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.