## Milvus

> [Milvus](https://milvus.io/docs/overview.md) is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.

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

## Creating a Milvus vectorstore[​](#creating-a-milvus-vectorstore "Direct link to Creating a Milvus vectorstore")

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

I have used the cloud version of Milvus, thus I need `uri` and `token` as well.

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

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

```
%pip install --upgrade --quiet  pymilvus
```

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 Milvusfrom langchain_core.documents import Documentfrom langchain_openai import OpenAIEmbeddingsembeddings = 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},    ),]vector_store = Milvus.from_documents(    docs,    embedding=embeddings,    connection_args={"uri": "Use your uri:)", "token": "Use your 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.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, vector_store, 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='dinosaur' filter=None limit=None
```

```
[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='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}), 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, 'rating': 9.0, 'genre': 'thriller'})]
```

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

```
query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None
```

```
[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}), 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'})]
```

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

```
query='toys' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None
```

```
[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}), 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'})]
```

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

```
query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='thriller'), Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=9)]) limit=None
```

```
[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, 'rating': 9.0, 'genre': 'thriller'})]
```

```
# 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")
```

```
query='dinosaur' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='action')]) limit=None
```

```
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': '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,    vector_store,    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?")
```

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

```
[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='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'})]
```