# msglm
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
### Installation
Install the latest version from pypi
``` sh
$ 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.
``` python
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.
``` python
mk_msgs(["Hello, world!", "some assistant response"], api="openai")
```
``` js
[
{"role": "user", "content": "Hello, world!"},
{"role": "assistant", "content": "Some assistant response"}
]
```
#### anthropic
``` python
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
``` python
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.
``` python
mk_msg([img, "What's in this image?"], api="anthropic")
```
``` js
[
{
"role": "user",
"content": [
{"type": "image", "source": {"type": "base64", "media_type": media_type, "data": img}}
{"type": "text", "text": "What's in this image?"}
]
}
]
```
#### anthropic
``` python
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
``` python
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`](https://AnswerDotAI.github.io/msglm/core.html#mk_msg) and
[`mk_msgs`](https://AnswerDotAI.github.io/msglm/core.html#mk_msgs).
For Anthropic use
``` python
from msglm import mk_msg_anthropic as mk_msg, mk_msgs_anthropic as mk_msgs
```
For OpenAI use
``` python
from msglm import mk_msg_openai as mk_msg, mk_msgs_openai as mk_msgs
```
### Other use-cases
#### Prompt Caching
*msglm* supports [prompt
caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching)
for Anthropic models. Simply pass *cache=True* to *mk_msg* or *mk_msgs*.
``` python
from msglm import mk_msg_anthropic as mk_msg
mk_msg("please cache my message", cache=True)
```
This generates the expected cache block below
``` js
{
"role": "user",
"content": [
{"type": "text", "text": "Please cache my message", "cache_control": {"type": "ephemeral"}}
]
}
```
#### PDF chats
*msglm* offers PDF
[support](https://docs.anthropic.com/en/docs/build-with-claude/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.
``` python
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](https://answerdotai.github.io/msglm/).
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"description": "# msglm\n\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n### Installation\n\nInstall the latest version from pypi\n\n``` sh\n$ pip install msglm\n```\n\n## Usage\n\nTo use an LLM we need to structure our messages in a particular format.\n\nHere\u2019s an example of a text chat from the OpenAI docs.\n\n``` python\nfrom openai import OpenAI\nclient = OpenAI()\n\ncompletion = client.chat.completions.create(\n model=\"gpt-4o\",\n messages=[\n {\"role\": \"user\", \"content\": \"What's the Wild Atlantic Way?\"}\n ]\n)\n```\n\nGenerating the correct format for a particular API can get tedious. The\ngoal of *msglm* is to make it easier.\n\nThe examples below will show you how to use *msglm* for text and image\nchats with OpenAI and Anthropic.\n\n### Text Chats\n\nFor a text chat simply pass a list of strings and the api format\n(e.g.\u00a0\u201copenai\u201d) to **mk_msgs** and it will generate the correct format.\n\n``` python\nmk_msgs([\"Hello, world!\", \"some assistant response\"], api=\"openai\")\n```\n\n``` js\n[\n {\"role\": \"user\", \"content\": \"Hello, world!\"},\n {\"role\": \"assistant\", \"content\": \"Some assistant response\"}\n]\n```\n\n#### anthropic\n\n``` python\nfrom msglm import mk_msgs_anthropic as mk_msgs\nfrom anthropic import Anthropic\nclient = Anthropic()\n\nr = client.messages.create(\n model=\"claude-3-haiku-20240307\",\n max_tokens=1024,\n messages=[mk_msgs([\"Hello, world!\", \"some LLM response\"])]\n)\nprint(r.content[0].text)\n```\n\n#### openai\n\n``` python\nfrom msglm import mk_msgs_openai as mk_msgs\nfrom openai import OpenAI\n\nclient = OpenAI()\nr = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[mk_msgs([\"Hello, world!\", \"some LLM response\"])]\n)\nprint(r.choices[0].message.content)\n```\n\n### Image Chats\n\nFor an image chat simply pass the raw image bytes in a list with your\nquestion to *mk_msgs* and it will generate the correct format.\n\n``` python\nmk_msg([img, \"What's in this image?\"], api=\"anthropic\")\n```\n\n``` js\n[\n {\n \"role\": \"user\", \n \"content\": [\n {\"type\": \"image\", \"source\": {\"type\": \"base64\", \"media_type\": media_type, \"data\": img}}\n {\"type\": \"text\", \"text\": \"What's in this image?\"}\n ]\n }\n]\n```\n\n#### anthropic\n\n``` python\nimport httpx\nfrom msglm import mk_msg_anthropic as mk_msg\nfrom anthropic import Anthropic\n\nclient = Anthropic()\n\nimg_url = \"https://www.atshq.org/wp-content/uploads/2022/07/shutterstock_1626122512.jpg\"\nimg = httpx.get(img_url).content\n\nr = client.messages.create(\n model=\"claude-3-haiku-20240307\",\n max_tokens=1024,\n messages=[mk_msg([img, \"Describe the image\"])]\n)\nprint(r.content[0].text)\n```\n\n#### openai\n\n``` python\nimport httpx\nfrom msglm import mk_msg_openai as mk_msg\nfrom openai import OpenAI\n\nimg_url = \"https://www.atshq.org/wp-content/uploads/2022/07/shutterstock_1626122512.jpg\"\nimg = httpx.get(img_url).content\n\nclient = OpenAI()\nr = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[mk_msg([img, \"Describe the image\"])]\n)\nprint(r.choices[0].message.content)\n```\n\n### API Wrappers\n\nTo make life a little easier, msglm comes with api specific wrappers for\n[`mk_msg`](https://AnswerDotAI.github.io/msglm/core.html#mk_msg) and\n[`mk_msgs`](https://AnswerDotAI.github.io/msglm/core.html#mk_msgs).\n\nFor Anthropic use\n\n``` python\nfrom msglm import mk_msg_anthropic as mk_msg, mk_msgs_anthropic as mk_msgs\n```\n\nFor OpenAI use\n\n``` python\nfrom msglm import mk_msg_openai as mk_msg, mk_msgs_openai as mk_msgs\n```\n\n### Other use-cases\n\n#### Prompt Caching\n\n*msglm* supports [prompt\ncaching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching)\nfor Anthropic models. Simply pass *cache=True* to *mk_msg* or *mk_msgs*.\n\n``` python\nfrom msglm import mk_msg_anthropic as mk_msg\n\nmk_msg(\"please cache my message\", cache=True)\n```\n\nThis generates the expected cache block below\n\n``` js\n{\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"text\", \"text\": \"Please cache my message\", \"cache_control\": {\"type\": \"ephemeral\"}}\n ]\n}\n```\n\n#### PDF chats\n\n*msglm* offers PDF\n[support](https://docs.anthropic.com/en/docs/build-with-claude/pdf-support)\nfor Anthropic. 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