llmuri 0.1.0

LLM URI Parsing.

llmuri -- URI support for LLMs

This project defines a standard URI for specifying LLMs and provides functionality to convert LLM strings for use with popular LLM packages.

Example

from llmuri import uri_to_litellm
from litellm import completion

for uri in [
    "llm:openai/gpt-4",
    "openai/gpt-4",
    "llm:ollama/llama2",
    "llm:mistralai/mistral-medium?temperature=0.2&max_tokens=4",
    "llm:[e-mail]:11434/llama2",
]:
    kwargs = uri_to_litellm(uri)
    response = completion(
        messages=[{"content": "respond in 20 words. who are you?", "role": "user"}],
        **kwargs,
    )
    print(response)

Installation

pip install llmuri

URI Specification

[scheme:]provider[@host[:port]]/model-name[?parameter1=value1[&parameter2=value2]...

examples:

ollama/llama2
llm:ollama/llama2?temperature=0.2
llms:[e-mail]:11434/llama2

• scheme can be "llm" or "llms" to specify that the LLM is hosted behind an https web service. If the scheme is ommitted, "llm" is assumed.

• provider is the name of the LLM API or service.

• host is an optional hostname where the LLM service is located.

• port is the service's port number.

• model-name is the name of the model to be executed.

• parameter-list is a list of model-specific parameters.

Canonical Abbreviations

The canonical set of well-known services is in canonical-abbreviations.csv.

In-process References

If the port portion of the URI is "mem", then the LLM is not accessed externally, but is instantiated in the current process.

The semantics of in-process references is a WIP and subject to change.

• provider and host are available for llm specification.

llm:A@B:mem/mymodel  #  A,B are available
llmp:A@B:C/mymodel   #  A,B,C are available if we introduce a new scheme. bad idea?

Examples:

llm:MyTransformer@:mem        # Instantiate an instance of MyTransformer
llm:openai@:mem/gpt-3.5-turbo # Instantiate an instance of OpenAI() using gpt-3.5-turbo
llm:completions@mistralai:mem/mistral-medium  # completions interface?