Vector store components in Langflow

Vector databases store vector data, which backs AI workloads like chatbots and Retrieval Augmented Generation.

Vector database components establish connections to existing vector databases or create in-memory vector stores for storing and retrieving vector data.

Vector database components are distinct from memory components, which are built specifically for storing and retrieving chat messages from external databases.

Use a vector store component in a flow

This example uses the Astra DB vector store component. Your vector store component's parameters and authentication may be different, but the document ingestion workflow is the same. A document is loaded from a local machine and chunked. The Astra DB vector store generates embeddings with the connected model component, and stores them in the connected Astra DB database.

This vector data can then be retrieved for workloads like Retrieval Augmented Generation.

The user's chat input is embedded and compared to the vectors embedded during document ingestion for a similarity search. The results are output from the vector database component as a Data object and parsed into text. This text fills the {context} variable in the Prompt component, which informs the Open AI model component's responses.

Alternatively, connect the vector database component's Retriever port to a retriever tool, and then to an agent component. This enables the agent to use your vector database as a tool and make decisions based on the available data.

Astra DB Vector Store

This component implements a Vector Store using Astra DB with search capabilities.

For more information, see the DataStax documentation.

Inputs

Name	Display Name	Info
token	Astra DB Application Token	The authentication token for accessing Astra DB.
environment	Environment	The environment for the Astra DB API Endpoint. For example, dev or prod.
database_name	Database	The database name for the Astra DB instance.
api_endpoint	Astra DB API Endpoint	The API endpoint for the Astra DB instance. This supersedes the database selection.
collection_name	Collection	The name of the collection within Astra DB where the vectors are stored.
keyspace	Keyspace	An optional keyspace within Astra DB to use for the collection.
embedding_choice	Embedding Model or Astra Vectorize	Choose an embedding model or use Astra vectorize.
embedding_model	Embedding Model	Specify the embedding model. Not required for Astra vectorize collections.
number_of_results	Number of Search Results	The number of search results to return (default: 4).
search_type	Search Type	The search type to use. The options are Similarity, Similarity with score threshold, and MMR (Max Marginal Relevance).
search_score_threshold	Search Score Threshold	The minimum similarity score threshold for search results when using the Similarity with score threshold option.
advanced_search_filter	Search Metadata Filter	An optional dictionary of filters to apply to the search query.
autodetect_collection	Autodetect Collection	A boolean flag to determine whether to autodetect the collection.
content_field	Content Field	A field to use as the text content field for the vector store.
deletion_field	Deletion Based On Field	When provided, documents in the target collection with metadata field values matching the input metadata field value are deleted before new data is loaded.
ignore_invalid_documents	Ignore Invalid Documents	A boolean flag to determine whether to ignore invalid documents at runtime.
astradb_vectorstore_kwargs	AstraDBVectorStore Parameters	An optional dictionary of additional parameters for the AstraDBVectorStore.

Outputs

Name	Display Name	Info
vector_store	Vector Store	Astra DB vector store instance configured with the specified parameters.
search_results	Search Results	The results of the similarity search as a list of Data objects.

Generate embeddings

The Astra DB Vector Store component offers two methods for generating embeddings.

1. Embedding Model: Use your own embedding model by connecting an Embeddings component in Langflow.

2. Astra Vectorize: Use Astra DB's built-in embedding generation service. When creating a new collection, choose the embeddings provider and models, including NVIDIA's NV-Embed-QA model hosted by Datastax.

important

The embedding model selection is made when creating a new collection and cannot be changed later.

For an example of using the Astra DB Vector Store component with an embedding model, see the Vector Store RAG starter project.

For more information, see the Astra DB Serverless documentation.

Hybrid search

The Astra DB component includes hybrid search, which is enabled by default.

The component fields related to hybrid search are Search Query, Lexical Terms, and Reranker.

• Search Query finds results by vector similarity.
• Lexical Terms is a comma-separated string of keywords, like features, data, attributes, characteristics.
• Reranker is the re-ranker model used in the hybrid search. The re-ranker model is nvidia/llama-3.2-nv.reranker.

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.

important

To use hybrid search, your collection must be created with vector, lexical, and rerank capabilities enabled. These capabilities are enabled by default when you create a collection in a database in the AWS us-east-2 region. For more information, see the DataStax documentation.

To use Hybrid search in the Astra DB component, do the following:

1. Click New Flow > RAG > Hybrid Search RAG.
2. In the OpenAI model component, add your OpenAI API key.
3. In the Astra DB vector store component, add your Astra DB Application Token.
4. In the Database field, select your database.
5. In the Collection field, select or create a collection with hybrid search capabilities enabled.
6. In the Playground, enter a question about your data, such as What are the features of my data? Your query is sent to two components: an OpenAI model component and the Astra DB vector database component. The OpenAI component contains a prompt for creating the lexical query from your input:

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

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You should convert the query into:

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

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

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

7. To view the keywords and questions the OpenAI component generates from your collection, in the OpenAI component, click .

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1. Keywords: features, data, attributes, characteristics

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

8. To view the DataFrame generated from the OpenAI component's response, in the Structured Output component, click . The DataFrame is passed to a Parser component, which parses the contents of the Keywords column into a string.

   This string of comma-separated words is passed to the Lexical Terms port of the Astra DB component. Note that the Search Query port of the Astra DB port is connected to the Chat Input component from step 6. This Search Query is vectorized, and both the Search Query and Lexical Terms content are sent to the reranker at the find_and_rerank endpoint.

   The reranker compares the vector search results against the string of terms from the lexical search. The highest-ranked results of your hybrid search are returned to the Playground.

For more information, see the DataStax documentation.

AstraDB Graph vector store

This component implements a Vector Store using AstraDB with graph capabilities. For more information, see the Astra DB Serverless documentation.

Inputs

Name	Display Name	Info
collection_name	Collection Name	The name of the collection within AstraDB where the vectors will be stored (required)
token	Astra DB Application Token	Authentication token for accessing AstraDB (required)
api_endpoint	API Endpoint	API endpoint URL for the AstraDB service (required)
search_input	Search Input	Query string for similarity search
ingest_data	Ingest Data	Data to be ingested into the vector store
namespace	Namespace	Optional namespace within AstraDB to use for the collection
embedding	Embedding Model	Embedding model to use
metric	Metric	Distance metric for vector comparisons (options: "cosine", "euclidean", "dot_product")
setup_mode	Setup Mode	Configuration mode for setting up the vector store (options: "Sync", "Async", "Off")
pre_delete_collection	Pre Delete Collection	Boolean flag to determine whether to delete the collection before creating a new one
number_of_results	Number of Results	Number of results to return in similarity search (default: 4)
search_type	Search Type	Search type to use (options: "Similarity", "Graph Traversal", "Hybrid")
traversal_depth	Traversal Depth	Maximum depth for graph traversal searches (default: 1)
search_score_threshold	Search Score Threshold	Minimum similarity score threshold for search results
search_filter	Search Metadata Filter	Optional dictionary of filters to apply to the search query

Outputs

Name	Display Name	Info
vector_store	Vector Store	Astra DB graph vector store instance configured with the specified parameters.
search_results	Search Results	The results of the similarity search as a list of Data objects.

Cassandra

This component creates a Cassandra Vector Store with search capabilities. For more information, see the Cassandra documentation.

Inputs

Name	Type	Description
database_ref	String	Contact points for the database or AstraDB database ID
username	String	Username for the database (leave empty for AstraDB)
token	SecretString	User password for the database or AstraDB token
keyspace	String	Table Keyspace or AstraDB namespace
table_name	String	Name of the table or AstraDB collection
ttl_seconds	Integer	Time-to-live for added texts
batch_size	Integer	Number of data to process in a single batch
setup_mode	String	Configuration mode for setting up the Cassandra table
cluster_kwargs	Dict	Additional keyword arguments for the Cassandra cluster
search_query	String	Query for similarity search
ingest_data	Data	Data to be ingested into the vector store
embedding	Embeddings	Embedding function to use
number_of_results	Integer	Number of results to return in search
search_type	String	Type of search to perform
search_score_threshold	Float	Minimum similarity score for search results
search_filter	Dict	Metadata filters for search query
body_search	String	Document textual search terms
enable_body_search	Boolean	Flag to enable body search

Outputs

Name	Type	Description
vector_store	Cassandra	A Cassandra vector store instance configured with the specified parameters.
search_results	List[Data]	The results of the similarity search as a list of Data objects.

Cassandra Graph Vector Store

This component implements a Cassandra Graph Vector Store with search capabilities.

Inputs

Name	Display Name	Info
database_ref	Contact Points / Astra Database ID	Contact points for the database or AstraDB database ID (required)
username	Username	Username for the database (leave empty for AstraDB)
token	Password / AstraDB Token	User password for the database or AstraDB token (required)
keyspace	Keyspace	Table Keyspace or AstraDB namespace (required)
table_name	Table Name	The name of the table or AstraDB collection where vectors will be stored (required)
setup_mode	Setup Mode	Configuration mode for setting up the Cassandra table (options: "Sync", "Off", default: "Sync")
cluster_kwargs	Cluster arguments	Optional dictionary of additional keyword arguments for the Cassandra cluster
search_query	Search Query	Query string for similarity search
ingest_data	Ingest Data	Data to be ingested into the vector store (list of Data objects)
embedding	Embedding	Embedding model to use
number_of_results	Number of Results	Number of results to return in similarity search (default: 4)
search_type	Search Type	Search type to use (options: "Traversal", "MMR traversal", "Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)", default: "Traversal")
depth	Depth of traversal	The maximum depth of edges to traverse (for "Traversal" or "MMR traversal" search types, default: 1)
search_score_threshold	Search Score Threshold	Minimum similarity score threshold for search results (for "Similarity with score threshold" search type)
search_filter	Search Metadata Filter	Optional dictionary of filters to apply to the search query

Outputs

Name	Display Name	Info
vector_store	Vector Store	A Cassandra Graph vector store instance configured with the specified parameters.
search_results	Search Results	The results of the similarity search as a list of Data objects.

Chroma DB

This component creates a Chroma Vector Store with search capabilities.

The Chroma DB component creates an ephemeral vector database for experimentation and vector storage.

1. To use this component in a flow, connect it to a component that outputs Data or DataFrame. This example splits text from a URL component, and computes embeddings with the connected OpenAI Embeddings component. Chroma DB computes embeddings by default, but you can connect your own embeddings model, as seen in this example.

2. In the Chroma DB component, in the Collection field, enter a name for your embeddings collection.
3. Optionally, to persist the Chroma database, in the Persist field, enter a directory to store the chroma.sqlite3 file. This example uses ./chroma-db to create a directory relative to where Langflow is running.
4. To load data and embeddings into your Chroma database, in the Chroma DB component, click .

tip

When loading duplicate documents, enable the Allow Duplicates option in Chroma DB if you want to store multiple copies of the same content, or disable it to automatically deduplicate your data.

5. To view the split data, in the Split Text component, click .
6. To query your loaded data, open the Playground and query your database. Your input is converted to vector data and compared to the stored vectors in a vector similarity search.

For more information, see the Chroma documentation.

Inputs

Name	Type	Description
collection_name	String	The name of the Chroma collection. Default: "langflow".
persist_directory	String	The directory to persist the Chroma database.
search_query	String	The query to search for in the vector store.
ingest_data	Data	The data to ingest into the vector store (list of Data objects).
embedding	Embeddings	The embedding function to use for the vector store.
chroma_server_cors_allow_origins	String	CORS allow origins for the Chroma server.
chroma_server_host	String	Host for the Chroma server.
chroma_server_http_port	Integer	HTTP port for the Chroma server.
chroma_server_grpc_port	Integer	gRPC port for the Chroma server.
chroma_server_ssl_enabled	Boolean	Enable SSL for the Chroma server.
allow_duplicates	Boolean	Allow duplicate documents in the vector store.
search_type	String	Type of search to perform: "Similarity" or "MMR".
number_of_results	Integer	Number of results to return from the search. Default: 10.
limit	Integer	Limit the number of records to compare when Allow Duplicates is False.

Outputs

Name	Type	Description
vector_store	Chroma	Chroma vector store instance
search_results	List[Data]	Results of similarity search

Clickhouse

This component implements a Clickhouse Vector Store with search capabilities. For more information, see the CLickhouse Documentation.

Inputs

Name	Display Name	Info
host	hostname	Clickhouse server hostname (required, default: "localhost")
port	port	Clickhouse server port (required, default: 8123)
database	database	Clickhouse database name (required)
table	Table name	Clickhouse table name (required)
username	The ClickHouse user name.	Username for authentication (required)
password	The password for username.	Password for authentication (required)
index_type	index_type	Type of the index (options: "annoy", "vector_similarity", default: "annoy")
metric	metric	Metric to compute distance (options: "angular", "euclidean", "manhattan", "hamming", "dot", default: "angular")
secure	Use https/TLS	Overrides inferred values from the interface or port arguments (default: false)
index_param	Param of the index	Index parameters (default: "'L2Distance',100")
index_query_params	index query params	Additional index query parameters
search_query	Search Query	Query string for similarity search
ingest_data	Ingest Data	Data to be ingested into the vector store
embedding	Embedding	Embedding model to use
number_of_results	Number of Results	Number of results to return in similarity search (default: 4)
score_threshold	Score threshold	Threshold for similarity scores

Outputs

Name	Display Name	Info
vector_store	Vector Store	Built Clickhouse vector store
search_results	Search Results	Results of the similarity search as a list of Data objects

Couchbase

This component creates a Couchbase Vector Store with search capabilities. For more information, see the Couchbase documentation.

Inputs

Name	Type	Description
couchbase_connection_string	SecretString	Couchbase Cluster connection string (required).
couchbase_username	String	Couchbase username (required).
couchbase_password	SecretString	Couchbase password (required).
bucket_name	String	Name of the Couchbase bucket (required).
scope_name	String	Name of the Couchbase scope (required).
collection_name	String	Name of the Couchbase collection (required).
index_name	String	Name of the Couchbase index (required).
search_query	String	The query to search for in the vector store.
ingest_data	Data	The data to ingest into the vector store (list of Data objects).
embedding	Embeddings	The embedding function to use for the vector store.
number_of_results	Integer	Number of results to return from the search. Default: 4 (advanced).

Outputs

Name	Type	Description
vector_store	CouchbaseVectorStore	A Couchbase vector store instance configured with the specified parameters.

Local DB

The Local DB component is Langflow's enhanced version of Chroma DB.

The component adds a user-friendly interface with two modes (Ingest and Retrieve), automatic collection management, and built-in persistence in Langflow's cache directory.

Local DB includes Ingest and Retrieve modes.

The Ingest mode works similarly to ChromaDB, and persists your database to the Langflow cache directory. The Langflow cache directory location is specified in LANGFLOW_CONFIG_DIR. For more information, see Environment variables.

The Retrieve mode can query your Chroma DB collections.

For more information, see the Chroma documentation.

Inputs

Name	Type	Description
collection_name	String	The name of the Chroma collection. Default: "langflow".
persist_directory	String	Custom base directory to save the vector store. Collections will be stored under {directory}/vector_stores/{collection_name}. If not specified, it will use your system's cache folder.
existing_collections	String	Select a previously created collection to search through its stored data.
embedding	Embeddings	The embedding function to use for the vector store.
allow_duplicates	Boolean	If false, will not add documents that are already in the Vector Store.
search_type	String	Type of search to perform: "Similarity" or "MMR".
ingest_data	Data/DataFrame	Data to store. It will be embedded and indexed for semantic search.
search_query	String	Enter text to search for similar content in the selected collection.
number_of_results	Integer	Number of results to return. Default: 10.
limit	Integer	Limit the number of records to compare when Allow Duplicates is False.

Outputs

Name	Type	Description
vector_store	Chroma	A local Chroma vector store instance configured with the specified parameters.
search_results	ListData	Results of similarity search.

Elasticsearch

This component creates an Elasticsearch Vector Store with search capabilities. For more information, see the Elasticsearch documentation.

Inputs

Name	Type	Description
es_url	String	Elasticsearch server URL
es_user	String	Username for Elasticsearch authentication
es_password	SecretString	Password for Elasticsearch authentication
index_name	String	Name of the Elasticsearch index
strategy	String	Strategy for vector search ("approximate_k_nearest_neighbors" or "script_scoring")
distance_strategy	String	Strategy for distance calculation ("COSINE", "EUCLIDEAN_DISTANCE", "DOT_PRODUCT")
search_query	String	Query for similarity search
ingest_data	Data	Data to be ingested into the vector store
embedding	Embeddings	Embedding function to use
number_of_results	Integer	Number of results to return in search (default: 4)

Outputs

Name	Type	Description
vector_store	ElasticsearchStore	Elasticsearch vector store instance
search_results	List[Data]	Results of similarity search

FAISS

This component creates a FAISS Vector Store with search capabilities. For more information, see the FAISS documentation.

Inputs

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

Outputs

Name	Type	Description
vector_store	FAISS	A FAISS vector store instance configured with the specified parameters.

Graph RAG

This component performs Graph RAG (Retrieval Augmented Generation) traversal in a vector store, enabling graph-based document retrieval. For more information, see the Graph RAG documentation.

For an example flow, see the Graph RAG template.

Inputs

Name	Display Name	Info
embedding_model	Embedding Model	Specify the embedding model. This is not required for collections embedded with Astra vectorize.
vector_store	Vector Store Connection	Connection to the vector store.
edge_definition	Edge Definition	Edge definition for the graph traversal. For more information, see the GraphRAG documentation.
strategy	Traversal Strategies	The strategy to use for graph traversal. Strategy options are dynamically loaded from available strategies.
search_query	Search Query	The query to search for in the vector store.
graphrag_strategy_kwargs	Strategy Parameters	Optional dictionary of additional parameters for the retrieval strategy. For more information, see the strategy documentation.

Outputs

Name	Type	Description
search_results	List[Data]	Results of the graph-based document retrieval as a list of Data objects.

Hyper-Converged Database (HCD) Vector Store

This component implements a Vector Store using HCD.

To use the HCD vector store, add your deployment's collection name, username, password, and HCD Data API endpoint. The endpoint must be formatted like http[s]://**DOMAIN_NAME** or **IP_ADDRESS**[:port], for example, http://192.0.2.250:8181.

Replace DOMAIN_NAME or IP_ADDRESS with the domain name or IP address of your HCD Data API connection.

To use the HCD vector store for embeddings ingestion, connect it to an embeddings model and a file loader:

Inputs

Name	Display Name	Info
collection_name	Collection Name	The name of the collection within HCD where the vectors will be stored (required)
username	HCD Username	Authentication username for accessing HCD (default: "hcd-superuser", required)
password	HCD Password	Authentication password for accessing HCD (required)
api_endpoint	HCD API Endpoint	API endpoint URL for the HCD service (required)
search_input	Search Input	Query string for similarity search
ingest_data	Ingest Data	Data to be ingested into the vector store
namespace	Namespace	Optional namespace within HCD to use for the collection (default: "default_namespace")
ca_certificate	CA Certificate	Optional CA certificate for TLS connections to HCD
metric	Metric	Optional distance metric for vector comparisons (options: "cosine", "dot_product", "euclidean")
batch_size	Batch Size	Optional number of data to process in a single batch
bulk_insert_batch_concurrency	Bulk Insert Batch Concurrency	Optional concurrency level for bulk insert operations
bulk_insert_overwrite_concurrency	Bulk Insert Overwrite Concurrency	Optional concurrency level for bulk insert operations that overwrite existing data
bulk_delete_concurrency	Bulk Delete Concurrency	Optional concurrency level for bulk delete operations
setup_mode	Setup Mode	Configuration mode for setting up the vector store (options: "Sync", "Async", "Off", default: "Sync")
pre_delete_collection	Pre Delete Collection	Boolean flag to determine whether to delete the collection before creating a new one
metadata_indexing_include	Metadata Indexing Include	Optional list of metadata fields to include in the indexing
embedding	Embedding or Astra Vectorize	Allows either an embedding model or an Astra Vectorize configuration
metadata_indexing_exclude	Metadata Indexing Exclude	Optional list of metadata fields to exclude from the indexing
collection_indexing_policy	Collection Indexing Policy	Optional dictionary defining the indexing policy for the collection
number_of_results	Number of Results	Number of results to return in similarity search (default: 4)
search_type	Search Type	Search type to use (options: "Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)", default: "Similarity")
search_score_threshold	Search Score Threshold	Minimum similarity score threshold for search results (default: 0)
search_filter	Search Metadata Filter	Optional dictionary of filters to apply to the search query

Outputs

Name	Display Name	Info
vector_store	Vector Store	An HCD vector store instance The results of the similarity search as a list of Data objects.
search_results	Search Results	The results of the similarity search as a list of Data objects.

Milvus

This component creates a Milvus Vector Store with search capabilities. For more information, see the Milvus documentation.

Inputs

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

Outputs

Name	Type	Description
vector_store	Milvus	A Milvus vector store instance configured with the specified parameters.

MongoDB Atlas

This component creates a MongoDB Atlas Vector Store with search capabilities. For more information, see the MongoDB Atlas documentation.

Inputs

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

Outputs

Name	Type	Description
vector_store	MongoDBAtlasVectorSearch	MongoDB Atlas vector store instance
search_results	List[Data]	Results of similarity search

Opensearch

This component creates an Opensearch vector store with search capabilities For more information, see Opensearch documentation.

Inputs

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

Outputs

Name	Type	Description
vector_store	OpenSearchVectorSearch	OpenSearch vector store instance
search_results	List[Data]	Results of similarity search

PGVector

This component creates a PGVector Vector Store with search capabilities. For more information, see the PGVector documentation.

Inputs

Name	Type	Description
pg_server_url	SecretString	PostgreSQL server connection string
collection_name	String	Table name for the vector store
search_query	String	Query for similarity search
ingest_data	Data	Data to be ingested into the vector store
embedding	Embeddings	Embedding function to use
number_of_results	Integer	Number of results to return in search

Outputs

Name	Type	Description
vector_store	PGVector	PGVector vector store instance
search_results	List[Data]	Results of similarity search

Pinecone

This component creates a Pinecone Vector Store with search capabilities. For more information, see the Pinecone documentation.

Inputs

Name	Type	Description
index_name	String	Name of the Pinecone index
namespace	String	Namespace for the index
distance_strategy	String	Strategy for calculating distance between vectors
pinecone_api_key	SecretString	API key for Pinecone
text_key	String	Key in the record to use as text
search_query	String	Query for similarity search
ingest_data	Data	Data to be ingested into the vector store
embedding	Embeddings	Embedding function to use
number_of_results	Integer	Number of results to return in search

Outputs

Name	Type	Description
vector_store	Pinecone	Pinecone vector store instance
search_results	List[Data]	Results of similarity search

Qdrant

This component creates a Qdrant Vector Store with search capabilities. For more information, see the Qdrant documentation.

Inputs

Name	Type	Description
collection_name	String	Name of the Qdrant collection
host	String	Qdrant server host
port	Integer	Qdrant server port
grpc_port	Integer	Qdrant gRPC port
api_key	SecretString	API key for Qdrant
prefix	String	Prefix for Qdrant
timeout	Integer	Timeout for Qdrant operations
path	String	Path for Qdrant
url	String	URL for Qdrant
distance_func	String	Distance function for vector similarity
content_payload_key	String	Key for content payload
metadata_payload_key	String	Key for metadata payload
search_query	String	Query for similarity search
ingest_data	Data	Data to be ingested into the vector store
embedding	Embeddings	Embedding function to use
number_of_results	Integer	Number of results to return in search

Outputs

Name	Type	Description
vector_store	Qdrant	Qdrant vector store instance
search_results	List[Data]	Results of similarity search

Redis

This component creates a Redis Vector Store with search capabilities. For more information, see the Redis documentation.

Inputs

Name	Type	Description
redis_server_url	SecretString	Redis server connection string
redis_index_name	String	Name of the Redis index
code	String	Custom code for Redis (advanced)
schema	String	Schema for Redis index
search_query	String	Query for similarity search
ingest_data	Data	Data to be ingested into the vector store
number_of_results	Integer	Number of results to return in search
embedding	Embeddings	Embedding function to use

Outputs

Name	Type	Description
vector_store	Redis	Redis vector store instance
search_results	List[Data]	Results of similarity search

Supabase

This component creates a connection to a Supabase Vector Store with search capabilities. For more information, see the Supabase documentation.

Inputs

Name	Type	Description
supabase_url	String	URL of the Supabase instance
supabase_service_key	SecretString	Service key for Supabase authentication
table_name	String	Name of the table in Supabase
query_name	String	Name of the query to use
search_query	String	Query for similarity search
ingest_data	Data	Data to be ingested into the vector store
embedding	Embeddings	Embedding function to use
number_of_results	Integer	Number of results to return in search

Outputs

Name	Type	Description
vector_store	SupabaseVectorStore	Supabase vector store instance
search_results	List[Data]	Results of similarity search

Upstash

This component creates an Upstash Vector Store with search capabilities. For more information, see the Upstash documentation.

Inputs

Name	Type	Description
index_url	String	The URL of the Upstash index
index_token	SecretString	The token for the Upstash index
text_key	String	The key in the record to use as text
namespace	String	Namespace for the index
search_query	String	Query for similarity search
metadata_filter	String	Filters documents by metadata
ingest_data	Data	Data to be ingested into the vector store
embedding	Embeddings	Embedding function to use (optional)
number_of_results	Integer	Number of results to return in search

Outputs

Name	Type	Description
vector_store	UpstashVectorStore	Upstash vector store instance
search_results	List[Data]	Results of similarity search

Vectara

This component creates a Vectara Vector Store with search capabilities. For more information, see the Vectara documentation.

Inputs

Name	Type	Description
vectara_customer_id	String	Vectara customer ID
vectara_corpus_id	String	Vectara corpus ID
vectara_api_key	SecretString	Vectara API key
embedding	Embeddings	Embedding function to use (optional)
ingest_data	List[Document/Data]	Data to be ingested into the vector store
search_query	String	Query for similarity search
number_of_results	Integer	Number of results to return in search

Outputs

Name	Type	Description
vector_store	VectaraVectorStore	Vectara vector store instance
search_results	List[Data]	Results of similarity search

Vectara Search

This component searches a Vectara Vector Store for documents based on the provided input. For more information, see the Vectara documentation.

Inputs

Name	Type	Description
search_type	String	Type of search, such as "Similarity" or "MMR"
input_value	String	Search query
vectara_customer_id	String	Vectara customer ID
vectara_corpus_id	String	Vectara corpus ID
vectara_api_key	SecretString	Vectara API key
files_url	List[String]	Optional URLs for file initialization

Outputs

Name	Type	Description
search_results	List[Data]	Results of similarity search

Weaviate

This component facilitates a Weaviate Vector Store setup, optimizing text and document indexing and retrieval. For more information, see the Weaviate Documentation.

Inputs

Name	Type	Description
weaviate_url	String	Default instance URL
search_by_text	Boolean	Indicates whether to search by text
api_key	SecretString	Optional API key for authentication
index_name	String	Optional index name
text_key	String	Default text extraction key
input	Document	Document or record
embedding	Embeddings	Model used
attributes	List[String]	Optional additional attributes

Outputs

Name	Type	Description
vector_store	WeaviateVectorStore	Weaviate vector store instance

Weaviate Search

This component searches a Weaviate Vector Store for documents similar to the input. For more information, see the Weaviate Documentation.

Inputs

Name	Type	Description
search_type	String	Type of search, such as "Similarity" or "MMR"
input_value	String	Search query
weaviate_url	String	Default instance URL
search_by_text	Boolean	Indicates whether to search by text
api_key	SecretString	Optional API key for authentication
index_name	String	Optional index name
text_key	String	Default text extraction key
embedding	Embeddings	Model used
attributes	List[String]	Optional additional attributes

Outputs

Name	Type	Description
search_results	List[Data]	Results of similarity search