## PGVector (Postgres)

> [PGVector](https://github.com/pgvector/pgvector) is a vector similarity search package for `Postgres` data base.

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

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

First we’ll want to create a PGVector 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`). We also need the \`\` package.

```
%pip install --upgrade --quiet  lark pgvector psycopg2-binary
```

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 PGVectorfrom langchain_core.documents import Documentfrom langchain_openai import OpenAIEmbeddingscollection = "Name of your collection"embeddings = OpenAIEmbeddings()
```

```
docs = [    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 = PGVector.from_documents(    docs,    embeddings,    collection_name=collection,)
```

## 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")
```

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

```
# This example 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 film?")
```

```
# 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")
```

## 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")
```