This project provides a semantic cache for natural language queries, ideal for applications involving AI responses or text classification. It enables caching based on meaning rather than exact string matches, improving efficiency and reducing latency for similar but not identical queries. The target audience includes developers working with LLMs, chatbots, and natural language processing tasks.

How It Works

The cache leverages semantic similarity by storing cache entries based on their meaning, using an underlying Upstash Vector database. When a query is made, it's converted into a vector embedding and compared against existing cache entries. A configurable proximity threshold determines if a cache hit occurs, allowing for flexible matching of synonyms and paraphrased queries. This approach allows for effective caching of natural language inputs that traditional lexical caches would miss.

Quick Start & Requirements

• Install via npm: npm install @upstash/semantic-cache @upstash/vector
• Requires an Upstash Vector database.
• Needs Upstash Vector URL and Token credentials (set via .env file).
• Supports Node.js applications.
• Official docs: https://github.com/upstash/semantic-cache

Highlighted Details

• Caches based on semantic similarity, not just exact matches.
• Handles synonyms and multi-language queries (if embedding model supports it).
• Customizable proximity threshold for cache hit sensitivity.
• Supports namespacing for data partitioning.

Maintenance & Community

The project is maintained by Upstash. Further community or contribution details are not explicitly detailed in the README.

Licensing & Compatibility

• MIT License.
• Permissive license suitable for commercial use and integration into closed-source applications.

Limitations & Caveats

A 1-second delay is noted as necessary after setting data to allow the vector index to update, which might impact real-time cache population in certain scenarios. The effectiveness of multilingual support is dependent on the chosen embedding model.