msglm

msglm makes it a little easier to create messages for language models like Claude and OpenAI GPTs.

Installation

Install the latest version from pypi

$ pip install msglm

Usage

To use an LLM we need to structure our messages in a particular format.

Here’s an example of a text chat from the OpenAI docs.

from openai import OpenAI
client = OpenAI()

completion = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {"role": "user", "content": "What's the Wild Atlantic Way?"}
  ]
)

Generating the correct format for a particular API can get tedious. The goal of msglm is to make it easier.

The examples below will show you how to use msglm for text and image chats with OpenAI and Anthropic.

Text Chats

For a text chat simply pass a list of strings and the api format (e.g. “openai”) to mk_msgs and it will generate the correct format.

mk_msgs(["Hello, world!", "some assistant response"], api="openai")

[
    {"role": "user", "content": "Hello, world!"},
    {"role": "assistant", "content": "Some assistant response"}
]

anthropic

from msglm import mk_msgs_anthropic as mk_msgs
from anthropic import Anthropic
client = Anthropic()

r = client.messages.create(
    model="claude-3-haiku-20240307",
    max_tokens=1024,
    messages=[mk_msgs(["Hello, world!", "some LLM response"])]
)
print(r.content[0].text)

openai

from msglm import mk_msgs_openai as mk_msgs
from openai import OpenAI

client = OpenAI()
r = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[mk_msgs(["Hello, world!", "some LLM response"])]
)
print(r.choices[0].message.content)

Image Chats

For an image chat simply pass the raw image bytes in a list with your question to mk_msgs and it will generate the correct format.

mk_msg([img, "What's in this image?"], api="anthropic")

[
    {
        "role": "user", 
        "content": [
            {"type": "image", "source": {"type": "base64", "media_type": media_type, "data": img}}
            {"type": "text", "text": "What's in this image?"}
        ]
    }
]

anthropic

import httpx
from msglm import mk_msg_anthropic as mk_msg
from anthropic import Anthropic

client = Anthropic()

img_url = "https://www.atshq.org/wp-content/uploads/2022/07/shutterstock_1626122512.jpg"
img = httpx.get(img_url).content

r = client.messages.create(
    model="claude-3-haiku-20240307",
    max_tokens=1024,
    messages=[mk_msg([img, "Describe the image"])]
)
print(r.content[0].text)

openai

import httpx
from msglm import mk_msg_openai as mk_msg
from openai import OpenAI

img_url = "https://www.atshq.org/wp-content/uploads/2022/07/shutterstock_1626122512.jpg"
img = httpx.get(img_url).content

client = OpenAI()
r = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[mk_msg([img, "Describe the image"])]
)
print(r.choices[0].message.content)

API Wrappers

To make life a little easier, msglm comes with api specific wrappers for mk_msg and mk_msgs.

For Anthropic use

from msglm import mk_msg_anthropic as mk_msg, mk_msgs_anthropic as mk_msgs

For OpenAI use

from msglm import mk_msg_openai as mk_msg, mk_msgs_openai as mk_msgs

Other use-cases

Prompt Caching

msglm supports prompt caching for Anthropic models. Simply pass cache=True to mk_msg or mk_msgs.

from msglm import mk_msg_anthropic as mk_msg

mk_msg("please cache my message", cache=True)

This generates the expected cache block below

{
    "role": "user",
    "content": [
        {"type": "text", "text": "Please cache my message", "cache_control": {"type": "ephemeral"}}
    ]
}

PDF chats

msglm offers PDF support for Anthropic. Just like an image chat all you need to do is pass the raw pdf bytes in a list with your question to mk_msg and it will generate the correct format as shown in the example below.

import httpx
from msglm import mk_msg_anthropic as mk_msg
from anthropic import Anthropic

client = Anthropic(default_headers={'anthropic-beta': 'pdfs-2024-09-25'})

url = "https://assets.anthropic.com/m/1cd9d098ac3e6467/original/Claude-3-Model-Card-October-Addendum.pdf"
pdf = httpx.get(url).content

r = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[mk_msg([pdf, "Which model has the highest human preference win rates across each use-case?"])]
)
print(r.content[0].text)

Note: this feature is currently in beta so you’ll need to:

• use the Anthropic beta client (e.g. anthropic.Anthropic(default_headers={'anthropic-beta': 'pdfs-2024-09-25'}))
• use the claude-3-5-sonnet-20241022 model

Summary

We hope msglm will make your life a little easier when chatting to LLMs. To learn more about the package please read this doc.