## Redis

> [Redis](https://redis.com/) is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.

In the notebook, we’ll demo the `SelfQueryRetriever` wrapped around a `Redis` vector store.

## Creating a Redis vector store[​](#creating-a-redis-vector-store "Direct link to Creating a Redis vector store")

First we’ll want to create a Redis vector store 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`) along with integration-specific requirements.

```
%pip install --upgrade --quiet  redis redisvl langchain-openai tiktoken lark
```

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

```
import getpassimport osos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
```

```
from langchain_community.vectorstores import Redisfrom 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,            "director": "Steven Spielberg",            "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",            "genre": "science fiction",            "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",            "genre": "science fiction",            "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",            "genre": "drama",            "rating": 8.3,        },    ),    Document(        page_content="Toys come alive and have a blast doing so",        metadata={            "year": 1995,            "director": "John Lasseter",            "genre": "animated",            "rating": 9.1,        },    ),    Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "year": 1979,            "rating": 9.9,            "director": "Andrei Tarkovsky",            "genre": "science fiction",        },    ),]
```

```
index_schema = {    "tag": [{"name": "genre"}],    "text": [{"name": "director"}],    "numeric": [{"name": "year"}, {"name": "rating"}],}vectorstore = Redis.from_documents(    docs,    embeddings,    redis_url="redis://localhost:6379",    index_name="movie_reviews",    index_schema=index_schema,)
```

```
`index_schema` does not match generated metadata schema.If you meant to manually override the schema, please ignore this message.index_schema: {'tag': [{'name': 'genre'}], 'text': [{'name': 'director'}], 'numeric': [{'name': 'year'}, {'name': 'rating'}]}generated_schema: {'text': [{'name': 'director'}, {'name': 'genre'}], 'numeric': [{'name': 'year'}, {'name': 'rating'}], 'tag': []}
```

## 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 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")
```

```
/Users/bagatur/langchain/libs/langchain/langchain/chains/llm.py:278: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.  warnings.warn(
```

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

```
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'id': 'doc:movie_reviews:7b5481d753bc4135851b66fa61def7fb', 'director': 'Steven Spielberg', 'genre': 'science fiction', 'year': '1993', 'rating': '7.7'}), Document(page_content='Toys come alive and have a blast doing so', metadata={'id': 'doc:movie_reviews:9e4e84daa0374941a6aa4274e9bbb607', 'director': 'John Lasseter', 'genre': 'animated', 'year': '1995', 'rating': '9.1'}), Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'id': 'doc:movie_reviews:2cc66f38bfbd438eb3a045d90a1a4088', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'year': '1979', 'rating': '9.9'}), Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'id': 'doc:movie_reviews:edf567b1d5334e02b2a4c692d853c80c', 'director': 'Satoshi Kon', 'genre': 'science fiction', 'year': '2006', 'rating': '8.6'})]
```

```
# This example only specifies a filterretriever.get_relevant_documents("I want to watch a movie rated higher than 8.4")
```

```
query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.4) limit=None
```

```
[Document(page_content='Toys come alive and have a blast doing so', metadata={'id': 'doc:movie_reviews:9e4e84daa0374941a6aa4274e9bbb607', 'director': 'John Lasseter', 'genre': 'animated', 'year': '1995', 'rating': '9.1'}), Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'id': 'doc:movie_reviews:2cc66f38bfbd438eb3a045d90a1a4088', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'year': '1979', 'rating': '9.9'}), Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'id': 'doc:movie_reviews:edf567b1d5334e02b2a4c692d853c80c', 'director': 'Satoshi Kon', 'genre': 'science fiction', 'year': '2006', 'rating': '8.6'})]
```

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

```
query='women' 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={'id': 'doc:movie_reviews:bb899807b93c442083fd45e75a4779d5', 'director': 'Greta Gerwig', 'genre': 'drama', 'year': '2019', 'rating': '8.3'})]
```

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

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

```
[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'id': 'doc:movie_reviews:2cc66f38bfbd438eb3a045d90a1a4088', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'year': '1979', 'rating': '9.9'}), Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'id': 'doc:movie_reviews:edf567b1d5334e02b2a4c692d853c80c', 'director': 'Satoshi Kon', 'genre': 'science fiction', 'year': '2006', 'rating': '8.6'})]
```

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

```
query='toys' 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.CONTAIN: 'contain'>, attribute='genre', value='animated')]) limit=None
```

```
[Document(page_content='Toys come alive and have a blast doing so', metadata={'id': 'doc:movie_reviews:9e4e84daa0374941a6aa4274e9bbb607', 'director': 'John Lasseter', 'genre': 'animated', 'year': '1995', 'rating': '9.1'})]
```

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

```
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'id': 'doc:movie_reviews:7b5481d753bc4135851b66fa61def7fb', 'director': 'Steven Spielberg', 'genre': 'science fiction', 'year': '1993', 'rating': '7.7'}), Document(page_content='Toys come alive and have a blast doing so', metadata={'id': 'doc:movie_reviews:9e4e84daa0374941a6aa4274e9bbb607', 'director': 'John Lasseter', 'genre': 'animated', 'year': '1995', 'rating': '9.1'})]
```