Skip to main content

Vector Stores

Vector databases are used to store and search for vectors. They can be used to store embeddings, search for similar vectors, and perform other vector operations.

Astra DB Vector Store​

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

For more information, see the DataStax documentation.

Parameters​

Inputs​

NameDisplay NameInfo
collection_nameCollection NameThe name of the collection within Astra DB where the vectors will be stored (required)
tokenAstra DB Application TokenAuthentication token for accessing Astra DB (required)
api_endpointAPI EndpointAPI endpoint URL for the Astra DB service (required)
search_inputSearch InputQuery string for similarity search
ingest_dataIngest DataData to be ingested into the vector store
namespaceNamespaceOptional namespace within Astra DB to use for the collection
embedding_serviceEmbedding Model or Astra VectorizeDetermines whether to use an Embedding Model or Astra Vectorize for the collection
embeddingEmbedding ModelAllows an embedding model configuration (when using Embedding Model)
providerVectorize ProviderProvider for Astra Vectorize (when using Astra Vectorize)
metricMetricOptional distance metric for vector comparisons
batch_sizeBatch SizeOptional number of data to process in a single batch
setup_modeSetup ModeConfiguration mode for setting up the vector store (options: "Sync", "Async", "Off", default: "Sync")
pre_delete_collectionPre Delete CollectionBoolean flag to determine whether to delete the collection before creating a new one
number_of_resultsNumber of ResultsNumber of results to return in similarity search (default: 4)
search_typeSearch TypeSearch type to use (options: "Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)")
search_score_thresholdSearch Score ThresholdMinimum similarity score threshold for search results
search_filterSearch Metadata FilterOptional dictionary of filters to apply to the search query

Outputs​

NameDisplay NameInfo
vector_storeVector StoreBuilt Astra DB vector store
search_resultsSearch ResultsResults 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.

Parameters​

Inputs​

NameTypeDescription
database_refStringContact points for the database or AstraDB database ID
usernameStringUsername for the database (leave empty for AstraDB)
tokenSecretStringUser password for the database or AstraDB token
keyspaceStringTable Keyspace or AstraDB namespace
table_nameStringName of the table or AstraDB collection
ttl_secondsIntegerTime-to-live for added texts
batch_sizeIntegerNumber of data to process in a single batch
setup_modeStringConfiguration mode for setting up the Cassandra table
cluster_kwargsDictAdditional keyword arguments for the Cassandra cluster
search_queryStringQuery for similarity search
ingest_dataDataData to be ingested into the vector store
embeddingEmbeddingsEmbedding function to use
number_of_resultsIntegerNumber of results to return in search
search_typeStringType of search to perform
search_score_thresholdFloatMinimum similarity score for search results
search_filterDictMetadata filters for search query
body_searchStringDocument textual search terms
enable_body_searchBooleanFlag to enable body search

Outputs​

NameTypeDescription
vector_storeCassandraCassandra vector store instance
search_resultsList[Data]Results of similarity search

Cassandra Graph Vector Store​

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

Parameters​

Inputs​

NameDisplay NameInfo
database_refContact Points / Astra Database IDContact points for the database or AstraDB database ID (required)
usernameUsernameUsername for the database (leave empty for AstraDB)
tokenPassword / AstraDB TokenUser password for the database or AstraDB token (required)
keyspaceKeyspaceTable Keyspace or AstraDB namespace (required)
table_nameTable NameThe name of the table or AstraDB collection where vectors will be stored (required)
setup_modeSetup ModeConfiguration mode for setting up the Cassandra table (options: "Sync", "Off", default: "Sync")
cluster_kwargsCluster argumentsOptional dictionary of additional keyword arguments for the Cassandra cluster
search_querySearch QueryQuery string for similarity search
ingest_dataIngest DataData to be ingested into the vector store (list of Data objects)
embeddingEmbeddingEmbedding model to use
number_of_resultsNumber of ResultsNumber of results to return in similarity search (default: 4)
search_typeSearch TypeSearch type to use (options: "Traversal", "MMR traversal", "Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)", default: "Traversal")
depthDepth of traversalThe maximum depth of edges to traverse (for "Traversal" or "MMR traversal" search types, default: 1)
search_score_thresholdSearch Score ThresholdMinimum similarity score threshold for search results (for "Similarity with score threshold" search type)
search_filterSearch Metadata FilterOptional dictionary of filters to apply to the search query

Outputs​

NameDisplay NameInfo
vector_storeVector StoreBuilt Cassandra Graph vector store
search_resultsSearch ResultsResults 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.

Parameters​

Inputs​

NameTypeDescription
collection_nameStringThe name of the Chroma collection. Default: "langflow".
persist_directoryStringThe directory to persist the Chroma database.
search_queryStringThe query to search for in the vector store.
ingest_dataDataThe data to ingest into the vector store (list of Data objects).
embeddingEmbeddingsThe embedding function to use for the vector store.
chroma_server_cors_allow_originsStringCORS allow origins for the Chroma server.
chroma_server_hostStringHost for the Chroma server.
chroma_server_http_portIntegerHTTP port for the Chroma server.
chroma_server_grpc_portIntegergRPC port for the Chroma server.
chroma_server_ssl_enabledBooleanEnable SSL for the Chroma server.
allow_duplicatesBooleanAllow duplicate documents in the vector store.
search_typeStringType of search to perform: "Similarity" or "MMR".
number_of_resultsIntegerNumber of results to return from the search. Default: 10.
limitIntegerLimit the number of records to compare when Allow Duplicates is False.

Outputs​

NameTypeDescription
vector_storeChromaChroma vector store instance
search_resultsList[Data]Results of similarity search

Clickhouse​

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

Parameters​

Inputs​

NameDisplay NameInfo
hosthostnameClickhouse server hostname (required, default: "localhost")
portportClickhouse server port (required, default: 8123)
databasedatabaseClickhouse database name (required)
tableTable nameClickhouse table name (required)
usernameThe ClickHouse user name.Username for authentication (required)
passwordThe password for username.Password for authentication (required)
index_typeindex_typeType of the index (options: "annoy", "vector_similarity", default: "annoy")
metricmetricMetric to compute distance (options: "angular", "euclidean", "manhattan", "hamming", "dot", default: "angular")
secureUse https/TLSOverrides inferred values from the interface or port arguments (default: false)
index_paramParam of the indexIndex parameters (default: "'L2Distance',100")
index_query_paramsindex query paramsAdditional index query parameters
search_querySearch QueryQuery string for similarity search
ingest_dataIngest DataData to be ingested into the vector store
embeddingEmbeddingEmbedding model to use
number_of_resultsNumber of ResultsNumber of results to return in similarity search (default: 4)
score_thresholdScore thresholdThreshold for similarity scores

Outputs​

NameDisplay NameInfo
vector_storeVector StoreBuilt Clickhouse vector store
search_resultsSearch ResultsResults 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.

Parameters​

Inputs​

NameTypeDescription
couchbase_connection_stringSecretStringCouchbase Cluster connection string (required).
couchbase_usernameStringCouchbase username (required).
couchbase_passwordSecretStringCouchbase password (required).
bucket_nameStringName of the Couchbase bucket (required).
scope_nameStringName of the Couchbase scope (required).
collection_nameStringName of the Couchbase collection (required).
index_nameStringName of the Couchbase index (required).
search_queryStringThe query to search for in the vector store.
ingest_dataDataThe data to ingest into the vector store (list of Data objects).
embeddingEmbeddingsThe embedding function to use for the vector store.
number_of_resultsIntegerNumber of results to return from the search. Default: 4 (advanced).

Outputs​

NameTypeDescription
vector_storeCouchbaseVectorStoreA 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.

Parameters​

Inputs​

NameTypeDescription
index_nameStringThe name of the FAISS index. Default: "langflow_index".
persist_directoryStringPath to save the FAISS index. It will be relative to where Langflow is running.
search_queryStringThe query to search for in the vector store.
ingest_dataDataThe data to ingest into the vector store (list of Data objects or documents).
allow_dangerous_deserializationBooleanSet to True to allow loading pickle files from untrusted sources. Default: True (advanced).
embeddingEmbeddingsThe embedding function to use for the vector store.
number_of_resultsIntegerNumber of results to return from the search. Default: 4 (advanced).

Outputs​

NameTypeDescription
vector_storeFAISSA FAISS vector store instance configured with the specified parameters.

Hyper-Converged Database (HCD) Vector Store​

This component implements a Vector Store using HCD.

Parameters​

Inputs​

NameDisplay NameInfo
collection_nameCollection NameThe name of the collection within HCD where the vectors will be stored (required)
usernameHCD UsernameAuthentication username for accessing HCD (default: "hcd-superuser", required)
passwordHCD PasswordAuthentication password for accessing HCD (required)
api_endpointHCD API EndpointAPI endpoint URL for the HCD service (required)
search_inputSearch InputQuery string for similarity search
ingest_dataIngest DataData to be ingested into the vector store
namespaceNamespaceOptional namespace within HCD to use for the collection (default: "default_namespace")
ca_certificateCA CertificateOptional CA certificate for TLS connections to HCD
metricMetricOptional distance metric for vector comparisons (options: "cosine", "dot_product", "euclidean")
batch_sizeBatch SizeOptional number of data to process in a single batch
bulk_insert_batch_concurrencyBulk Insert Batch ConcurrencyOptional concurrency level for bulk insert operations
bulk_insert_overwrite_concurrencyBulk Insert Overwrite ConcurrencyOptional concurrency level for bulk insert operations that overwrite existing data
bulk_delete_concurrencyBulk Delete ConcurrencyOptional concurrency level for bulk delete operations
setup_modeSetup ModeConfiguration mode for setting up the vector store (options: "Sync", "Async", "Off", default: "Sync")
pre_delete_collectionPre Delete CollectionBoolean flag to determine whether to delete the collection before creating a new one
metadata_indexing_includeMetadata Indexing IncludeOptional list of metadata fields to include in the indexing
embeddingEmbedding or Astra VectorizeAllows either an embedding model or an Astra Vectorize configuration
metadata_indexing_excludeMetadata Indexing ExcludeOptional list of metadata fields to exclude from the indexing
collection_indexing_policyCollection Indexing PolicyOptional dictionary defining the indexing policy for the collection
number_of_resultsNumber of ResultsNumber of results to return in similarity search (default: 4)
search_typeSearch TypeSearch type to use (options: "Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)", default: "Similarity")
search_score_thresholdSearch Score ThresholdMinimum similarity score threshold for search results (default: 0)
search_filterSearch Metadata FilterOptional dictionary of filters to apply to the search query

Outputs​

NameDisplay NameInfo
vector_storeVector StoreBuilt HCD vector store instance
search_resultsSearch ResultsResults 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.

Parameters​

Inputs​

NameTypeDescription
collection_nameStringName of the Milvus collection
collection_descriptionStringDescription of the Milvus collection
uriStringConnection URI for Milvus
passwordSecretStringPassword for Milvus
usernameSecretStringUsername for Milvus
batch_sizeIntegerNumber of data to process in a single batch
search_queryStringQuery for similarity search
ingest_dataDataData to be ingested into the vector store
embeddingEmbeddingsEmbedding function to use
number_of_resultsIntegerNumber of results to return in search
search_typeStringType of search to perform
search_score_thresholdFloatMinimum similarity score for search results
search_filterDictMetadata filters for search query
setup_modeStringConfiguration mode for setting up the vector store
vector_dimensionsIntegerNumber of dimensions of the vectors
pre_delete_collectionBooleanWhether to delete the collection before creating a new one

Outputs​

NameTypeDescription
vector_storeMilvusA 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.

Parameters​

Inputs​

NameTypeDescription
mongodb_atlas_cluster_uriSecretStringMongoDB Atlas Cluster URI
db_nameStringDatabase name
collection_nameStringCollection name
index_nameStringIndex name
search_queryStringQuery for similarity search
ingest_dataDataData to be ingested into the vector store
embeddingEmbeddingsEmbedding function to use
number_of_resultsIntegerNumber of results to return in search

Outputs​

NameTypeDescription
vector_storeMongoDBAtlasVectorSearchMongoDB Atlas vector store instance
search_resultsList[Data]Results of similarity search

PGVector​

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

Parameters​

Inputs​

NameTypeDescription
pg_server_urlSecretStringPostgreSQL server connection string
collection_nameStringTable name for the vector store
search_queryStringQuery for similarity search
ingest_dataDataData to be ingested into the vector store
embeddingEmbeddingsEmbedding function to use
number_of_resultsIntegerNumber of results to return in search

Outputs​

NameTypeDescription
vector_storePGVectorPGVector vector store instance
search_resultsList[Data]Results of similarity search

Pinecone​

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

Parameters​

Inputs​

NameTypeDescription
index_nameStringName of the Pinecone index
namespaceStringNamespace for the index
distance_strategyStringStrategy for calculating distance between vectors
pinecone_api_keySecretStringAPI key for Pinecone
text_keyStringKey in the record to use as text
search_queryStringQuery for similarity search
ingest_dataDataData to be ingested into the vector store
embeddingEmbeddingsEmbedding function to use
number_of_resultsIntegerNumber of results to return in search

Outputs​

NameTypeDescription
vector_storePineconePinecone vector store instance
search_resultsList[Data]Results of similarity search

Qdrant​

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

Parameters​

Inputs​

NameTypeDescription
collection_nameStringName of the Qdrant collection
hostStringQdrant server host
portIntegerQdrant server port
grpc_portIntegerQdrant gRPC port
api_keySecretStringAPI key for Qdrant
prefixStringPrefix for Qdrant
timeoutIntegerTimeout for Qdrant operations
pathStringPath for Qdrant
urlStringURL for Qdrant
distance_funcStringDistance function for vector similarity
content_payload_keyStringKey for content payload
metadata_payload_keyStringKey for metadata payload
search_queryStringQuery for similarity search
ingest_dataDataData to be ingested into the vector store
embeddingEmbeddingsEmbedding function to use
number_of_resultsIntegerNumber of results to return in search

Outputs​

NameTypeDescription
vector_storeQdrantQdrant vector store instance
search_resultsList[Data]Results of similarity search

Redis​

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

Parameters​

Inputs​

NameTypeDescription
redis_server_urlSecretStringRedis server connection string
redis_index_nameStringName of the Redis index
codeStringCustom code for Redis (advanced)
schemaStringSchema for Redis index
search_queryStringQuery for similarity search
ingest_dataDataData to be ingested into the vector store
number_of_resultsIntegerNumber of results to return in search
embeddingEmbeddingsEmbedding function to use

Outputs​

NameTypeDescription
vector_storeRedisRedis vector store instance
search_resultsList[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.

Parameters​

Inputs​

NameTypeDescription
supabase_urlStringURL of the Supabase instance
supabase_service_keySecretStringService key for Supabase authentication
table_nameStringName of the table in Supabase
query_nameStringName of the query to use
search_queryStringQuery for similarity search
ingest_dataDataData to be ingested into the vector store
embeddingEmbeddingsEmbedding function to use
number_of_resultsIntegerNumber of results to return in search

Outputs​

NameTypeDescription
vector_storeSupabaseVectorStoreSupabase vector store instance
search_resultsList[Data]Results of similarity search

Upstash​

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

Parameters​

Inputs​

NameTypeDescription
index_urlStringThe URL of the Upstash index
index_tokenSecretStringThe token for the Upstash index
text_keyStringThe key in the record to use as text
namespaceStringNamespace for the index
search_queryStringQuery for similarity search
metadata_filterStringFilters documents by metadata
ingest_dataDataData to be ingested into the vector store
embeddingEmbeddingsEmbedding function to use (optional)
number_of_resultsIntegerNumber of results to return in search

Outputs​

NameTypeDescription
vector_storeUpstashVectorStoreUpstash vector store instance
search_resultsList[Data]Results of similarity search

Vectara​

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

Parameters​

Inputs​

NameTypeDescription
vectara_customer_idStringVectara customer ID
vectara_corpus_idStringVectara corpus ID
vectara_api_keySecretStringVectara API key
embeddingEmbeddingsEmbedding function to use (optional)
ingest_dataList[Document/Data]Data to be ingested into the vector store
search_queryStringQuery for similarity search
number_of_resultsIntegerNumber of results to return in search

Outputs​

NameTypeDescription
vector_storeVectaraVectorStoreVectara vector store instance
search_resultsList[Data]Results of similarity search

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

Parameters​

Inputs​

NameTypeDescription
search_typeStringType of search, such as "Similarity" or "MMR"
input_valueStringSearch query
vectara_customer_idStringVectara customer ID
vectara_corpus_idStringVectara corpus ID
vectara_api_keySecretStringVectara API key
files_urlList[String]Optional URLs for file initialization

Outputs​

NameTypeDescription
search_resultsList[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.

Parameters​

Inputs​

NameTypeDescription
weaviate_urlStringDefault instance URL
search_by_textBooleanIndicates whether to search by text
api_keySecretStringOptional API key for authentication
index_nameStringOptional index name
text_keyStringDefault text extraction key
inputDocumentDocument or record
embeddingEmbeddingsModel used
attributesList[String]Optional additional attributes

Outputs​

NameTypeDescription
vector_storeWeaviateVectorStoreWeaviate vector store instance

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

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

Parameters​

Inputs​

NameTypeDescription
search_typeStringType of search, such as "Similarity" or "MMR"
input_valueStringSearch query
weaviate_urlStringDefault instance URL
search_by_textBooleanIndicates whether to search by text
api_keySecretStringOptional API key for authentication
index_nameStringOptional index name
text_keyStringDefault text extraction key
embeddingEmbeddingsModel used
attributesList[String]Optional additional attributes

Outputs​

NameTypeDescription
search_resultsList[Data]Results of similarity search

Hi, how can I help you?