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|---|---|---|---|---|---|---|---|
6a869dda8824e6129431dfaf-0 | 6a869dda8824e6129431dfaf | self-managed-search | null | https://mongodb.com/docs/search/self-managed/current/limitations | Known Limitations for Self-managed mongot | # Known Limitations for Self-managed mongot
This page describes limitations and unsupported behavior for self-managed `mongot` deployments.
## Limitations by Category
The following sections describe known limitations for self-managed `mongot` deployments, grouped by category.
### Deployment, Topology, and Platform ... | [
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6a869dda8824e6129431dfaf-1 | 6a869dda8824e6129431dfaf | self-managed-search | null | https://mongodb.com/docs/search/self-managed/current/limitations | Known Limitations for Self-managed mongot | ### Operations and Upgrades
<table>
<tr>
<th id="Limitation">
Limitation
</th>
<th id="Applies%20to">
Applies to
</th>
<th id="Description">
Description
</th>
</tr>
<tr>
<td headers="Limitation">
One mongot Instance Can't Serve Multiple mongod Deployments
</td>
<td headers="Applies%20to">
All deployment paths.
</... | [
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6a869dda8824e6129431dfaf-2 | 6a869dda8824e6129431dfaf | self-managed-search | null | https://mongodb.com/docs/search/self-managed/current/limitations | Known Limitations for Self-managed mongot | ### Deployment and Lifecycle Management
The following table compares how Atlas and self-managed deployments handle operational aspects of deploying and managing the lifecycle of `mongot`.
<table>
<tr>
<th id="Operational%20Aspect">
Operational Aspect
</th>
<th id="Atlas">
Atlas
</th>
<th id="Self-managed%20mongot">... | [
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6a869dda8824e6129431dfb8-0 | 6a869dda8824e6129431dfb8 | self-managed-search | null | https://mongodb.com/docs/search/self-managed/current/resource-planning-sizing/introduction | Introduction to mongot Deployment Sizing | # Introduction to mongot Deployment Sizing
This guide helps you choose the right starting configuration for your `mongot` deployment. Follow these steps to determine an effective starting configuration and refine it for your specific needs.
## Get Started
### Identify your primary workload class
First, determine wh... | [
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6a869dda8824e6129431dfbc-0 | 6a869dda8824e6129431dfbc | self-managed-search | null | https://mongodb.com/docs/search/self-managed/current/security/authentication-and-authorization | Configure Authentication and Authorization for `mongot` | # Configure Authentication and Authorization for `mongot`
Use this page to configure authentication and authorization for your `mongot` and MongoDB Community Edition deployments. For information on using `mongot` with MongoDB Controllers for Kubernetes Operator, see MongoDB Search and Vector Search with Kubernetes.
`... | [
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6a869dda8824e6129431dfbc-1 | 6a869dda8824e6129431dfbc | self-managed-search | null | https://mongodb.com/docs/search/self-managed/current/security/authentication-and-authorization | Configure Authentication and Authorization for `mongot` | ### Add the `x509` block to your `mongot` configuration.
Add the `x509` block under `syncSource.replicaSet`. For sharded clusters, add the same block under `syncSource.router`.
In the `x509` block, `tlsCertificateKeyFile` and `caFile` are required. List multiple hosts in `hostAndPort` so `mongot` can still discover t... | [
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-... |
68a95c2c5e79d242619df768-0 | 68a95c2c5e79d242619df768 | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/release-notes | Release Notes | # Release Notes
Learn what's new in:
- Version 11.1
- Version 11.0
- Version 10.7
- Version 10.6
- Version 10.5
- Version 10.4
- Version 10.3
- Version 10.2
- Version 10.1.1
- Version 10.1.0
- Version 10.0
## MongoDB Connector for Spark 11.1
The 11.1 connector release includes the following changes and ne... | [
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68a95c2c5e79d242619df76b-0 | 68a95c2c5e79d242619df76b | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/batch-mode/batch-write-config | Batch Write Configuration Options | # Batch Write Configuration Options
## Overview
You can configure the following properties when writing data to MongoDB in batch mode.
If you use `SparkConf` to set the connector's write configurations, prefix `spark.mongodb.write.` to each property.
<table>
<tr>
<th id="Property%20name">
Property name
</th>
<th i... | [
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-... |
68a95c2c5e79d242619df7df-0 | 68a95c2c5e79d242619df7df | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/streaming-mode/streaming-read-config | Streaming Read Configuration Options | # Streaming Read Configuration Options
## Overview
You can configure the following properties when reading data from MongoDB in streaming mode.
If you use `SparkConf` to set the connector's read configurations, prefix `spark.mongodb.read.` to each property.
<table>
<tr>
<th id="Property%20name">
Property name
</th... | [
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0.004944434855133295,
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-0.0... |
68a95c2c5e79d242619df7df-1 | 68a95c2c5e79d242619df7df | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/streaming-mode/streaming-read-config | Streaming Read Configuration Options | ## Change Stream Configuration
You can configure the following properties when reading a change stream from MongoDB:
<table>
<tr>
<th id="Property%20name">
Property name
</th>
<th id="Description">
Description
</th>
</tr>
<tr>
<td headers="Property%20name">
`change.stream.lookup.full.document`
</td>
<td headers="D... | [
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0.026498645544052124,
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0.021508675068616867,
-0.03355952352285385,
-0... |
68a95c2c5e79d242619df787-0 | 68a95c2c5e79d242619df787 | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current | MongoDB Connector for Spark | # MongoDB Connector for Spark
The MongoDB Connector for Spark provides integration between MongoDB and Apache Spark.
With the connector, you have access to all Spark libraries for use with MongoDB datasets: `Dataset` for analysis with SQL (benefiting from automatic schema inference), streaming, machine learning, and ... | [
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0.046960558742284775,
0.0030950093641877174,
-0.05135030299425125,
-0.011566830798983574,
0.01871747523546219,
-0.024742290377616882,
-0.... |
68a95c2c5e79d242619df7ec-0 | 68a95c2c5e79d242619df7ec | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/streaming-mode/streaming-write-config | Streaming Write Configuration Options | # Streaming Write Configuration Options
## Overview
You can configure the following properties when writing data to MongoDB in streaming mode.
If you use `SparkConf` to set the connector's write configurations, prefix `spark.mongodb.write.` to each property.
<table>
<tr>
<th id="Property%20name">
Property name
</t... | [
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0.026393162086606026,
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0.037313468754291534,
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0.05238109081983566,
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0.002552029909566045,
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... |
68a95c2c5e79d242619df790-0 | 68a95c2c5e79d242619df790 | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/batch-mode/batch-read-config | Batch Read Configuration Options | # Batch Read Configuration Options
## Overview
You can configure the following properties when reading data from MongoDB in batch mode.
If you use `SparkConf` to set the connector's read configurations, prefix `spark.mongodb.read.` to each property.
<table>
<tr>
<th id="Property%20name">
Property name
</th>
<th id... | [
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0.020622618496418,
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0.018232150003314018,
0.001394702703692019,
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0.03347310796380043,
-0.04310670122504234,
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-0.0015558131271973252,
0.00543646514415741,
-0.010503411293029785,
-0.0... |
68a95c2c5e79d242619df790-1 | 68a95c2c5e79d242619df790 | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/batch-mode/batch-read-config | Batch Read Configuration Options | ### SamplePartitioner Configuration
The `SamplePartitioner` configuration is similar to the AutoBucketPartitioner configuration, but does not use the `$bucketAuto` aggregation stage. This configuration lets you specify a partition field, partition size, and number of samples per partition.
To use this configuration, ... | [
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0.030477801337838173,
0.005645096302032471,
-... |
68a95c2c5e79d242619df775-0 | 68a95c2c5e79d242619df775 | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/streaming-mode | Streaming Mode | # Streaming Mode
## Overview
The Spark Connector supports streaming mode, which uses Spark Structured Streaming to process data as soon as it's available instead of waiting for a time interval to pass. Spark Structured Streaming is a data-stream-processing engine that you can access by using the Dataset or DataFrame ... | [
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0.0842113345861435,
0.027407048270106316,
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-0.013268762268126011,
0.021519144997000694,
-0.04580008611083031,
-0.... |
68a95c2c5e79d242619df7cf-0 | 68a95c2c5e79d242619df7cf | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/batch-mode | Batch Mode | # Batch Mode
## Overview
In batch mode, you can use the Spark Dataset and DataFrame APIs to process data at a specified time interval.
The following sections show you how to use the Spark Connector to read data from MongoDB and write data to MongoDB in batch mode:
- Read from MongoDB in Batch Mode
- Write to Mongo... | [
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... |
68a95c2c5e79d242619df761-0 | 68a95c2c5e79d242619df761 | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/streaming-mode/streaming-read | Read from MongoDB in Streaming Mode | # Read from MongoDB in Streaming Mode
## Overview
When reading a stream from a MongoDB database, the MongoDB Spark Connector supports both *micro-batch processing* and *continuous processing*. Micro-batch processing, the default processing engine, achieves end-to-end latencies as low as 100 milliseconds with exactly-... | [
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-0.0... |
68a95c2c5e79d242619df761-1 | 68a95c2c5e79d242619df761 | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/streaming-mode/streaming-read | Read from MongoDB in Streaming Mode | ## Example
The following example shows how to stream data from MongoDB to your console.
<Tabs>
<Tab name="Java (Sync)">
1. Create a `DataStreamReader` object that reads from MongoDB.
2. Create a `DataStreamWriter` object by calling the `writeStream()` method on the streaming `Dataset` object that you created with ... | [
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-0.0... |
68a95c2c5e79d242619df76f-0 | 68a95c2c5e79d242619df76f | spark-connector | MongoDB Spark Connector | https://mongodb.com/docs/spark-connector/current/configuration | Configuring Spark | # Configuring Spark
## Overview
You can configure read and write operations in both batch and streaming mode. To learn more about the available configuration options, see the following pages:
- Batch Read Configuration Options
- Batch Write Configuration Options
- Streaming Read Configuration Options
- Streaming ... | [
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6997fd716fd39a1e7ec5abd2-0 | 6997fd716fd39a1e7ec5abd2 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/tutorials/connect-tutorial | Connect to and Query Data with Free SQL Tools | # Connect to and Query Data with Free SQL Tools
This tutorial guides you through connecting to an Atlas dedicated cluster or a federated database instance with the free tools MongoDB JDBC (Java Database Connectivity) Driver and DBeaver (Community Edition).
If you loaded sample data into your deployment, this tutorial... | [
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6997fd716fd39a1e7ec5abd2-1 | 6997fd716fd39a1e7ec5abd2 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/tutorials/connect-tutorial | Connect to and Query Data with Free SQL Tools | ### Create a database connection.
- In DBeaver, click Database and select New Database Connection from the dropdown menu to open the Connect to a database modal.
- From the list of databases, select the `MongoDB` database driver that you created in the previous step.
If you don't see `MongoDB`, select the All cate... | [
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6a630539d3c0cfaf89e39ced-0 | 6a630539d3c0cfaf89e39ced | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/schema/adf | Atlas Data Federation Schema Commands | # Atlas Data Federation Schema Commands
In Atlas Data Federation, you manage the schema that the SQL Interface uses through three commands: `sqlGenerateSchema`, `sqlGetSchema`, and `sqlSetSchema`. You run these commands from `mongosh` against a federated database instance, or you perform the equivalent actions in the ... | [
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6a630539d3c0cfaf89e39ced-1 | 6a630539d3c0cfaf89e39ced | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/schema/adf | Atlas Data Federation Schema Commands | ### Output
The command returns the following output if the collection or view does not have a schema:
```json
{ "ok" : 1, "metadata" : { }, "schema" : { } }
```
The command returns output similar to the following if the collection or view has a schema:
```json
{
"ok": 1,
"metadata": {
"description": "<descr... | [
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6a630539d3c0cfaf89e39ced-2 | 6a630539d3c0cfaf89e39ced | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/schema/adf | Atlas Data Federation Schema Commands | ### Set a Schema
The following `sqlSetSchema` command sets the schema for the `egData` collection:
```sh
db.getSiblingDB("sampleDB").runCommand({
sqlSetSchema : "egData",
"schema" : {
"version" : NumberLong(1),
"jsonSchema" : {
"bsonType" : [ "object" ],
"properties" : {
... | [
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-0... |
6a630539d3c0cfaf89e39ced-3 | 6a630539d3c0cfaf89e39ced | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/schema/adf | Atlas Data Federation Schema Commands | ## Learn More
- Schema Management
- MongoDB SQL Interface Overview | [
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6997fd716fd39a1e7ec5abd7-0 | 6997fd716fd39a1e7ec5abd7 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/connect-with-sql-composable | SQL Interface Server Setup | # SQL Interface Server Setup
You can use the MongoDB SQL Interface with an Atlas dedicated cluster, an Atlas federated database instance, or a self-managed Enterprise Advanced (EA) database. Choose your deployment type to get started:
## Prerequisites
- A MongoDB database user with which to connect.
## Procedure
Y... | [
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6997fd716fd39a1e7ec5abd7-1 | 6997fd716fd39a1e7ec5abd7 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/connect-with-sql-composable | SQL Interface Server Setup | ### Generate a Schema
Dedicated clusters manage schemas with the MongoDB SQL Schema Builder CLI only, the same way self-managed Enterprise Advanced (EA) deployments do. After you enable the SQL Interface, download and run the CLI to generate a schema for the collections that you want to query. To learn more, see Mongo... | [
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6a630539d3c0cfaf89e39cee-0 | 6a630539d3c0cfaf89e39cee | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/schema/schema-builder-cli | MongoDB SQL Schema Builder CLI | # MongoDB SQL Schema Builder CLI
The MongoDB SQL Schema Builder CLI is the schema-management tool for the SQL Interface on Enterprise Advanced (EA) self-managed deployments and on Atlas dedicated clusters (`M10` and above). You download and run the CLI against your cluster to generate or update the schema for your col... | [
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6a630539d3c0cfaf89e39cee-1 | 6a630539d3c0cfaf89e39cee | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/schema/schema-builder-cli | MongoDB SQL Schema Builder CLI | ## CLI Flag Reference
The MongoDB SQL Schema Builder CLI accepts the following flags. You must provide either `--uri` or `--file` so that the CLI can connect to your cluster.
The path to a configuration file. Command-line arguments take precedence over values in the configuration file.
The connection string for your... | [
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6997fd716fd39a1e7ec5abd8-0 | 6997fd716fd39a1e7ec5abd8 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/connect | Connect Your SQL Tool to MongoDB | # Connect Your SQL Tool to MongoDB
Connect your SQL tool, such as Tableau, Power BI, or DBeaver to your MongoDB Atlas or Enterprise Advanced (EA) database.
All three SQL Interface deployment options use the same JDBC (Java Database Connectivity) and ODBC (Open Database Connectivity) drivers, so the steps on this page... | [
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6997fd716fd39a1e7ec5abd8-1 | 6997fd716fd39a1e7ec5abd8 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/connect | Connect Your SQL Tool to MongoDB | ### Configure Tableau Logging
To change the logging configuration for the MongoDB JDBC (Java Database Connectivity) driver, add logging properties to the `mongodb_jdbc.properties` file. The location of this file depends on your platform:
<table>
<tr>
<th id="Platform">
Platform
</th>
<th id="Path">
Path
</th>
</tr>... | [
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... |
6997fd716fd39a1e7ec5abd8-2 | 6997fd716fd39a1e7ec5abd8 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/connect | Connect Your SQL Tool to MongoDB | ### Procedure
To connect from DBeaver:
#### Get your MongoDB database connection information.
Select the tab for your deployment type for instructions on how to get your connection information:
<Tabs>
<Tab name="Atlas">
- Go to your Atlas dedicated cluster or federated database instance for your project.
- Click... | [
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6997fd716fd39a1e7ec5abdf-0 | 6997fd716fd39a1e7ec5abdf | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/changelog | SQL Interface Changelog | # SQL Interface Changelog
MongoDB releases SQL Interface drivers and connectors in a continuous deployment manner. This release cadence allows us to continuously improve SQL Interface performance and stability. These release notes capture only those releases that contain direct user feature changes. If a particular SQ... | [
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6997fd716fd39a1e7ec5abdf-1 | 6997fd716fd39a1e7ec5abdf | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/changelog | SQL Interface Changelog | - Updates the ODBC (Open Database Connectivity) driver to version 2.0.7.
- Improves compatibility with applications such as SAS that look up the data source name during connection setup.
- Fixes an error that could occur when your Business Intelligence tool retrieves the list of databases from your MongoDB data s... | [
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-0.0... |
6997fd716fd39a1e7ec5abdf-2 | 6997fd716fd39a1e7ec5abdf | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/changelog | SQL Interface Changelog | - Updates the JDBC (Java Database Connectivity) driver to 2.3.0.
- Introduces a new property `disableclientcache` to disable client caching in the JDBC driver. You must use this property with care and it is intended for server environments where the JDBC (Java Database Connectivity) driver is used to connect to a vari... | [
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6997fd716fd39a1e7ec5abce-0 | 6997fd716fd39a1e7ec5abce | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/schema-management | Schema Management | # Schema Management
The SQL Interface schemas are JSON schemas that describe data as it exists in MongoDB, including its polymorphism, sparseness, and nested, structured data. Schemas are necessary when connecting SQL-based tools to MongoDB data sources. The SQL Interface also requires schema information to translate ... | [
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6997fd716fd39a1e7ec5abac-0 | 6997fd716fd39a1e7ec5abac | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/dbeaver/connect | Connect from DBeaver | # Connect from DBeaver
This page describes how to connect to your federated database instance with DBeaver.
## Prerequisites
- A federated database instance mapped to one or more data stores.
If some or all of your data comes from an Atlas cluster, you must use MongoDB version 5.0 or greater for that cluster to t... | [
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6997fd716fd39a1e7ec5abc4-0 | 6997fd716fd39a1e7ec5abc4 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface | MongoDB SQL Interface Overview | # MongoDB SQL Interface Overview
SQL InterfaceThe SQL Interface for Atlas and Enterprise Advanced (EA) lets non-developers query MongoDB data from the SQL tools they already use. It provides live, read-only access over standard ODBC (Open Database Connectivity) and JDBC (Java Database Connectivity) connections, elimin... | [
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6997fd716fd39a1e7ec5abc4-1 | 6997fd716fd39a1e7ec5abc4 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface | MongoDB SQL Interface Overview | ## MongoDB SQL Interface Compatibility Considerations
When using the SQL Interface, consider the following points to ensure your setup is compatible and up-to-date:
- There is no official EOL (End of Life) for SQL Interface components, including drivers, connectors, and the MongoDB SQL Schema Builder. Instead, their ... | [
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6997fd716fd39a1e7ec5abd3-0 | 6997fd716fd39a1e7ec5abd3 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/query-with-asql-statements | Query with MongoSQL Statements | # Query with MongoSQL Statements
The page gives example MongoSQL queries. Basic examples use SQL syntax to query collections, and more advanced ones use `FLATTEN` and `UNWIND` to work with nested data.
MongoDB doesn't provide a SQL IDE. Run these queries from your preferred SQL tool, such as DBeaver, Tableau, or Powe... | [
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6997fd716fd39a1e7ec5abd3-1 | 6997fd716fd39a1e7ec5abd3 | sql-interface | MongoDB SQL Interface | https://mongodb.com/docs/sql-interface/query-with-asql-statements | Query with MongoSQL Statements | ### Unwind Example: Nested and Sibling Arrays
In an example scenario, a `customerInfo` collection contains documents that are structured as follows:
```json
{
"id": 1,
"location": "New York",
"customer": {
"age": 50,
"email": "customer@email.com",
"satisfaction": 5
},
"visits": [
{
"ye... | [
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... |
69eb0c75d0423fff078dfb02-0 | 69eb0c75d0423fff078dfb02 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/query/vector-search-playground | Chatbot Demo Builder in Search Playground | # Chatbot Demo Builder in Search Playground
Quickly try MongoDB Vector Search in the MongoDB Search Playground by using a RAG chatbot that answers questions on your data with vector search. The Chatbot Demo Builder in the MongoDB Search Playground allows you to bring your own data, try different chunking strategies, g... | [
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69fc3180a8c6c8a96605288d-0 | 69fc3180a8c6c8a96605288d | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/models | Models for Automated Embedding | # Models for Automated Embedding
Automated Embedding uses Voyage AI's embedding models, which Atlas hosts and manages in the Data Plane in a multi-tenant environment.
## Supported Models
Automated Embedding supports the following Voyage AI embedding models:
<table>
<tr>
<th id="Embedding%20Model">
Embedding Model
... | [
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... |
69fc3180a8c6c8a96605288d-1 | 69fc3180a8c6c8a96605288d | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/models | Models for Automated Embedding | ### Index Insert and Update Rate Limits
The index rate limits restrict the maximum frequency and number of tokens at which embeddings are generated during certain operations on MongoDB Vector Search Automated Embedding indexes. These operations include inserts (new data is added to your index) or updates (existing dat... | [
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69eb0c75d0423fff078dfade-0 | 69eb0c75d0423fff078dfade | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/deployment/deployment-options | Review Deployment Options | # Review Deployment Options
The following sections describe deployment options for MongoDB Vector Search on Atlas.
You can structure your cluster with different deployment types, cloud providers, and cluster tiers to meet the needs of a pre-production or production environment. Use these recommendations to select the... | [
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69eb0c75d0423fff078dfade-1 | 69eb0c75d0423fff078dfade | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/deployment/deployment-options | Review Deployment Options | ### Deployment Type
For testing MongoDB Vector Search queries, you can deploy a Flex cluster, a dedicated cluster, or use a local Atlas deployment.
<Tabs>
<Tab name="Atlas Cluster">
#### Cluster Tiers
Free clusters are a no-cost cluster type. Flex clusters are low-cost cluster types suitable for teams that are lea... | [
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69eb0c75d0423fff078dfade-2 | 69eb0c75d0423fff078dfade | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/deployment/deployment-options | Review Deployment Options | ### Node Architecture
MongoDB Vector SearchHow to Index Fields for Vector SearchRun Vector Search ANN and ENN QueriesFor production environments, we recommend a node architecture in which MongoDB processes and MongoDB Search processes run on separate nodes. To deploy separate Search Nodes, see Migrate to Dedicated Sea... | [
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69eb0c75d0423fff078dfade-3 | 69eb0c75d0423fff078dfade | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/deployment/deployment-options | Review Deployment Options | ### Migrate to Dedicated Search Nodes
Dedicated Search Nodes allow you to both size and scale your search deployment separately from your cluster. It also eliminates any resource contention that you might experience on a cluster that runs both the database and search processes on the same node.
To migrate to dedicate... | [
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6a514deed727379c91ba2ea9-0 | 6a514deed727379c91ba2ea9 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/query/native-reranking/managing | Manage Settings for $rerank Stage | # Manage Settings for $rerank Stage
You can perform the following tasks in the Atlas UI:
- Enable or disable Native Reranking for all projects in your organization or per project.
- Monitor Token Usage.
- View rate limits.
- View metrics.
## Permissions Required
To enable or disable Native Reranking, you must ha... | [
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6a514deed727379c91ba2ea9-1 | 6a514deed727379c91ba2ea9 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/query/native-reranking/managing | Manage Settings for $rerank Stage | ## Monitor Metrics for Native Reranking
You can monitor `$rerank` usage through the following metrics in the Atlas UI:
<table>
<tr>
<th id="Metric">
Metric
</th>
<th id="Description">
Description
</th>
</tr>
<tr>
<td headers="Metric">
$rerank Query Status Rate
</td>
<td headers="Description">
The percentage of `$r... | [
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69fc3180a8c6c8a96605288c-0 | 69fc3180a8c6c8a96605288c | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/crud-embeddings/automated-embedding | Automated Embedding Overview | # Automated Embedding Overview
<Tabs>
<Tab name="Atlas">
You can configure MongoDB Vector Search to automatically generate and manage vector embeddings for the text data in your Atlas cluster. When you enable Automated Embedding, MongoDB Vector Search automatically generates embeddings using the specified Voyage AI ... | [
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-... |
69fc3180a8c6c8a96605288c-1 | 69fc3180a8c6c8a96605288c | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/crud-embeddings/automated-embedding | Automated Embedding Overview | ## Embeddings Storage
Automated embedding indexes generate vector embeddings asynchronously and persist to your MongoDB cluster on a separate reserve database. Each Automated Embedding index has exactly one corresponding generated embeddings collection. The generated embeddings collection is stored in a dedicated inte... | [
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-0... |
69fc3180a8c6c8a966052890-0 | 69fc3180a8c6c8a966052890 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/billing | Manage Billing for Automated Embedding | # Manage Billing for Automated Embedding
## Overview
You can manage payments and billing for Automated Embedding through the billing and payments interface in the Atlas UI. Atlas sends billing charges to the Atlas account linked to your cluster. Your billing profile for Atlas is the same as the billing profile for yo... | [
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... |
69fc3180a8c6c8a966052890-1 | 69fc3180a8c6c8a966052890 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/billing | Manage Billing for Automated Embedding | ### View Invoices
To view the most recent and pending charges for Automated Embedding in an organization:
#### Log in to Atlas.
#### Go to the Billing page for your organization.
- If it's not already displayed, select your desired organization from the Organizations menu in the navigation bar.
- In the sidebar, ... | [
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69eb0c75d0423fff078dfadb-0 | 69eb0c75d0423fff078dfadb | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/benchmark/performance-recommendations | Additional Performance Recommendations | # Additional Performance Recommendations
This page provides additional recommendations for improving the performance of your MongoDB Vector Search queries.
## Ensure Sufficient Memory
Hierarchical Navigable Small Worlds works efficiently when vector data is held in memory. You must ensure that the data nodes have su... | [
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6a514deed727379c91ba2e95-0 | 6a514deed727379c91ba2e95 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/benchmark/benchmark-tests | Benchmark for MongoDB Vector Search | # Benchmark for MongoDB Vector Search
This section contains the following pages, which provide information about our MongoDB Vector Search performance benchmark and how you can use it to test, evaluate, and improve your own vector search performance:
- Benchmark Overview
- Benchmark Results
- Additional Recommendat... | [
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0.00... |
69eb0c75d0423fff078dfadc-0 | 69eb0c75d0423fff078dfadc | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/deployment/compatibility-limitations | MongoDB Vector Search Compatibility & Limitations | # MongoDB Vector Search Compatibility & Limitations
This page describes the compatibility and limitations of MongoDB Vector Search features on Atlas. For details on MongoDB Vector Search compatibility with self-managed deployments, see the Deployment Options documentation.
## Feature Compatibility
### MongoDB Versio... | [
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-0.03... |
69eb0c75d0423fff078dfae4-0 | 69eb0c75d0423fff078dfae4 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/about/ai-agents | Build AI Agents with MongoDB | # Build AI Agents with MongoDB
MongoDB provides several features for building AI agents. As both a vector and document database, MongoDB supports various search methods for agentic RAG, as well as storing agent interactions in the same database for short and long-term agent memory.
## What is an AI agent?
In the con... | [
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69eb0c75d0423fff078dfae4-1 | 69eb0c75d0423fff078dfae4 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/about/ai-agents | Build AI Agents with MongoDB | ### Agent Memory
Memory for agents involves storing information about previous interactions, so that the agent can learn from past experiences and provide more relevant and personalized responses. This is particularly important for tasks that require context, such as conversational agents, where the agent needs to rem... | [
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69eb0c75d0423fff078dfae4-2 | 69eb0c75d0423fff078dfae4 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/about/ai-agents | Build AI Agents with MongoDB | ### Procedure
This AI agent can be used to answer questions about a custom data source and perform calculations. It can also remember previous interactions to inform its responses. It uses the following components:
- **Perception**: Text inputs.
- **Planning**: An LLM and various prompts to reason through the task.
... | [
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-0.... |
69eb0c75d0423fff078dfae4-3 | 69eb0c75d0423fff078dfae4 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/about/ai-agents | Build AI Agents with MongoDB | - `retrieve_session_history`: to get all interactions for a specific session by using the `session_id` field.
```python
from config import memory_collection
from datetime import datetime
from typing import List
def store_chat_message(session_id: str, role: str, content: str) -> None:
message = {
"session_... | [
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-0.03935619071125984,
-0.064... |
69eb0c75d0423fff078dfae4-4 | 69eb0c75d0423fff078dfae4 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/about/ai-agents | Build AI Agents with MongoDB | - Ask questions. The agent generates a response based on your tools, the previous interactions, and the prompts defined in the planning phase.
Refer to the example output for a sample interaction:
```none
python main.py
```
```none
Ingest sample data? (y/n): y
Successfully split PDF into 104 chunks.
Generating embed... | [
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0.019450247287750244,
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0.021183185279369354,
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0.016528485342860222,
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-0.05... |
69eb0c75d0423fff078dfae4-5 | 69eb0c75d0423fff078dfae4 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/about/ai-agents | Build AI Agents with MongoDB | // run the helper method to ensure the index is created
const result = await vectorCollection.createSearchIndex(index);
console.log(`New index named ${result} is building.`);
// wait for the index to be ready to query
console.log(
'Polling to check if the index is ready. This may take up to a... | [
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-0.0... |
69eb0c75d0423fff078dfae4-6 | 69eb0c75d0423fff078dfae4 | vector-search | Atlas Vector Search | https://mongodb.com/docs/vector-search/about/ai-agents | Build AI Agents with MongoDB | let response;
if (tool === 'vector_search_tool') {
const contextResults = await vectorSearchTool(toolInput);
const context = contextResults
.map((doc) => doc.document?.pageContent || JSON.stringify(doc))
.join('\n---\n');
const systemMessageContent = `
Answer the user's question based on the... | [
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-0.0... |
68a95c2d5e79d242619df8bf-0 | 68a95c2d5e79d242619df8bf | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current/install | Install the MongoDB C# Analyzer | # Install the MongoDB C# Analyzer
## Overview
Learn how to install the C# Analyzer in your .NET application.
## Installation
To install the C# Analyzer, run the following command from the root directory of your .NET project:
```shell
dotnet add package MongoDB.Analyzer -v 2.0.0
```
To view the NuGet page for the ... | [
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... |
68a95c2d5e79d242619df88e-0 | 68a95c2d5e79d242619df88e | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current/code-type/linq | Analyze LINQ Expressions | # Analyze LINQ Expressions
## Overview
**LINQ** is a query syntax included in the C# language. The .NET/C# driver can translate a subset of LINQ expressions into MongoDB aggregation pipelines.
To learn more about LINQ, see the following resources:
- LINQ in the .NET/C# driver documentation
- LINQ in the Microsoft ... | [
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... |
68a95c2d5e79d242619df8ae-0 | 68a95c2d5e79d242619df8ae | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current/rules | Rules and Message Content | # Rules and Message Content
## Overview
In this guide, you can learn about the MongoDB C# Analyzer rules and attached messages. A rule evaluates different parts of your code and returns a message if a specific condition is met.
For example, the Analyzer might apply a rule if it detects a LINQ expression, which cause... | [
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0.... |
68a95c2d5e79d242619df8b3-0 | 68a95c2d5e79d242619df8b3 | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current/whats-new | What's New | # What's New
Learn what's new in:
- Version 2.0
- Version 1.5
- Version 1.4
## What's New in 2.0
The 2.0 MongoDB C# Analyzer release includes the following features, improvements, and fixes:
- Supports only .NET/C# driver v3.0 and higher.
- Drops support for the `MALinq2002` rule because .NET/C# driver v3.0 rem... | [
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... |
68a95c2d5e79d242619df8a0-0 | 68a95c2d5e79d242619df8a0 | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current/configuration | Configure the MongoDB C# Analyzer | # Configure the MongoDB C# Analyzer
## Overview
Learn how to configure the C# Analyzer in your .NET project and what configuration options are available.
## Configuration
To configure the C# Analyzer, perform the following actions:
1. Install the C# Analyzer as described in the installation guide.
2. Create a fil... | [
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... |
68a95c2d5e79d242619df8d0-0 | 68a95c2d5e79d242619df8d0 | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current/code-type/builders | Analyze Builders Expressions | # Analyze Builders Expressions
## Overview
A **builder** is a class provided by the .NET/C# driver to help you construct common operations like queries and updates.
To learn more about builders, see Operations with Builders in the .NET/C# driver documentation.
## Translate into the MongoDB Query API
Use the C# Ana... | [
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0... |
68a95c2d5e79d242619df88b-0 | 68a95c2d5e79d242619df88b | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current/analyze-code | Analyze Your Code | # Analyze Your Code
## Overview
Learn how to use the C# Analyzer to analyze your MongoDB .NET/C# Driver code. The C# Analyzer can analyze expressions created with the following patterns:
- Builders Class
- Language Integrated Query (LINQ)
- Plain Old CLR/Class Objects (POCOs)
## Use the MongoDB C# Analyzer From t... | [
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... |
68a95c2d5e79d242619df8a6-0 | 68a95c2d5e79d242619df8a6 | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current/issues-and-help | Issues & Help | # Issues & Help
The MongoDB developer community is highly engaged and encompasses users with a range of Visual Studio Code experience.
You can find support for general questions by using the MongoDB Stack Overflow tag or the MongoDB Reddit community.
Refer to our support channels documentation for more information.
... | [
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-0... |
68a95c2d5e79d242619df8a3-0 | 68a95c2d5e79d242619df8a3 | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current | MongoDB C# Analyzer | # MongoDB C# Analyzer
## Overview
The C# Analyzer is a tool that helps you understand the following aspects of your MongoDB .NET/C# Driver code:
- How your code translates into the MongoDB Query API
- If your code includes unsupported LINQ or builder expressions
- How your POCO instances serialize to JSON format
... | [
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... |
68a95c2d5e79d242619df895-0 | 68a95c2d5e79d242619df895 | visual-studio-extension | C# Analyzer | https://mongodb.com/docs/mongodb-analyzer/current/faq | Frequently Asked Questions | # Frequently Asked Questions
## Does the C# Analyzer support JetBrains Rider?
No, the C# Analyzer does not officially support JetBrains Rider.
We have internally tested the C# Analyzer on JetBrains Rider and have seen some success though. We welcome you to test the C# Analyzer on your JetBrains Rider deployment, and... | [
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... |
6a5878fec2014ca239cc44d3-0 | 6a5878fec2014ca239cc44d3 | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/management/azure-foundry | Deploy Voyage AI by MongoDB Models Using Azure AI Foundry | # Deploy Voyage AI by MongoDB Models Using Azure AI Foundry
You can use Azure AI Foundry to discover and deploy Voyage AI by MongoDB models in your Azure environment. Azure AI Foundry provides an Azure-native path for model discovery, deployment, billing, and access management.
Using Voyage AI through Azure AI Foundr... | [
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6a5878fec2014ca239cc44d3-1 | 6a5878fec2014ca239cc44d3 | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/management/azure-foundry | Deploy Voyage AI by MongoDB Models Using Azure AI Foundry | ### Deployment Settings
Review the following settings carefully during deployment:
<table>
<tr>
<th id="Setting">
Setting
</th>
<th id="Guidance">
Guidance
</th>
</tr>
<tr>
<td headers="Setting">
Subscription
</td>
<td headers="Guidance">
Use the Azure subscription that should receive billing for the deployment.
... | [
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6997fd706fd39a1e7ec5ab6c-0 | 6997fd706fd39a1e7ec5ab6c | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/management/rate-limits | Manage Rate Limits | # Manage Rate Limits
Rate limits are restrictions on the frequency and number of tokens you can request from Voyage AI within a specified period of time. To learn more about rate limits, see Best Practices.
Atlas enforces rate limits based on the model API key usage (requests per minute (RPM) and tokens per minute (T... | [
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6997fd706fd39a1e7ec5ab6c-1 | 6997fd706fd39a1e7ec5ab6c | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/management/rate-limits | Manage Rate Limits | ### Organization Rate Limits
The following tables show the default rate limits (TPM (Tokens Per Minute) and RPM (Requests Per Minute)) based on usage tier for each Voyage AI embedding model.
<Tabs>
<Tab name="Usage Tier 1">
<table>
<tr>
<th id="Model">
Model
</th>
<th id="Tokens%20Per%20Min%20(TPM)">
Tokens Per Mi... | [
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0.03809564933180809,
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-... |
6997fd706fd39a1e7ec5ab6c-2 | 6997fd706fd39a1e7ec5ab6c | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/management/rate-limits | Manage Rate Limits | ### Project Rate Limits
By default, projects inherit the rate limits based on the rate limits for the organization. However, you can set different limits for each project at the project level. Project level rate limits can't exceed the rate limits for the organization. Rate limits set at the project level apply to all... | [
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0.0694236159324646,
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0.04142575338482857,
-0.03618508577346802,
... |
6997fd706fd39a1e7ec5ab59-0 | 6997fd706fd39a1e7ec5ab59 | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/management/monitor-usage | Monitor Usage | # Monitor Usage
You can monitor the usage of your model API keys in the Atlas UI by tracking API requests, token consumption, and costs. Usage monitoring is available at both the organization and project levels.
## View Usage
You can view your API key usage at the organization or project level.
<Tabs>
<Tab name="O... | [
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0.029862387105822563,
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... |
6997fd706fd39a1e7ec5ab6f-0 | 6997fd706fd39a1e7ec5ab6f | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/management/gcp-marketplace | Deploy Voyage AI Models Using Model Garden on Gemini Enterprise Agent Platform | # Deploy Voyage AI Models Using Model Garden on Gemini Enterprise Agent Platform
You can explore and deploy Voyage AI by MongoDB models from Model Garden on Gemini Enterprise Agent Platform, formally GCP Vertex AI.
Model Garden manages licenses for Voyage AI by MongoDB models and provides deployment options using eit... | [
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6997fd706fd39a1e7ec5ab6f-1 | 6997fd706fd39a1e7ec5ab6f | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/management/gcp-marketplace | Deploy Voyage AI Models Using Model Garden on Gemini Enterprise Agent Platform | ### Fill out the deployment form.
A form opens that allows you to review and edit the deployment options. Gemini Enterprise Agent Platform provides default settings that are optimized for the model, but you can customize them as needed. For example, you can select the machine type, GPU type, and number of replicas. Th... | [
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6997fd706fd39a1e7ec5ab7b-0 | 6997fd706fd39a1e7ec5ab7b | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/api-reference/overview | Embedding and Reranking API Overview | # Embedding and Reranking API Overview
The Embedding and Reranking API provides programmatic access to the latest Voyage AI embedding and reranking models through a RESTful interface. This page provides an overview of the API and its features.
For detailed information and parameters, see the API specification.
You c... | [
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0.025643866509199142,
-0.031457334756851196,
... |
6997fd706fd39a1e7ec5ab7b-1 | 6997fd706fd39a1e7ec5ab7b | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/api-reference/overview | Embedding and Reranking API Overview | ## Rate Limits and Usage Tiers
The Embedding and Reranking API implements rate limiting to ensure fair usage and optimal performance. Rate limits are applied per API key and measured in two dimensions. Your rate limits increase as you advance through usage tiers.
- **TPM (Tokens Per Minute)**: Maximum number of token... | [
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-... |
6997fd706fd39a1e7ec5ab66-0 | 6997fd706fd39a1e7ec5ab66 | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/tutorials/migrate-to-atlas | Migrate Your Applications to use the Atlas Embedding and Reranking API | # Migrate Your Applications to use the Atlas Embedding and Reranking API
If you are an existing Voyage AI user, complete the following steps to migrate your application to use the Atlas Embedding and Reranking API.
## Procedure
### Sign up for a free Atlas account or log in.
If you're new to Atlas, it creates an or... | [
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6997fd706fd39a1e7ec5ab5a-0 | 6997fd706fd39a1e7ec5ab5a | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/management/organization-project-access | Organization and Project Access | # Organization and Project Access
An organization is the top-level entity. Billing, teams, and resource policies are managed at the organization level. Rate limits and API keys are also managed at this level but can be further constrained within projects, which are logical grouping of database deployments or clusters,... | [
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... |
6997fd706fd39a1e7ec5ab70-0 | 6997fd706fd39a1e7ec5ab70 | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/models/multimodal-embeddings | Multimodal Embeddings | # Multimodal Embeddings
Multimodal embedding models transform unstructured data from multiple modalities into a shared vector space. Voyage multimodal embedding models support text, images, and video, such as figures, photos, slide decks, document screenshots, and video clips. This removes the need for text extraction... | [
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0... |
6997fd706fd39a1e7ec5ab70-1 | 6997fd706fd39a1e7ec5ab70 | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/models/multimodal-embeddings | Multimodal Embeddings | ### Example
```javascript
const fs = require('fs');
const apiKey = process.env.VOYAGE_API_KEY;
const url = "https://ai.mongodb.com/v1/multimodalembeddings";
const headers = {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json"
};
// Read and encode image to base64
const imageBuffer = fs.r... | [
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6997fd706fd39a1e7ec5ab7c-0 | 6997fd706fd39a1e7ec5ab7c | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/tutorials/semantic-search | Semantic Search with Voyage AI Embeddings | # Semantic Search with Voyage AI Embeddings
This guide describes how to perform semantic search with Voyage AI models. This page includes examples for basic and advanced semantic search use cases, including search with reranking, multilingual, multimodal, contextualized chunk, and large corpus retrieval.
## Perform S... | [
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6997fd706fd39a1e7ec5ab7c-1 | 6997fd706fd39a1e7ec5ab7c | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/tutorials/semantic-search | Semantic Search with Voyage AI Embeddings | ### Run semantic search queries.
Expand each section to get code examples for each type of semantic search.
- Create a file named `semantic_search_basic.py` in your project and paste the following code into it:
```python
import voyageai
import numpy as np
# Initialize Voyage AI client
vo = voyageai.Client()
# Samp... | [
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-... |
6997fd706fd39a1e7ec5ab7c-2 | 6997fd706fd39a1e7ec5ab7c | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/tutorials/semantic-search | Semantic Search with Voyage AI Embeddings | # Perform semantic search in Spanish
spanish_query = "ganancias de empresas tecnológicas"
# Generate embeddings for Spanish documents
spanish_embeddings = vo.embed(
texts=spanish_docs,
model="voyage-4-large",
input_type="document"
).embeddings
# Generate embedding for Spanish query
spanish_query_embedding... | [
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-0.... |
6997fd706fd39a1e7ec5ab7c-3 | 6997fd706fd39a1e7ec5ab7c | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/tutorials/semantic-search | Semantic Search with Voyage AI Embeddings | - Create a file named `semantic_search_large_corpus.py` in your project and paste the following code into it:
```python
import voyageai
import numpy as np
from datasets import load_dataset
from collections import defaultdict
# Initialize Voyage AI client
vo = voyageai.Client()
# Load legal benchmark dataset
corpus_d... | [
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6997fd706fd39a1e7ec5ab7c-4 | 6997fd706fd39a1e7ec5ab7c | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/tutorials/semantic-search | Semantic Search with Voyage AI Embeddings | ## What is Semantic Search?
Semantic search is a search method that returns results based on your data's semantic, or underlying, meaning. Unlike traditional full-text search which finds text matches, semantic search finds vectors that are close to your search query in multi-dimensional space. The closer the vectors a... | [
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6997fd706fd39a1e7ec5ab62-0 | 6997fd706fd39a1e7ec5ab62 | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/models/rerankers | Rerankers | # Rerankers
A reranker receives a query and many documents, and returns a ranked list of relevancy between the query and documents. The documents are often the preliminary results from an embedding-based retrieval system, and the reranker refines the ranks of these candidate documents and provides more accurate releva... | [
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6997fd706fd39a1e7ec5ab62-1 | 6997fd706fd39a1e7ec5ab62 | voyageai | Voyage AI | https://mongodb.com/docs/voyageai/models/rerankers | Rerankers | ### Example
```python
import voyageai
vo = voyageai.Client()
# This will automatically use the environment variable VOYAGE_API_KEY.
# Alternatively, you can use vo = voyageai.Client(api_key="<model-api-key>")
query = "When is Apple's conference call scheduled?"
documents = [
"The Mediterranean diet emphasizes fi... | [
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