## Astra DB (Cassandra)

> [DataStax Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on `Cassandra` and made conveniently available through an easy-to-use JSON API.

In the walkthrough, we’ll demo the `SelfQueryRetriever` with an `Astra DB` vector store.

## Creating an Astra DB vector store[​](#creating-an-astra-db-vector-store "Direct link to Creating an Astra DB vector store")

First we’ll want to create an Astra DB 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 `astrapy` package.

```
%pip install --upgrade --quiet lark astrapy langchain-openai
```

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

```
import osfrom getpass import getpassfrom langchain_openai.embeddings import OpenAIEmbeddingsos.environ["OPENAI_API_KEY"] = getpass("OpenAI API Key:")embeddings = OpenAIEmbeddings()
```

Create the Astra DB VectorStore:

*   the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`
*   the Token looks like `AstraCS:6gBhNmsk135....`

```
ASTRA_DB_API_ENDPOINT = input("ASTRA_DB_API_ENDPOINT = ")ASTRA_DB_APPLICATION_TOKEN = getpass("ASTRA_DB_APPLICATION_TOKEN = ")
```

```
from langchain.vectorstores import AstraDBfrom langchain_core.documents import Documentdocs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},    ),    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, "director": "Satoshi Kon", "rating": 8.6},    ),    Document(        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",        metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},    ),    Document(        page_content="Toys come alive and have a blast doing so",        metadata={"year": 1995, "genre": "animated"},    ),    Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "year": 1979,            "director": "Andrei Tarkovsky",            "genre": "science fiction",            "rating": 9.9,        },    ),]vectorstore = AstraDB.from_documents(    docs,    embeddings,    collection_name="astra_self_query_demo",    api_endpoint=ASTRA_DB_API_ENDPOINT,    token=ASTRA_DB_APPLICATION_TOKEN,)
```

## 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.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrievermetadata_field_info = [    AttributeInfo(        name="genre",        description="The genre of the movie",        type="string or list[string]",    ),    AttributeInfo(        name="year",        description="The year the movie was released",        type="integer",    ),    AttributeInfo(        name="director",        description="The name of the movie director",        type="string",    ),    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("I want to watch a movie rated higher than 8.5")
```

```
# This example only specifies a query and a filterretriever.get_relevant_documents("Has Greta Gerwig directed any movies about women")
```

```
# This example specifies a composite filterretriever.get_relevant_documents(    "What's a highly rated (above 8.5), science fiction movie ?")
```

```
# This example specifies a query and composite filterretriever.get_relevant_documents(    "What's a movie about toys after 1990 but before 2005, and is animated")
```

## 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?")
```

## Cleanup[​](#cleanup "Direct link to Cleanup")

If you want to completely delete the collection from your Astra DB instance, run this.

_(You will lose the data you stored in it.)_

```
vectorstore.delete_collection()
```