Using Pinecone 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 Pinecone-specific client SDK or logic directly, we can simply use the high-level API 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. Create a serverless index.

2. Load the movie data into the index.

3. Use the movie recommendation service.

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 a free Pinecone account

Note: If you already have a Pinecone account and API key, please skip this section.

1. Sign up for a free Pinecone account.

2. You need an API key to make API calls to your Pinecone project. To get your key, open the Pinecone console, select your project, go to API Keys, and copy your API key.

3. Note down the API key because it will be used in subsequent steps. ...

Create a