llmtrack 1.0.0

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LLMTrack is a Python package for enabling caching (avoiding repeated API calls) and recording token usage on a per-model basis.

Why do we call it "per-model"? Because we want to track the token usage and cache for each model separately under the root directory, following the rule {root_dir}/{client name}/{model name}.

Installation

pip install llmtrack

Root Directory for Saving Cache and Token Usage

By default, the root directory for saving cache and token usage is the current working directory (os.getcwd()). You can change it, as follows:

from llmtrack import set_root_dir, get_root_dir
set_root_dir("~/my_project/llmtrack")
print(get_root_dir())

Caching and Recording Token Usage

You can use get_llm to get a language model instance, as follows:

from llmtrack import get_llm
client_name = "openai"
model_name = "gpt-4o-mini"
llm = get_llm(f"{client_name}/{model_name}", cache=True, token_usage=True)
usr_message = "ONLY generate a positve word"
client_response = llm.respond(usr_message, verbal=True)

After running the code above, the cache and token-usage files will be stored in ~/my_project/llmtrack/openai/gpt-4o-mini, following the rule {root_dir}/{client name}/{model name}. Now, if you invoke the same model with the same prompt, the cache will be used.

You can check the token usage and cache by:

# check token usage
print('Token Usage')
usage = llm.token_usage
print(usage)

# check cache
print('\n\nCache')
cache_key = llm.get_cache_key(usr_message)
print(llm.cache[cache_key])

Supported Clients and Model Names

Public LLM APIs are specified by simply specifying model_name consisting of API providers and model names. The supported APIs include :

• OpenAI, e.g., "openai/xxxx" (xxxx should be replaced by specific model names)
  • The environment variable has to be setup: OPENAI_API_KEY
  • All Available model_name: See the document
• Azure OpenAI, e.g., "azure_openai/chatgpt-4k"
  • The three environment variables have to be setup: AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, AZURE_OPENAI_API_VERSION
  • Ask providers for specific model names
• MoonShot, e.g., "moonshot/moonshot-v1-8k"
  • The environment variable has to be setup: MOONSHOT_API_KEY
• Groq
  • All Available model_name: See the document