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.

Chroma DB

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

Parameters

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

FAISS

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

Parameters

Intputs

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.

Milvus

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

Parameters

Intputs

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.

PGVector

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

Parameters

Intputs

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

Qdrant

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

Parameters

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.

Parameters

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 Supabase Vector Store with search capabilities. For more information, see the Supabase documentation.

Parameters

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

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

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.