## DashVector

> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements. The vector retrieval service `DashVector` is based on the `Proxima` core of the efficient vector engine independently developed by `DAMO Academy`, and provides a cloud-native, fully managed vector retrieval service with horizontal expansion capabilities. `DashVector` exposes its powerful vector management, vector query and other diversified capabilities through a simple and easy-to-use SDK/API interface, which can be quickly integrated by upper-layer AI applications, thereby providing services including large model ecology, multi-modal AI search, molecular structure A variety of application scenarios, including analysis, provide the required efficient vector retrieval capabilities.

In this notebook, we’ll demo the `SelfQueryRetriever` with a `DashVector` vector store.

## Create DashVector vectorstore[​](#create-dashvector-vectorstore "Direct link to Create DashVector vectorstore")

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

To use DashVector, you have to have `dashvector` package installed, and you must have an API key and an Environment. Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html).

NOTE: The self-query retriever requires you to have `lark` package installed.

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

```
import osimport dashvectorclient = dashvector.Client(api_key=os.environ["DASHVECTOR_API_KEY"])
```

```
from langchain_community.embeddings import DashScopeEmbeddingsfrom langchain_community.vectorstores import DashVectorfrom langchain_core.documents import Documentembeddings = DashScopeEmbeddings()# create DashVector collectionclient.create("langchain-self-retriever-demo", dimension=1536)
```

```
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, "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 = DashVector.from_documents(    docs, embeddings, collection_name="langchain-self-retriever-demo")
```

## Create your self-querying retriever[​](#create-your-self-querying-retriever "Direct link to Create your 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_community.llms import Tongyimetadata_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 = Tongyi(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")
```

```
query='dinosaurs' filter=None limit=None
```

```
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}), Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}), 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': 8.199999809265137}), 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': 8.600000381469727})]
```

```
# This example only specifies a filterretriever.get_relevant_documents("I want to watch a movie rated higher than 8.5")
```

```
query=' ' filter=Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5) limit=None
```

```
[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, '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, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]
```

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

```
query='Greta Gerwig' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None
```

```
[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': 8.300000190734863})]
```

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

```
query='science fiction' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)]) limit=None
```

```
[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'})]
```

## 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,    enable_limit=True,    verbose=True,)
```

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

```
query='dinosaurs' filter=None limit=2
```

```
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}), Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]
```