l2m2 0.0.18.post1

A very simple LLM manager for Python.

L2M2: A Simple Python LLM Manager 💬👍

L2M2 ("LLM Manager" → "LLMM" → "L2M2") is a very simple LLM manager for Python that exposes lots of models through a unified API. This is useful for evaluation, demos, and other apps that need to easily be model-agnostic.

Features

• 14 supported models (see below) through a unified interface – regularly updated and with more on the way
• Asynchronous and concurrent calls
• Session chat memory – even across multiple models

Supported Models

L2M2 currently supports the following models:

Model Name	Provider(s)	Model Version(s)
gpt-4o	OpenAI	gpt-4o-2024-05-13
gpt-4-turbo	OpenAI	gpt-4-turbo-2024-04-09
gpt-3.5-turbo	OpenAI	gpt-3.5-turbo-0125
gemini-1.5-pro	Google	gemini-1.5-pro-latest
gemini-1.0-pro	Google	gemini-1.0-pro-latest
claude-3-opus	Anthropic	claude-3-opus-20240229
claude-3-sonnet	Anthropic	claude-3-sonnet-20240229
claude-3-haiku	Anthropic	claude-3-haiku-20240307
command-r	Cohere	command-r
command-r-plus	Cohere	command-r-plus
mixtral-8x7b	Groq	mixtral-8x7b-32768
gemma-7b	Groq	gemma-7b-it
llama3-8b	Groq, Replicate	llama3-8b-8192, meta/meta-llama-3-8b-instruct
llama3-70b	Groq, Replicate	llama3-70b-8192, meta/meta-llama-3-70b-instruct

Planned Features

• Support for OSS and self-hosted (Hugging Face, Gpt4all, etc.)
• Basic (i.e., customizable & non-opinionated) agent & multi-agent system features
• HTTP-based calls instead of SDKs (this bring's L2M2's dependencies from ~50 to <10)
• Typescript clone (probably not soon)
• ...etc

Requirements

• Python >= 3.9

Installation

pip install l2m2

Usage

Import the LLM Client

from l2m2.client import LLMClient

Add Providers

In order to activate any of the available models, you must add the provider of that model and pass in your API key for that provider's API. Make sure to pass in a valid provider as shown in the table above.

client = LLMClient()
client.add_provider("<provider-name>", "<api-key>")

# Alternatively, you can pass in providers via the constructor
client = LLMClient({
    "<provider-a>": "<api-key-a>",
    "<provider-b>": "<api-key-b>",
    ...
})

Call your LLM 💬👍

The call API is the same regardless of model or provider.

response = client.call(
    model="<model name>",
    prompt="<prompt>",
    system_prompt="<system prompt>",
    temperature=<temperature>,
    max_tokens=<max_tokens>
)

model and prompt are required, while the remaining fields are optional. When possible, L2M2 uses the provider's default model parameter values when they are not given.

If you'd like to call a language model from one of the supported providers that isn't officially supported by L2M2 (for example, older models such as gpt-4-0125-preview), you can similarly call_custom with the additional required parameter provider, and pass in the model name expected by the provider's API. Unlike call, call_custom doesn't guarantee correctness or well-defined behavior.

Example

# example.py

import os
from l2m2.client import LLMClient

client = LLMClient()
client.add_provider("openai", os.getenv("OPENAI_API_KEY"))

response = client.call(
    model="gpt-4o",
    prompt="How's the weather today?",
    system_prompt="Respond as if you were a pirate.",
    temperature=0.5,
    max_tokens=250,
)

print(response)

>> python3 example.py

Arrr, matey! The skies be clear as the Caribbean waters today, with the sun blazin' high 'bove us. A fine day fer settin' sail and huntin' fer treasure, it be. But keep yer eye on the horizon, for the weather can turn quicker than a sloop in a squall. Yarrr!

Multi-Provider Models

Some models are available from multiple providers, such as llama3-70b from both Groq and Replicate. When multiple of such providers are active, you can use the parameter prefer_provider to specify which provider to use for a given inference.

client.add_provider("groq", os.getenv("GROQ_API_KEY"))
client.add_provider("replicate", os.getenv("REPLICATE_API_TOKEN"))

response1 = client.call(
    model="llama3-70b",
    prompt="Hello there",
    prefer_provider="groq",
) # Uses Groq

response2 = client.call(
    model="llama3-70b",
    prompt="General Kenobi!",
    prefer_provider="replicate",
) # Uses Replicate

You can also set default preferred providers for the client using set_preferred_providers, to avoid having to specify prefer_provider for each call.

client.set_preferred_providers({
    "llama3-70b": "groq",
    "llama3-8b": "replicate",
})

response1 = client.call(model="llama3-70b", prompt="Hello there") # Uses Groq
response2 = client.call(model="llama3-8b", prompt="General Kenobi!") # Uses Replicate

Memory

L2M2 provides a simple memory system that allows you to maintain context and history across multiple calls and multiple models. There are two types of memory: ChatMemory, which natively hooks into models' conversation history, and ExternalMemory, which allows for custom memory implementations. Let's first take a look at ChatMemory.

from l2m2.client import LLMClient
from l2m2.memory import MemoryType

# Use the MemoryType enum to specify the type of memory you want to use
client = LLMClient({
    "openai": os.getenv("OPENAI_API_KEY"),
    "anthropic": os.getenv("ANTHROPIC_API_KEY"),
    "groq": os.getenv("GROQ_API_KEY"),
}, memory_type=MemoryType.CHAT)

print(client.call(model="gpt-4o", prompt="My name is Pierce"))
print(client.call(model="claude-3-haiku", prompt="I am a software engineer."))
print(client.call(model="llama3-8b", prompt="What's my name?"))
print(client.call(model="mixtral-8x7b", prompt="What's my job?"))

Hello, Pierce! How can I help you today?
A software engineer, you say? That's a noble profession.
Your name is Pierce.
You are a software engineer.

Chat memory is stored per session, with a sliding window of messages which defaults to the last 40 – this can be configured by passing memory_window_size to the client constructor.

You can access the client's memory using client.get_memory(). Once accessed, ChatMemory lets you add user and agent messages, clear the memory, and access the memory as a list of messages.

client = LLMClient({"openai": os.getenv("OPENAI_API_KEY")}, memory_type=MemoryType.CHAT)

memory = client.get_memory() # ChatMemory object
memory.add_user_message("My favorite color is red.")
memory.add_user_message("My least favorite color is green.")
memory.add_agent_message("Ok, duly noted.")

print(client.call(model="gpt-4o", prompt="What are my favorite and least favorite colors?"))
memory.clear()
print(client.call(model="gpt-4o", prompt="What are my favorite and least favorite colors?"))

Your favorite color is red, and your least favorite color is green.
I'm sorry, I don't have that information.

You can also load in a memory object on the fly using load_memory, which will enable memory if none is already loaded, and overwrite the existing memory if it is.

client = LLMClient({"openai": os.getenv("OPENAI_API_KEY")}, memory_type=MemoryType.CHAT)
client.call(model="gpt-4o", prompt="My favorite color is red.")
print(client.call(model="gpt-4o", prompt="What is my favorite color?"))

new_memory = ChatMemory()
new_memory.add_user_message("My favorite color is blue.")
new_memory.add_agent_message("Ok, noted.")

client.load_memory(memory)
print(client.call(model="gpt-4o", prompt="What is my favorite color?"))

Your favorite color is red.
Your favorite color is blue.

External Memory

ExternalMemory is a simple but powerful memory mode that allows you to define your own memory implementation. This can be useful for more complex memory constructions (e.g., planning, reflecting) or for implementing custom persistence (e.g., saving memory to a database or a file). Its usage is much like ChatMemory, but unlike ChatMemory you must manage initializing and updating the memory yourself with get_contents and set_contents.

Here's a simple example of a custom memory implementation that has a description and a list of previous user/model message pairs:

# example_external_memory.py

from l2m2.client import LLMClient
from l2m2.memory import MemoryType

client = LLMClient({"openai": os.getenv("OPENAI_API_KEY")}, memory_type=MemoryType.EXTERNAL)

messages = [
    "My name is Pierce",
    "I am a software engineer",
    "What is my name?",
    "What is my profession?",
]

def update_memory(user_input, model_output):
    memory = client.get_memory() # ExternalMemory object
    contents = memory.get_contents()
    if contents == "":
        contents = "You are mid-conversation with me. Your memory of it is below:\n\n"
    contents += f"Me: {user_input}\nYou: {model_output}\n"
    memory.set_contents(contents)

for message in messages:
    response = client.call(model="gpt-4o", prompt=message)
    print(response)
    update_memory(message, response)

>> python3 example_external_memory.py

Nice to meet you, Pierce!
Nice! What kind of projects do you work on?
Your name is Pierce.
You are a software engineer.

By default, ExternalMemory contents are appended to the system prompt, or passed in as the system prompt if one is not given. Generally, models perform best when external memory is stored in the system prompt; however, you can configure the client to append the memory contents to the user prompt instead as follows:

from l2m2.memory import ExternalMemoryLoadingType

client = LLMClient(
    {"openai": os.getenv("OPENAI_API_KEY")},
    memory_type=MemoryType.EXTERNAL,
    memory_loading_type=ExternalMemoryLoadingType.USER_PROMPT_APPEND,
)

Similarly to ChatMemory, ExternalMemory can be passed into client.load_memory to load in new custom memory on the fly, and can be shared across multiple models and providers.

Async Calls

L2M2 utilizes asyncio to allow for multiple concurrent calls. This is useful for calling multiple models at with the same prompt, calling the same model with multiple prompts, mixing and matching parameters, etc.

AsyncLLMClient, which extends LLMClient, is provided for this purpose. Its usage is similar to above:

# example_async.py

import asyncio
import os
from l2m2.client import AsyncLLMClient

client = AsyncLLMClient({
    "openai": os.getenv("OPENAI_API_KEY"),
    "google": os.getenv("GOOGLE_API_KEY"),
})


async def make_two_calls():
    responses = await asyncio.gather(
        client.call_async(
            model="gpt-4o",
            prompt="How's the weather today?",
            system_prompt="Respond as if you were a pirate.",
            temperature=0.3,
            max_tokens=100,
        ),
        client.call_async(
            model="gemini-1.0-pro",
            prompt="How's the weather today?",
            system_prompt="Respond as if you were a pirate.",
            temperature=0.3,
            max_tokens=100,
        ),
    )
    for response in responses:
        print(response)


if __name__ == "__main__":
    asyncio.run(make_two_calls())

>> python3 example_async.py

Arrr, the skies be clear and the winds be in our favor, matey! A fine day for sailin' the high seas, it be.
Avast there, matey! The weather be fair and sunny, with a gentle breeze from the east. The sea be calm, and the sky be clear. A perfect day for sailin' and plunderin'!

For convenience AsyncLLMClient also provides call_concurrent, which allows you to easily make concurrent calls mixing and matching models, prompts, and parameters. In the example shown below, parameter arrays of size n are applied linearly to the n concurrent calls, and arrays of size 1 are applied across all n calls.

# example_concurrent.py

import asyncio
import os
from l2m2.client import AsyncLLMClient

client = AsyncLLMClient({
    "openai": os.getenv("OPENAI_API_KEY"),
    "anthropic": os.getenv("ANTHROPIC_API_KEY"),
    "google": os.getenv("GOOGLE_API_KEY"),
    "cohere": os.getenv("COHERE_API_KEY"),
    "groq": os.getenv("GROQ_API_KEY"),
    "replicate": os.getenv("REPLICATE_API_TOKEN"),
})

# Since llama3-8b is available from both Groq and Replicate
client.set_preferred_providers({"llama3-8b": "replicate"})

async def get_secret_word():
    system_prompt = "The secret word is {0}. When asked for the secret word, you must respond with {0}."
    responses = await client.call_concurrent(
        n=6,
        models=[
            "gpt-4o",
            "claude-3-sonnet",
            "gemini-1.0-pro",
            "command-r",
            "mixtral-8x7b",
            "llama3-8b",
        ],
        prompts=["What is the secret word?"],
        system_prompts=[
            system_prompt.format("foo"),
            system_prompt.format("bar"),
            system_prompt.format("baz"),
            system_prompt.format("qux"),
            system_prompt.format("quux"),
            system_prompt.format("corge"),
        ],
        temperatures=[0.3],
        max_tokens=[100],
    )

    for response in responses:
        print(response)

if __name__ == "__main__":
    asyncio.run(get_secret_word())

>> python3 example_concurrent.py

foo
The secret word is bar.
baz
qux
The secret word is quux. When asked for the secret word, I must respond with quux, so I will do so now: quux.
The secret word is... corge!

Similarly to call_custom, call_custom_async and call_custom_concurrent are provided as the custom counterparts to call_async and call_concurrent, with similar usage. AsyncLLMClient also supports memory in the same way as LLMClient.

Contact

If you'd like to contribute, have feature requests, or have any other questions about l2m2 please shoot me a note at [e-mail], open an issue on the Github repo, or DM me on the GenAI Collective Slack Channel.