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Vector Stores

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This page may contain outdated information. It will be updated as soon as possible.

Astra DB​

The Astra DB initializes a vector store using Astra DB from Data. It creates Astra DB-based vector indexes to efficiently store and retrieve documents.

Parameters:

  • Input: Documents or Data for input.
  • Embedding or Astra vectorize: External or server-side model Astra DB uses.
  • Collection Name: Name of the Astra DB collection.
  • Token: Authentication token for Astra DB.
  • API Endpoint: API endpoint for Astra DB.
  • Namespace: Astra DB namespace.
  • Metric: Metric used by Astra DB.
  • Batch Size: Batch size for operations.
  • Bulk Insert Batch Concurrency: Concurrency level for bulk inserts.
  • Bulk Insert Overwrite Concurrency: Concurrency level for overwriting during bulk inserts.
  • Bulk Delete Concurrency: Concurrency level for bulk deletions.
  • Setup Mode: Setup mode for the vector store.
  • Pre Delete Collection: Option to delete the collection before setup.
  • Metadata Indexing Include: Fields to include in metadata indexing.
  • Metadata Indexing Exclude: Fields to exclude from metadata indexing.
  • Collection Indexing Policy: Indexing policy for the collection.

NOTE

Ensure you configure the necessary Astra DB token and API endpoint before starting.

Astra DB Search​

Astra DBSearch searches an existing Astra DB vector store for documents similar to the input. It uses the Astra DBcomponent's functionality for efficient retrieval.

Parameters:

  • Search Type: Type of search, such as Similarity or MMR.
  • Input Value: Value to search for.
  • Embedding or Astra vectorize: External or server-side model Astra DB uses.
  • Collection Name: Name of the Astra DB collection.
  • Token: Authentication token for Astra DB.
  • API Endpoint: API endpoint for Astra DB.
  • Namespace: Astra DB namespace.
  • Metric: Metric used by Astra DB.
  • Batch Size: Batch size for operations.
  • Bulk Insert Batch Concurrency: Concurrency level for bulk inserts.
  • Bulk Insert Overwrite Concurrency: Concurrency level for overwriting during bulk inserts.
  • Bulk Delete Concurrency: Concurrency level for bulk deletions.
  • Setup Mode: Setup mode for the vector store.
  • Pre Delete Collection: Option to delete the collection before setup.
  • Metadata Indexing Include: Fields to include in metadata indexing.
  • Metadata Indexing Exclude: Fields to exclude from metadata indexing.
  • Collection Indexing Policy: Indexing policy for the collection.

Chroma​

Chroma sets up a vector store using Chroma for efficient vector storage and retrieval within language processing workflows.

Parameters:

  • Collection Name: Name of the collection.
  • Persist Directory: Directory to persist the Vector Store.
  • Server CORS Allow Origins (Optional): CORS allow origins for the Chroma server.
  • Server Host (Optional): Host for the Chroma server.
  • Server Port (Optional): Port for the Chroma server.
  • Server gRPC Port (Optional): gRPC port for the Chroma server.
  • Server SSL Enabled (Optional): SSL configuration for the Chroma server.
  • Input: Input data for creating the Vector Store.
  • Embedding: Embeddings used for the Vector Store.

For detailed documentation and integration guides, please refer to the Chroma Component Documentation.

Chroma Search​

ChromaSearch searches a Chroma collection for documents similar to the input text. It leverages Chroma to ensure efficient document retrieval.

Parameters:

  • Input: Input text for search.
  • Search Type: Type of search, such as Similarity or MMR.
  • Collection Name: Name of the Chroma collection.
  • Index Directory: Directory where the Chroma index is stored.
  • Embedding: Embedding model used for vectorization.
  • Server CORS Allow Origins (Optional): CORS allow origins for the Chroma server.
  • Server Host (Optional): Host for the Chroma server.
  • Server Port (Optional): Port for the Chroma server.
  • Server gRPC Port (Optional): gRPC port for the Chroma server.
  • Server SSL Enabled (Optional): SSL configuration for the Chroma server.

Couchbase​

Couchbase builds a Couchbase vector store from Data, streamlining the storage and retrieval of documents.

Parameters:

  • Embedding: Model used by Couchbase.
  • Input: Documents or Data.
  • Couchbase Cluster Connection String: Cluster Connection string.
  • Couchbase Cluster Username: Cluster Username.
  • Couchbase Cluster Password: Cluster Password.
  • Bucket Name: Bucket identifier in Couchbase.
  • Scope Name: Scope identifier in Couchbase.
  • Collection Name: Collection identifier in Couchbase.
  • Index Name: Index identifier.

For detailed documentation and integration guides, please refer to the Couchbase Component Documentation.

Couchbase Search​

CouchbaseSearch leverages the Couchbase component to search for documents based on similarity metric.

Parameters:

  • Input: Search query.
  • Embedding: Model used in the Vector Store.
  • Couchbase Cluster Connection String: Cluster Connection string.
  • Couchbase Cluster Username: Cluster Username.
  • Couchbase Cluster Password: Cluster Password.
  • Bucket Name: Bucket identifier.
  • Scope Name: Scope identifier.
  • Collection Name: Collection identifier in Couchbase.
  • Index Name: Index identifier.

FAISS​

The FAISS component manages document ingestion into a FAISS Vector Store, optimizing document indexing and retrieval.

Parameters:

  • Embedding: Model used for vectorizing inputs.
  • Input: Documents to ingest.
  • Folder Path: Save path for the FAISS index, relative to Langflow.

For more details, see the FAISS Component Documentation.

FAISS Search​

FAISSSearch searches a FAISS Vector Store for documents similar to a given input, using similarity metrics for efficient retrieval.

Parameters:

  • Embedding: Model used in the FAISS Vector Store.
  • Folder Path: Path to load the FAISS index from, relative to Langflow.
  • Input: Search query.
  • Index Name: Index identifier.

MongoDB Atlas​

MongoDBAtlas builds a MongoDB Atlas-based vector store from Data, streamlining the storage and retrieval of documents.

Parameters:

  • Embedding: Model used by MongoDB Atlas.
  • Input: Documents or Data.
  • Collection Name: Collection identifier in MongoDB Atlas.
  • Database Name: Database identifier.
  • Index Name: Index identifier.
  • MongoDB Atlas Cluster URI: Cluster URI.
  • Search Kwargs: Additional search parameters.

NOTE

Ensure pymongo is installed for using MongoDB Atlas Vector Store.

MongoDB Atlas Search​

MongoDBAtlasSearch leverages the MongoDBAtlas component to search for documents based on similarity metrics.

Parameters:

  • Search Type: Type of search, such as "Similarity" or "MMR".
  • Input: Search query.
  • Embedding: Model used in the Vector Store.
  • Collection Name: Collection identifier.
  • Database Name: Database identifier.
  • Index Name: Index identifier.
  • MongoDB Atlas Cluster URI: Cluster URI.
  • Search Kwargs: Additional search parameters.

PGVector​

PGVector integrates a Vector Store within a PostgreSQL database, allowing efficient storage and retrieval of vectors.

Parameters:

  • Input: Value for the Vector Store.
  • Embedding: Model used.
  • PostgreSQL Server Connection String: Server URL.
  • Table: Table name in the PostgreSQL database.

For more details, see the PGVector Component Documentation.

NOTE

Ensure the PostgreSQL server is accessible and configured correctly.

PGVector Search​

PGVectorSearch extends PGVector to search for documents based on similarity metrics.

Parameters:

  • Input: Search query.
  • Embedding: Model used.
  • PostgreSQL Server Connection String: Server URL.
  • Table: Table name.
  • Search Type: Type of search, such as "Similarity" or "MMR".

Pinecone​

Pinecone constructs a Pinecone wrapper from Data, setting up Pinecone-based vector indexes for document storage and retrieval.

Parameters:

  • Input: Documents or Data.
  • Embedding: Model used.
  • Index Name: Index identifier.
  • Namespace: Namespace used.
  • Pinecone API Key: API key.
  • Pinecone Environment: Environment settings.
  • Search Kwargs: Additional search parameters.
  • Pool Threads: Number of threads.
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Ensure the Pinecone API key and environment are correctly configured.

Pinecone Search​

PineconeSearch searches a Pinecone Vector Store for documents similar to the input, using advanced similarity metrics.

Parameters:

  • Search Type: Type of search, such as "Similarity" or "MMR".
  • Input Value: Search query.
  • Embedding: Model used.
  • Index Name: Index identifier.
  • Namespace: Namespace used.
  • Pinecone API Key: API key.
  • Pinecone Environment: Environment settings.
  • Search Kwargs: Additional search parameters.
  • Pool Threads: Number of threads.

Qdrant​

Qdrant allows efficient similarity searches and retrieval operations, using a list of texts to construct a Qdrant wrapper.

Parameters:

  • Input: Documents or Data.
  • Embedding: Model used.
  • API Key: Qdrant API key.
  • Collection Name: Collection identifier.
  • Advanced Settings: Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL.

Qdrant Search​

QdrantSearch extends Qdrant to search for documents similar to the input based on advanced similarity metrics.

Parameters:

  • Search Type: Type of search, such as "Similarity" or "MMR".
  • Input Value: Search query.
  • Embedding: Model used.
  • API Key: Qdrant API key.
  • Collection Name: Collection identifier.
  • Advanced Settings: Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL.

Redis​

Redis manages a Vector Store in a Redis database, supporting efficient vector storage and retrieval.

Parameters:

  • Index Name: Default index name.
  • Input: Data for building the Redis Vector Store.
  • Embedding: Model used.
  • Schema: Optional schema file (.yaml) for document structure.
  • Redis Server Connection String: Server URL.
  • Redis Index: Optional index name.

For detailed documentation, refer to the Redis Documentation.

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Ensure the Redis server URL and index name are configured correctly. Provide a schema if no documents are available.

Redis Search​

RedisSearch searches a Redis Vector Store for documents similar to the input.

Parameters:

  • Search Type: Type of search, such as "Similarity" or "MMR".
  • Input Value: Search query.
  • Index Name: Default index name.
  • Embedding: Model used.
  • Schema: Optional schema file (.yaml) for document structure.
  • Redis Server Connection String: Server URL.
  • Redis Index: Optional index name.

Supabase​

Supabase initializes a Supabase Vector Store from texts and embeddings, setting up an environment for efficient document retrieval.

Parameters:

  • Input: Documents or data.
  • Embedding: Model used.
  • Query Name: Optional query name.
  • Search Kwargs: Advanced search parameters.
  • Supabase Service Key: Service key.
  • Supabase URL: Instance URL.
  • Table Name: Optional table name.
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Ensure the Supabase service key, URL, and table name are properly configured.

Supabase Search​

SupabaseSearch searches a Supabase Vector Store for documents similar to the input.

Parameters:

  • Search Type: Type of search, such as "Similarity" or "MMR".
  • Input Value: Search query.
  • Embedding: Model used.
  • Query Name: Optional query name.
  • Search Kwargs: Advanced search parameters.
  • Supabase Service Key: Service key.
  • Supabase URL: Instance URL.
  • Table Name: Optional table name.

Upstash Vector​

UpstashVector searches a Upstash Vector Store for documents similar to the input. It has it's own embedding model which can be used to search documents without needing an external embedding model.

Parameters:

  • Index URL: The URL of the Upstash index.
  • Index Token: The token for the Upstash index.
  • Text Key: The key in the record to use as text.
  • Namespace: The namespace name. A new namespace is created if not found. Leave empty for default namespace.
  • Search Query: The search query.
  • Metadata Filter: The metadata filter. Filters documents by metadata. Look at the docs for more information.
  • Embedding: The embedding model used. To use Upstash's embeddings, don't provide an embedding.
  • Number of Results: The number of results to return.

Vectara​

Vectara sets up a Vectara Vector Store from files or upserted data, optimizing document retrieval.

Parameters:

  • Vectara Customer ID: Customer ID.
  • Vectara Corpus ID: Corpus ID.
  • Vectara API Key: API key.
  • Files Url: Optional URLs for file initialization.
  • Input: Optional data for corpus upsert.

For more information, consult the Vectara Component Documentation.

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If inputs or files_url are provided, they will be processed accordingly.

Vectara Search​

VectaraSearch searches a Vectara Vector Store for documents based on the provided input.

Parameters:

  • Search Type: Type of search, such as "Similarity" or "MMR".
  • Input Value: Search query.
  • Vectara Customer ID: Customer ID.
  • Vectara Corpus ID: Corpus ID.
  • Vectara API Key: API key.
  • Files Url: Optional URLs for file initialization.

Weaviate​

Weaviate facilitates a Weaviate Vector Store setup, optimizing text and document indexing and retrieval.

Parameters:

  • Weaviate URL: Default instance URL.
  • Search By Text: Indicates whether to search by text.
  • API Key: Optional API key for authentication.
  • Index Name: Optional index name.
  • Text Key: Default text extraction key.
  • Input: Document or record.
  • Embedding: Model used.
  • Attributes: Optional additional attributes.

For more details, see the Weaviate Component Documentation.

NOTE

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

Weaviate Search​

WeaviateSearch searches a Weaviate Vector Store for documents similar to the input.

Parameters:

  • Search Type: Type of search, such as "Similarity" or "MMR".
  • Input Value: Search query.
  • Weaviate URL: Default instance URL.
  • Search By Text: Indicates whether to search by text.
  • API Key: Optional API key for authentication.
  • Index Name: Optional index name.
  • Text Key: Default text extraction key.
  • Embedding: Model used.
  • Attributes: Optional additional attributes.

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