> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# MongoDB atlas integrations

> Integrate with MongoDB atlas using LangChain Python.

> [MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud
> database available in AWS, Azure, and GCP.  It now has support for native
> Vector Search on the MongoDB document data.

## Installation and setup

See [detail configuration instructions](/oss/python/integrations/vectorstores/mongodb_atlas).

We need to install `langchain-mongodb` python package.

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install langchain-mongodb
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langchain-mongodb
  ```
</CodeGroup>

## Vector store

See a [usage example](/oss/python/integrations/vectorstores/mongodb_atlas).

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_mongodb import MongoDBAtlasVectorSearch
```

## Retrievers

### Full text search retriever

> `Hybrid Search Retriever` performs full-text searches using
> Lucene’s standard (`BM25`) analyzer.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_mongodb.retrievers import MongoDBAtlasFullTextSearchRetriever
```

### Hybrid search retriever

> `Hybrid Search Retriever` combines vector and full-text searches weighting
> them the via `Reciprocal Rank Fusion` (`RRF`) algorithm.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_mongodb.retrievers import MongoDBAtlasHybridSearchRetriever
```

## Model caches

### MongoDBCache

An abstraction to store a simple cache in MongoDB. This does not use Semantic Caching, nor does it require an index to be made on the collection before generation.

To import this cache:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_mongodb.cache import MongoDBCache
```

To use this cache with your LLMs:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_core.globals import set_llm_cache

# use any embedding provider...
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings

mongodb_atlas_uri = "<YOUR_CONNECTION_STRING>"
COLLECTION_NAME="<YOUR_CACHE_COLLECTION_NAME>"
DATABASE_NAME="<YOUR_DATABASE_NAME>"

set_llm_cache(MongoDBCache(
    connection_string=mongodb_atlas_uri,
    collection_name=COLLECTION_NAME,
    database_name=DATABASE_NAME,
))
```

### MongoDBAtlasSemanticCache

Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends MongoDBAtlas as both a cache and a vectorstore.
The MongoDBAtlasSemanticCache inherits from `MongoDBAtlasVectorSearch` and needs an Atlas Vector Search Index defined to work. Please look at the [usage example](/oss/python/integrations/vectorstores/mongodb_atlas) on how to set up the index.

To import this cache:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_mongodb.cache import MongoDBAtlasSemanticCache
```

To use this cache with your LLMs:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_core.globals import set_llm_cache

# use any embedding provider...
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings

mongodb_atlas_uri = "<YOUR_CONNECTION_STRING>"
COLLECTION_NAME="<YOUR_CACHE_COLLECTION_NAME>"
DATABASE_NAME="<YOUR_DATABASE_NAME>"

set_llm_cache(MongoDBAtlasSemanticCache(
    embedding=FakeEmbeddings(),
    connection_string=mongodb_atlas_uri,
    collection_name=COLLECTION_NAME,
    database_name=DATABASE_NAME,
))
```

***

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