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

Vector databases can be populated from within Langflow with document ingestion pipelines, like the following

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
collection_name	Collection Name	The name of the collection within Astra DB where the vectors will be stored (required)
token	Astra DB Application Token	Authentication token for accessing Astra DB (required)
api_endpoint	API Endpoint	API endpoint URL for the Astra DB 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 Astra DB to use for the collection
embedding_choice	Embedding Model or Astra Vectorize	Determines whether to use an Embedding Model or Astra Vectorize for the collection
embedding	Embedding Model	Allows an embedding model configuration (when using Embedding Model)
provider	Vectorize Provider	Provider for Astra Vectorize (when using Astra Vectorize)
metric	Metric	Optional distance metric for vector comparisons
batch_size	Batch Size	Optional number of data to process in a single batch
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
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)")
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	Built Astra DB vector store
search_results	Search Results	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	Cassandra vector store instance
search_results	List[Data]	Results of similarity search

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	Built Cassandra Graph vector store
search_results	Search Results	Results of the similarity search as a list of Data objects

Chroma DB

This component creates a Chroma Vector Store with search capabilities. 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.

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.

Hyper-Converged Database (HCD) Vector Store

This component implements a Vector Store using HCD.

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	Built HCD vector store instance
search_results	Search Results	Results of 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	MongoDB Atlas Cluster URI
db_name	String	Database name
collection_name	String	Collection name
index_name	String	Index name
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	MongoDBAtlasVectorSearch	MongoDB Atlas 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

Vectara RAG

This component leverages Vectara's Retrieval Augmented Generation (RAG) capabilities to search and summarize documents based on the provided input. 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
search_query	String	The query to receive an answer on
lexical_interpolation	Float	Hybrid search factor (0.005 to 0.1)
filter	String	Metadata filters to narrow the search
reranker	String	Reranker type (mmr, rerank_multilingual_v1, none)
reranker_k	Integer	Number of results to rerank (1 to 100)
diversity_bias	Float	Diversity bias for MMR reranker (0 to 1)
max_results	Integer	Maximum number of search results to summarize (1 to 100)
response_lang	String	Language code for the response (for example, "eng", "auto")
prompt	String	Prompt name for summarization

Outputs

Name	Type	Description
answer	Message	Generated RAG response

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

Note: Ensure Weaviate instance is running and accessible. Verify API key, index name, text key, and attributes are set correctly.

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