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.