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‚ÄãTimescale Vector is available on Timescale, 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 to Timescale, create a new database and follow this notebook!See the Timescale Vector explainer blog for more details and performance benchmarks.See the installation instructions for more details on using Timescale Vector in python.Creating a TimescaleVector vectorstore‚ÄãFirst 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 lark#!pip install timescale-vectorIn 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 load_dotenv, find_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, and create a new database.The URI will look something like this:
Timescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.
Timescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL. ->: 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‚ÄãTimescale Vector is available on Timescale, 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 to Timescale, create a new database and follow this notebook!See the Timescale Vector explainer blog for more details and performance benchmarks.See the installation instructions for more details on using Timescale Vector in python.Creating a TimescaleVector vectorstore‚ÄãFirst 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 lark#!pip install timescale-vectorIn 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 load_dotenv, find_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, and create a new database.The URI will look something like this: