---
title: MongoDB
slug: /bundles-mongodb
---
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';
[**Bundles**](/components-bundle-components) 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`](https://docs.langchain.com/oss/python/integrations/vectorstores/mongodb_atlas).
About vector store instances
### 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](https://www.mongodb.com/docs/atlas/atlas-vector-search/tutorials/vector-search-quick-start/) or inspect [component code](/concepts-components#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". |