Gptcache: Cache large language model responses | Software on the Web
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Gptcache

Cache large language model responses

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Overview

Gptcache is a library for creating semantic cache for large language model queries, reducing API costs and boosting speed

Gptcache has been fully integrated with LangChain and has a released server docker image, allowing any language to use it

Details

Key Features

  • Creates a semantic cache for large language model queries
  • Reduces LLM API costs by up to 10x
  • Boosts speed by up to 100x
  • Supports exact and similar matching with caching

Best For

  • Developers working with large language models
  • Applications with high traffic levels and frequent LLM API calls
  • Those looking to reduce LLM API costs and improve response times

Integrations

  • LangChain
  • OpenAI API

Pros

  • Reduces expenses related to LLM API calls
  • Improves response times for LLM queries
  • Easy to install and use, with a simple example usage provided

Limitations

  • The API may be subject to change at any time due to swift development
Read full editorial notes

What is Gptcache?

Gptcache is a project dedicated to building a semantic cache for storing large language model responses, helping reduce expenses and improve response times. It is designed to work with large language models like ChatGPT

What are the key features of Gptcache?

  • Creates a semantic cache for large language model queries

  • Reduces LLM API costs by up to 10x

  • Boosts speed by up to 100x

  • Supports exact and similar matching with caching

Who is Gptcache best for?

  • Developers working with large language models

  • Applications with high traffic levels and frequent LLM API calls

  • Those looking to reduce LLM API costs and improve response times

What integrations and ecosystem support does Gptcache offer?

  • LangChain

  • OpenAI API

What are the pros and limitations of Gptcache?

  • Pros: Reduces expenses related to LLM API calls; Improves response times for LLM queries; Easy to install and use, with a simple example usage provided

  • Limitations: The API may be subject to change at any time due to swift development

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