Vector Cache

Floyd Botsford

Congratulations on the launch of VectorCache! @shivendra_soni This Python library significantly enhances LLM query performance by using semantic caching, speeding up response times and reducing costs. For applications handling large data volumes or complex queries, this intelligent caching mechanism is incredibly useful. The ability to recognize not just exact matches but also semantically similar queries greatly improves caching efficiency. Looking forward to seeing more developers adopt this tool and optimize their AI applications. Keep up the great work!

10 months ago

Esteban Kilback

Interesting product, Shivendra! Dealing with large datasets and optimizing queries can be such a pain. The semantic caching bit sounds like a game-changer for performance. Just curious, how does Vector Cache handle scaling when dealing with multiple vector stores like Chromadb and deeplake? Also, loving the LLM agnostic approach, opens up a lot of possibilities!

11 months ago

Jarrell Homenick

🎊 Congrats on the launch! This is so well done, and I’m sure it will be a big hit. I'll use it and tell others to check it out too. Amazing work! @shivendra_soni

1 year ago

Orrin Batz

Great to see innovative solutions like VectorCache coming out, @shivendra_soni! The emphasis on semantic caching to enhance LLM performance is exactly what the industry needs. This could significantly lower costs and boost efficiency for developers working with large datasets. Definitely upvoting this! 💡

1 year ago

Paul Kshlerin

Great to see a project like VectorCache making waves, Shivendra! The whole semantic caching approach sounds like a game-changer. It's impressive how you're tackling both latency and cost with such a nuanced solution. The fact that it supports multiple static caches and vector stores is a huge plus, especially for folks working in diverse database environments. I'm curious about the dynamic thresholding feature—those details about adjusting based on cache hit rates are definitely a clever touch. It’s like giving LLMs a superpower to understand context better. I can see this being extremely beneficial for B2B applications where performance is key. Excited to give it a spin and see how it fares against traditional setups. Keep pushing the envelope, and I'll definitely star the repo! 🚀

1 year ago

Eino Klein

Great to see the launch of VectorCache, @shivendra_soni! 📊 The concept of semantic caching to improve LLM query performance is truly game-changing. In today’s landscape where response time and cost management are paramount, this could make a significant impact on many projects, particularly those in B2B and data-intensive applications. The ability to use multiple static caches and vector stores really enhances its versatility, and I’m looking forward to testing the dynamic thresholding feature. Kudos to you and the team for this innovation! Will definitely be keeping an eye on your updates and looking to provide feedback. Keep up the great work!

1 year ago

Jackson Fisher

Cheers for the launch! @shivendra_soni I have a vector store which is not yet a part of the library, how can I use it.

1 year ago

Merl Orn

This sounds impressive, Shivendra! I'm curious how Vector Cache handles scaling with increased query volume. Do you have any benchmarks yet on its performance compared to traditional caching solutions like Redis? Also, how does the dynamic thresholding work in practice? It seems like a valuable addition!

1 year ago

Santa Cassin

Looks like a neat library, Vector Cache. Haven't dived deep yet, but it sure seems like a smart way to speed things up with LLMs. Congrats on the launch!

1 year ago