README.md

LLM Proxy Cache Stats 📊🤖🧠💾

Ahoy, mateys! LLM Proxy Cache Stats be a Python application that caches requests to the OpenAI API and provides basic analytics on the requests and responses. Inspired by the legendary Helicone, this here treasure be simpler and easier to set up. Arrr! No need for a database, nor Cloudflare.

🚀 Quick Start ⚓

To run the cache, ye need to have Python 3. Clone this repository and install the dependencies using pip, me hearties:

git clone  https://github.com/lukestanley/llm_proxy_cache_stats.git
cd  llm_proxy_cache_stats
pip install  -r  requirements.txt
python cache_openai.py

🧭 To use the cache, set the API base to the cache server:

import openai
openai.api_base = "http://localhost:5000/v1"

⌛ The cache be savin' to disk up to every 5 seconds.

To see ye crazy swashbuckling requests, gander the honest beauty of ye stats dashboard: http://localhost:5000/stats

The statistics be updated on ye old fashioned way, by refreshin' the page. ⛵

🦜️🔗 Aye this be workin' with LangChain too!

import openai
openai.api_base = "http://localhost:5000/v1"
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMChain

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)

chain = LLMChain(llm=llm, prompt=prompt)
print(chain.run("reliable socks for pirates"))

⚙️ Configuration 📜

Ye can customise the behaviour of the cache by modifyin' the cache_openai.py file, me hearties. Tweak it to your likin' and sail the high seas of AI analytics with ease! 🏴‍☠️ 🦜