> For the complete MongoDB documentation index, see www.mongodb.com/docs/llms.txt

<!--
Tab options on this page. Append to the .md URL to filter:
  ?tabs=<id,...>   select specific tabs (e.g. ?tabs=nodejs,shell)
  ?allTabs=true    include every tab
  (no param)       default: one tab per tabset

Available tabs:
  other tabs: cloud, local, semantic-search, semantic-search-filter, llamaindex-rag, llamaindex-rag-filtering
-->

# Get Started with the LlamaIndex Integration

You can integrate MongoDB Vector Search with [LlamaIndex](https://llamaindex.ai/) to implement retrieval-augmented generation (RAG) in your LLM application. This tutorial demonstrates how to start using MongoDB Vector Search with LlamaIndex to perform semantic search on your data and build a RAG (Retrieval-Augmented Generation) implementation. Specifically, you perform the following actions:

1. Set up the environment.

2. Store custom data in MongoDB.

3. Create a MongoDB Vector Search index on your data.

4. Run the following vector search queries:

   - Semantic search.

   - Semantic search with metadata pre-filtering.

5. Implement RAG (Retrieval-Augmented Generation) by using MongoDB Vector Search to answer questions on your data.

Work with a runnable version of this tutorial as a [Python notebook.](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/llamaindex.ipynb)

## Background

LlamaIndex is an open-source framework designed to simplify how you connect custom data sources to LLM (Large Language Model)s. It provides several tools such as data connectors, indexes, and query engines to help you load and prepare vector embeddings for RAG (Retrieval-Augmented Generation) applications.

By integrating MongoDB Vector Search with LlamaIndex, you can use MongoDB as a vector database and use MongoDB Vector Search to implement RAG (Retrieval-Augmented Generation) by retrieving semantically similar documents from your data. To learn more about RAG (Retrieval-Augmented Generation), see [Retrieval-Augmented Generation (RAG) with MongoDB.](https://www.mongodb.com/docs/vector-search/tutorials/rag.md#std-label-ai-key-concepts)

## Procedure

### Prerequisites

To complete this tutorial, you must have the following:

- One of the following MongoDB cluster types:
  - An [Atlas cluster](https://www.mongodb.com/docs/atlas/tutorial/create-new-cluster.md#std-label-create-new-cluster) running MongoDB version 6.0.11, 7.0.2, or later. Ensure that your IP address (Internet Protocol address) is included in your Atlas project's [access list.](https://www.mongodb.com/docs/atlas/security/ip-access-list.md#std-label-access-list)

  - A local Atlas deployment created using Python and Docker. Install `atlas-local-lib-py` (`pip install atlas-local-lib-py`) to programmatically create and manage local deployments. To learn more, see the [atlas-local-lib-py repository.](https://github.com/mongodb/atlas-local-lib-py)

  - A MongoDB Community cluster with [Search and Vector Search](https://www.mongodb.com/docs/search/self-managed/current/installation/linux.md#std-label-mongot-deployment-on-linux) installed.

- An OpenAI API Key. You must have an OpenAI account with credits available for API requests. To learn more about registering an OpenAI account, see the [OpenAI API website.](https://openai.com/api/)

- A Voyage AI API (Application Programming Interface) key. To create an API (Application Programming Interface) key, see [Manage Voyage AI Model API Keys.](https://www.mongodb.com/docs/voyageai/management/api-keys.md#std-label-voyage-api-keys)

- An environment to run interactive Python notebooks such as [Colab.](https://colab.research.google.com)

### Set Up the Environment

Set up the environment for this tutorial. Create an interactive Python notebook by saving a file with the `.ipynb` extension. This notebook allows you to run Python code snippets individually, and you'll use it to run the code in this tutorial.

To set up your notebook environment:

1. Install and import dependencies.

   Run the following command:

   ```python
   pip install --quiet --upgrade llama-index llama-index-vector-stores-mongodb llama-index-llms-openai llama-index-embeddings-voyageai pymongo atlas-local-lib-py
   ```

   Then, run the following code to import the required packages:

   ```python
   import os, pymongo, pprint
   from pymongo.operations import SearchIndexModel
   from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, StorageContext
   from llama_index.core.settings import Settings
   from llama_index.core.retrievers import VectorIndexRetriever
   from llama_index.core.vector_stores import MetadataFilter, MetadataFilters, ExactMatchFilter, FilterOperator
   from llama_index.core.query_engine import RetrieverQueryEngine
   from llama_index.embeddings.voyageai import VoyageEmbedding
   from llama_index.llms.openai import OpenAI
   from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
   ```

2. Define environment variables and connection string.

   Run the following code, replacing the placeholders with the following values:

   - Your OpenAI API Key.

   - Your Voyage AI API Key.

   For your MongoDB connection, choose one of the following options:

   **Option 1: Local Deployment (Recommended for Testing)**

   Use `atlas-local-lib-py` to create or reuse a local deployment and retrieve the connection string programmatically:

   ```python
   from atlas_local import LocalDeployment

   os.environ["OPENAI_API_KEY"] = "<openai-api-key>"
   os.environ["VOYAGEAI_API_KEY"] = "<voyageai-api-key>"

   # Create or reuse a local Atlas deployment
   deployment = LocalDeployment.get_or_create(
       name="llamaindex-local-deployment"
   )
   MONGODB_URI = deployment.connection_string()
   ```

   **Option 2: Cloud Cluster**

   Manually specify your MongoDB cluster's SRV (DNS Service Record) [connection string:](https://www.mongodb.com/docs/manual/reference/connection-string/#find-your-mongodb-atlas-connection-string)

   ```python
   os.environ["OPENAI_API_KEY"] = "<openai-api-key>"
   os.environ["VOYAGEAI_API_KEY"] = "<voyageai-api-key>"
   MONGODB_URI = "<connection-string>"
   ```

   **Note:**

   Replace `<connection-string>` with the connection string for your Atlas cluster or local Atlas deployment.

   ### Atlas Cluster

   Your connection string should use the following format:

   ```text
   mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
   ```

   IMPORTANT: Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see [Connect to a Cluster via Client Libraries.](https://www.mongodb.com/docs/atlas/driver-connection.md#std-label-connect-via-driver)

3. Configure LlamaIndex settings.

   Run the following code to configure settings that are specific to LlamaIndex. These settings specify the following:

   - `voyage-3-large` as the embedding model used by your application to generate vector embeddings from your data.

   - OpenAI as the LLM used by your application to answer questions on your data.

   - [Chunk size and overlap](https://docs.llamaindex.ai/en/stable/optimizing/basic_strategies/basic_strategies/#chunk-sizes) to customize how LlamaIndex partitions your data for storage.

   ```python
   from llama_index.embeddings.voyageai import VoyageEmbedding

   embed_model= VoyageEmbedding(
     voyage_api_key = os.environ["VOYAGEAI_API_KEY"],
     model_name = "voyage-3-large",
   )

   Settings.llm = OpenAI()
          Settings.embed_model = embed_model
   Settings.chunk_size = 100
   Settings.chunk_overlap = 10
   ```

### Use MongoDB as a Vector Store

Then, load custom data into MongoDB and instantiate your MongoDB cluster as a vector database, also called a [vector store](https://docs.llamaindex.ai/en/stable/module_guides/storing/vector_stores/). Copy and paste the following code snippets into your notebook.

1. Load the sample data.

   For this tutorial, you use a publicly accessible PDF document that contains that contains a [recent MongoDB earnings report](https://investors.mongodb.com/node/13176/pdf) as the data source for your vector store. This document describes MongoDB's financial results for the fourth quarter and full year of fiscal 2025.

   To load the sample data, run the following code snippet. It does the following:

   - Creates a new directory called `data`.

   - Retrieves the PDF from the specified URL and saves it as a file in the directory.

   - Uses the `SimpleDirectoryReader` [data connector](https://docs.llamaindex.ai/en/stable/module_guides/loading/connector/) to extract raw text and metadata from the file. It also formats the data into documents.

   ```python
   # Load the sample data
   from urllib.request import urlretrieve
   urlretrieve("https://investors.mongodb.com/node/13176/pdf", "mongodb-earnings-report.pdf")
   sample_data = SimpleDirectoryReader(input_files=["mongodb-earnings-report.pdf"]).load_data()

   # Print the first document
   sample_data[0]
   ```

   **Output:**

   ```text
   Document(id_='62b7cace-30c0-4687-9d87-e178547ae357', embedding=None,
   metadata={'page_label': '1', 'file_name': 'mongodb-earnings-report.pdf',
   'file_path': 'data/mongodb-earnings-report.pdf', 'file_type':
   'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28',
   'last_modified_date': '2025-05-28'},
   excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size',
   'creation_date', 'last_modified_date', 'last_accessed_date'],
   excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size',
   'creation_date', 'last_modified_date', 'last_accessed_date'],
   relationships={}, metadata_template='{key}: {value}', metadata_separator='\n',
   text_resource=MediaResource(embeddings=None, data=None, text='MongoDB, Inc.
   Announces Fourth Quarter and Full Year Fiscal 2025 Financial Results\nMarch 5,
   2025\nFourth Quarter Fiscal 2025 Total Revenue of $548.4 million, up 20%
   Year-over-Year\nFull Year Fiscal 2025 Total Revenue of $2.01 billion, up 19%
   Year-over-Year\nContinued Strong Customer Growth with Over 54,500 Customers as
   of January 31, 2025\nMongoDB Atlas Revenue up 24% Year-over-Year; 71% of Total
   Q4 Revenue\nNEW YORK , March 5, 2025 /PRNewswire/ -- MongoDB, Inc. (NASDAQ:
   MDB) today announced its financial results for the fourth quarter and
   fiscal\nyear ended January 31, 2025.\n\xa0\n  \xa0\n"MongoDB  delivered a
   strong end to fiscal 2025 with 24% Atlas revenue growth and significant margin
   expansion. Atlas consumption in the quarter\nwas better than expected and we
   continue to see good performance in new workload wins due to the flexibility,
   scalability and performance of the\nMongoDB  platform. In fiscal year 2026 we
   expect to see stable consumption growth in Atlas, our main growth driver,"
   said Dev Ittycheria, President\nand Chief Executive Officer of MongoDB
   .\n"Looking ahead, we remain incredibly excited about our long-term growth
   opportunity. MongoDB  removes the constraints of legacy databases,\nenabling
   businesses to innovate at AI speed with our flexible document model and
   seamless scalability. Following the Voyage AI acquisition, we\ncombine
   real-time data, sophisticated embedding and retrieval models and semantic
   search directly in the database, simplifying the development of\ntrustworthy
   AI-powered apps."\nFourth Quarter Fiscal 2025 Financial Highlights\nRevenue:
   Total revenue was $548.4 million for the fourth quarter of fiscal 2025, an
   increase of 20% year-over-year.\nSubscription revenue was $531.0 million, an
   increase of 19% year-over-year, and services revenue was $17.4 million,
   an\nincrease of 34% year-over-year.\nGross Profit: Gross profit was $399.4
   million for the fourth quarter of fiscal 2025, representing a 73% gross
   margin\ncompared to 75% in the year-ago period. Non-GAAP gross profit was
   $411.7 million, representing a 75% non-GAAP gross\nmargin, compared to a
   non-GAAP gross margin of 77% in the year-ago period.\nLoss from Operations:
   Loss from operations was $18.6 million for the fourth quarter of fiscal 2025,
   compared to a loss\nfrom operations of $71.0 million in the year-ago period.
   Non-GAAP income from operations was $112.5 million, compared\nto non-GAAP
   income from operations of $69.2 million in the year-ago period.\nNet Income
   (Loss): Net income was $15.8 million, or $0.20 per share, based on 77.6
   million weighted-average shares\noutstanding, for the fourth quarter of fiscal
   2025. This compares to a net loss of $55.5 million, or $0.77 per share, in
   the\nyear-ago period. Non-GAAP net income was $108.4 million, or $1.28 per
   share, based on 84.6 million fully diluted\nweighted-average shares
   outstanding. This compares to a non-GAAP net income of $71.1 million, or $0.86
   per share, in\nthe year-ago period.\nCash Flow: As of January 31, 2025,
   MongoDB  had $2.3 billion in cash, cash equivalents, short-term investments
   and\nrestricted cash. During the three months ended January 31, 2025, MongoDB
   generated $50.5 million of cash from\noperations, compared to $54.6 million of
   cash from operations in the year-ago period. MongoDB  used $26.0 million of
   cash\nin capital expenditures and used $1.6 million of cash in principal
   payments of finance leases, leading to free cash flow of\n$22.9 million,
   compared to free cash flow of $50.5 million in the year-ago period.\nFull Year
   Fiscal 2025 Financial Highlights\nRevenue: Total revenue was $2.01 billion for
   the full year fiscal 2025, an increase of 19% year-over-year.
   Subscription\nrevenue was $1.94 billion, an increase of 19% year-over-year,
   and services revenue was $62.6 million, an increase of
   12%\nyear-over-year.\nGross Profit: Gross profit was $1.47 billion for the
   full year fiscal 2025, representing a 73% gross margin compared to',
   path=None, url=None, mimetype=None), image_resource=None, audio_resource=None,
   video_resource=None, text_template='{metadata_str}\n\n{content}')
   ```

2. Instantiate the vector store.

   Run the following code to create a vector store by using the `MongoDBAtlasVectorSearch` method, which specifies the following:

   - A connection to your MongoDB cluster.

   - `llamaindex_db.test` as the MongoDB database and collection used to store the documents.

   - `vector_index` as the index to use for querying the vector store.

   Then, you save the vector store to a [storage context](https://docs.llamaindex.ai/en/stable/api_reference/storage/storage_context/), which is a LlamaIndex container object used to prepare your data for storage.

   ```python
   # Connect to your MongoDB cluster
   mongo_client = pymongo.MongoClient(MONGODB_URI)

   # Instantiate the vector store
   vector_store = MongoDBAtlasVectorSearch(
       mongo_client,
       db_name = "llamaindex_db",
       collection_name = "test",
       vector_index_name = "vector_index"
   )
   vector_store_context = StorageContext.from_defaults(vector_store=vector_store)
   ```

3. Store your data as vector embeddings.

   Once you've loaded your data and instantiated Atlas as a vector store, generate vector embeddings from your data and store them in Atlas. To do this, you must build a [vector store index](https://docs.llamaindex.ai/en/stable/understanding/indexing/indexing/). This type of index is a LlamaIndex data structure that splits, embeds, and then stores your data in the vector store.

   The following code uses the `VectorStoreIndex.from_documents` method to build the vector store index on your sample data. It turns your sample data into vector embeddings and stores these embeddings as documents in the `llamaindex_db.test` collection in your MongoDB cluster, as specified by the vector store's storage context.

   **Note:**

   This method uses the embedding model and chunk settings that you configured when you set up your environment.

   ```python
   vector_store_index = VectorStoreIndex.from_documents(
      sample_data, storage_context=vector_store_context, show_progress=True
   )
   ```

   **Tip:**

   After running the sample code, if you're using Atlas, you can verify your vector embeddings by navigating to the `llamaindex_db.test` namespace [in the Atlas UI.](https://www.mongodb.com/docs/atlas/atlas-ui/collections.md#std-label-atlas-ui-view-collections)

### Create the MongoDB Vector Search Index

To enable vector search queries on your vector store, create a MongoDB Vector Search index on the `llamaindex_db.test` collection.

In your notebook, run the following code to create an index of the [vectorSearch](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-avs-types-vector-search) type that indexes the following fields:

- `embedding` field as the [vector](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-avs-types-vector-search) type. The `embedding` field contains the embeddings created using VoyageAI's `voyage-3-large` embedding model. The index definition specifies `1024` vector dimensions and measures similarity using `cosine`.

- `metadata.page_label` field as the [filter](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-avs-types-vector-search) type for pre-filtering data by the page number in the PDF.

```python
# Specify the collection for which to create the index
collection = mongo_client["llamaindex_db"]["test"]

# Create your index model, then create the search index
search_index_model = SearchIndexModel(
  definition={
    "fields": [
      {
        "type": "vector",
        "path": "embedding",
        "numDimensions": 1024,
        "similarity": "cosine"
      },
      {
        "type": "filter",
        "path": "metadata.page_label"
      }
    ]
  },
  name="vector_index",
  type="vectorSearch"
)

collection.create_search_index(model=search_index_model)
```

The index should take about one minute to build. While it builds, the index is in an [initial sync](https://www.mongodb.com/docs/search/performance/index-performance.md#std-label-troubleshoot-initial-sync) state. When it finishes building, you can start querying the data in your collection.

### Prerequisites

To complete this tutorial, you must have the following:

- One of the following MongoDB cluster types:
  - An [Atlas cluster](https://www.mongodb.com/docs/atlas/tutorial/create-new-cluster.md#std-label-create-new-cluster) running MongoDB version 6.0.11, 7.0.2, or later. Ensure that your IP address (Internet Protocol address) is included in your Atlas project's [access list.](https://www.mongodb.com/docs/atlas/security/ip-access-list.md#std-label-access-list)

  - A local Atlas deployment created using Python and Docker. Install `atlas-local-lib-py` (`pip install atlas-local-lib-py`) to programmatically create and manage local deployments. To learn more, see the [atlas-local-lib-py repository.](https://github.com/mongodb/atlas-local-lib-py)

  - A MongoDB Community cluster with [Search and Vector Search](https://www.mongodb.com/docs/search/self-managed/current/installation/linux.md#std-label-mongot-deployment-on-linux) installed.

- An OpenAI API Key. You must have an OpenAI account with credits available for API requests. To learn more about registering an OpenAI account, see the [OpenAI API website.](https://openai.com/api/)

- An environment to run interactive Python notebooks such as [Colab.](https://colab.research.google.com)

### Set Up the Environment

Set up the environment for this tutorial. Create an interactive Python notebook by saving a file with the `.ipynb` extension. This notebook allows you to run Python code snippets individually, and you'll use it to run the code in this tutorial.

To set up your notebook environment:

1. Install and import dependencies.

   Run the following command:

   ```python
   pip install --quiet --upgrade llama-index llama-index-vector-stores-mongodb llama-index-embeddings-openai pymongo
   ```

   Then, run the following code to import the required packages:

   ```python
   import os, pymongo, pprint
   from pymongo.operations import SearchIndexModel
   from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, StorageContext
   from llama_index.core.settings import Settings
   from llama_index.core.retrievers import VectorIndexRetriever
   from llama_index.core.vector_stores import MetadataFilter, MetadataFilters, ExactMatchFilter, FilterOperator
   from llama_index.core.query_engine import RetrieverQueryEngine
   from llama_index.embeddings.openai import OpenAIEmbedding
   from llama_index.llms.openai import OpenAI
   from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
   ```

2. Define environment variables.

   Run the following code, replacing the placeholders with the following values:

   - Your OpenAI API Key.

   - Your MongoDB cluster's connection string.

   ```python
   os.environ["OPENAI_API_KEY"] = "<api-key>"
   MONGODB_URI = "<connection-string>"
   ```

   **Note:**

   Replace `<connection-string>` with the connection string for your Atlas cluster or local Atlas deployment.

   ### Atlas Cluster

   Your connection string should use the following format:

   ```text
   mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
   ```

   IMPORTANT: Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see [Connect to a Cluster via Client Libraries.](https://www.mongodb.com/docs/atlas/driver-connection.md#std-label-connect-via-driver)

3. Configure LlamaIndex settings.

   Run the following code to configure settings that are specific to LlamaIndex. These settings specify the following:

   - OpenAI as the LLM used by your application to answer questions on your data.

   - `text-embedding-ada-002` as the embedding model used by your application to generate vector embeddings from your data.

   - [Chunk size and overlap](https://docs.llamaindex.ai/en/stable/optimizing/basic_strategies/basic_strategies/#chunk-sizes) to customize how LlamaIndex partitions your data for storage.

   ```python
   Settings.llm = OpenAI()
   Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
   Settings.chunk_size = 100
   Settings.chunk_overlap = 10
   ```

### Use MongoDB as a Vector Store

Then, load custom data into MongoDB and instantiate your MongoDB cluster as a vector database, also called a [vector store](https://docs.llamaindex.ai/en/stable/module_guides/storing/vector_stores/). Copy and paste the following code snippets into your notebook.

1. Load the sample data.

   For this tutorial, you use a publicly accessible PDF document that contains that contains a [recent MongoDB earnings report](https://investors.mongodb.com/node/13176/pdf) as the data source for your vector store. This document describes MongoDB's financial results for the fourth quarter and full year of fiscal 2025.

   To load the sample data, run the following code snippet. It does the following:

   - Creates a new directory called `data`.

   - Retrieves the PDF from the specified URL and saves it as a file in the directory.

   - Uses the `SimpleDirectoryReader` [data connector](https://docs.llamaindex.ai/en/stable/module_guides/loading/connector/) to extract raw text and metadata from the file. It also formats the data into documents.

   ```python
   # Load the sample data
   from urllib.request import urlretrieve
   urlretrieve("https://investors.mongodb.com/node/13176/pdf", "mongodb-earnings-report.pdf")
   sample_data = SimpleDirectoryReader(input_files=["./data/mongodb-earnings-report.pdf"]).load_data()

   # Print the first document
   sample_data[0]
   ```

   **Output:**

   ```text
   Document(id_='62b7cace-30c0-4687-9d87-e178547ae357', embedding=None,
   metadata={'page_label': '1', 'file_name': 'mongodb-earnings-report.pdf',
   'file_path': 'data/mongodb-earnings-report.pdf', 'file_type':
   'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28',
   'last_modified_date': '2025-05-28'},
   excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size',
   'creation_date', 'last_modified_date', 'last_accessed_date'],
   excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size',
   'creation_date', 'last_modified_date', 'last_accessed_date'],
   relationships={}, metadata_template='{key}: {value}', metadata_separator='\n',
   text_resource=MediaResource(embeddings=None, data=None, text='MongoDB, Inc.
   Announces Fourth Quarter and Full Year Fiscal 2025 Financial Results\nMarch 5,
   2025\nFourth Quarter Fiscal 2025 Total Revenue of $548.4 million, up 20%
   Year-over-Year\nFull Year Fiscal 2025 Total Revenue of $2.01 billion, up 19%
   Year-over-Year\nContinued Strong Customer Growth with Over 54,500 Customers as
   of January 31, 2025\nMongoDB Atlas Revenue up 24% Year-over-Year; 71% of Total
   Q4 Revenue\nNEW YORK , March 5, 2025 /PRNewswire/ -- MongoDB, Inc. (NASDAQ:
   MDB) today announced its financial results for the fourth quarter and
   fiscal\nyear ended January 31, 2025.\n\xa0\n  \xa0\n"MongoDB  delivered a
   strong end to fiscal 2025 with 24% Atlas revenue growth and significant margin
   expansion. Atlas consumption in the quarter\nwas better than expected and we
   continue to see good performance in new workload wins due to the flexibility,
   scalability and performance of the\nMongoDB  platform. In fiscal year 2026 we
   expect to see stable consumption growth in Atlas, our main growth driver,"
   said Dev Ittycheria, President\nand Chief Executive Officer of MongoDB
   .\n"Looking ahead, we remain incredibly excited about our long-term growth
   opportunity. MongoDB  removes the constraints of legacy databases,\nenabling
   businesses to innovate at AI speed with our flexible document model and
   seamless scalability. Following the Voyage AI acquisition, we\ncombine
   real-time data, sophisticated embedding and retrieval models and semantic
   search directly in the database, simplifying the development of\ntrustworthy
   AI-powered apps."\nFourth Quarter Fiscal 2025 Financial Highlights\nRevenue:
   Total revenue was $548.4 million for the fourth quarter of fiscal 2025, an
   increase of 20% year-over-year.\nSubscription revenue was $531.0 million, an
   increase of 19% year-over-year, and services revenue was $17.4 million,
   an\nincrease of 34% year-over-year.\nGross Profit: Gross profit was $399.4
   million for the fourth quarter of fiscal 2025, representing a 73% gross
   margin\ncompared to 75% in the year-ago period. Non-GAAP gross profit was
   $411.7 million, representing a 75% non-GAAP gross\nmargin, compared to a
   non-GAAP gross margin of 77% in the year-ago period.\nLoss from Operations:
   Loss from operations was $18.6 million for the fourth quarter of fiscal 2025,
   compared to a loss\nfrom operations of $71.0 million in the year-ago period.
   Non-GAAP income from operations was $112.5 million, compared\nto non-GAAP
   income from operations of $69.2 million in the year-ago period.\nNet Income
   (Loss): Net income was $15.8 million, or $0.20 per share, based on 77.6
   million weighted-average shares\noutstanding, for the fourth quarter of fiscal
   2025. This compares to a net loss of $55.5 million, or $0.77 per share, in
   the\nyear-ago period. Non-GAAP net income was $108.4 million, or $1.28 per
   share, based on 84.6 million fully diluted\nweighted-average shares
   outstanding. This compares to a non-GAAP net income of $71.1 million, or $0.86
   per share, in\nthe year-ago period.\nCash Flow: As of January 31, 2025,
   MongoDB  had $2.3 billion in cash, cash equivalents, short-term investments
   and\nrestricted cash. During the three months ended January 31, 2025, MongoDB
   generated $50.5 million of cash from\noperations, compared to $54.6 million of
   cash from operations in the year-ago period. MongoDB  used $26.0 million of
   cash\nin capital expenditures and used $1.6 million of cash in principal
   payments of finance leases, leading to free cash flow of\n$22.9 million,
   compared to free cash flow of $50.5 million in the year-ago period.\nFull Year
   Fiscal 2025 Financial Highlights\nRevenue: Total revenue was $2.01 billion for
   the full year fiscal 2025, an increase of 19% year-over-year.
   Subscription\nrevenue was $1.94 billion, an increase of 19% year-over-year,
   and services revenue was $62.6 million, an increase of
   12%\nyear-over-year.\nGross Profit: Gross profit was $1.47 billion for the
   full year fiscal 2025, representing a 73% gross margin compared to',
   path=None, url=None, mimetype=None), image_resource=None, audio_resource=None,
   video_resource=None, text_template='{metadata_str}\n\n{content}')
   ```

2. Instantiate the vector store.

   Run the following code to create a vector store by using the `MongoDBAtlasVectorSearch` method, which specifies the following:

   - A connection to your MongoDB cluster.

   - `llamaindex_db.test` as the MongoDB database and collection used to store the documents.

   - `vector_index` as the index to use for querying the vector store.

   Then, you save the vector store to a [storage context](https://docs.llamaindex.ai/en/stable/api_reference/storage/storage_context/), which is a LlamaIndex container object used to prepare your data for storage.

   ```python
   # Connect to your MongoDB cluster
   mongo_client = pymongo.MongoClient(MONGODB_URI)

   # Instantiate the vector store
   vector_store = MongoDBAtlasVectorSearch(
       mongo_client,
       db_name = "llamaindex_db",
       collection_name = "test",
       vector_index_name = "vector_index"
   )
   vector_store_context = StorageContext.from_defaults(vector_store=vector_store)
   ```

3. Store your data as vector embeddings.

   Once you've loaded your data and instantiated MongoDB as a vector store, generate vector embeddings from your data and store them in MongoDB. To do this, you must build a [vector store index](https://docs.llamaindex.ai/en/stable/understanding/indexing/indexing/). This type of index is a LlamaIndex data structure that splits, embeds, and then stores your data in the vector store.

   The following code uses the `VectorStoreIndex.from_documents` method to build the vector store index on your sample data. It turns your sample data into vector embeddings and stores these embeddings as documents in the `llamaindex_db.test` collection in your MongoDB cluster, as specified by the vector store's storage context.

   **Note:**

   This method uses the embedding model and chunk settings that you configured when you set up your environment.

   ```python
   vector_store_index = VectorStoreIndex.from_documents(
      sample_data, storage_context=vector_store_context, show_progress=True
   )
   ```

   **Tip:**

   After running the sample code, if you're using Atlas, you can verify your vector embeddings by navigating to the `llamaindex_db.test` namespace [in the Atlas UI.](https://www.mongodb.com/docs/atlas/atlas-ui/collections.md#std-label-atlas-ui-view-collections)

### Create the MongoDB Vector Search Index

To enable vector search queries on your vector store, create a MongoDB Vector Search index on the `llamaindex_db.test` collection.

In your notebook, run the following code to create an index of the [vectorSearch](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-avs-types-vector-search) type that indexes the following fields:

- `embedding` field as the [vector](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-avs-types-vector-search) type. The `embedding` field contains the embeddings created using OpenAI's `text-embedding-ada-002` embedding model. The index definition specifies `1536` vector dimensions and measures similarity using `cosine`.

- `metadata.page_label` field as the [filter](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-avs-types-vector-search) type for pre-filtering data by the page number in the PDF.

```python
# Specify the collection for which to create the index
collection = mongo_client["llamaindex_db"]["test"]

# Create your index model, then create the search index
search_index_model = SearchIndexModel(
  definition={
    "fields": [
      {
        "type": "vector",
        "path": "embedding",
        "numDimensions": 1536,
        "similarity": "cosine"
      },
      {
        "type": "filter",
        "path": "metadata.page_label"
      }
    ]
  },
  name="vector_index",
  type="vectorSearch"
)

collection.create_search_index(model=search_index_model)
```

The index should take about one minute to build. While it builds, the index is in an [initial sync](https://www.mongodb.com/docs/search/performance/index-performance.md#std-label-troubleshoot-initial-sync) state. When it finishes building, you can start querying the data in your collection.

### Run Vector Search Queries

After MongoDB builds your index, return to your notebook and run vector search queries on your data. The following examples demonstrate different queries that you can run on your vectorized data.

### Basic Semantic Search

This example performs a basic semantic search for the string `MongoDB Atlas security` and returns a list of documents ranked by [relevance score](https://www.mongodb.com/docs/search/query/score/overview.md#std-label-scoring-ref). It also specifies the following:

- MongoDB Vector Search as a [retriever](https://docs.llamaindex.ai/en/stable/module_guides/querying/retriever/) to perform semantic search.

- The  `similarity_top_k` parameter to return only the three most relevant documents.

```python
retriever = vector_store_index.as_retriever(similarity_top_k=3)
nodes = retriever.retrieve("MongoDB acquisition")

for node in nodes:
    print(node)
```

**Output:**

```JSON
Node ID: 479446ef-8a32-410d-a5e0-8650bd10d78d
Text: MongoDB  completed the redemption of 2026 Convertible Notes,
eliminating all debt from the balance sheet. Additionally, in
conjunction with the acquisition of Voyage, MongoDB  is announcing a
stock buyback program of $200 million, to offset the dilutive impact
of the acquisition consideration.
Score:  0.914

Node ID: 453137d9-8902-4fae-8d81-5f5d9b0836eb
Text: "Looking ahead, we remain incredibly excited about our long-term
growth opportunity. MongoDB  removes the constraints of legacy
databases, enabling businesses to innovate at AI speed with our
flexible document model and seamless scalability. Following the Voyage
AI acquisition, we combine real-time data, sophisticated embedding and
retrieval mod...
Score:  0.914

Node ID: f3c35db6-43e5-4da7-a297-d9b009b9d300
Text: Lombard Odier, a Swiss private bank, partnered with MongoDB  to
migrate and modernize its legacy banking technology systems on MongoDB
with generative AI. The initiative enabled the bank to migrate code
50-60 times quicker and move applications from a legacy relational
database to MongoDB  20 times faster than previous migrations.
Score:  0.912
```

### Answer Questions on Your Data

This section demonstrates how to implement RAG (Retrieval-Augmented Generation) in your application with MongoDB Vector Search and LlamaIndex. Now that you've learned how to run vector search queries to retrieve semantically similar documents, run the following code to use MongoDB Vector Search to retrieve documents and a LlamaIndex [query engine](https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/) to then answer questions based on those documents.

### Basic RAG

This example does the following:

- Instantiates MongoDB Vector Search as a [vector index retriever](https://docs.llamaindex.ai/en/stable/api_reference/retrievers/vector/), a specific type of retriever for vector stores. It includes the `similarity_top_k`  parameter so that MongoDB Vector Search retrieves only the 5 most relevant documents.

* Instantiates the `RetrieverQueryEngine` query engine to answer questions on your data. When prompted, the query engine performs the following actions:

  - Uses MongoDB Vector Search as a retriever to query for semantically similar documents based on the prompt.

  - Calls the LLM that you specified when you set up your environment to generate a context-aware response based on the retrieved documents.

* Prompts the LLM with a sample query about Atlas security recommendations.

* Returns the LLM (Large Language Model)'s response and the documents used as context. The generated response might vary.

```python
# Instantiate MongoDB Vector Search as a retriever
vector_store_retriever = VectorIndexRetriever(index=vector_store_index, similarity_top_k=5)

# Pass the retriever into the query engine
query_engine = RetrieverQueryEngine(retriever=vector_store_retriever)

# Prompt the LLM
response = query_engine.query("What was MongoDB's latest acquisition?")

print(response)
print("\nSource documents: ")
pprint.pprint(response.source_nodes)
```

**Output:**

```text
MongoDB's latest acquisition was Voyage AI, a pioneer in embedding and reranking models for next-generation AI applications.

Source documents:
[NodeWithScore(node=TextNode(id_='82a2a0c0-80b9-4a9e-a848-529b4ff8f301', embedding=None, metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='2171a7d3-482c-4f83-beee-8c37e0ebc747', node_type='4', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='ef623ef7400aa6e120f821b455b2ddce99b94c57365e7552b676abaa3eb23640'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='8cfe6680-8dec-486e-92c5-89ac1733b6c8', node_type='1', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='b6c412af868c29d67a6b030f266cd0e680f4a578a34c209c1818ff9a366c9d44'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='479446ef-8a32-410d-a5e0-8650bd10d78d', node_type='1', metadata={}, hash='b805543bf0ef0efc25492098daa9bd9c037043fb7228fb0c3270de235e668341')}, metadata_template='{key}: {value}', metadata_separator='\n', text="Fourth Quarter Fiscal 2025 and Recent Business Highlights\nMongoDB  acquired Voyage AI, a pioneer in state-of-the-art embedding and reranking models that power next-generation\nAI applications. Integrating Voyage AI's technology with MongoDB  will enable organizations to easily build trustworthy,\nAI-powered applications by offering highly accurate and relevant information retrieval deeply integrated with operational\ndata.", mimetype='text/plain', start_char_idx=1678, end_char_idx=2101, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.9279670119285583),
 NodeWithScore(node=TextNode(id_='453137d9-8902-4fae-8d81-5f5d9b0836eb', embedding=None, metadata={'page_label': '1', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='62b7cace-30c0-4687-9d87-e178547ae357', node_type='4', metadata={'page_label': '1', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='cb1dbd172c17e53682296ccc966ebdbb5605acb4fbf3872286e3a202c1d3650d'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='b6ae7c13-5bec-47f5-887f-835fc7bae374', node_type='1', metadata={'page_label': '1', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='a4835102686cdf03d1106946237d50031d00a0861eea892e38b928dd5e44e295'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='3d4034d3-bac5-4985-8926-9213f8a87318', node_type='1', metadata={}, hash='f103b351f2bda28ec3d2f1bb4f40d93ac1698ea5f7630a5297688a4caa419389')}, metadata_template='{key}: {value}', metadata_separator='\n', text='"Looking ahead, we remain incredibly excited about our long-term growth opportunity. MongoDB  removes the constraints of legacy databases,\nenabling businesses to innovate at AI speed with our flexible document model and seamless scalability. Following the Voyage AI acquisition, we\ncombine real-time data, sophisticated embedding and retrieval models and semantic search directly in the database, simplifying the development of\ntrustworthy AI-powered apps."', mimetype='text/plain', start_char_idx=1062, end_char_idx=1519, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.921961784362793),
 NodeWithScore(node=TextNode(id_='85dd431c-2d4c-4336-ab39-e87a97b30c59', embedding=None, metadata={'page_label': '4', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='311532cc-f526-4fc3-adb6-49e76afdd580', node_type='4', metadata={'page_label': '4', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='37f0ad7fcb7f204226ea7c6c475360e2db55bb77447f1742a164efb9c1da5dc0'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='6175bcb6-9e2a-4196-85f7-0585bcbbdd3b', node_type='1', metadata={}, hash='0e92e55a50f8b6dbfe7bcaedb0ccc42345a185048efcd440e3ee1935875e7cbf')}, metadata_template='{key}: {value}', metadata_separator='\n', text="Headquartered in New York, MongoDB's mission is to empower innovators to create, transform, and disrupt industries with software and data.\nMongoDB's unified, intelligent data platform was built to power the next generation of applications, and MongoDB  is the most widely available, globally\ndistributed database on the market.", mimetype='text/plain', start_char_idx=0, end_char_idx=327, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.9217028021812439),
 NodeWithScore(node=TextNode(id_='f3c35db6-43e5-4da7-a297-d9b009b9d300', embedding=None, metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='2171a7d3-482c-4f83-beee-8c37e0ebc747', node_type='4', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='ef623ef7400aa6e120f821b455b2ddce99b94c57365e7552b676abaa3eb23640'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='3008736c-29f0-4b41-ac0f-efdb469319b9', node_type='1', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='cd3647350e6d7fcd89e2303fe1995b8f91b633c5f33e14b3b4c18a16738ea86f'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='c9bef874-77ee-40bc-a1fe-ca42d1477cb3', node_type='1', metadata={}, hash='c7d7af8a1b43b587a9c47b27f57e7cb8bc35bd90390a078db21e3f5253ee7cc1')}, metadata_template='{key}: {value}', metadata_separator='\n', text='Lombard Odier, a Swiss private bank, partnered with MongoDB  to migrate and modernize its legacy banking technology\nsystems on MongoDB  with generative AI. The initiative enabled the bank to migrate code 50-60 times quicker and move\napplications from a legacy relational database to MongoDB  20 times faster than previous migrations.', mimetype='text/plain', start_char_idx=2618, end_char_idx=2951, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.9197831153869629),
 NodeWithScore(node=TextNode(id_='479446ef-8a32-410d-a5e0-8650bd10d78d', embedding=None, metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='2171a7d3-482c-4f83-beee-8c37e0ebc747', node_type='4', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='ef623ef7400aa6e120f821b455b2ddce99b94c57365e7552b676abaa3eb23640'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='82a2a0c0-80b9-4a9e-a848-529b4ff8f301', node_type='1', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='688872b911c388c239669970f562d4014aaec4753903e75f4bdfcf1eb1daf5ab'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='3008736c-29f0-4b41-ac0f-efdb469319b9', node_type='1', metadata={}, hash='a854a9bf103e429ce78b45603df9e2341e5d0692aa95e544e6c82616be29b28e')}, metadata_template='{key}: {value}', metadata_separator='\n', text='MongoDB  completed the redemption of 2026 Convertible Notes, eliminating all debt from the balance sheet. Additionally, in\nconjunction with the acquisition of Voyage, MongoDB  is announcing a stock buyback program of $200 million, to offset the\ndilutive impact of the acquisition consideration.', mimetype='text/plain', start_char_idx=2102, end_char_idx=2396, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.9183852672576904)]
```

### Next Steps

To explore LlamaIndex's full library of tools for RAG (Retrieval-Augmented Generation) applications, which includes data connectors, indexes, and query engines, see [LlamaHub.](https://llamahub.ai)

To extend the application in this tutorial to have back-and-forth conversations, see [Chat Engine.](https://docs.llamaindex.ai/en/stable/module_guides/deploying/chat_engines/)

MongoDB also provides the following developer resources:

- [MongoDB Developer GitHub Repository](https://github.com//mongodb-developer)

**See also:**

- [LlamaIndex Documentation](https://docs.llamaindex.ai/en/stable/examples/vector_stores/MongoDBAtlasVectorSearchRAGOpenAI/)

- [LlamaIndex API Reference](https://docs.llamaindex.ai/en/stable/api_reference/storage/vector_store/mongodb/)