MongoDB - Langflow — ContextQMD

import Icon from "@site/src/components/icon"; import PartialParams from '@site/docs/_partial-hidden-params.mdx'; import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx'; import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx'; import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';

<Icon name="Blocks" aria-hidden="true" /> Bundles contain custom components that support specific third-party integrations with Langflow.

This page describes the components that are available in the MongoDB bundle.

MongoDB Atlas

The MongoDB Atlas component reads and writes to MongoDB Atlas vector stores using an instance of MongoDBAtlasVectorSearch.

<details> <summary>About vector store instances</summary> <PartialVectorStoreInstance /> </details> <PartialVectorSearchResults />

MongoDB Atlas parameters

You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.

For information about accepted values and functionality, see the MongoDB Atlas documentation or inspect component code.

Name	Type	Description
mongodb_atlas_cluster_uri	SecretString	Input parameter. The connection URI for your MongoDB Atlas cluster. Required.
enable_mtls	Boolean	Input parameter. Enable mutual TLS authentication. Default: `false`.
mongodb_atlas_client_cert	SecretString	Input parameter. Client certificate combined with private key for mTLS authentication. Required if mTLS is enabled.
db_name	String	Input parameter. The name of the database to use. Required.
collection_name	String	Input parameter. The name of the collection to use. Required.
index_name	String	Input parameter. The name of the Atlas Search index, it should be a Vector Search. Required.
insert_mode	String	Input parameter. How to insert new documents into the collection. The options are "append" or "overwrite". Default: "append".
embedding	Embeddings	Input parameter. The embedding model to use.
number_of_results	Integer	Input parameter. Number of results to return in similarity search. Default: 4.
index_field	String	Input parameter. The field to index. Default: "embedding".
filter_field	String	Input parameter. The field to filter the index.
number_dimensions	Integer	Input parameter. Embedding vector dimension count. Default: 1536.
similarity	String	Input parameter. The method used to measure similarity between vectors. The options are "cosine", "euclidean", or "dotProduct". Default: "cosine".
quantization	String	Input parameter. Quantization reduces memory costs by converting 32-bit floats to smaller data types. The options are "scalar" or "binary".