Part 9/11:
The embedding process for 25 sentences took approximately 3.2 seconds on CPU, with plans to optimize further using GPU acceleration for quicker processing.
Integrating Embeddings into Microservices and Future Optimization
After successfully generating and inserting embeddings, the next step was to query the collection to retrieve relevant results based on semantic similarity. Testing confirmed that the engine was capable of returning fast, relevant results with minimal latency.
The author also expressed intent to incorporate such semantic search capabilities into their own microservices, emphasizing the importance of performance and scalability.
Key takeaways from their experimentation include:
- The speed advantage of minimal, lightweight engines like Quadrant.