Semantic Search with OpenAI Embeddings and Pinecone
The "Semantic Search with OpenAI Embeddings and Pinecone" project exemplifies an innovative approach to creating a semantic search engine by integrating OpenAI Embeddings with the Pinecone vector database. This combination provides a powerful tool for searching and retrieving information based on semantic similarity, rather than simple keyword matching.
Overview
The main highlight of this project is a demo application which effectively demonstrates how OpenAI's advanced embedding capabilities can be paired with Pinecone's vector database to achieve robust semantic search functionality. Interested users can explore this application freely, taking advantage of the free tiers offered by both Pinecone and OpenAI, making it accessible and cost-effective for experimentation.
For users experiencing authentication issues, an alternative demo link is provided to ensure smooth access.
Tech Stack
This project leverages a modern and efficient technology stack, including:
- Next.js: A powerful React-based framework for building web applications.
- NextAuth.js: A flexible authentication solution for securing web applications.
- Prisma: An ORM (Object-Relational Mapping) tool that simplifies database interaction.
- Tailwind CSS: A utility-first CSS framework for designing responsive interfaces.
- tRPC: An RPC (Remote Procedure Call) framework for building typesafe APIs.
- Pinecone vector db: A scalable and fast vector database that supports semantic search.
- OpenAI Embeddings: Advanced AI models that convert text into numerical vectors that capture semantic meaning.
- NeonDB serverless postgres db: A serverless PostgreSQL database for resilient data management.
How It Works
The demonstration includes several components that help users understand how semantic search is implemented:
- Input Record: A visual representation showing how data is initially processed and stored.
- Query Handling: Illustrates how queries are interpreted semantically, with results retrieved based on meaning rather than exact match.
More Learning Resources
For those interested in digging deeper into how embeddings function and how they transform the search landscape, a recommended YouTube video by AssemblyAI offers an insightful explanation on embeddings.
Overall, the project is geared towards providing an advanced yet accessible introduction to semantic search technologies, opening avenues for businesses and developers to explore and implement more intuitive search solutions.