This article discusses the implementation of semantic search using PostgreSQL and OpenAI Embeddings. It explains how word embeddings capture semantic relationships between words and demonstrates how to utilize text-embedding-ada model and cosine similarity for sorting reviews. The article also covers the use of vector databases, specifically the open-source PostgreSQL extension pgvector, for storing and searching embeddings. The potential applications of semantic search extend beyond text data to other forms of data such as sound, video, and images.
Implementing Semantic Search with PostgreSQL and OpenAI Embeddings
Implementing semantic search within corporate databases can be challenging, but it doesn’t have to be. In this article, we show you how to use PostgreSQL and OpenAI Embeddings to implement semantic search on your data. We also provide links to free embedding models if you prefer not to use OpenAI Embeddings API.
What are Word Embeddings?
Word embeddings are dense vector representations of words in a vector space. They capture semantic relationships between words, allowing for semantic search on available data.
Using OpenAI’s Text-Embedding-Ada Model
We can use OpenAI’s text-embedding-ada model to generate embeddings. The choice of distance function doesn’t matter much, but cosine similarity is recommended by OpenAI.
Storing Embeddings in a Database
Once a word or document is transformed into an embedding, it can be stored in a database. However, the database needs to support fast operations on the vector to be considered a vector database.
Using pgvector with PostgreSQL
We can use pgvector, an open-source PostgreSQL extension, to enable vector similarity search functionalities. The article provides instructions on how to run PostgreSQL with pgvector and demonstrates how to create a table, insert embeddings, and select similar items.
The Potential Applications
Implementing semantic search with PostgreSQL and OpenAI Embeddings has vast applications. It can be used for corporate searches, medical record systems, and similarity calculations for various types of data such as sound, video, and images.
Evolve Your Company with AI and Semantic Search
If you want to stay competitive and redefine your way of work, consider implementing Semantic Search with PostgreSQL and OpenAI Embeddings. Here’s how you can get started:
- Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
If you need advice on AI KPI management or want to stay updated on leveraging AI, connect with us at hello@itinai.com or follow us on Telegram at t.me/itinainews and Twitter @itinaicom.
Spotlight on a Practical AI Solution: AI Sales Bot
Discover how AI can redefine your sales processes and customer engagement with our AI Sales Bot. It automates customer engagement 24/7 and manages interactions across all customer journey stages. Explore our AI Sales Bot solution at itinai.com/aisalesbot.
List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Semantic Search with PostgreSQL and OpenAI Embeddings
- Towards Data Science – Medium
- Twitter – @itinaicom
