## Get started
We'll use a Pinecone vector store in this example.

First we'll want to create a `Pinecone` vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.

To use Pinecone, you need to have `pinecone` package installed and you must have an API key and an environment. Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart).

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


```python
# !pip install lark pinecone-client
```


```python
import os

import pinecone


pinecone.init(api_key=os.environ["PINECONE_API_KEY"], environment=os.environ["PINECONE_ENV"])
```


```python
from langchain.schema import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone

embeddings = OpenAIEmbeddings()
# create new index
pinecone.create_index("langchain-self-retriever-demo", dimension=1536)
```


```python
docs = [
    Document(page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose", metadata={"year": 1993, "rating": 7.7, "genre": ["action", "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, "rating": 9.9, "director": "Andrei Tarkovsky", "genre": ["science fiction", "thriller"], "rating": 9.9})
]
vectorstore = Pinecone.from_documents(
    docs, embeddings, index_name="langchain-self-retriever-demo"
)
```

## 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.


```python
from langchain.llms import OpenAI
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo

metadata_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
And now we can actually try using our retriever!


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

<CodeOutputBlock lang="python">

```
    query='dinosaur' filter=None


    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': ['action', 'science fiction'], 'rating': 7.7, 'year': 1993.0}),
     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0}),
     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),
     Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'director': 'Christopher Nolan', 'rating': 8.2, 'year': 2010.0})]
```

</CodeOutputBlock>


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

<CodeOutputBlock lang="python">

```
    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)


    [Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),
     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]
```

</CodeOutputBlock>


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

<CodeOutputBlock lang="python">

```
    query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig')


    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'director': 'Greta Gerwig', 'rating': 8.3, 'year': 2019.0})]
```

</CodeOutputBlock>


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

<CodeOutputBlock lang="python">

```
    query=' ' 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)])


    [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]
```

</CodeOutputBlock>


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

<CodeOutputBlock lang="python">

```
    query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990.0), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005.0), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')])


    [Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0})]
```

</CodeOutputBlock>

## 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.


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


```python
# This example only specifies a relevant query
retriever.get_relevant_documents("What are two movies about dinosaurs")
```
