## MongoDB Atlas

> [MongoDB Atlas](https://www.mongodb.com/) is a document database that can be used as a vector database.

In the walkthrough, we’ll demo the `SelfQueryRetriever` with a `MongoDB Atlas` vector store.

## Creating a MongoDB Atlas vectorstore[​](#creating-a-mongodb-atlas-vectorstore "Direct link to Creating a MongoDB Atlas vectorstore")

First we’ll want to create a MongoDB Atlas VectorStore and seed it with some data. We’ve created a small demo set of documents that contain summaries of movies.

NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `pymongo` package.

```
%pip install --upgrade --quiet  lark pymongo
```

We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key.

```
import osOPENAI_API_KEY = "Use your OpenAI key"os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
```

```
from langchain_community.vectorstores import MongoDBAtlasVectorSearchfrom langchain_core.documents import Documentfrom langchain_openai import OpenAIEmbeddingsfrom pymongo import MongoClientCONNECTION_STRING = "Use your MongoDB Atlas connection string"DB_NAME = "Name of your MongoDB Atlas database"COLLECTION_NAME = "Name of your collection in the database"INDEX_NAME = "Name of a search index defined on the collection"MongoClient = MongoClient(CONNECTION_STRING)collection = MongoClient[DB_NAME][COLLECTION_NAME]embeddings = OpenAIEmbeddings()
```

```
docs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"year": 1993, "rating": 7.7, "genre": "action"},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"year": 2010, "genre": "thriller", "rating": 8.2},    ),    Document(        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",        metadata={"year": 2019, "rating": 8.3, "genre": "drama"},    ),    Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={"year": 1979, "rating": 9.9, "genre": "science fiction"},    ),    Document(        page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",        metadata={"year": 2006, "genre": "thriller", "rating": 9.0},    ),    Document(        page_content="Toys come alive and have a blast doing so",        metadata={"year": 1995, "genre": "animated", "rating": 9.3},    ),]vectorstore = MongoDBAtlasVectorSearch.from_documents(    docs,    embeddings,    collection=collection,    index_name=INDEX_NAME,)
```

Now, let’s create a vector search index on your cluster. In the below example, `embedding` is the name of the field that contains the embedding vector. Please refer to the [documentation](https://www.mongodb.com/docs/atlas/atlas-search/field-types/knn-vector) to get more details on how to define an Atlas Vector Search index. You can name the index `{COLLECTION_NAME}` and create the index on the namespace `{DB_NAME}.{COLLECTION_NAME}`. Finally, write the following definition in the JSON editor on MongoDB Atlas:

```
{  "mappings": {    "dynamic": true,    "fields": {      "embedding": {        "dimensions": 1536,        "similarity": "cosine",        "type": "knnVector"      },      "genre": {        "type": "token"      },      "ratings": {        "type": "number"      },      "year": {        "type": "number"      }    }  }}
```

## Creating our self-querying retriever[​](#creating-our-self-querying-retriever "Direct link to Creating our self-querying retriever")

Now we can instantiate our retriever. To do this we’ll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.

```
from langchain.chains.query_constructor.base import AttributeInfofrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain_openai import OpenAImetadata_field_info = [    AttributeInfo(        name="genre",        description="The genre of the movie",        type="string",    ),    AttributeInfo(        name="year",        description="The year the movie was released",        type="integer",    ),    AttributeInfo(        name="rating", description="A 1-10 rating for the movie", type="float"    ),]document_content_description = "Brief summary of a movie"
```

```
llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, document_content_description, metadata_field_info, verbose=True)
```

## Testing it out[​](#testing-it-out "Direct link to Testing it out")

And now we can try actually using our retriever!

```
# This example only specifies a relevant queryretriever.get_relevant_documents("What are some movies about dinosaurs")
```

```
# This example specifies a filterretriever.get_relevant_documents("What are some highly rated movies (above 9)?")
```

```
# This example only specifies a query and a filterretriever.get_relevant_documents(    "I want to watch a movie about toys rated higher than 9")
```

```
# This example specifies a composite filterretriever.get_relevant_documents(    "What's a highly rated (above or equal 9) thriller film?")
```

```
# This example specifies a query and composite filterretriever.get_relevant_documents(    "What's a movie after 1990 but before 2005 that's all about dinosaurs, \    and preferably has a lot of action")
```

## Filter k[​](#filter-k "Direct link to Filter k")

We can also use the self query retriever to specify `k`: the number of documents to fetch.

We can do this by passing `enable_limit=True` to the constructor.

```
retriever = SelfQueryRetriever.from_llm(    llm,    vectorstore,    document_content_description,    metadata_field_info,    verbose=True,    enable_limit=True,)
```

```
# This example only specifies a relevant queryretriever.get_relevant_documents("What are two movies about dinosaurs?")
```