I am integrating Pinecone’s vector database within our PHP Laravel application to enhance search functionality with a focus on context-based results. We use OpenAI’s text-embedding-3-large model for converting text into vector embeddings.
To give you a better understanding of our scenario, consider two templates:
- Template One Tags: diwali, special, exhibition, poster, posters, festival, festivals, divali, dewali, deepavali, deepawali, red, elegant
- Template Two Tags: diwali, festival, lights, festivals, deepawali, deepavali, greetings, wish, wishes, card, greeting, design, celebration, dipawali, दीपावली, dewali, deewali, dipavali, दिवाळी, divali, dipawli, wishfully, festivel, featival, dipabali, fastivel, depavali, lighted
Could you provide a detailed explanation on how Pinecone’s vector search algorithm evaluates and ranks search results in this context? Specifically, I am interested in understanding why the algorithm might rank one template higher than the other based on their semantic relevance to a search query like “diwali.” What factors does Pinecone consider in its ranking process?
Your insights will be invaluable as we aim to optimize our application’s search capabilities to better align with user intents.
Thank you for your time and assistance.