Vector store components in LLM Controls

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.

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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.

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LLMC Vector DB

LLMC Vector DB helps you store and search through your personal documents in a way that feels fast, secure, and tailored just for you. Whether you’re uploading files, adding notes, or saving content from the web, everything is stored in a way that makes it easy to find later , without digging through folders.

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Key Benefits

Private and Secure
Your content is always separated from others. Everything you add is linked to your account only, ensuring complete privacy. Organized Just for You
You’ll only see your own collections. No clutter. No mix-ups. It automatically shows the content that belongs to you neatly filtered and personalized. Flexible Setup
While you focus on your content, LLM Controls is smartly configured to perform at its best, adapting to different needs and environments. Developers can still fine-tune things like speed, search preferences, and more if needed. Seamless Content Capture
Just upload a document or paste a link, and LLM Controls takes care of the rest. It understands and organizes the content instantly, so you can find what you need later in just a few words.

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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

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	The 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.

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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 LLM Controls.
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.

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.

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AstraDB Graph vector store

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

Parameters

Inputs

Name	Display Name	Info
collection_name	Collection Name	The name of the collection within AstraDB where the vectors are 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. The options are “cosine”, “euclidean”, “dot_product”.
setup_mode	Setup Mode	Configuration mode for setting up the vector store. The options are “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. The options are “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	The Graph RAG vector store instance configured with the specified parameters.
search_results	Search Results	The results of the similarity search as a list of Data objects.

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Cassandra

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

Parameters

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	The 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.

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Cassandra Graph Vector Store

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

Parameters

Inputs

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

Outputs

Name	Display Name	Info
vector_store	Vector Store	The 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.

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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 LLM Controls is running.
4. To load data and embeddings into your Chroma database, in the Chroma DB component, click Run component.

5. To view the split data, in the Split Text component, click Inspect output.
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.

Parameters

Inputs

Name	Type	Description
collection_name	String	The name of the Chroma collection. Default: “LLM Controls”.
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	The CORS allow origins for the Chroma server.
chroma_server_host	String	The host for the Chroma server.
chroma_server_http_port	Integer	The HTTP port for the Chroma server.
chroma_server_grpc_port	Integer	The 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	The type of search to perform: “Similarity” or “MMR”.
number_of_results	Integer	The number of results to return from the search. Default: 10.
limit	Integer	The limit of the number of records to compare when Allow Duplicates is False.

Outputs

Name	Type	Description
vector_store	Chroma	The Chroma vector store instance.
search_results	List[Data]	The results of the similarity search as a list of Data objects.

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Clickhouse

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

Parameters

Inputs

Name	Display Name	Info
host	hostname	The Clickhouse server hostname. Required. Default: “localhost”
port	port	The Clickhouse server port. Required. Default: 8123.
database	database	The Clickhouse database name. Required.
table	Table name	The 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. The options are “annoy” and “vector_similarity”. Default: “annoy”.
metric	metric	Metric to compute distance. The options are “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	The query string for similarity search.
ingest_data	Ingest Data	The data to be ingested into the vector store.
embedding	Embedding	The embedding model to use.
number_of_results	Number of Results	The number of results to return in similarity search. Default: 4.
score_threshold	Score threshold	The threshold for similarity scores.

Outputs

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

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Couchbase

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

Parameters

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 list of data to ingest into the vector store.
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.

Outputs

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

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Local DB

The Local DB component is LLM Controls’ 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 Lang’s cache directory. Local DB includes Ingest and Retrieve modes. The Ingest mode works similarly to ChromaDB, and persists your database to the LLM Controls cache directory. The LLM Controls cache directory location is specified in LLMC_CONFIG_DIR. For more information. The Retrieve mode can query your Chroma DB collections. For more information, see the Chroma documentation.

Parameters

Inputs

Name	Type	Description
collection_name	String	The name of the Chroma collection. Default: “LLMC”.
persist_directory	String	Custom base directory to save the vector store. Collections are stored under $DIRECTORY/vector_stores/$COLLECTION_NAME. If not specified, it uses 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 is 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	The results of the similarity search as a list of Data objects.

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Elasticsearch

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

Parameters

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. The options are “approximate_k_nearest_neighbors” or “script_scoring”.
distance_strategy	String	Strategy for distance calculation. The options are “COSINE”, “EUCLIDEAN_DISTANCE”, or “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	The Elasticsearch vector store instance.
search_results	List[Data]	The results of the similarity search as a list of Data objects.

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FAISS

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

Parameters

Inputs

Name	Type	Description
index_name	String	The name of the FAISS index. Default: “llmc_index”.
persist_directory	String	Path to save the FAISS index. It is relative to where LLM Controls is running.
search_query	String	The query to search for in the vector store.
ingest_data	Data	The list of data to ingest into the vector store.
allow_dangerous_deserialization	Boolean	Set to True to allow loading pickle files from untrusted sources. Default: True.
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.

Outputs

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

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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.

Parameters

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.

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Hyper-Converged Database (HCD)

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:

Parameters

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 is “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 is “default_namespace”.
ca_certificate	CA Certificate	Optional CA certificate for TLS connections to HCD.
metric	Metric	Optional distance metric for vector comparisons. Options are “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 are “Sync”, “Async”, “Off”. Default is “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 is 4.
search_type	Search Type	Search type to use. Options are “Similarity”, “Similarity with score threshold”, “MMR (Max Marginal Relevance)”. Default is “Similarity”.
search_score_threshold	Search Score Threshold	Minimum similarity score threshold for search results. Default is 0.
search_filter	Search Metadata Filter	Optional dictionary of filters to apply to the search query.

Outputs

Name	Type	Description
vector_store	HyperConvergedDatabaseVectorStore	The HCD vector store instance.
search_results	List[Data]	The results of the similarity search as a list of Data objects.

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Milvus

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

Parameters

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.

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MongoDB Atlas

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

Parameters

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. The options are “append” or “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. The options are “cosine”, “euclidean”, or “dotProduct”. Default: “cosine”.
quantization	String	Quantization reduces memory costs by converting 32-bit floats to smaller data types. The options are “scalar” or “binary”.

Outputs

Name	Type	Description
vector_store	MongoDBAtlasVectorSearch	The MongoDB Atlas vector store instance.
search_results	List[Data]	The results of the similarity search as a list of Data objects.

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Opensearch

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

Parameters

Inputs

Name	Type	Description
opensearch_url	String	URL for OpenSearch cluster, such as https://192.168.1.1:9200.
index_name	String	The index name where the vectors are 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	The data to be ingested into the vector store.
embedding	Embeddings	The embedding function to use.
search_type	String	The options are “similarity”, “similarity_score_threshold”, “mmr”.
number_of_results	Integer	The number of results to return in search.
search_score_threshold	Float	The minimum similarity score threshold for search results.
username	String	The username for the opensource cluster.
password	SecretString	The 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]	The results of the similarity search as a list of Data objects.

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PGVector

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

Parameters

Inputs

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

Outputs

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

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Pinecone

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

Parameters

Inputs

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

Outputs

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

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

Outputs

Name	Type	Description
vector_store	Redis	Redis vector store instance
search_results	List[Data]	The results of the similarity search as a list of Data objects.

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Supabase

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

Parameters

Inputs

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

Outputs

Name	Type	Description
vector_store	SupabaseVectorStore	A Supabase vector store instance.
search_results	List[Data]	The results of the similarity search as a list of Data objects.

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Upstash

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

Parameters

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	The namespace for the index.
search_query	String	The query for similarity search.
metadata_filter	String	Filter documents by metadata.
ingest_data	Data	The data to be ingested into the vector store.
embedding	Embeddings	The embedding function to use.
number_of_results	Integer	The number of results to return in search.

Outputs

Name	Type	Description
vector_store	UpstashVectorStore	An Upstash vector store instance.
search_results	List[Data]	The results of the similarity search as a list of Data objects.

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Vectara

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

Parameters

Inputs

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

Outputs

Name	Type	Description
vector_store	VectaraVectorStore	Vectara vector store instance.
search_results	List[Data]	The results of the similarity search as a list of Data objects.

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Vectara Search

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

Parameters

Inputs

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

Outputs

Name	Type	Description
search_results	List[Data]	The results of the similarity search as a list of Data objects.

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

Outputs

Name	Type	Description
vector_store	WeaviateVectorStore	The Weaviate vector store instance.

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Weaviate Search

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

Parameters

Inputs

Name	Type	Description
search_type	String	The type of search, such as “Similarity” or “MMR”
input_value	String	The search query.
weaviate_url	String	The default instance URL.
search_by_text	Boolean	A boolean value that indicates whether to search by text.
api_key	SecretString	The optional API key for authentication.
index_name	String	The optional index name.
text_key	String	The default text extraction key.
embedding	Embeddings	The embeddings model used.
attributes	List[String]	Optional additional attributes.

Outputs

Name	Type	Description
search_results	List[Data]	The results of the similarity search as a list of Data objects.