How do you handle text conversion to embeddings and upsert in Pinecone for real-time recommendation systems

Question

With the help of proper code and explanation, can you tell me how you handle text conversion to embeddings and upsert in Pinecone for real-time recommendation systems?

Answer 1

To handle text conversion to embeddings and upsert into Pinecone for real-time recommendation systems, you can follow the following steps:

• Convert Text to Embeddings: Use a pre-trained model like SentenceTransformer to generate vector embeddings for the text.
• Upsert into Pinecone: Store the embeddings in Pinecone with unique IDs and metadata for recommendations.

Here is the code snippet showing the above steps:

In the above code, we are using the following key steps:

• Text to Embeddings: Use a lightweight model for real-time applications.
• Upsert: Store vectors with associated metadata for filtering.
• Query: Retrieve recommendations based on similarity to the query embedding.

Hence, this approach ensures efficient, real-time recommendations using Pinecone's fast vector search capabilities.