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Build a Question Answering App Using Pinecone And Python
How to use Pinecone to add semantic search to your app
Project Motivation
Let’s imagine you own a website where customers can find the answers to common problems. Instead of maintaining a customer support service, you choose to create a powerful and intelligent search engine. People can simply type their issues in a search field and see similar questions. This way, you reduce duplicate topics, and customers can easily find what they are looking for.
How to ensure that the search is smart enough to show the best matching results to the users? This is when the semantic search comes into play. It tries to understand the intent and context of the search query to provide more accurate results.
In this article, we’ll create a similarity search engine. We’ll use Python and Pinecone, a fully managed vector database, to achieve this goal.
Let’s get started!
Pinecone Key Concepts
Vector representations of objects
In Machine Learning(ML), vector embeddings represent a collection of continuous numbers to determine similarities between various objects. We calculate how close the objects’ vectors are in the vector space.
Distance metrics
The distance metrics depend on the use case. For example, Pinecone supports some of the most frequently used metrics: Euclidean, cosine, and dot product.
In our demo app, we’ll use the cosine metric. It’s used to find similarities between different documents. The result scores range between [0,1].
Pinecone Quick Overview
Pinecone is a freemium tool. You can sign up for free and use the services for a smaller project in a single pod. It should be enough for around 1M vectors with 100ms latency. For advanced plans, check out the pricing page.
What can you build with Pinecone?
- Semantic text search
- Image similarity search
- Video/Audio/Text recommendations
- Time series similarity…
