## Timescale Vector (Postgres)

> [Timescale Vector](https://www.timescale.com/ai) is `PostgreSQL++` for AI applications. It enables you to efficiently store and query billions of vector embeddings in `PostgreSQL`.
> 
> [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) also known as `Postgres`, is a free and open-source relational database management system (RDBMS) emphasizing extensibility and `SQL` compliance.

This notebook shows how to use the Postgres vector database (`TimescaleVector`) to perform self-querying. In the notebook we’ll demo the `SelfQueryRetriever` wrapped around a TimescaleVector vector store.

## What is Timescale Vector?[​](#what-is-timescale-vector "Direct link to What is Timescale Vector?")

**[Timescale Vector](https://www.timescale.com/ai) is PostgreSQL++ for AI applications.**

Timescale Vector enables you to efficiently store and query millions of vector embeddings in `PostgreSQL`. - Enhances `pgvector` with faster and more accurate similarity search on 1B+ vectors via DiskANN inspired indexing algorithm. - Enables fast time-based vector search via automatic time-based partitioning and indexing. - Provides a familiar SQL interface for querying vector embeddings and relational data.

Timescale Vector is cloud PostgreSQL for AI that scales with you from POC to production: - Simplifies operations by enabling you to store relational metadata, vector embeddings, and time-series data in a single database. - Benefits from rock-solid PostgreSQL foundation with enterprise-grade feature liked streaming backups and replication, high-availability and row-level security. - Enables a worry-free experience with enterprise-grade security and compliance.

## How to access Timescale Vector[​](#how-to-access-timescale-vector "Direct link to How to access Timescale Vector")

Timescale Vector is available on [Timescale](https://www.timescale.com/ai), the cloud PostgreSQL platform. (There is no self-hosted version at this time.)

LangChain users get a 90-day free trial for Timescale Vector. - To get started, [signup](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) to Timescale, create a new database and follow this notebook! - See the [Timescale Vector explainer blog](https://www.timescale.com/blog/how-we-made-postgresql-the-best-vector-database/?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) for more details and performance benchmarks. - See the [installation instructions](https://github.com/timescale/python-vector) for more details on using Timescale Vector in python.

## Creating a TimescaleVector vectorstore[​](#creating-a-timescalevector-vectorstore "Direct link to Creating a TimescaleVector vectorstore")

First we’ll want to create a Timescale Vector vectorstore 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 `timescale-vector` package.

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

```
%pip install --upgrade --quiet  timescale-vector
```

In this example, we’ll use `OpenAIEmbeddings`, so let’s load your OpenAI API key.

```
# Get openAI api key by reading local .env file# The .env file should contain a line starting with `OPENAI_API_KEY=sk-`import osfrom dotenv import find_dotenv, load_dotenv_ = load_dotenv(find_dotenv())OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]# Alternatively, use getpass to enter the key in a prompt# import os# import getpass# os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
```

To connect to your PostgreSQL database, you’ll need your service URI, which can be found in the cheatsheet or `.env` file you downloaded after creating a new database.

If you haven’t already, [signup for Timescale](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral), and create a new database.

The URI will look something like this: `postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require`

```
# Get the service url by reading local .env file# The .env file should contain a line starting with `TIMESCALE_SERVICE_URL=postgresql://`_ = load_dotenv(find_dotenv())TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]# Alternatively, use getpass to enter the key in a prompt# import os# import getpass# TIMESCALE_SERVICE_URL = getpass.getpass("Timescale Service URL:")
```

```
from langchain_community.vectorstores.timescalevector import TimescaleVectorfrom langchain_core.documents import Documentfrom langchain_openai import OpenAIEmbeddingsembeddings = OpenAIEmbeddings()
```

Here’s the sample documents we’ll use for this demo. The data is about movies, and has both content and metadata fields with information about particular movie.

```
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,        },    ),]
```

Finally, we’ll create our Timescale Vector vectorstore. Note that the collection name will be the name of the PostgreSQL table in which the documents are stored in.

```
COLLECTION_NAME = "langchain_self_query_demo"vectorstore = TimescaleVector.from_documents(    embedding=embeddings,    documents=docs,    collection_name=COLLECTION_NAME,    service_url=TIMESCALE_SERVICE_URL,)
```

## 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 OpenAI# Give LLM info about the metadata fieldsmetadata_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"# Instantiate the self-query retriever from an LLMllm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, document_content_description, metadata_field_info, verbose=True)
```

## Self Querying Retrieval with Timescale Vector[​](#self-querying-retrieval-with-timescale-vector "Direct link to Self Querying Retrieval with Timescale Vector")

And now we can try actually using our retriever!

Run the queries below and note how you can specify a query, filter, composite filter (filters with AND, OR) in natural language and the self-query retriever will translate that query into SQL and perform the search on the Timescale Vector (Postgres) vectorstore.

This illustrates the power of the self-query retriever. You can use it to perform complex searches over your vectorstore without you or your users having to write any SQL directly!

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

```
/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/libs/langchain/langchain/chains/llm.py:275: 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={'year': 1993, 'genre': 'science fiction', 'rating': 7.7}), Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7}), Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}), Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]
```

```
# 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.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, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'}), Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'}), 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': 8.6, 'director': 'Satoshi Kon'}), 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': 8.6, 'director': 'Satoshi Kon'})]
```

```
# 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={'year': 2019, 'rating': 8.3, 'director': 'Greta Gerwig'}), Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'rating': 8.3, 'director': 'Greta Gerwig'})]
```

```
# 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.EQ: 'eq'>, attribute='genre', value='science fiction')]) limit=None
```

```
[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'}), Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'})]
```

```
# 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.EQ: 'eq'>, attribute='genre', value='animated')]) limit=None
```

```
[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': '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 specifies a query with a LIMIT valueretriever.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, 'genre': 'science fiction', 'rating': 7.7}), Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7})]
```