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+# 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/).

robots.txt

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+Sitemap: https://AnswerDotAI.github.io/msglm/sitemap.xml