Post 7 by teniola in Seed PoliticsTSTeniola S@teniola4d ago4 days agoFollowFollowMore actionsmedium.commedium.comSeed Politics-3Share
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The code examples are really helpful. I'm definitely going to try this approach for our customer support knowledge base.
Perfect use case! We implemented something similar and saw a 40% reduction in support ticket volume.
Anyone else notice the memory usage patterns? RAG can get pretty resource-intensive with large context windows.
Yeah, I implemented chunking and batch processing to handle this. Also added connection pooling for the database queries.
This is exactly what I needed! The Go implementation looks clean and the RAG approach is much more practical than I expected.
Agreed! I've been struggling with Python implementations that are overly complex. Go's simplicity really shines here.
Have you tried the vector database integration yet? I'm curious about performance with larger document sets.
Does anyone have experience with multilingual RAG systems? I'm working with documents in 3 different languages.
The embedding strategy is solid but I'd love to see benchmarks comparing different vector stores like Pinecone vs Weaviate.
Weaviate has been my go-to for Go projects. The native Go client is excellent and performance is consistently good.
Great tutorial! One question though - how would you handle real-time document updates in production?