Resk-Caching is a Bun-based backend library and server designed for secure caching, embeddings orchestration, and vector database access. It prioritizes security, high performance, and deep observability. - Resk-Security/resk-caching
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Maker
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Resk-Caching is a Bun-based backend library/server designed to cache Large Language Model (LLM) responses using vector databases, significantly reducing API costs while maintaining response quality and relevance.
🎯 Primary Purpose: Cost Optimization for LLM APIs
This library addresses the high costs associated with LLM API calls by implementing intelligent caching strategies. Instead of making expensive API calls to services like OpenAI, Claude, or other LLMs, Resk-Caching stores pre-computed responses in a vector database and retrieves them based on semantic similarity to incoming queries.
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Bun plus vector caching is such an intriguing combo. I’m curious about how you’re managing cache invalidation as LLMs evolve?
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Is this ready to work with Milvus or Pinecone right out of the box, or will we need to create some adapters?
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Is this ready to work with Milvus or Pinecone right out of the box, or will we need to create some adapters?
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This almost seems too good to be true—are there any benchmarks comparing cached responses to fresh calls?
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I’m just getting started with LLMs, and honestly, the costs are a bit intimidating—this feels like a real game changer. Thanks for creating something so practical! 🙌
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Maker
@azra_malek Hey! Thanks a lot. I try to create useful tools for the community. Your feedback is very valuable.
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this is exactly what makes AI accessible for smaller developers like me. Huge congrats on the launch! 🚀
Bun plus vector caching is such an intriguing combo. I’m curious about how you’re managing cache invalidation as LLMs evolve?
Is this ready to work with Milvus or Pinecone right out of the box, or will we need to create some adapters?
Is this ready to work with Milvus or Pinecone right out of the box, or will we need to create some adapters?
This almost seems too good to be true—are there any benchmarks comparing cached responses to fresh calls?
I’m just getting started with LLMs, and honestly, the costs are a bit intimidating—this feels like a real game changer. Thanks for creating something so practical! 🙌
@azra_malek Hey! Thanks a lot. I try to create useful tools for the community. Your feedback is very valuable.
this is exactly what makes AI accessible for smaller developers like me. Huge congrats on the launch! 🚀