Unlock the Power of AI-Powered Search for Your Digital Platform using vector databases and embeddings

Ever wondered how to transform your customers’ user experience experience on your e-commerce site, job board, community app, or SaaS product with cutting-edge AI technology?

Here are ten innovative ways AI-powered search can revolutionize your online platform. 

Some of the ideas come from real-world projects our team worked on and are meant to stir up your imagination:

1. E-Commerce: Seamlessly match products to shopper queries, enhancing customer satisfaction and boosting sales.

2. Job Boards and ATS: Precisely align resumes with job descriptions, streamlining the hiring process.

3. Community Apps: Connect users based on shared interests, fostering meaningful interactions.

4. Grocery Stores: Link ingredients to recipes and dietary needs, simplifying meal planning.

5. Directories: Accurately match listings to visitor inquiries, improving user experience.

6. Fashion Retail: Recommend products based on customers' past purchases and style preferences, personalizing shopping.

7. Insurance: Efficiently retrieve relevant photos for claims processing, speeding up resolution times.

8. Art Industry: Suggest artwork that aligns with users' tastes, enhancing discovery and appreciation.

9. Marketing: Predict lead conversion likelihood based on user behavior, refining targeting strategies.

10. Publishing: Offer personalized article recommendations, keeping readers engaged and informed.

If you want to dig deeper, here’s a (more technical) description of how to integrate this into your website (or ask your team to do it). For the sake of clarity, I’ll use an example of an e-commerce shop selling electronics.

1. Vectorize your existing database

First, you need to create the so-called embeddings of your products. This means we need to transform the texts that describe each of your products such as TV sets, fridges or microwaves into corresponding vectors. So for example, a product like “PANASONIC TX-50MX700E, Ultra HD 4K” plus its description, color, size, etc. will be transformed into a vector that looks like an array of numbers; eg:

To do this you can use the OpenAI endpoint https://api.openai.com/v1/embeddings but there are alternative ways to do it as well.

1. Store your embeddings in a vector database

Now you need to store the vectors in a database provider that has some support for vector storing, retrieval, and distance calculations. You can look at vendors ranging from SingleStore, Pinecone to PostgreSQL with pgvector extension, Amazon’s vector store in OpenSearch service or Microsoft Azure Cosmos DB.

Example: 

When choosing a vector database, consider factors like scalability, ease of integration, support for your specific use cases, and the community or vendor support available.

1. Now you can retrieve your TV sets by performing a similarity search. 

For this, you need to take the input from your user, such as “I need a 4K tv that’s at least 50 inches (125 cm) wide” and convert it to a vector such as [1.1, 1.55, -1.12, 1.75, …]. Then you need to query your database and retrieve the vectors, i.e. the products, that are closest to your customer’s query. 

For example, to retrieve the 5 most suitable products that will match your user’s request, you can use a query such as this one (PostgreSQL):

In conclusion, integrating AI-powered search into your digital platform is not just about keeping up with technological trends; it's about enhancing the way users interact with your service. By leveraging vector databases and embeddings, you're not only improving search accuracy and efficiency but also creating a more personalized and engaging experience for your users. This approach can lead to increased satisfaction, higher conversion rates, and a competitive edge in your industry.

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