Using PostgreSQL as a Vector Store with LangChain

In this lesson, we will see how to use langchaingo vector store component to improve the integration process. Instead of dealing with PostgreSQL-specific logic, we can simply use the pgvector implementation available in langchaingo.

We will continue to use the movie recommendation example and walk through how to implement a service that provides movie recommendations based on user-provided search criteria. This is split into these steps, which will be executed in order:

1. Load the movie data into the table.

2. Use the movie recommendation service.

Note: We don't need an additional step to enable pgvector extension and table creation in the database since the langchaingo pgvector implementation can do that automatically during the initialization phase.

Below is the high-level architecture of the solution.

• The movie data (including name and description) is converted into vector embeddings and loaded into PostgreSQL database.

• When a user queries for movie recommendations, the application executes a similarity search in the PostgreSQL database.

Set up managed PostgreSQL service

Note: If you already configured the PostgreSQL service from the previous lessons, please skip this section.

Aiven is a fully managed cloud database platform that provides various open-source database services, including PostgreSQL. It offers an easy way to set up and manage a PostgreSQL database with ...