
cluster_kwargs	Dict	Input parameter. Additional keyword arguments for a Cassandra cluster.
search_query	String	Input parameter. Query string for similarity search. Only relevant for reads.
ingest_data	Data	Input parameter. Data to be loaded into the vector store as raw chunks and embeddings. Only relevant for writes.
embedding	Embeddings	Input parameter. Embedding function to use.
number_of_results	Integer	Input parameter. Number of results to return in search. Only relevant for reads.
search_type	String	Input parameter. Type of search to perform. Only relevant for reads.
search_score_threshold	Float	Input parameter. Minimum similarity score for search results. Only relevant for reads.
search_filter	Dict	Input parameter. An optional dictionary of metadata search filters to apply in addition to vector search. Only relevant for reads.
body_search	String	Input parameter. Document textual search terms. Only relevant for reads.
enable_body_search	Boolean	Input parameter. Flag to enable body search. Only relevant for reads.

Cassandra Graph

The Cassandra Graph component uses a CassandraGraphVectorStore instance for graph traversal and graph-based document retrieval in a compatible Cassandra cluster. It also supports writing to the vector store.

Cassandra Graph parameters

Name	Display Name	Info
database_ref	Contact Points / Astra Database ID	Input parameter. The contact points for the database or an Astra database ID. Required.
username	Username	Input parameter. The username for the database. Leave empty for Astra DB.
token	Password / Astra DB Token	Input parameter. The user password for the database or an Astra application token. Required.
keyspace	Keyspace	Input parameter. The name of the keyspace containing the vector store specified in Table Name (table_name). Required.
table_name	Table Name	Input parameter. The name of the table or collection that is the vector store. Required.
setup_mode	Setup Mode	Input parameter. The configuration mode for setting up the Cassandra table. The options are Sync (default) or Off.
cluster_kwargs	Cluster arguments	Input parameter. An optional dictionary of additional keyword arguments for the Cassandra cluster.
search_query	Search Query	Input parameter. The query string for similarity search. Only relevant for reads.
ingest_data	Ingest Data	Input parameter. Data to be loaded into the vector store as raw chunks and embeddings. Only relevant for writes.
embedding	Embedding	Input parameter. The embedding model to use.
number_of_results	Number of Results	Input parameter. The number of results to return in similarity search. Only relevant for reads. Default: 4.
search_type	Search Type	Input parameter. The search type to use. The options are Traversal (default), MMR Traversal, Similarity, Similarity with score threshold, or MMR (Max Marginal Relevance).
depth	Depth of traversal	Input parameter. The maximum depth of edges to traverse. Only relevant if Search Type (search_type) is Traversal or MMR Traversal. Default: 1.
search_score_threshold	Search Score Threshold	Input parameter. The minimum similarity score threshold for search results. Only relevant for reads using the Similarity with score threshold search type.
search_filter	Search Metadata Filter	Input parameter. An optional dictionary of metadata search filters to apply in addition to graph traversal and similarity search.

Chroma

The Chroma DB and Local DB components read and write to Chroma vector stores using an instance of Chroma vector store. Includes support for remote or in-memory instances with or without persistence.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Chroma documentation

Chroma DB

You can use the Chroma DB component to read and write to a Chroma database in local storage or a remote Chroma server with options for persistence and caching. When writing, the component can create a new database or collection at the specified location.

tip

An ephemeral (non-persistent) local Chroma vector store is helpful for testing vector search flows where you don't need to retain the database.

The following example flow uses one Chroma DB component for both reads and writes:

• When writing, it splits Data from a URL component into chunks, computes embeddings with attached Embedding Model component, and then loads the chunks and embeddings into the Chroma vector store. To trigger writes, click Run component on the Chroma DB component.

• When reading, it uses chat input to perform a similarity search on the vector store, and then print the search results to the chat. To trigger reads, open the Playground and enter a chat message.

After running the flow once, you can click Inspect Output on each component to understand how the data transformed as it passed from component to component.

Chroma DB parameters

Name	Type	Description
Collection Name (collection_name)	String	Input parameter. The name of your Chroma vector store collection. Default: langflow.
Persist Directory (persist_directory)	String	Input parameter. To persist the Chroma database, enter a relative or absolute path to a directory to store the chroma.sqlite3 file. Leave empty for an ephemeral database. When reading or writing to an existing persistent database, specify the path to the persistent directory.
Ingest Data (ingest_data)	Data or DataFrame	Input parameter. Data or DataFrame input containing the records to write to the vector store. Only relevant for writes.
Search Query (search_query)	String	Input parameter. The query to use for vector search. Only relevant for reads.
Cache Vector Store (cache_vector_store)	Boolean	Input parameter. If true, the component caches the vector store in memory for faster reads. Default: Enabled (true).
Embedding (embedding)	Embeddings	Input parameter. The embedding function to use for the vector store. By default, Chroma DB uses its built-in embeddings model, or you can attach an Embedding Model component to use a different provider or model.
CORS Allow Origins (chroma_server_cors_allow_origins)	String	Input parameter. The CORS allow origins for the Chroma server.
Chroma Server Host (chroma_server_host)	String	Input parameter. The host for the Chroma server.
Chroma Server HTTP Port (chroma_server_http_port)	Integer	Input parameter. The HTTP port for the Chroma server.
Chroma Server gRPC Port (chroma_server_grpc_port)	Integer	Input parameter. The gRPC port for the Chroma server.
Chroma Server SSL Enabled (chroma_server_ssl_enabled)	Boolean	Input parameter. Enable SSL for the Chroma server.
Allow Duplicates (allow_duplicates)	Boolean	Input parameter. If true (default), writes don't check for existing duplicates in the collection, allowing you to store multiple copies of the same content. If false, writes won't add documents that match existing documents already present in the collection. If false, it can strictly enforce deduplication by searching the entire collection or only search the number of records, specified in limit. Only relevant for writes.
Search Type (search_type)	String	Input parameter. The type of search to perform, either Similarity or MMR. Only relevant for reads.
Number of Results (number_of_results)	Integer	Input parameter. The number of search results to return. Default: 10. Only relevant for reads.
Limit (limit)	Integer	Input parameter. Limit the number of records to compare when Allow Duplicates is false. This can help improve performance when writing to large collections, but it can result in some duplicate records. Only relevant for writes.

Local DB

The Local DB component reads and writes to a persistent, in-memory Chroma DB instance intended for use with Langflow. It has separate modes for reads and writes, automatic collection management, and default persistence in your Langflow cache directory.

Set the Mode parameter to reflect the operation you want the component to perform, and the configure the other parameters accordingly. Some parameters are only available for one mode.

Ingest

To create or write to your local Chroma vector store, use Ingest mode.

The following parameters are available in Ingest mode:

Name	Type	Description
Name Your Collection (collection_name)	String	Input parameter. The name for your Chroma vector store collection. Default: langflow. Only available in Ingest mode.
Persist Directory (persist_directory)	String	Input parameter. The base directory where you want to create and persist the vector store. If you use the Local DB component in multiple flows or to create multiple collections, collections are stored at $PERSISTENT_DIRECTORY/vector_stores/$COLLECTION_NAME. If not specified, the default location is your Langflow cache directory (LANGFLOW_CONFIG_DIR). For more information, see Memory management options.
Embedding (embedding)	Embeddings	Input parameter. The embedding function to use for the vector store.
Allow Duplicates (allow_duplicates)	Boolean	Input parameter. If true (default), writes don't check for existing duplicates in the collection, allowing you to store multiple copies of the same content. If false, writes won't add documents that match existing documents already present in the collection. If false, it can strictly enforce deduplication by searching the entire collection or only search the number of records, specified in limit. Only available in Ingest mode.
Ingest Data (ingest_data)	Data or DataFrame	Input parameter. The records to write to the collection. Records are embedded and indexed for semantic search. Only available in Ingest mode.
Limit (limit)	Integer	Input parameter. Limit the number of records to compare when Allow Duplicates is false. This can help improve performance when writing to large collections, but it can result in some duplicate records. Only available in Ingest mode.

Retrieve

To read from your local Chroma vector store, use Retrieve mode.

The following parameters are available in Retrieve mode:

Name	Type	Description
Persist Directory (persist_directory)	String	Input parameter. The base directory where you want to create and persist the vector store. If you use the Local DB component in multiple flows or to create multiple collections, collections are stored at $PERSISTENT_DIRECTORY/vector_stores/$COLLECTION_NAME. If not specified, the default location is your Langflow cache directory (LANGFLOW_CONFIG_DIR). For more information, see Memory management options.
Existing Collections (existing_collections)	String	Input parameter. Select a previously-created collection to search. Only available in Retrieve mode.
Embedding (embedding)	Embeddings	Input parameter. The embedding function to use for the vector store.
Search Type (search_type)	String	Input parameter. The type of search to perform, either Similarity or MMR. Only available in Retrieve mode.
Search Query (search_query)	String	Input parameter. Enter a query for similarity search. Only available in Retrieve mode.
Number of Results (number_of_results)	Integer	Input parameter. Number of search results to return. Default: 10. Only available in Retrieve mode.

Clickhouse

The Clickhouse component reads and writes to a Clickhouse vector store using an instance of Clickhouse vector store.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Clickhouse Documentation

Clickhouse parameters

Name	Display Name	Info
host	hostname	Input parameter. The Clickhouse server hostname. Required. Default: localhost.
port	port	Input parameter. The Clickhouse server port. Required. Default: 8123.
database	database	Input parameter. The Clickhouse database name. Required.
table	Table name	Input parameter. The Clickhouse table name. Required.
username	Username	Input parameter. Clickhouse username for authentication. Required.
password	Password	Input parameter. Clickhouse password for authentication. Required.
index_type	index_type	Input parameter. Type of the index, either annoy (default) or vector_similarity.
metric	metric	Input parameter. Metric to compute distance for similarity search. The options are angular (default), euclidean, manhattan, hamming, dot.
secure	Use HTTPS/TLS	Input parameter. If true, enables HTTPS/TLS for the Clickhouse server and overrides inferred values for interface or port arguments. Default: false.
index_param	Param of the index	Input parameter. Index parameters. Default: 100,'L2Distance'.
index_query_params	index query params	Input parameter. Additional index query parameters.
search_query	Search Query	Input parameter. The query string for similarity search. Only relevant for reads.
ingest_data	Ingest Data	Input parameter. The records to load into the vector store.
cache_vector_store	Cache Vector Store	Input parameter. If true, the component caches the vector store in memory for faster reads. Default: Enabled (true).
embedding	Embedding	Input parameter. The embedding model to use.
number_of_results	Number of Results	Input parameter. The number of search results to return. Default: 4. Only relevant for reads.
score_threshold	Score threshold	Input parameter. The threshold for similarity score comparison. Default: Unset (no threshold). Only relevant for reads.

Couchbase

The Couchbase component reads and writes to a Couchbase vector store using an instance of CouchbaseSearchVectorStore.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Couchbase documentation

Couchbase parameters

Name	Type	Description
couchbase_connection_string	SecretString	Input parameter. Couchbase Cluster connection string. Required.
couchbase_username	String	Input parameter. Couchbase username for authentication. Required.
couchbase_password	SecretString	Input parameter. Couchbase password for authentication. Required.
bucket_name	String	Input parameter. Name of the Couchbase bucket. Required.
scope_name	String	Input parameter. Name of the Couchbase scope. Required.
collection_name	String	Input parameter. Name of the Couchbase collection. Required.
index_name	String	Input parameter. Name of the Couchbase index. Required.
ingest_data	Data	Input parameter. The records to load into the vector store. Only relevant for writes.
search_query	String	Input parameter. The query string for vector search. Only relevant for reads.
cache_vector_store	Boolean	Input parameter. If true, the component caches the vector store in memory for faster reads. Default: Enabled (true).
embedding	Embeddings	Input parameter. The embedding function to use for the vector store.
number_of_results	Integer	Input parameter. Maximum number of search results to return. Default: 4. Only relevant for reads.

DataStax

The following components support DataStax vector stores.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Astra DB Serverless documentation
• Hyper-Converged Database (HCD) documentation

Astra DB

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

important

It is recommend 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.

Astra DB parameters

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. Always use 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.
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.
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 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 shows the default reranker even if the selected collection doesn't support hybrid search. To verify 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 following 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. For more information, see Vector store instances.

Hybrid search example

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:

1. Use the Data API to create a collection that supports hybrid search if you haven't already created 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.

2. Create a flow based on the Hybrid Search RAG template, which includes an Astra DB component that is pre-configured for hybrid search.

3. In the Language Model components, add your OpenAI API key.

4. 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.

5. 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.

6. In the component's header menu, click Controls, find the Lexical Terms field, enable the Show toggle, and then click Close.

7. 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.

8. Click the Structured Output component to expose the component's header menu, click Controls, find the Format Instructions row, click Expand, and then replace the prompt with the following text:

   
   

   _10

   You are a database query planner that takes a user's requests, and then converts to a search against the subject matter in question.

   _10

   You should convert the query into:

   _10

   1. A list of keywords to use against a Lucene text analyzer index, no more than 4. Strictly unigrams.

   _10

   2. A question to use as the basis for a QA embedding engine.

   _10

   Avoid common keywords associated with the user's subject matter.

   
   

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

10. 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 DataFrame that is transformed into a text string Message by another Parser component. Finally, the Chat Output component prints the Message response to the Playground.

11. Optional: Exit the Playground, and then click 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 Data 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:

      
      

      _10

      1. Keywords: features, data, attributes, characteristics

      _10

      2. Question: What characteristics can be identified in my data?

      
      

    • Parser component: The output is the string of keywords extracted from the structured output Data, and then used as lexical terms for the hybrid search.

    • Astra DB component: The output is the DataFrame containing the results of the hybrid search as returned by the Data API.

Astra DB Graph

The Astra DB Graph component uses a AstraDBGraphVectorStore instance 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.

Astra DB Graph parameters

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 for 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. An 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. An 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.

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.

tip

This component was meant as a Graph RAG extension for the Astra DB vector store component. However, the Astra DB Graph component includes both the vector store connection and Graph RAG functionality.

Graph RAG parameters

Name	Display Name	Info
embedding_model	Embedding Model	Input parameter. Specify the embedding model to use. Not required if the connected vector store has an vectorize integration.
vector_store	Vector Store Connection	Input parameter. A vector_store instance inherited from an 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 Data objects or as a tabular DataFrame. 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 the Data API server to read and write to an HCD vector store. Because the underlying functions call the Data API, which originated from Astra DB, the component uses an instance of AstraDBVectorStore.

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

HCD parameters

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 ofr 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 for 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. An 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. An 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.

Elasticsearch

The Elasticsearch component reads and writes to an Elasticsearch instance using ElasticsearchStore.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Elasticsearch documentation

Elasticsearch parameters

Name	Type	Description
es_url	String	Input parameter. Elasticsearch server URL.
es_user	String	Input parameter. Username for Elasticsearch authentication.
es_password	SecretString	Input parameter. Password for Elasticsearch authentication.
index_name	String	Input parameter. Name of the Elasticsearch index.
strategy	String	Input parameter. Strategy for vector search, either approximate_k_nearest_neighbors or script_scoring.
distance_strategy	String	Input parameter. Strategy for distance calculation, either COSINE, EUCLIDEAN_DISTANCE, or DOT_PRODUCT.
search_query	String	Input parameter. Query string for similarity search.
ingest_data	Data	Input parameter. Records to load into the vector store.
embedding	Embeddings	Input parameter. The embedding model to use.
number_of_results	Integer	Input parameter. Number of search results to return. Default: 4.

FAISS

The FAISS component providese access to the Facebook AI Similarity Search (FAISS) library through an instance of FAISS vector store.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• FAISS documentation

FAISS parameters

Name	Type	Description
index_name	String	Input parameter. The name of the FAISS index. Default: "langflow_index".
persist_directory	String	Input parameter. Path to save the FAISS index. It is relative to where Langflow is running.
search_query	String	Input parameter. The query to search for in the vector store.
ingest_data	Data	Input parameter. The list of data to ingest into the vector store.
allow_dangerous_deserialization	Boolean	Input parameter. Set to True to allow loading pickle files from untrusted sources. Default: True.
embedding	Embeddings	Input parameter. The embedding function to use for the vector store.
number_of_results	Integer	Input parameter. Number of results to return from the search. Default: 4.

Milvus

The Milvus component reads and writes to Milvus vector stores using an instance of Milvus vector store.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Milvus documentation

Milvus parameters

Name	Type	Description
collection_name	String	Input parameter. Name of the Milvus collection.
collection_description	String	Input parameter. Description of the Milvus collection.
uri	String	Input parameter. Connection URI for Milvus.
password	SecretString	Input parameter. Password for Milvus.
username	SecretString	Input parameter. Username for Milvus.
batch_size	Integer	Input parameter. Number of data to process in a single batch.
search_query	String	Input parameter. Query for similarity search.
ingest_data	Data	Input parameter. Data to be ingested into the vector store.
embedding	Embeddings	Input parameter. Embedding function to use.
number_of_results	Integer	Input parameter. Number of results to return in search.
search_type	String	Input parameter. Type of search to perform.
search_score_threshold	Float	Input parameter. Minimum similarity score for search results.
search_filter	Dict	Input parameter. Metadata filters for search query.
setup_mode	String	Input parameter. Configuration mode for setting up the vector store.
vector_dimensions	Integer	Input parameter. Number of dimensions of the vectors.
pre_delete_collection	Boolean	Input parameter. Whether to delete the collection before creating a new one.

MongoDB Atlas

The MongoDB Atlas component reads and writes to MongoDB Atlas vector stores using an instance of MongoDBAtlasVectorSearch.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• MongoDB Atlas documentation

MongoDB Atlas parameters

Name	Type	Description
mongodb_atlas_cluster_uri	SecretString	Input parameter. The connection URI for your MongoDB Atlas cluster. Required.
enable_mtls	Boolean	Input parameter. Enable mutual TLS authentication. Default: false.
mongodb_atlas_client_cert	SecretString	Input parameter. Client certificate combined with private key for mTLS authentication. Required if mTLS is enabled.
db_name	String	Input parameter. The name of the database to use. Required.
collection_name	String	Input parameter. The name of the collection to use. Required.
index_name	String	Input parameter. The name of the Atlas Search index, it should be a Vector Search. Required.
insert_mode	String	Input parameter. How to insert new documents into the collection. The options are "append" or "overwrite". Default: "append".
embedding	Embeddings	Input parameter. The embedding model to use.
number_of_results	Integer	Input parameter. Number of results to return in similarity search. Default: 4.
index_field	String	Input parameter. The field to index. Default: "embedding".
filter_field	String	Input parameter. The field to filter the index.
number_dimensions	Integer	Input parameter. Embedding context length. Default: 1536.
similarity	String	Input parameter. The method used to measure similarity between vectors. The options are "cosine", "euclidean", or "dotProduct". Default: "cosine".
quantization	String	Input parameter. Quantization reduces memory costs by converting 32-bit floats to smaller data types. The options are "scalar" or "binary".

OpenSearch

The OpenSearch component reads and writes to OpenSearch instances using OpenSearchVectorSearch.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• OpenSearch documentation

OpenSearch parameters

Name	Type	Description
opensearch_url	String	Input parameter. URL for OpenSearch cluster, such as https://192.168.1.1:9200.
index_name	String	Input parameter. The index name where the vectors are stored in OpenSearch cluster.
search_input	String	Input parameter. Enter a search query. Leave empty to retrieve all documents or if hybrid search is being used.
ingest_data	Data	Input parameter. The data to be ingested into the vector store.
embedding	Embeddings	Input parameter. The embedding function to use.
search_type	String	Input parameter. The options are "similarity", "similarity_score_threshold", "mmr".
number_of_results	Integer	Input parameter. The number of results to return in search.
search_score_threshold	Float	Input parameter. The minimum similarity score threshold for search results.
username	String	Input parameter. The username for the opensource cluster.
password	SecretString	Input parameter. The password for the opensource cluster.
use_ssl	Boolean	Input parameter. Use SSL.
verify_certs	Boolean	Input parameter. Verify certificates.
hybrid_search_query	String	Input parameter. Provide a custom hybrid search query in JSON format. This allows you to combine vector similarity and keyword matching.

PGVector

The PGVector component reads and writes to PostgreSQL vector stores using an instance of PGVector.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• PGVector documentation

PGVector parameters

Name	Type	Description
pg_server_url	SecretString	Input parameter. The PostgreSQL server connection string.
collection_name	String	Input parameter. The table name for the vector store.
search_query	String	Input parameter. The query for similarity search.
ingest_data	Data	Input parameter. The data to be ingested into the vector store.
embedding	Embeddings	Input parameter. The embedding function to use.
number_of_results	Integer	Input parameter. The number of results to return in search.

Pinecone

The Pinecone component reads and writes to Pinecone vector stores using an instance of PineconeVectorStore.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Pinecone documentation

Pinecone parameters

Name	Type	Description
index_name	String	Input parameter. The name of the Pinecone index.
namespace	String	Input parameter. The namespace for the index.
distance_strategy	String	Input parameter. The strategy for calculating distance between vectors.
pinecone_api_key	SecretString	Input parameter. The API key for Pinecone.
text_key	String	Input parameter. The key in the record to use as text.
search_query	String	Input parameter. The query for similarity search.
ingest_data	Data	Input parameter. The data to be ingested into the vector store.
embedding	Embeddings	Input parameter. The embedding function to use.
number_of_results	Integer	Input parameter. The number of results to return in search.

Qdrant

The Qdrant component reads and writes to Qdrant vector stores using an instance of QdrantVectorStore.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Qdrant documentation

Qdrant parameters

Name	Type	Description
collection_name	String	Input parameter. The name of the Qdrant collection.
host	String	Input parameter. The Qdrant server host.
port	Integer	Input parameter. The Qdrant server port.
grpc_port	Integer	Input parameter. The Qdrant gRPC port.
api_key	SecretString	Input parameter. The API key for Qdrant.
prefix	String	Input parameter. The prefix for Qdrant.
timeout	Integer	Input parameter. The timeout for Qdrant operations.
path	String	Input parameter. The path for Qdrant.
url	String	Input parameter. The URL for Qdrant.
distance_func	String	Input parameter. The distance function for vector similarity.
content_payload_key	String	Input parameter. The content payload key.
metadata_payload_key	String	Input parameter. The metadata payload key.
search_query	String	Input parameter. The query for similarity search.
ingest_data	Data	Input parameter. The data to be ingested into the vector store.
embedding	Embeddings	Input parameter. The embedding function to use.
number_of_results	Integer	Input parameter. The number of results to return in search.

Redis

The Redis component reads and writes to Redis vector stores using an instance of Redis vector store.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Redis documentation

Redis parameters

Name	Type	Description
redis_server_url	SecretString	Input parameter. The Redis server connection string.
redis_index_name	String	Input parameter. The name of the Redis index.
code	String	Input parameter. The custom code for Redis (advanced).
schema	String	Input parameter. The schema for Redis index.
search_query	String	Input parameter. The query for similarity search.
ingest_data	Data	Input parameter. The data to be ingested into the vector store.
number_of_results	Integer	Input parameter. The number of results to return in search.
embedding	Embeddings	Input parameter. The embedding function to use.

Supabase

The Supabase component reads and writes to Supabase vector stores using an instance of SupabaseVectorStore.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Supabase documentation

Supabase parameters

Name	Type	Description
supabase_url	String	Input parameter. The URL of the Supabase instance.
supabase_service_key	SecretString	Input parameter. The service key for Supabase authentication.
table_name	String	Input parameter. The name of the table in Supabase.
query_name	String	Input parameter. The name of the query to use.
search_query	String	Input parameter. The query for similarity search.
ingest_data	Data	Input parameter. The data to be ingested into the vector store.
embedding	Embeddings	Input parameter. The embedding function to use.
number_of_results	Integer	Input parameter. The number of results to return in search.

Upstash

The Upstash component reads and writes to Upstash vector stores using an instance of UpstashVectorStore.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Upstash documentation

Upstash parameters

Name	Type	Description
index_url	String	Input parameter. The URL of the Upstash index.
index_token	SecretString	Input parameter. The token for the Upstash index.
text_key	String	Input parameter. The key in the record to use as text.
namespace	String	Input parameter. The namespace for the index.
search_query	String	Input parameter. The query for similarity search.
metadata_filter	String	Input parameter. Filter documents by metadata.
ingest_data	Data	Input parameter. The data to be ingested into the vector store.
embedding	Embeddings	Input parameter. The embedding function to use.
number_of_results	Integer	Input parameter. The number of results to return in search.

Vectara Platform

The Vectara and Vectara RAG components support Vectara vector store, search, and RAG functionality using instances of Vectara vector store.

For more information, see the following:

• Hidden parameters
• Vector store instances
• Vectara documentation

Vectara

The Vectara component reads and writes to Vectara vector stores, and then produces search results output.

Vectara parameters

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.

Weaviate

The Weaviate component reads and writes to Weaviate vector stores using an instance of Weaviate vector store.

For more information, see the following:

• Hidden parameters
• Search results output
• Vector store instances
• Weaviate Documentation

Weaviate parameters

Name	Type	Description
weaviate_url	String	Input parameter. The default instance URL.
search_by_text	Boolean	Input parameter. Indicates whether to search by text.
api_key	SecretString	Input parameter. The optional API key for authentication.
index_name	String	Input parameter. The optional index name.
text_key	String	Input parameter. The default text extraction key.
input	Document	Input parameter. The document or record.
embedding	Embeddings	Input parameter. The embedding model used.
attributes	List[String]	Input parameter. Optional additional attributes.