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+
+ +
+ + +
+ + + +
+ +
+
+

core

+
+ +
+
+ Create messages for language models like Claude and OpenAI GPTs. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

API Exploration

+

Anthropic’s Claude and OpenAI’s GPT models are some of the most popular LLMs.

+

Let’s take a look at their APIs and to learn how we should structure our messages for a simple text chat.

+
+

openai

+
+
from openai import OpenAI
+
+
+
client = OpenAI()
+
+client.chat.completions.create(
+  model="gpt-4o-mini",
+  messages=[ {"role": "user", "content": "Hello, world!"} ]
+)
+
+
ChatCompletion(id='chatcmpl-ADNgo1gSrIolZfS9UW8glyIiFIe0Y', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Hello! How can I assist you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1727752690, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_74ba47b4ac', usage=CompletionUsage(completion_tokens=9, prompt_tokens=11, total_tokens=20, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0)))
+
+
+
+
+

anthropic

+
+
from anthropic import Anthropic
+
+
+
client = Anthropic()
+
+client.messages.create(
+    model="claude-3-haiku-20240307",
+    max_tokens=1024,
+    messages=[ {"role": "user", "content": "Hello, world!"} ]
+)
+
+

Hello! It’s nice to meet you. How can I assist you today?

+
+
    +
  • id: msg_01GtzcT5ezjzXSmYehbhAhZH
  • +
  • content: [{'text': "Hello! It's nice to meet you. How can I assist you today?", 'type': 'text'}]
  • +
  • model: claude-3-haiku-20240307
  • +
  • role: assistant
  • +
  • stop_reason: end_turn
  • +
  • stop_sequence: None
  • +
  • type: message
  • +
  • usage: {'input_tokens': 11, 'output_tokens': 19}
  • +
+
+
+
+

As we can see both APIs use the exact same message structure.

+
+
+

mk_msg

+

Ok, let’s build the first version of mk_msg to handle this case

+
+
def mk_msg(content:str, role:str="user")->dict:
+    "Create an OpenAI/Anthropic compatible message."
+    return dict(role=role, content=content)
+
+

Let’s test it out with the OpenAI API. To do that we’ll need to setup two things:

+
    +
  • install the openai SDK by running pip install openai
  • +
  • add your openai api key to your env vars export OPENAI_API_KEY="YOUR_OPEN_API_KEY"
  • +
+
+
oa_cli = OpenAI()
+
+r = oa_cli.chat.completions.create(
+  model="gpt-4o-mini",
+  messages=[mk_msg("Hello, world!")]
+)
+r.choices[0].message.content
+
+
'Hello! How can I assist you today?'
+
+
+

Now, let’s test out mk_msg on the Anthropic API. To do that we’ll need to setup two things:

+
    +
  • install the openai SDK by running pip install anthropic
  • +
  • add your anthropic api key to your env vars export ANTHROPIC_API_KEY="YOUR_ANTHROPIC_API_KEY"
  • +
+
+
a_cli = Anthropic()
+
+r = a_cli.messages.create(
+    model="claude-3-haiku-20240307",
+    max_tokens=1024,
+    messages=[mk_msg("Hello, world!")]
+)
+r.content[0].text
+
+
"Hello! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with all sorts of tasks, from research and analysis to creative projects and problem-solving. Please let me know if there's anything I can assist you with."
+
+
+

So far so good!

+
+

Helper Functions

+

Before going any further, let’s create some helper functions to make it a little easier to call the OpenAI and Anthropic APIs. We’re going to be making a bunch of API calls to test our code and typing the full expressions out each time will become a little tedious. These functions won’t be included in the final package.

+
+
def openai_chat(msgs: list)->tuple:
+    "call the openai chat completions endpoint with `msgs`."
+    r = oa_cli.chat.completions.create(model="gpt-4o-mini", messages=msgs)
+    return r, r.choices[0].message.content
+
+

Let’s double check that mk_msg still works with our simple text example from before.

+
+
_, text = openai_chat([mk_msg("Hello, world!")])
+text
+
+
'Hello! How can I assist you today?'
+
+
+
+
def anthropic_chat(msgs: list)->tuple:
+    "call the anthropic messages endpoint with `msgs`."
+    r = a_cli.messages.create(model="claude-3-haiku-20240307", max_tokens=1024, messages=msgs)
+    return r, r.content[0].text
+
+

and Anthropic…

+
+
_, text = anthropic_chat([mk_msg("Hello, world!")])
+text
+
+
"Hello! It's nice to meet you. How can I assist you today?"
+
+
+
+
+
+

Images

+

Ok, let’s see how both APIs handle image messages.

+

+
+

openai

+
+
import base64, httpx
+
+
+
img_url = "https://claudette.answer.ai/index_files/figure-html/cell-35-output-1.jpeg"
+
+
+
mtype = "image/jpeg"
+img = base64.b64encode(httpx.get(img_url).content).decode("utf-8")
+
+client = OpenAI()
+client.chat.completions.create(
+    model="gpt-4o-mini",
+    messages=[
+        {
+            "role":"user",
+            "content": [
+                {"type":"text","text":"What's in this image?"},
+                {"type":"image_url","image_url":{"url":f"data:{mtype};base64,{img}"}},
+            ],
+        }
+    ],
+)
+
+
ChatCompletion(id='chatcmpl-ADNjLoa2oJmG7yPU1qnBI426h1TB2', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The image features a small puppy lying on grass, with a background of purple flowers. The puppy has a white coat with brown markings, and it appears to be resting or playing in a garden setting.', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1727752847, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_74ba47b4ac', usage=CompletionUsage(completion_tokens=40, prompt_tokens=8512, total_tokens=8552, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0)))
+
+
+
+
+

anthropic

+
+
mtype = "image/jpeg"
+img = base64.b64encode(httpx.get(img_url).content).decode("utf-8")
+
+client = Anthropic()
+client.messages.create(
+    model="claude-3-haiku-20240307",
+    max_tokens=1024,
+    messages=[
+        {
+            "role":"user",
+            "content": [
+                {"type":"text","text":"What's in this image?"},
+                {"type":"image","source":{"type":"base64","media_type":mtype,"data":img}}
+            ],
+        }
+    ],
+)
+
+

This image shows a cute puppy lying in a grassy area with purple flowers in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a fluffy white and brown coat. The puppy has a friendly, playful expression on its face as it looks directly at the camera. The image captures a charming, serene outdoor scene with the puppy as the main focus.

+
+
    +
  • id: msg_015JmJ2GR9fkisz9Wsh1CKEK
  • +
  • content: [{'text': 'This image shows a cute puppy lying in a grassy area with purple flowers in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a fluffy white and brown coat. The puppy has a friendly, playful expression on its face as it looks directly at the camera. The image captures a charming, serene outdoor scene with the puppy as the main focus.', 'type': 'text'}]
  • +
  • model: claude-3-haiku-20240307
  • +
  • role: assistant
  • +
  • stop_reason: end_turn
  • +
  • stop_sequence: None
  • +
  • type: message
  • +
  • usage: {'input_tokens': 104, 'output_tokens': 90}
  • +
+
+
+
+

Both APIs format images slightly differently and the structure of the message content is a litte more complex.

+

In a text chat, content is a simple string but for a multimodal chat (text+images) we can see that content is a list of dictionaries.

+
+
+
+

Msg Class

+

To handle the additional complexity of the multimodal messages let’s build a Msg class for the content data structure:

+
{
+    "role": "user",
+    "content": [{"type": "text", "text": "What's in this image?"}],
+}
+
+
class Msg:
+    "Helper class to create a message for the OpenAI and Anthropic APIs."
+    def __call__(self, role:str, content:[list,str], **kw)->dict:
+        "Create an OpenAI/Anthropic compatible message with `role` and `content`."
+        if content is not None and not isinstance(content, list): content = [content]
+        return dict(role=role, content=[{"type":"text", "content":item} for item in content], **kw)
+
+

Now, let’s update mk_msg to use Msg.

+
+
def mk_msg(content:str, role:str="user", **kw)->dict:
+    "Create an OpenAI/Anthropic compatible message."
+    return Msg()(role, content, **kw)
+
+
+
mk_msg(["Hello world", "how are you?"])
+
+
{'role': 'user',
+ 'content': [{'type': 'text', 'content': 'Hello world'},
+  {'type': 'text', 'content': 'how are you?'}]}
+
+
+

Now, let’s update Msg so that it can format our images correctly.

+

First, we’ll add a method mk_content where we can check the type of each input and format the data appropriately. Let’s start by adding a check for text data.

+
+
class Msg:
+    "Helper class to create a message for the OpenAI and Anthropic APIs."
+    def __call__(self, role:str, content:[list,str], **kw)->dict:
+        "Create an OpenAI/Anthropic compatible message with `role` and `content`."
+        if content is not None and not isinstance(content, list): content = [content]
+        content = [self.mk_content(o) for o in content] if content else ''
+        return dict(role=role, content=content, **kw)
+    
+    def text_msg(self, s:str)->dict: 
+        "Convert `s` to a text message"
+        return {"type": "text", "text":s}
+    
+    def mk_content(self, content:str)->dict:
+        "Create the appropriate data structure based the content type."
+        if isinstance(content, str): return self.text_msg(content)
+        return content
+
+

Here’s where things get a little tricker. As both APIs handle images differently let’s subclass Msg for each API and handle the image formatting in a method called img_msg.

+
+
class Msg:
+    "Helper class to create a message for the OpenAI and Anthropic APIs."
+    def __call__(self, role:str, content:[list,str], **kw)->dict:
+        "Create an OpenAI/Anthropic compatible message with `role` and `content`."
+        if content is not None and not isinstance(content, list): content = [content]
+        content = [self.mk_content(o) for o in content] if content else ''
+        return dict(role=role, content=content, **kw)
+    
+    def img_msg(self, *args)->dict: 
+        "Convert bytes to an image message"
+        raise NotImplemented
+        
+    def text_msg(self, s:str)->dict: 
+        "Convert `s` to a text message"
+        return {"type": "text", "text":s}
+    
+    def mk_content(self, content:str)->dict:
+        "Create the appropriate data structure based the content type."
+        if isinstance(content, str): return self.text_msg(content)
+        if isinstance(content, bytes): return self.img_msg(content)
+        return content
+
+
+
class OpenAiMsg(Msg):
+    "Helper class to create a message for the OpenAI API."
+    def img_msg(self, data:bytes)->dict:
+        "Convert `data` to an image message"
+        img = base64.b64encode(data).decode("utf-8")
+        mtype = mimetypes.types_map["." + imghdr.what(None, h=data)]
+        r = {"url": f"data:{mtype};base64,{img}"}
+        return {"type": "image_url", "image_url": r}
+
+
+
class AnthropicMsg(Msg):
+    "Helper class to create a message for the Anthropic API."
+    def img_msg(self, data:bytes)->dict:
+        "Convert `data` to an image message"
+        img = base64.b64encode(data).decode("utf-8")
+        mtype = mimetypes.types_map["." + imghdr.what(None, h=data)]
+        r = {"type": "base64", "media_type": mtype, "data":img}
+        return {"type": "image", "source": r}
+
+

Now, let’s update mk_msg so that it chooses the appropriate Msg subclass.

+
+
def mk_msg(content:str, role:str="user", *args, api:str="openai", **kw) -> dict:
+    "Create an OpenAI/Anthropic compatible message."
+    m = OpenAiMsg() if api == "openai" else AnthropicMsg()
+    return m(role, content, **kw)
+
+

Ok, let’s test our changes…

+
+
from IPython.display import Image, display
+
+
+
img = httpx.get(img_url).content
+display(Image(img))
+
+
+
+

+
+
+
+
+
+
msg = mk_msg([img, "describe this picture"], api="openai")
+_, text = openai_chat([msg])
+text
+
+
'The image features an adorable puppy lying on the grass. It has a primarily white coat with brown markings, particularly around its ears and eyes. The puppy has a curious and playful expression. In the background, there are purple flowers adding a touch of color to the scene. The overall setting appears to be a well-kept garden, creating a charming and serene atmosphere.'
+
+
+
+
msg = mk_msg([img, "describe this picture"], api="anthropic")
+_, text = anthropic_chat([msg])
+text
+
+
"The image shows a cute puppy nestled in some grass, with purple flowers blooming in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a brown and white coat. It has a friendly, curious expression as it looks out at the viewer. The scene creates a peaceful, natural setting, with the puppy's soft fur and the vibrant flowers providing a lovely visual contrast. Overall, this image captures the endearing charm and beauty of a young dog in a picturesque outdoor environment."
+
+
+

Great! Before moving on let’s create _mk_img to make our code a little DRY’r.

+

Let’s use _mk_img in our Msg subclasses

+
+
class OpenAiMsg(Msg):
+    "Helper class to create a message for the OpenAI API."
+    def img_msg(self, data:bytes)->dict:
+        "Convert `data` to an image message"
+        img, mtype = _mk_img(data)
+        r = {"url": f"data:{mtype};base64,{img}"}
+        return {"type": "image_url", "image_url": r}
+
+
+
class AnthropicMsg(Msg):
+    "Helper class to create a message for the Anthropic API."
+    def img_msg(self, data:bytes)->dict:
+        "Convert `data` to an image message"
+        img, mtype = _mk_img(data)
+        r = {"type": "base64", "media_type": mtype, "data":img}
+        return {"type": "image", "source": r}
+
+
+
+

Text Only Models

+

Ok, what next? Some text only models that follow the OpenAI API spec such as Deepseek, expect messages to have the following format

+
{"role": "user", "content": "Hello, world!"}
+

Let’s update our code to support this use-case.

+
+
class Msg:
+    "Helper class to create a message for the OpenAI and Anthropic APIs."
+    def __call__(self, role:str, content:[list, str], text_only:bool=False, **kw)->dict:
+        "Create an OpenAI/Anthropic compatible message with `role` and `content`."
+        if content is not None and not isinstance(content, list): content = [content]
+        content = [self.mk_content(o, text_only=text_only) for o in content] if content else ''
+        return dict(role=role, content=content[0] if text_only else content, **kw)
+    
+    def img_msg(self, *args, **kw)->dict: 
+        "Convert bytes to an image message"
+        raise NotImplemented
+        
+    def text_msg(self, s:str, text_only=False, **kw): 
+        "Convert `s` to a text message"
+        return s if text_only else {"type":"text", "text":s}
+    
+    def mk_content(self, content, text_only=False, **kw):
+        if isinstance(content, str): return self.text_msg(content, text_only=text_only)
+        if isinstance(content, bytes): return self.img_msg(content)
+        return content
+
+
+
class OpenAiMsg(Msg):
+    "Helper class to create a message for the OpenAI API."
+    def img_msg(self, data)->dict:
+        "Convert `data` to an image message"
+        img, mtype = _mk_img(data)
+        r = {"url": f"data:{mtype};base64,{img}"}
+        return {"type": "image_url", "image_url": r}
+
+
+
class AnthropicMsg(Msg):
+    "Helper class to create a message for the Anthropic API."
+    def img_msg(self, data)->dict:
+        "Convert `data` to an image message"
+        img, mtype = _mk_img(data)
+        r = dict(type='base64', media_type=mtype, data=img)
+        return {"type": "image", "source": r}
+
+
+

source

+
+
+

mk_msg

+
+
 mk_msg (content:Union[list,str], role:str='user', *args,
+         api:str='openai', text_only=False, **kw)
+
+

Create an OpenAI/Anthropic compatible message.

+
+
mk_msg("describe this picture", text_only=True)
+
+
{'role': 'user', 'content': 'describe this picture'}
+
+
+
+
mk_msg("describe this picture")
+
+
{'role': 'user',
+ 'content': [{'type': 'text', 'text': 'describe this picture'}]}
+
+
+
+
+

Conversation

+

LLMs are stateless. To continue a conversation we need to include the entire message history in every API call. By default the role in each message alternates between user and assistant.

+

Let’s add a method that alternates the roles for us and then calls mk_msgs.

+
+

source

+
+
+

mk_msgs

+
+
 mk_msgs (msgs:list, *args, api:str='openai', **kw)
+
+

Create a list of messages compatible with OpenAI/Anthropic.

+
+
mk_msgs(["Hello", "Some assistant response", "tell me a joke"])
+
+
[{'role': 'user', 'content': [{'type': 'text', 'text': 'Hello'}]},
+ {'role': 'assistant',
+  'content': [{'type': 'text', 'text': 'Some assistant response'}]},
+ {'role': 'user', 'content': [{'type': 'text', 'text': 'tell me a joke'}]}]
+
+
+
+
+

SDK Objects

+

To make our lives even easier, it would be nice if mk_msg could format the SDK objects returned from a previous chat so that we can pass them straight to mk_msgs.

+

The OpenAI SDK accepts objects like ChatCompletion as messages. Anthropic is different and expects every message to have the role, content format that we’ve seen so far.

+
+

source

+
+
+

Msg

+
+
 Msg ()
+
+

Helper class to create a message for the OpenAI and Anthropic APIs.

+
+

source

+
+
+

AnthropicMsg

+
+
 AnthropicMsg ()
+
+

Helper class to create a message for the OpenAI and Anthropic APIs.

+
+

source

+
+
+

OpenAiMsg

+
+
 OpenAiMsg ()
+
+

Helper class to create a message for the OpenAI and Anthropic APIs.

+

Let’s test our changes.

+
+
msgs = ["tell me a joke"]
+r, text = openai_chat(mk_msgs(msgs))
+text
+
+
'Why did the scarecrow win an award? \n\nBecause he was outstanding in his field!'
+
+
+
+
msgs += [r, "tell me another joke that's similar to your first joke"]
+r, text = openai_chat(mk_msgs(msgs))
+text
+
+
'Why did the bicycle fall over?\n\nBecause it was two-tired!'
+
+
+
+
+

Usage

+

To make msglm a little easier to use let’s create OpenAI and Anthropic wrappers for mk_msg and mk_msgs.

+
+
mk_msg_anthropic = partial(mk_msg, api="anthropic")
+mk_msgs_anthropic = partial(mk_msgs, api="anthropic")
+
+

If you’re using OpenAI you should be able to use the import below

+
from msglm import mk_msg_openai as mk_msg, mk_msgs_openai as mk_msgs
+

Similarily for Anthropic

+
from msglm import mk_msg_anthropic as mk_msg, mk_msgs_anthropic as mk_msgs
+
+
+

Caching

+

Anthropic currently offers prompt caching, which can reduce cost and latency.

+

To cache a message, we simply add a cache_control field to our content as shown below.

+
{
+    "role": "user",
+    "content": [
+        {
+            "type": "text",
+            "text": "Hello, can you tell me more about the solar system?",
+            "cache_control": {"type": "ephemeral"}
+        }
+    ]
+}
+

Let’s update our mk_msg and mk_msgs Anthropic wrappers to support caching.

+
+

source

+
+
+

mk_msgs_anthropic

+
+
 mk_msgs_anthropic (*args, cache=False, api:str='openai')
+
+

Create a list of Anthropic compatible messages.

+
+

source

+
+
+

mk_msg_anthropic

+
+
 mk_msg_anthropic (*args, cache=False, role:str='user', api:str='openai',
+                   text_only=False)
+
+

Create an Anthropic compatible message.

+

Let’s see caching in action

+
+
mk_msg_anthropic("Don't cache my message")
+
+
{'role': 'user',
+ 'content': [{'type': 'text', 'text': "Don't cache my message"}]}
+
+
+
+
mk_msg_anthropic("Please cache my message", cache=True)
+
+
{'role': 'user',
+ 'content': [{'type': 'text',
+   'text': 'Please cache my message',
+   'cache_control': {'type': 'ephemeral'}}]}
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/core.html.md b/core.html.md new file mode 100644 index 0000000..aeb2ba1 --- /dev/null +++ b/core.html.md @@ -0,0 +1,703 @@ +# core + + + + +## API Exploration + +Anthropic’s Claude and OpenAI’s GPT models are some of the most popular +LLMs. + +Let’s take a look at their APIs and to learn how we should structure our +messages for a simple text chat. + +#### openai + +``` python +from openai import OpenAI +``` + +``` python +client = OpenAI() + +client.chat.completions.create( + model="gpt-4o-mini", + messages=[ {"role": "user", "content": "Hello, world!"} ] +) +``` + + ChatCompletion(id='chatcmpl-ADNgo1gSrIolZfS9UW8glyIiFIe0Y', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Hello! How can I assist you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1727752690, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_74ba47b4ac', usage=CompletionUsage(completion_tokens=9, prompt_tokens=11, total_tokens=20, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0))) + +#### anthropic + +``` python +from anthropic import Anthropic +``` + +``` python +client = Anthropic() + +client.messages.create( + model="claude-3-haiku-20240307", + max_tokens=1024, + messages=[ {"role": "user", "content": "Hello, world!"} ] +) +``` + +Hello! It’s nice to meet you. How can I assist you today? + +
+ +- id: `msg_01GtzcT5ezjzXSmYehbhAhZH` +- content: + `[{'text': "Hello! It's nice to meet you. How can I assist you today?", 'type': 'text'}]` +- model: `claude-3-haiku-20240307` +- role: `assistant` +- stop_reason: `end_turn` +- stop_sequence: `None` +- type: `message` +- usage: `{'input_tokens': 11, 'output_tokens': 19}` + +
+ +As we can see both APIs use the exact same message structure. + +### mk_msg + +Ok, let’s build the first version of +[`mk_msg`](https://AnswerDotAI.github.io/msglm/core.html#mk_msg) to +handle this case + +``` python +def mk_msg(content:str, role:str="user")->dict: + "Create an OpenAI/Anthropic compatible message." + return dict(role=role, content=content) +``` + +Let’s test it out with the OpenAI API. To do that we’ll need to setup +two things: + +- install the openai SDK by running `pip install openai` +- add your openai api key to your env vars + `export OPENAI_API_KEY="YOUR_OPEN_API_KEY"` + +``` python +oa_cli = OpenAI() + +r = oa_cli.chat.completions.create( + model="gpt-4o-mini", + messages=[mk_msg("Hello, world!")] +) +r.choices[0].message.content +``` + + 'Hello! How can I assist you today?' + +Now, let’s test out +[`mk_msg`](https://AnswerDotAI.github.io/msglm/core.html#mk_msg) on the +Anthropic API. To do that we’ll need to setup two things: + +- install the openai SDK by running `pip install anthropic` +- add your anthropic api key to your env vars + `export ANTHROPIC_API_KEY="YOUR_ANTHROPIC_API_KEY"` + +``` python +a_cli = Anthropic() + +r = a_cli.messages.create( + model="claude-3-haiku-20240307", + max_tokens=1024, + messages=[mk_msg("Hello, world!")] +) +r.content[0].text +``` + + "Hello! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with all sorts of tasks, from research and analysis to creative projects and problem-solving. Please let me know if there's anything I can assist you with." + +So far so good! + +#### Helper Functions + +Before going any further, let’s create some helper functions to make it +a little easier to call the OpenAI and Anthropic APIs. We’re going to be +making a bunch of API calls to test our code and typing the full +expressions out each time will become a little tedious. These functions +won’t be included in the final package. + +``` python +def openai_chat(msgs: list)->tuple: + "call the openai chat completions endpoint with `msgs`." + r = oa_cli.chat.completions.create(model="gpt-4o-mini", messages=msgs) + return r, r.choices[0].message.content +``` + +Let’s double check that +[`mk_msg`](https://AnswerDotAI.github.io/msglm/core.html#mk_msg) still +works with our simple text example from before. + +``` python +_, text = openai_chat([mk_msg("Hello, world!")]) +text +``` + + 'Hello! How can I assist you today?' + +``` python +def anthropic_chat(msgs: list)->tuple: + "call the anthropic messages endpoint with `msgs`." + r = a_cli.messages.create(model="claude-3-haiku-20240307", max_tokens=1024, messages=msgs) + return r, r.content[0].text +``` + +and Anthropic… + +``` python +_, text = anthropic_chat([mk_msg("Hello, world!")]) +text +``` + + "Hello! It's nice to meet you. How can I assist you today?" + +### Images + +Ok, let’s see how both APIs handle image messages. + + + +#### openai + +``` python +import base64, httpx +``` + +``` python +img_url = "https://claudette.answer.ai/index_files/figure-html/cell-35-output-1.jpeg" +``` + +``` python +mtype = "image/jpeg" +img = base64.b64encode(httpx.get(img_url).content).decode("utf-8") + +client = OpenAI() +client.chat.completions.create( + model="gpt-4o-mini", + messages=[ + { + "role":"user", + "content": [ + {"type":"text","text":"What's in this image?"}, + {"type":"image_url","image_url":{"url":f"data:{mtype};base64,{img}"}}, + ], + } + ], +) +``` + + ChatCompletion(id='chatcmpl-ADNjLoa2oJmG7yPU1qnBI426h1TB2', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The image features a small puppy lying on grass, with a background of purple flowers. The puppy has a white coat with brown markings, and it appears to be resting or playing in a garden setting.', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1727752847, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_74ba47b4ac', usage=CompletionUsage(completion_tokens=40, prompt_tokens=8512, total_tokens=8552, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0))) + +#### anthropic + +``` python +mtype = "image/jpeg" +img = base64.b64encode(httpx.get(img_url).content).decode("utf-8") + +client = Anthropic() +client.messages.create( + model="claude-3-haiku-20240307", + max_tokens=1024, + messages=[ + { + "role":"user", + "content": [ + {"type":"text","text":"What's in this image?"}, + {"type":"image","source":{"type":"base64","media_type":mtype,"data":img}} + ], + } + ], +) +``` + +This image shows a cute puppy lying in a grassy area with purple flowers +in the background. The puppy appears to be a Cavalier King Charles +Spaniel, with a fluffy white and brown coat. The puppy has a friendly, +playful expression on its face as it looks directly at the camera. The +image captures a charming, serene outdoor scene with the puppy as the +main focus. + +
+ +- id: `msg_015JmJ2GR9fkisz9Wsh1CKEK` +- content: + `[{'text': 'This image shows a cute puppy lying in a grassy area with purple flowers in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a fluffy white and brown coat. The puppy has a friendly, playful expression on its face as it looks directly at the camera. The image captures a charming, serene outdoor scene with the puppy as the main focus.', 'type': 'text'}]` +- model: `claude-3-haiku-20240307` +- role: `assistant` +- stop_reason: `end_turn` +- stop_sequence: `None` +- type: `message` +- usage: `{'input_tokens': 104, 'output_tokens': 90}` + +
+ +Both APIs format images slightly differently and the structure of the +message `content` is a litte more complex. + +In a text chat, `content` is a simple string but for a multimodal chat +(text+images) we can see that `content` is a list of dictionaries. + +### Msg Class + +To handle the additional complexity of the multimodal messages let’s +build a [`Msg`](https://AnswerDotAI.github.io/msglm/core.html#msg) class +for the `content` data structure: + +``` json +{ + "role": "user", + "content": [{"type": "text", "text": "What's in this image?"}], +} +``` + +``` python +class Msg: + "Helper class to create a message for the OpenAI and Anthropic APIs." + def __call__(self, role:str, content:[list,str], **kw)->dict: + "Create an OpenAI/Anthropic compatible message with `role` and `content`." + if content is not None and not isinstance(content, list): content = [content] + return dict(role=role, content=[{"type":"text", "content":item} for item in content], **kw) +``` + +Now, let’s update +[`mk_msg`](https://AnswerDotAI.github.io/msglm/core.html#mk_msg) to use +[`Msg`](https://AnswerDotAI.github.io/msglm/core.html#msg). + +``` python +def mk_msg(content:str, role:str="user", **kw)->dict: + "Create an OpenAI/Anthropic compatible message." + return Msg()(role, content, **kw) +``` + +``` python +mk_msg(["Hello world", "how are you?"]) +``` + + {'role': 'user', + 'content': [{'type': 'text', 'content': 'Hello world'}, + {'type': 'text', 'content': 'how are you?'}]} + +Now, let’s update +[`Msg`](https://AnswerDotAI.github.io/msglm/core.html#msg) so that it +can format our images correctly. + +First, we’ll add a method `mk_content` where we can check the type of +each input and format the data appropriately. Let’s start by adding a +check for text data. + +``` python +class Msg: + "Helper class to create a message for the OpenAI and Anthropic APIs." + def __call__(self, role:str, content:[list,str], **kw)->dict: + "Create an OpenAI/Anthropic compatible message with `role` and `content`." + if content is not None and not isinstance(content, list): content = [content] + content = [self.mk_content(o) for o in content] if content else '' + return dict(role=role, content=content, **kw) + + def text_msg(self, s:str)->dict: + "Convert `s` to a text message" + return {"type": "text", "text":s} + + def mk_content(self, content:str)->dict: + "Create the appropriate data structure based the content type." + if isinstance(content, str): return self.text_msg(content) + return content +``` + +Here’s where things get a little tricker. As both APIs handle images +differently let’s subclass +[`Msg`](https://AnswerDotAI.github.io/msglm/core.html#msg) for each API +and handle the image formatting in a method called `img_msg`. + +``` python +class Msg: + "Helper class to create a message for the OpenAI and Anthropic APIs." + def __call__(self, role:str, content:[list,str], **kw)->dict: + "Create an OpenAI/Anthropic compatible message with `role` and `content`." + if content is not None and not isinstance(content, list): content = [content] + content = [self.mk_content(o) for o in content] if content else '' + return dict(role=role, content=content, **kw) + + def img_msg(self, *args)->dict: + "Convert bytes to an image message" + raise NotImplemented + + def text_msg(self, s:str)->dict: + "Convert `s` to a text message" + return {"type": "text", "text":s} + + def mk_content(self, content:str)->dict: + "Create the appropriate data structure based the content type." + if isinstance(content, str): return self.text_msg(content) + if isinstance(content, bytes): return self.img_msg(content) + return content +``` + +``` python +class OpenAiMsg(Msg): + "Helper class to create a message for the OpenAI API." + def img_msg(self, data:bytes)->dict: + "Convert `data` to an image message" + img = base64.b64encode(data).decode("utf-8") + mtype = mimetypes.types_map["." + imghdr.what(None, h=data)] + r = {"url": f"data:{mtype};base64,{img}"} + return {"type": "image_url", "image_url": r} +``` + +``` python +class AnthropicMsg(Msg): + "Helper class to create a message for the Anthropic API." + def img_msg(self, data:bytes)->dict: + "Convert `data` to an image message" + img = base64.b64encode(data).decode("utf-8") + mtype = mimetypes.types_map["." + imghdr.what(None, h=data)] + r = {"type": "base64", "media_type": mtype, "data":img} + return {"type": "image", "source": r} +``` + +Now, let’s update +[`mk_msg`](https://AnswerDotAI.github.io/msglm/core.html#mk_msg) so that +it chooses the appropriate +[`Msg`](https://AnswerDotAI.github.io/msglm/core.html#msg) subclass. + +``` python +def mk_msg(content:str, role:str="user", *args, api:str="openai", **kw) -> dict: + "Create an OpenAI/Anthropic compatible message." + m = OpenAiMsg() if api == "openai" else AnthropicMsg() + return m(role, content, **kw) +``` + +Ok, let’s test our changes… + +``` python +from IPython.display import Image, display +``` + +``` python +img = httpx.get(img_url).content +display(Image(img)) +``` + +![](00_core_files/figure-commonmark/cell-26-output-1.jpeg) + +``` python +msg = mk_msg([img, "describe this picture"], api="openai") +_, text = openai_chat([msg]) +text +``` + + 'The image features an adorable puppy lying on the grass. It has a primarily white coat with brown markings, particularly around its ears and eyes. The puppy has a curious and playful expression. In the background, there are purple flowers adding a touch of color to the scene. The overall setting appears to be a well-kept garden, creating a charming and serene atmosphere.' + +``` python +msg = mk_msg([img, "describe this picture"], api="anthropic") +_, text = anthropic_chat([msg]) +text +``` + + "The image shows a cute puppy nestled in some grass, with purple flowers blooming in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a brown and white coat. It has a friendly, curious expression as it looks out at the viewer. The scene creates a peaceful, natural setting, with the puppy's soft fur and the vibrant flowers providing a lovely visual contrast. Overall, this image captures the endearing charm and beauty of a young dog in a picturesque outdoor environment." + +Great! Before moving on let’s create +[`_mk_img`](https://AnswerDotAI.github.io/msglm/core.html#_mk_img) to +make our code a little DRY’r. + +Let’s use +[`_mk_img`](https://AnswerDotAI.github.io/msglm/core.html#_mk_img) in +our [`Msg`](https://AnswerDotAI.github.io/msglm/core.html#msg) +subclasses + +``` python +class OpenAiMsg(Msg): + "Helper class to create a message for the OpenAI API." + def img_msg(self, data:bytes)->dict: + "Convert `data` to an image message" + img, mtype = _mk_img(data) + r = {"url": f"data:{mtype};base64,{img}"} + return {"type": "image_url", "image_url": r} +``` + +``` python +class AnthropicMsg(Msg): + "Helper class to create a message for the Anthropic API." + def img_msg(self, data:bytes)->dict: + "Convert `data` to an image message" + img, mtype = _mk_img(data) + r = {"type": "base64", "media_type": mtype, "data":img} + return {"type": "image", "source": r} +``` + +### Text Only Models + +Ok, what next? Some text only models that follow the OpenAI API spec +such as Deepseek, expect messages to have the following format + +``` json +{"role": "user", "content": "Hello, world!"} +``` + +Let’s update our code to support this use-case. + +``` python +class Msg: + "Helper class to create a message for the OpenAI and Anthropic APIs." + def __call__(self, role:str, content:[list, str], text_only:bool=False, **kw)->dict: + "Create an OpenAI/Anthropic compatible message with `role` and `content`." + if content is not None and not isinstance(content, list): content = [content] + content = [self.mk_content(o, text_only=text_only) for o in content] if content else '' + return dict(role=role, content=content[0] if text_only else content, **kw) + + def img_msg(self, *args, **kw)->dict: + "Convert bytes to an image message" + raise NotImplemented + + def text_msg(self, s:str, text_only=False, **kw): + "Convert `s` to a text message" + return s if text_only else {"type":"text", "text":s} + + def mk_content(self, content, text_only=False, **kw): + if isinstance(content, str): return self.text_msg(content, text_only=text_only) + if isinstance(content, bytes): return self.img_msg(content) + return content +``` + +``` python +class OpenAiMsg(Msg): + "Helper class to create a message for the OpenAI API." + def img_msg(self, data)->dict: + "Convert `data` to an image message" + img, mtype = _mk_img(data) + r = {"url": f"data:{mtype};base64,{img}"} + return {"type": "image_url", "image_url": r} +``` + +``` python +class AnthropicMsg(Msg): + "Helper class to create a message for the Anthropic API." + def img_msg(self, data)->dict: + "Convert `data` to an image message" + img, mtype = _mk_img(data) + r = dict(type='base64', media_type=mtype, data=img) + return {"type": "image", "source": r} +``` + +------------------------------------------------------------------------ + +source + +### mk_msg + +> mk_msg (content:Union[list,str], role:str='user', *args, +> api:str='openai', text_only=False, **kw) + +*Create an OpenAI/Anthropic compatible message.* + +``` python +mk_msg("describe this picture", text_only=True) +``` + + {'role': 'user', 'content': 'describe this picture'} + +``` python +mk_msg("describe this picture") +``` + + {'role': 'user', + 'content': [{'type': 'text', 'text': 'describe this picture'}]} + +### Conversation + +LLMs are stateless. To continue a conversation we need to include the +entire message history in every API call. By default the role in each +message alternates between `user` and `assistant`. + +Let’s add a method that alternates the roles for us and then calls +[`mk_msgs`](https://AnswerDotAI.github.io/msglm/core.html#mk_msgs). + +------------------------------------------------------------------------ + +source + +### mk_msgs + +> mk_msgs (msgs:list, *args, api:str='openai', **kw) + +*Create a list of messages compatible with OpenAI/Anthropic.* + +``` python +mk_msgs(["Hello", "Some assistant response", "tell me a joke"]) +``` + + [{'role': 'user', 'content': [{'type': 'text', 'text': 'Hello'}]}, + {'role': 'assistant', + 'content': [{'type': 'text', 'text': 'Some assistant response'}]}, + {'role': 'user', 'content': [{'type': 'text', 'text': 'tell me a joke'}]}] + +### SDK Objects + +To make our lives even easier, it would be nice if +[`mk_msg`](https://AnswerDotAI.github.io/msglm/core.html#mk_msg) could +format the SDK objects returned from a previous chat so that we can pass +them straight to +[`mk_msgs`](https://AnswerDotAI.github.io/msglm/core.html#mk_msgs). + +The OpenAI SDK accepts objects like `ChatCompletion` as messages. +Anthropic is different and expects every message to have the `role`, +`content` format that we’ve seen so far. + +------------------------------------------------------------------------ + +source + +### Msg + +> Msg () + +*Helper class to create a message for the OpenAI and Anthropic APIs.* + +------------------------------------------------------------------------ + +source + +### AnthropicMsg + +> AnthropicMsg () + +*Helper class to create a message for the OpenAI and Anthropic APIs.* + +------------------------------------------------------------------------ + +source + +### OpenAiMsg + +> OpenAiMsg () + +*Helper class to create a message for the OpenAI and Anthropic APIs.* + +Let’s test our changes. + +``` python +msgs = ["tell me a joke"] +r, text = openai_chat(mk_msgs(msgs)) +text +``` + + 'Why did the scarecrow win an award? \n\nBecause he was outstanding in his field!' + +``` python +msgs += [r, "tell me another joke that's similar to your first joke"] +r, text = openai_chat(mk_msgs(msgs)) +text +``` + + 'Why did the bicycle fall over?\n\nBecause it was two-tired!' + +### Usage + +To make `msglm` a little easier to use let’s create OpenAI and Anthropic +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). + +``` python +mk_msg_anthropic = partial(mk_msg, api="anthropic") +mk_msgs_anthropic = partial(mk_msgs, api="anthropic") +``` + +If you’re using OpenAI you should be able to use the import below + +``` python +from msglm import mk_msg_openai as mk_msg, mk_msgs_openai as mk_msgs +``` + +Similarily for Anthropic + +``` python +from msglm import mk_msg_anthropic as mk_msg, mk_msgs_anthropic as mk_msgs +``` + +### Caching + +Anthropic currently offers [prompt +caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching), +which can reduce cost and latency. + +To cache a message, we simply add a `cache_control` field to our content +as shown below. + +``` js +{ + "role": "user", + "content": [ + { + "type": "text", + "text": "Hello, can you tell me more about the solar system?", + "cache_control": {"type": "ephemeral"} + } + ] +} +``` + +Let’s update our +[`mk_msg`](https://AnswerDotAI.github.io/msglm/core.html#mk_msg) and +[`mk_msgs`](https://AnswerDotAI.github.io/msglm/core.html#mk_msgs) +Anthropic wrappers to support caching. + +------------------------------------------------------------------------ + +source + +### mk_msgs_anthropic + +> mk_msgs_anthropic (*args, cache=False, api:str='openai') + +*Create a list of Anthropic compatible messages.* + +------------------------------------------------------------------------ + +source + +### mk_msg_anthropic + +> mk_msg_anthropic (*args, cache=False, role:str='user', api:str='openai', +> text_only=False) + +*Create an Anthropic compatible message.* + +Let’s see caching in action + +``` python +mk_msg_anthropic("Don't cache my message") +``` + + {'role': 'user', + 'content': [{'type': 'text', 'text': "Don't cache my message"}]} + +``` python +mk_msg_anthropic("Please cache my message", cache=True) +``` + + {'role': 'user', + 'content': [{'type': 'text', + 'text': 'Please cache my message', + 'cache_control': {'type': 'ephemeral'}}]} diff --git a/index.html b/index.html new file mode 100644 index 0000000..59b73e4 --- /dev/null +++ b/index.html @@ -0,0 +1,783 @@ + + + + + + + + + + +msglm + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

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": [{"type": "text", "text": "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 LLM response"], api="openai")
+
[
+    {"role": "user", "content": [{"type": "text", "text": "Hello, world!"}]},
+    {"role": "assistant", "content": [{"type": "text", "text": "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 your 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"}}
+    ]
+}
+
+
+

Text only models

+

msglm supports text only models such as DeepSeek that use the OpenAI API format. Simply pass text_only=True to mk_msg or mk_msgs

+
from msglm import mk_msg_openai as mk_msg
+
+mk_msg("please format my text only prompt", text_only=True)
+

This generates the expected cache block below

+
{
+    "role": "user",
+    "content": "please format my text only prompt"
+}
+
+
+
+

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.

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/index.html.md b/index.html.md new file mode 100644 index 0000000..c13c1d5 --- /dev/null +++ b/index.html.md @@ -0,0 +1,209 @@ +# msglm + + + + +### 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": [{"type": "text", "text": "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 LLM response"], api="openai") +``` + +``` js +[ + {"role": "user", "content": [{"type": "text", "text": "Hello, world!"}]}, + {"role": "assistant", "content": [{"type": "text", "text": "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 your 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"}} + ] +} +``` + +#### Text only models + +*msglm* supports text only models such as DeepSeek that use the OpenAI +API format. Simply pass *text_only=True* to *mk_msg* or *mk_msgs* + +``` python +from msglm import mk_msg_openai as mk_msg + +mk_msg("please format my text only prompt", text_only=True) +``` + +This generates the expected cache block below + +``` js +{ + "role": "user", + "content": "please format my text only prompt" +} +``` + +### 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](./core.html). diff --git a/robots.txt b/robots.txt new file mode 100644 index 0000000..ac31e34 --- /dev/null +++ b/robots.txt @@ -0,0 +1 @@ +Sitemap: https://AnswerDotAI.github.io/msglm/sitemap.xml diff --git a/search.json b/search.json new file mode 100644 index 0000000..c7d01c8 --- /dev/null +++ b/search.json @@ -0,0 +1,42 @@ +[ + { + "objectID": "index.html", + "href": "index.html", + "title": "msglm", + "section": "", + "text": "Install the latest version from pypi\n$ pip install msglm", + "crumbs": [ + "msglm" + ] + }, + { + "objectID": "index.html#usage", + "href": "index.html#usage", + "title": "msglm", + "section": "Usage", + "text": "Usage\nTo use an LLM we need to structure our messages in a particular format.\nHere’s an example of a text chat from the OpenAI docs.\nfrom openai import OpenAI\nclient = OpenAI()\n\ncompletion = client.chat.completions.create(\n model=\"gpt-4o\",\n messages=[\n {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"What's the Wild Atlantic Way?\"}]}\n ]\n)\nGenerating the correct format for a particular API can get tedious. The goal of msglm is to make it easier.\nThe examples below will show you how to use msglm for text and image chats with OpenAI and Anthropic.\n\nText Chats\nFor 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.\nmk_msgs([\"Hello, world!\", \"some LLM response\"], api=\"openai\")\n[\n {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"Hello, world!\"}]},\n {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"Some assistant response\"}]}\n]\n\nanthropic\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\nopenai\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\nImage Chats\nFor 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.\nmk_msg([img, \"What's in this image?\"], api=\"anthropic\")\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\nanthropic\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\nopenai\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\nAPI Wrappers\nTo make your life a little easier msglm comes with api specific wrappers for mk_msg and mk_msgs.\nFor Anthropic use\nfrom msglm import mk_msg_anthropic as mk_msg, mk_msgs_anthropic as mk_msgs\nFor OpenAI use\nfrom msglm import mk_msg_openai as mk_msg, mk_msgs_openai as mk_msgs\n\n\nOther use-cases\n\nPrompt Caching\nmsglm supports prompt caching for Anthropic models. Simply pass cache=True to mk_msg or mk_msgs.\nfrom msglm import mk_msg_anthropic as mk_msg\n\nmk_msg(\"please cache my message\", cache=True)\nThis generates the expected cache block below\n{\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"text\", \"text\": \"Please cache my message\", \"cache_control\": {\"type\": \"ephemeral\"}}\n ]\n}\n\n\nText only models\nmsglm supports text only models such as DeepSeek that use the OpenAI API format. Simply pass text_only=True to mk_msg or mk_msgs\nfrom msglm import mk_msg_openai as mk_msg\n\nmk_msg(\"please format my text only prompt\", text_only=True)\nThis generates the expected cache block below\n{\n \"role\": \"user\",\n \"content\": \"please format my text only prompt\"\n}\n\n\n\nSummary\nWe hope msglm will make your life a little easier when chatting to LLMs. To learn more about the package please read this doc.", + "crumbs": [ + "msglm" + ] + }, + { + "objectID": "core.html", + "href": "core.html", + "title": "core", + "section": "", + "text": "Anthropic’s Claude and OpenAI’s GPT models are some of the most popular LLMs.\nLet’s take a look at their APIs and to learn how we should structure our messages for a simple text chat.\n\n\n\nfrom openai import OpenAI\n\n\nclient = OpenAI()\n\nclient.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[ {\"role\": \"user\", \"content\": \"Hello, world!\"} ]\n)\n\nChatCompletion(id='chatcmpl-ADNgo1gSrIolZfS9UW8glyIiFIe0Y', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Hello! How can I assist you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1727752690, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_74ba47b4ac', usage=CompletionUsage(completion_tokens=9, prompt_tokens=11, total_tokens=20, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0)))\n\n\n\n\n\n\nfrom anthropic import Anthropic\n\n\nclient = Anthropic()\n\nclient.messages.create(\n model=\"claude-3-haiku-20240307\",\n max_tokens=1024,\n messages=[ {\"role\": \"user\", \"content\": \"Hello, world!\"} ]\n)\n\nHello! It’s nice to meet you. How can I assist you today?\n\n\nid: msg_01GtzcT5ezjzXSmYehbhAhZH\ncontent: [{'text': \"Hello! It's nice to meet you. How can I assist you today?\", 'type': 'text'}]\nmodel: claude-3-haiku-20240307\nrole: assistant\nstop_reason: end_turn\nstop_sequence: None\ntype: message\nusage: {'input_tokens': 11, 'output_tokens': 19}\n\n\n\n\nAs we can see both APIs use the exact same message structure.\n\n\n\nOk, let’s build the first version of mk_msg to handle this case\n\ndef mk_msg(content:str, role:str=\"user\")->dict:\n \"Create an OpenAI/Anthropic compatible message.\"\n return dict(role=role, content=content)\n\nLet’s test it out with the OpenAI API. To do that we’ll need to setup two things:\n\ninstall the openai SDK by running pip install openai\nadd your openai api key to your env vars export OPENAI_API_KEY=\"YOUR_OPEN_API_KEY\"\n\n\noa_cli = OpenAI()\n\nr = oa_cli.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[mk_msg(\"Hello, world!\")]\n)\nr.choices[0].message.content\n\n'Hello! How can I assist you today?'\n\n\nNow, let’s test out mk_msg on the Anthropic API. To do that we’ll need to setup two things:\n\ninstall the openai SDK by running pip install anthropic\nadd your anthropic api key to your env vars export ANTHROPIC_API_KEY=\"YOUR_ANTHROPIC_API_KEY\"\n\n\na_cli = Anthropic()\n\nr = a_cli.messages.create(\n model=\"claude-3-haiku-20240307\",\n max_tokens=1024,\n messages=[mk_msg(\"Hello, world!\")]\n)\nr.content[0].text\n\n\"Hello! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with all sorts of tasks, from research and analysis to creative projects and problem-solving. Please let me know if there's anything I can assist you with.\"\n\n\nSo far so good!\n\n\nBefore going any further, let’s create some helper functions to make it a little easier to call the OpenAI and Anthropic APIs. We’re going to be making a bunch of API calls to test our code and typing the full expressions out each time will become a little tedious. These functions won’t be included in the final package.\n\ndef openai_chat(msgs: list)->tuple:\n \"call the openai chat completions endpoint with `msgs`.\"\n r = oa_cli.chat.completions.create(model=\"gpt-4o-mini\", messages=msgs)\n return r, r.choices[0].message.content\n\nLet’s double check that mk_msg still works with our simple text example from before.\n\n_, text = openai_chat([mk_msg(\"Hello, world!\")])\ntext\n\n'Hello! How can I assist you today?'\n\n\n\ndef anthropic_chat(msgs: list)->tuple:\n \"call the anthropic messages endpoint with `msgs`.\"\n r = a_cli.messages.create(model=\"claude-3-haiku-20240307\", max_tokens=1024, messages=msgs)\n return r, r.content[0].text\n\nand Anthropic…\n\n_, text = anthropic_chat([mk_msg(\"Hello, world!\")])\ntext\n\n\"Hello! It's nice to meet you. How can I assist you today?\"\n\n\n\n\n\n\nOk, let’s see how both APIs handle image messages.\n\n\n\n\nimport base64, httpx\n\n\nimg_url = \"https://claudette.answer.ai/index_files/figure-html/cell-35-output-1.jpeg\"\n\n\nmtype = \"image/jpeg\"\nimg = base64.b64encode(httpx.get(img_url).content).decode(\"utf-8\")\n\nclient = OpenAI()\nclient.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\n \"role\":\"user\",\n \"content\": [\n {\"type\":\"text\",\"text\":\"What's in this image?\"},\n {\"type\":\"image_url\",\"image_url\":{\"url\":f\"data:{mtype};base64,{img}\"}},\n ],\n }\n ],\n)\n\nChatCompletion(id='chatcmpl-ADNjLoa2oJmG7yPU1qnBI426h1TB2', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The image features a small puppy lying on grass, with a background of purple flowers. The puppy has a white coat with brown markings, and it appears to be resting or playing in a garden setting.', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1727752847, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_74ba47b4ac', usage=CompletionUsage(completion_tokens=40, prompt_tokens=8512, total_tokens=8552, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0)))\n\n\n\n\n\n\nmtype = \"image/jpeg\"\nimg = base64.b64encode(httpx.get(img_url).content).decode(\"utf-8\")\n\nclient = Anthropic()\nclient.messages.create(\n model=\"claude-3-haiku-20240307\",\n max_tokens=1024,\n messages=[\n {\n \"role\":\"user\",\n \"content\": [\n {\"type\":\"text\",\"text\":\"What's in this image?\"},\n {\"type\":\"image\",\"source\":{\"type\":\"base64\",\"media_type\":mtype,\"data\":img}}\n ],\n }\n ],\n)\n\nThis image shows a cute puppy lying in a grassy area with purple flowers in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a fluffy white and brown coat. The puppy has a friendly, playful expression on its face as it looks directly at the camera. The image captures a charming, serene outdoor scene with the puppy as the main focus.\n\n\nid: msg_015JmJ2GR9fkisz9Wsh1CKEK\ncontent: [{'text': 'This image shows a cute puppy lying in a grassy area with purple flowers in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a fluffy white and brown coat. The puppy has a friendly, playful expression on its face as it looks directly at the camera. The image captures a charming, serene outdoor scene with the puppy as the main focus.', 'type': 'text'}]\nmodel: claude-3-haiku-20240307\nrole: assistant\nstop_reason: end_turn\nstop_sequence: None\ntype: message\nusage: {'input_tokens': 104, 'output_tokens': 90}\n\n\n\n\nBoth APIs format images slightly differently and the structure of the message content is a litte more complex.\nIn a text chat, content is a simple string but for a multimodal chat (text+images) we can see that content is a list of dictionaries.\n\n\n\n\nTo handle the additional complexity of the multimodal messages let’s build a Msg class for the content data structure:\n{\n \"role\": \"user\",\n \"content\": [{\"type\": \"text\", \"text\": \"What's in this image?\"}],\n}\n\nclass Msg:\n \"Helper class to create a message for the OpenAI and Anthropic APIs.\"\n def __call__(self, role:str, content:[list,str], **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message with `role` and `content`.\"\n if content is not None and not isinstance(content, list): content = [content]\n return dict(role=role, content=[{\"type\":\"text\", \"content\":item} for item in content], **kw)\n\nNow, let’s update mk_msg to use Msg.\n\ndef mk_msg(content:str, role:str=\"user\", **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message.\"\n return Msg()(role, content, **kw)\n\n\nmk_msg([\"Hello world\", \"how are you?\"])\n\n{'role': 'user',\n 'content': [{'type': 'text', 'content': 'Hello world'},\n {'type': 'text', 'content': 'how are you?'}]}\n\n\nNow, let’s update Msg so that it can format our images correctly.\nFirst, we’ll add a method mk_content where we can check the type of each input and format the data appropriately. Let’s start by adding a check for text data.\n\nclass Msg:\n \"Helper class to create a message for the OpenAI and Anthropic APIs.\"\n def __call__(self, role:str, content:[list,str], **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message with `role` and `content`.\"\n if content is not None and not isinstance(content, list): content = [content]\n content = [self.mk_content(o) for o in content] if content else ''\n return dict(role=role, content=content, **kw)\n \n def text_msg(self, s:str)->dict: \n \"Convert `s` to a text message\"\n return {\"type\": \"text\", \"text\":s}\n \n def mk_content(self, content:str)->dict:\n \"Create the appropriate data structure based the content type.\"\n if isinstance(content, str): return self.text_msg(content)\n return content\n\nHere’s where things get a little tricker. As both APIs handle images differently let’s subclass Msg for each API and handle the image formatting in a method called img_msg.\n\nclass Msg:\n \"Helper class to create a message for the OpenAI and Anthropic APIs.\"\n def __call__(self, role:str, content:[list,str], **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message with `role` and `content`.\"\n if content is not None and not isinstance(content, list): content = [content]\n content = [self.mk_content(o) for o in content] if content else ''\n return dict(role=role, content=content, **kw)\n \n def img_msg(self, *args)->dict: \n \"Convert bytes to an image message\"\n raise NotImplemented\n \n def text_msg(self, s:str)->dict: \n \"Convert `s` to a text message\"\n return {\"type\": \"text\", \"text\":s}\n \n def mk_content(self, content:str)->dict:\n \"Create the appropriate data structure based the content type.\"\n if isinstance(content, str): return self.text_msg(content)\n if isinstance(content, bytes): return self.img_msg(content)\n return content\n\n\nclass OpenAiMsg(Msg):\n \"Helper class to create a message for the OpenAI API.\"\n def img_msg(self, data:bytes)->dict:\n \"Convert `data` to an image message\"\n img = base64.b64encode(data).decode(\"utf-8\")\n mtype = mimetypes.types_map[\".\" + imghdr.what(None, h=data)]\n r = {\"url\": f\"data:{mtype};base64,{img}\"}\n return {\"type\": \"image_url\", \"image_url\": r}\n\n\nclass AnthropicMsg(Msg):\n \"Helper class to create a message for the Anthropic API.\"\n def img_msg(self, data:bytes)->dict:\n \"Convert `data` to an image message\"\n img = base64.b64encode(data).decode(\"utf-8\")\n mtype = mimetypes.types_map[\".\" + imghdr.what(None, h=data)]\n r = {\"type\": \"base64\", \"media_type\": mtype, \"data\":img}\n return {\"type\": \"image\", \"source\": r}\n\nNow, let’s update mk_msg so that it chooses the appropriate Msg subclass.\n\ndef mk_msg(content:str, role:str=\"user\", *args, api:str=\"openai\", **kw) -> dict:\n \"Create an OpenAI/Anthropic compatible message.\"\n m = OpenAiMsg() if api == \"openai\" else AnthropicMsg()\n return m(role, content, **kw)\n\nOk, let’s test our changes…\n\nfrom IPython.display import Image, display\n\n\nimg = httpx.get(img_url).content\ndisplay(Image(img))\n\n\n\n\n\n\n\n\n\nmsg = mk_msg([img, \"describe this picture\"], api=\"openai\")\n_, text = openai_chat([msg])\ntext\n\n'The image features an adorable puppy lying on the grass. It has a primarily white coat with brown markings, particularly around its ears and eyes. The puppy has a curious and playful expression. In the background, there are purple flowers adding a touch of color to the scene. The overall setting appears to be a well-kept garden, creating a charming and serene atmosphere.'\n\n\n\nmsg = mk_msg([img, \"describe this picture\"], api=\"anthropic\")\n_, text = anthropic_chat([msg])\ntext\n\n\"The image shows a cute puppy nestled in some grass, with purple flowers blooming in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a brown and white coat. It has a friendly, curious expression as it looks out at the viewer. The scene creates a peaceful, natural setting, with the puppy's soft fur and the vibrant flowers providing a lovely visual contrast. Overall, this image captures the endearing charm and beauty of a young dog in a picturesque outdoor environment.\"\n\n\nGreat! Before moving on let’s create _mk_img to make our code a little DRY’r.\nLet’s use _mk_img in our Msg subclasses\n\nclass OpenAiMsg(Msg):\n \"Helper class to create a message for the OpenAI API.\"\n def img_msg(self, data:bytes)->dict:\n \"Convert `data` to an image message\"\n img, mtype = _mk_img(data)\n r = {\"url\": f\"data:{mtype};base64,{img}\"}\n return {\"type\": \"image_url\", \"image_url\": r}\n\n\nclass AnthropicMsg(Msg):\n \"Helper class to create a message for the Anthropic API.\"\n def img_msg(self, data:bytes)->dict:\n \"Convert `data` to an image message\"\n img, mtype = _mk_img(data)\n r = {\"type\": \"base64\", \"media_type\": mtype, \"data\":img}\n return {\"type\": \"image\", \"source\": r}\n\n\n\n\nOk, what next? Some text only models that follow the OpenAI API spec such as Deepseek, expect messages to have the following format\n{\"role\": \"user\", \"content\": \"Hello, world!\"}\nLet’s update our code to support this use-case.\n\nclass Msg:\n \"Helper class to create a message for the OpenAI and Anthropic APIs.\"\n def __call__(self, role:str, content:[list, str], text_only:bool=False, **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message with `role` and `content`.\"\n if content is not None and not isinstance(content, list): content = [content]\n content = [self.mk_content(o, text_only=text_only) for o in content] if content else ''\n return dict(role=role, content=content[0] if text_only else content, **kw)\n \n def img_msg(self, *args, **kw)->dict: \n \"Convert bytes to an image message\"\n raise NotImplemented\n \n def text_msg(self, s:str, text_only=False, **kw): \n \"Convert `s` to a text message\"\n return s if text_only else {\"type\":\"text\", \"text\":s}\n \n def mk_content(self, content, text_only=False, **kw):\n if isinstance(content, str): return self.text_msg(content, text_only=text_only)\n if isinstance(content, bytes): return self.img_msg(content)\n return content\n\n\nclass OpenAiMsg(Msg):\n \"Helper class to create a message for the OpenAI API.\"\n def img_msg(self, data)->dict:\n \"Convert `data` to an image message\"\n img, mtype = _mk_img(data)\n r = {\"url\": f\"data:{mtype};base64,{img}\"}\n return {\"type\": \"image_url\", \"image_url\": r}\n\n\nclass AnthropicMsg(Msg):\n \"Helper class to create a message for the Anthropic API.\"\n def img_msg(self, data)->dict:\n \"Convert `data` to an image message\"\n img, mtype = _mk_img(data)\n r = dict(type='base64', media_type=mtype, data=img)\n return {\"type\": \"image\", \"source\": r}\n\n\nsource\n\n\n\n\n mk_msg (content:Union[list,str], role:str='user', *args,\n api:str='openai', text_only=False, **kw)\n\nCreate an OpenAI/Anthropic compatible message.\n\nmk_msg(\"describe this picture\", text_only=True)\n\n{'role': 'user', 'content': 'describe this picture'}\n\n\n\nmk_msg(\"describe this picture\")\n\n{'role': 'user',\n 'content': [{'type': 'text', 'text': 'describe this picture'}]}\n\n\n\n\n\nLLMs are stateless. To continue a conversation we need to include the entire message history in every API call. By default the role in each message alternates between user and assistant.\nLet’s add a method that alternates the roles for us and then calls mk_msgs.\n\nsource\n\n\n\n\n mk_msgs (msgs:list, *args, api:str='openai', **kw)\n\nCreate a list of messages compatible with OpenAI/Anthropic.\n\nmk_msgs([\"Hello\", \"Some assistant response\", \"tell me a joke\"])\n\n[{'role': 'user', 'content': [{'type': 'text', 'text': 'Hello'}]},\n {'role': 'assistant',\n 'content': [{'type': 'text', 'text': 'Some assistant response'}]},\n {'role': 'user', 'content': [{'type': 'text', 'text': 'tell me a joke'}]}]\n\n\n\n\n\nTo make our lives even easier, it would be nice if mk_msg could format the SDK objects returned from a previous chat so that we can pass them straight to mk_msgs.\nThe OpenAI SDK accepts objects like ChatCompletion as messages. Anthropic is different and expects every message to have the role, content format that we’ve seen so far.\n\nsource\n\n\n\n\n Msg ()\n\nHelper class to create a message for the OpenAI and Anthropic APIs.\n\nsource\n\n\n\n\n AnthropicMsg ()\n\nHelper class to create a message for the OpenAI and Anthropic APIs.\n\nsource\n\n\n\n\n OpenAiMsg ()\n\nHelper class to create a message for the OpenAI and Anthropic APIs.\nLet’s test our changes.\n\nmsgs = [\"tell me a joke\"]\nr, text = openai_chat(mk_msgs(msgs))\ntext\n\n'Why did the scarecrow win an award? \\n\\nBecause he was outstanding in his field!'\n\n\n\nmsgs += [r, \"tell me another joke that's similar to your first joke\"]\nr, text = openai_chat(mk_msgs(msgs))\ntext\n\n'Why did the bicycle fall over?\\n\\nBecause it was two-tired!'\n\n\n\n\n\nTo make msglm a little easier to use let’s create OpenAI and Anthropic wrappers for mk_msg and mk_msgs.\n\nmk_msg_anthropic = partial(mk_msg, api=\"anthropic\")\nmk_msgs_anthropic = partial(mk_msgs, api=\"anthropic\")\n\nIf you’re using OpenAI you should be able to use the import below\nfrom msglm import mk_msg_openai as mk_msg, mk_msgs_openai as mk_msgs\nSimilarily for Anthropic\nfrom msglm import mk_msg_anthropic as mk_msg, mk_msgs_anthropic as mk_msgs\n\n\n\nAnthropic currently offers prompt caching, which can reduce cost and latency.\nTo cache a message, we simply add a cache_control field to our content as shown below.\n{\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"Hello, can you tell me more about the solar system?\",\n \"cache_control\": {\"type\": \"ephemeral\"}\n }\n ]\n}\nLet’s update our mk_msg and mk_msgs Anthropic wrappers to support caching.\n\nsource\n\n\n\n\n mk_msgs_anthropic (*args, cache=False, api:str='openai')\n\nCreate a list of Anthropic compatible messages.\n\nsource\n\n\n\n\n mk_msg_anthropic (*args, cache=False, role:str='user', api:str='openai',\n text_only=False)\n\nCreate an Anthropic compatible message.\nLet’s see caching in action\n\nmk_msg_anthropic(\"Don't cache my message\")\n\n{'role': 'user',\n 'content': [{'type': 'text', 'text': \"Don't cache my message\"}]}\n\n\n\nmk_msg_anthropic(\"Please cache my message\", cache=True)\n\n{'role': 'user',\n 'content': [{'type': 'text',\n 'text': 'Please cache my message',\n 'cache_control': {'type': 'ephemeral'}}]}", + "crumbs": [ + "core" + ] + }, + { + "objectID": "core.html#api-exploration", + "href": "core.html#api-exploration", + "title": "core", + "section": "", + "text": "Anthropic’s Claude and OpenAI’s GPT models are some of the most popular LLMs.\nLet’s take a look at their APIs and to learn how we should structure our messages for a simple text chat.\n\n\n\nfrom openai import OpenAI\n\n\nclient = OpenAI()\n\nclient.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[ {\"role\": \"user\", \"content\": \"Hello, world!\"} ]\n)\n\nChatCompletion(id='chatcmpl-ADNgo1gSrIolZfS9UW8glyIiFIe0Y', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Hello! How can I assist you today?', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1727752690, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_74ba47b4ac', usage=CompletionUsage(completion_tokens=9, prompt_tokens=11, total_tokens=20, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0)))\n\n\n\n\n\n\nfrom anthropic import Anthropic\n\n\nclient = Anthropic()\n\nclient.messages.create(\n model=\"claude-3-haiku-20240307\",\n max_tokens=1024,\n messages=[ {\"role\": \"user\", \"content\": \"Hello, world!\"} ]\n)\n\nHello! It’s nice to meet you. How can I assist you today?\n\n\nid: msg_01GtzcT5ezjzXSmYehbhAhZH\ncontent: [{'text': \"Hello! It's nice to meet you. How can I assist you today?\", 'type': 'text'}]\nmodel: claude-3-haiku-20240307\nrole: assistant\nstop_reason: end_turn\nstop_sequence: None\ntype: message\nusage: {'input_tokens': 11, 'output_tokens': 19}\n\n\n\n\nAs we can see both APIs use the exact same message structure.\n\n\n\nOk, let’s build the first version of mk_msg to handle this case\n\ndef mk_msg(content:str, role:str=\"user\")->dict:\n \"Create an OpenAI/Anthropic compatible message.\"\n return dict(role=role, content=content)\n\nLet’s test it out with the OpenAI API. To do that we’ll need to setup two things:\n\ninstall the openai SDK by running pip install openai\nadd your openai api key to your env vars export OPENAI_API_KEY=\"YOUR_OPEN_API_KEY\"\n\n\noa_cli = OpenAI()\n\nr = oa_cli.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[mk_msg(\"Hello, world!\")]\n)\nr.choices[0].message.content\n\n'Hello! How can I assist you today?'\n\n\nNow, let’s test out mk_msg on the Anthropic API. To do that we’ll need to setup two things:\n\ninstall the openai SDK by running pip install anthropic\nadd your anthropic api key to your env vars export ANTHROPIC_API_KEY=\"YOUR_ANTHROPIC_API_KEY\"\n\n\na_cli = Anthropic()\n\nr = a_cli.messages.create(\n model=\"claude-3-haiku-20240307\",\n max_tokens=1024,\n messages=[mk_msg(\"Hello, world!\")]\n)\nr.content[0].text\n\n\"Hello! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with all sorts of tasks, from research and analysis to creative projects and problem-solving. Please let me know if there's anything I can assist you with.\"\n\n\nSo far so good!\n\n\nBefore going any further, let’s create some helper functions to make it a little easier to call the OpenAI and Anthropic APIs. We’re going to be making a bunch of API calls to test our code and typing the full expressions out each time will become a little tedious. These functions won’t be included in the final package.\n\ndef openai_chat(msgs: list)->tuple:\n \"call the openai chat completions endpoint with `msgs`.\"\n r = oa_cli.chat.completions.create(model=\"gpt-4o-mini\", messages=msgs)\n return r, r.choices[0].message.content\n\nLet’s double check that mk_msg still works with our simple text example from before.\n\n_, text = openai_chat([mk_msg(\"Hello, world!\")])\ntext\n\n'Hello! How can I assist you today?'\n\n\n\ndef anthropic_chat(msgs: list)->tuple:\n \"call the anthropic messages endpoint with `msgs`.\"\n r = a_cli.messages.create(model=\"claude-3-haiku-20240307\", max_tokens=1024, messages=msgs)\n return r, r.content[0].text\n\nand Anthropic…\n\n_, text = anthropic_chat([mk_msg(\"Hello, world!\")])\ntext\n\n\"Hello! It's nice to meet you. How can I assist you today?\"\n\n\n\n\n\n\nOk, let’s see how both APIs handle image messages.\n\n\n\n\nimport base64, httpx\n\n\nimg_url = \"https://claudette.answer.ai/index_files/figure-html/cell-35-output-1.jpeg\"\n\n\nmtype = \"image/jpeg\"\nimg = base64.b64encode(httpx.get(img_url).content).decode(\"utf-8\")\n\nclient = OpenAI()\nclient.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\n \"role\":\"user\",\n \"content\": [\n {\"type\":\"text\",\"text\":\"What's in this image?\"},\n {\"type\":\"image_url\",\"image_url\":{\"url\":f\"data:{mtype};base64,{img}\"}},\n ],\n }\n ],\n)\n\nChatCompletion(id='chatcmpl-ADNjLoa2oJmG7yPU1qnBI426h1TB2', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The image features a small puppy lying on grass, with a background of purple flowers. The puppy has a white coat with brown markings, and it appears to be resting or playing in a garden setting.', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1727752847, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_74ba47b4ac', usage=CompletionUsage(completion_tokens=40, prompt_tokens=8512, total_tokens=8552, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=0)))\n\n\n\n\n\n\nmtype = \"image/jpeg\"\nimg = base64.b64encode(httpx.get(img_url).content).decode(\"utf-8\")\n\nclient = Anthropic()\nclient.messages.create(\n model=\"claude-3-haiku-20240307\",\n max_tokens=1024,\n messages=[\n {\n \"role\":\"user\",\n \"content\": [\n {\"type\":\"text\",\"text\":\"What's in this image?\"},\n {\"type\":\"image\",\"source\":{\"type\":\"base64\",\"media_type\":mtype,\"data\":img}}\n ],\n }\n ],\n)\n\nThis image shows a cute puppy lying in a grassy area with purple flowers in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a fluffy white and brown coat. The puppy has a friendly, playful expression on its face as it looks directly at the camera. The image captures a charming, serene outdoor scene with the puppy as the main focus.\n\n\nid: msg_015JmJ2GR9fkisz9Wsh1CKEK\ncontent: [{'text': 'This image shows a cute puppy lying in a grassy area with purple flowers in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a fluffy white and brown coat. The puppy has a friendly, playful expression on its face as it looks directly at the camera. The image captures a charming, serene outdoor scene with the puppy as the main focus.', 'type': 'text'}]\nmodel: claude-3-haiku-20240307\nrole: assistant\nstop_reason: end_turn\nstop_sequence: None\ntype: message\nusage: {'input_tokens': 104, 'output_tokens': 90}\n\n\n\n\nBoth APIs format images slightly differently and the structure of the message content is a litte more complex.\nIn a text chat, content is a simple string but for a multimodal chat (text+images) we can see that content is a list of dictionaries.\n\n\n\n\nTo handle the additional complexity of the multimodal messages let’s build a Msg class for the content data structure:\n{\n \"role\": \"user\",\n \"content\": [{\"type\": \"text\", \"text\": \"What's in this image?\"}],\n}\n\nclass Msg:\n \"Helper class to create a message for the OpenAI and Anthropic APIs.\"\n def __call__(self, role:str, content:[list,str], **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message with `role` and `content`.\"\n if content is not None and not isinstance(content, list): content = [content]\n return dict(role=role, content=[{\"type\":\"text\", \"content\":item} for item in content], **kw)\n\nNow, let’s update mk_msg to use Msg.\n\ndef mk_msg(content:str, role:str=\"user\", **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message.\"\n return Msg()(role, content, **kw)\n\n\nmk_msg([\"Hello world\", \"how are you?\"])\n\n{'role': 'user',\n 'content': [{'type': 'text', 'content': 'Hello world'},\n {'type': 'text', 'content': 'how are you?'}]}\n\n\nNow, let’s update Msg so that it can format our images correctly.\nFirst, we’ll add a method mk_content where we can check the type of each input and format the data appropriately. Let’s start by adding a check for text data.\n\nclass Msg:\n \"Helper class to create a message for the OpenAI and Anthropic APIs.\"\n def __call__(self, role:str, content:[list,str], **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message with `role` and `content`.\"\n if content is not None and not isinstance(content, list): content = [content]\n content = [self.mk_content(o) for o in content] if content else ''\n return dict(role=role, content=content, **kw)\n \n def text_msg(self, s:str)->dict: \n \"Convert `s` to a text message\"\n return {\"type\": \"text\", \"text\":s}\n \n def mk_content(self, content:str)->dict:\n \"Create the appropriate data structure based the content type.\"\n if isinstance(content, str): return self.text_msg(content)\n return content\n\nHere’s where things get a little tricker. As both APIs handle images differently let’s subclass Msg for each API and handle the image formatting in a method called img_msg.\n\nclass Msg:\n \"Helper class to create a message for the OpenAI and Anthropic APIs.\"\n def __call__(self, role:str, content:[list,str], **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message with `role` and `content`.\"\n if content is not None and not isinstance(content, list): content = [content]\n content = [self.mk_content(o) for o in content] if content else ''\n return dict(role=role, content=content, **kw)\n \n def img_msg(self, *args)->dict: \n \"Convert bytes to an image message\"\n raise NotImplemented\n \n def text_msg(self, s:str)->dict: \n \"Convert `s` to a text message\"\n return {\"type\": \"text\", \"text\":s}\n \n def mk_content(self, content:str)->dict:\n \"Create the appropriate data structure based the content type.\"\n if isinstance(content, str): return self.text_msg(content)\n if isinstance(content, bytes): return self.img_msg(content)\n return content\n\n\nclass OpenAiMsg(Msg):\n \"Helper class to create a message for the OpenAI API.\"\n def img_msg(self, data:bytes)->dict:\n \"Convert `data` to an image message\"\n img = base64.b64encode(data).decode(\"utf-8\")\n mtype = mimetypes.types_map[\".\" + imghdr.what(None, h=data)]\n r = {\"url\": f\"data:{mtype};base64,{img}\"}\n return {\"type\": \"image_url\", \"image_url\": r}\n\n\nclass AnthropicMsg(Msg):\n \"Helper class to create a message for the Anthropic API.\"\n def img_msg(self, data:bytes)->dict:\n \"Convert `data` to an image message\"\n img = base64.b64encode(data).decode(\"utf-8\")\n mtype = mimetypes.types_map[\".\" + imghdr.what(None, h=data)]\n r = {\"type\": \"base64\", \"media_type\": mtype, \"data\":img}\n return {\"type\": \"image\", \"source\": r}\n\nNow, let’s update mk_msg so that it chooses the appropriate Msg subclass.\n\ndef mk_msg(content:str, role:str=\"user\", *args, api:str=\"openai\", **kw) -> dict:\n \"Create an OpenAI/Anthropic compatible message.\"\n m = OpenAiMsg() if api == \"openai\" else AnthropicMsg()\n return m(role, content, **kw)\n\nOk, let’s test our changes…\n\nfrom IPython.display import Image, display\n\n\nimg = httpx.get(img_url).content\ndisplay(Image(img))\n\n\n\n\n\n\n\n\n\nmsg = mk_msg([img, \"describe this picture\"], api=\"openai\")\n_, text = openai_chat([msg])\ntext\n\n'The image features an adorable puppy lying on the grass. It has a primarily white coat with brown markings, particularly around its ears and eyes. The puppy has a curious and playful expression. In the background, there are purple flowers adding a touch of color to the scene. The overall setting appears to be a well-kept garden, creating a charming and serene atmosphere.'\n\n\n\nmsg = mk_msg([img, \"describe this picture\"], api=\"anthropic\")\n_, text = anthropic_chat([msg])\ntext\n\n\"The image shows a cute puppy nestled in some grass, with purple flowers blooming in the background. The puppy appears to be a Cavalier King Charles Spaniel, with a brown and white coat. It has a friendly, curious expression as it looks out at the viewer. The scene creates a peaceful, natural setting, with the puppy's soft fur and the vibrant flowers providing a lovely visual contrast. Overall, this image captures the endearing charm and beauty of a young dog in a picturesque outdoor environment.\"\n\n\nGreat! Before moving on let’s create _mk_img to make our code a little DRY’r.\nLet’s use _mk_img in our Msg subclasses\n\nclass OpenAiMsg(Msg):\n \"Helper class to create a message for the OpenAI API.\"\n def img_msg(self, data:bytes)->dict:\n \"Convert `data` to an image message\"\n img, mtype = _mk_img(data)\n r = {\"url\": f\"data:{mtype};base64,{img}\"}\n return {\"type\": \"image_url\", \"image_url\": r}\n\n\nclass AnthropicMsg(Msg):\n \"Helper class to create a message for the Anthropic API.\"\n def img_msg(self, data:bytes)->dict:\n \"Convert `data` to an image message\"\n img, mtype = _mk_img(data)\n r = {\"type\": \"base64\", \"media_type\": mtype, \"data\":img}\n return {\"type\": \"image\", \"source\": r}\n\n\n\n\nOk, what next? Some text only models that follow the OpenAI API spec such as Deepseek, expect messages to have the following format\n{\"role\": \"user\", \"content\": \"Hello, world!\"}\nLet’s update our code to support this use-case.\n\nclass Msg:\n \"Helper class to create a message for the OpenAI and Anthropic APIs.\"\n def __call__(self, role:str, content:[list, str], text_only:bool=False, **kw)->dict:\n \"Create an OpenAI/Anthropic compatible message with `role` and `content`.\"\n if content is not None and not isinstance(content, list): content = [content]\n content = [self.mk_content(o, text_only=text_only) for o in content] if content else ''\n return dict(role=role, content=content[0] if text_only else content, **kw)\n \n def img_msg(self, *args, **kw)->dict: \n \"Convert bytes to an image message\"\n raise NotImplemented\n \n def text_msg(self, s:str, text_only=False, **kw): \n \"Convert `s` to a text message\"\n return s if text_only else {\"type\":\"text\", \"text\":s}\n \n def mk_content(self, content, text_only=False, **kw):\n if isinstance(content, str): return self.text_msg(content, text_only=text_only)\n if isinstance(content, bytes): return self.img_msg(content)\n return content\n\n\nclass OpenAiMsg(Msg):\n \"Helper class to create a message for the OpenAI API.\"\n def img_msg(self, data)->dict:\n \"Convert `data` to an image message\"\n img, mtype = _mk_img(data)\n r = {\"url\": f\"data:{mtype};base64,{img}\"}\n return {\"type\": \"image_url\", \"image_url\": r}\n\n\nclass AnthropicMsg(Msg):\n \"Helper class to create a message for the Anthropic API.\"\n def img_msg(self, data)->dict:\n \"Convert `data` to an image message\"\n img, mtype = _mk_img(data)\n r = dict(type='base64', media_type=mtype, data=img)\n return {\"type\": \"image\", \"source\": r}\n\n\nsource\n\n\n\n\n mk_msg (content:Union[list,str], role:str='user', *args,\n api:str='openai', text_only=False, **kw)\n\nCreate an OpenAI/Anthropic compatible message.\n\nmk_msg(\"describe this picture\", text_only=True)\n\n{'role': 'user', 'content': 'describe this picture'}\n\n\n\nmk_msg(\"describe this picture\")\n\n{'role': 'user',\n 'content': [{'type': 'text', 'text': 'describe this picture'}]}\n\n\n\n\n\nLLMs are stateless. To continue a conversation we need to include the entire message history in every API call. By default the role in each message alternates between user and assistant.\nLet’s add a method that alternates the roles for us and then calls mk_msgs.\n\nsource\n\n\n\n\n mk_msgs (msgs:list, *args, api:str='openai', **kw)\n\nCreate a list of messages compatible with OpenAI/Anthropic.\n\nmk_msgs([\"Hello\", \"Some assistant response\", \"tell me a joke\"])\n\n[{'role': 'user', 'content': [{'type': 'text', 'text': 'Hello'}]},\n {'role': 'assistant',\n 'content': [{'type': 'text', 'text': 'Some assistant response'}]},\n {'role': 'user', 'content': [{'type': 'text', 'text': 'tell me a joke'}]}]\n\n\n\n\n\nTo make our lives even easier, it would be nice if mk_msg could format the SDK objects returned from a previous chat so that we can pass them straight to mk_msgs.\nThe OpenAI SDK accepts objects like ChatCompletion as messages. Anthropic is different and expects every message to have the role, content format that we’ve seen so far.\n\nsource\n\n\n\n\n Msg ()\n\nHelper class to create a message for the OpenAI and Anthropic APIs.\n\nsource\n\n\n\n\n AnthropicMsg ()\n\nHelper class to create a message for the OpenAI and Anthropic APIs.\n\nsource\n\n\n\n\n OpenAiMsg ()\n\nHelper class to create a message for the OpenAI and Anthropic APIs.\nLet’s test our changes.\n\nmsgs = [\"tell me a joke\"]\nr, text = openai_chat(mk_msgs(msgs))\ntext\n\n'Why did the scarecrow win an award? \\n\\nBecause he was outstanding in his field!'\n\n\n\nmsgs += [r, \"tell me another joke that's similar to your first joke\"]\nr, text = openai_chat(mk_msgs(msgs))\ntext\n\n'Why did the bicycle fall over?\\n\\nBecause it was two-tired!'\n\n\n\n\n\nTo make msglm a little easier to use let’s create OpenAI and Anthropic wrappers for mk_msg and mk_msgs.\n\nmk_msg_anthropic = partial(mk_msg, api=\"anthropic\")\nmk_msgs_anthropic = partial(mk_msgs, api=\"anthropic\")\n\nIf you’re using OpenAI you should be able to use the import below\nfrom msglm import mk_msg_openai as mk_msg, mk_msgs_openai as mk_msgs\nSimilarily for Anthropic\nfrom msglm import mk_msg_anthropic as mk_msg, mk_msgs_anthropic as mk_msgs\n\n\n\nAnthropic currently offers prompt caching, which can reduce cost and latency.\nTo cache a message, we simply add a cache_control field to our content as shown below.\n{\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"Hello, can you tell me more about the solar system?\",\n \"cache_control\": {\"type\": \"ephemeral\"}\n }\n ]\n}\nLet’s update our mk_msg and mk_msgs Anthropic wrappers to support caching.\n\nsource\n\n\n\n\n mk_msgs_anthropic (*args, cache=False, api:str='openai')\n\nCreate a list of Anthropic compatible messages.\n\nsource\n\n\n\n\n mk_msg_anthropic (*args, cache=False, role:str='user', api:str='openai',\n text_only=False)\n\nCreate an Anthropic compatible message.\nLet’s see caching in action\n\nmk_msg_anthropic(\"Don't cache my message\")\n\n{'role': 'user',\n 'content': [{'type': 'text', 'text': \"Don't cache my message\"}]}\n\n\n\nmk_msg_anthropic(\"Please cache my message\", cache=True)\n\n{'role': 'user',\n 'content': [{'type': 'text',\n 'text': 'Please cache my message',\n 'cache_control': {'type': 'ephemeral'}}]}", + "crumbs": [ + "core" + ] + } +] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..285e444 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") 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0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3fb618;--bs-btn-disabled-border-color: #3fb618}.btn-info{--bs-btn-color: #fff;--bs-btn-bg: #9954bb;--bs-btn-border-color: #9954bb;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #82479f;--bs-btn-hover-border-color: #7a4396;--bs-btn-focus-shadow-rgb: 168, 110, 197;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7a4396;--bs-btn-active-border-color: #733f8c;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #9954bb;--bs-btn-disabled-border-color: #9954bb}.btn-warning{--bs-btn-color: #fff;--bs-btn-bg: #ff7518;--bs-btn-border-color: #ff7518;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #d96314;--bs-btn-hover-border-color: #cc5e13;--bs-btn-focus-shadow-rgb: 255, 138, 59;--bs-btn-active-color: #fff;--bs-btn-active-bg: #cc5e13;--bs-btn-active-border-color: #bf5812;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: 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none}.btn-outline-primary{--bs-btn-color: #2780e3;--bs-btn-border-color: #2780e3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2780e3;--bs-btn-hover-border-color: #2780e3;--bs-btn-focus-shadow-rgb: 39, 128, 227;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2780e3;--bs-btn-active-border-color: #2780e3;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #2780e3;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #2780e3;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: 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#ff0039;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ff0039;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248, 249, 250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 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reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 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0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end 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var(--bs-popover-border-width))}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before,.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{border-width:var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width)*.5) 0}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before{bottom:0;border-top-color:var(--bs-popover-arrow-border)}.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{bottom:var(--bs-popover-border-width);border-top-color:var(--bs-popover-bg)}.bs-popover-end>.popover-arrow,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow{left:calc(-1*(var(--bs-popover-arrow-height)) - 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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),d(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(yt),i.classList.remove(yt,c,l),this._isSliding=!1,r(ut)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return z.findOne(Et,this._element)}_getItems(){return z.find(At,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return p()?t===ct?lt:at:t===ct?at:lt}_orderToDirection(t){return p()?t===lt?ct:ht:t===lt?ht:ct}static jQueryInterface(t){return this.each((function(){const e=xt.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}N.on(document,bt,"[data-bs-slide], 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i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt,Nt),this._element.style[e]="",N.trigger(this._element,St)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(N.trigger(this._element,Dt).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,d(this._element),this._element.classList.add(Mt),this._element.classList.remove(Pt,Nt);for(const t of this._triggerArray){const e=z.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt),N.trigger(this._element,$t)}),this._element,!0)}_isShown(t=this._element){return 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e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ii(e,{elementContext:"reference"}),a=ii(e,{altBoundary:!0}),l=oi(r,n),c=oi(a,s,o),h=ri(l),d=ri(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},li={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=ee.reduce((function(t,i){return t[i]=function(t,e,i){var n=be(t),s=[Vt,zt].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return 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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/site_libs/quarto-html/quarto-syntax-highlighting.css b/site_libs/quarto-html/quarto-syntax-highlighting.css new file mode 100644 index 0000000..b30ce57 --- /dev/null +++ b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,205 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 0000000..ae0063a --- /dev/null +++ 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0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..d788a95 --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1290 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..2f25f71 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,11 @@ + + + + https://AnswerDotAI.github.io/msglm/index.html + 2024-10-18T17:54:58.683Z + + + https://AnswerDotAI.github.io/msglm/core.html + 2024-10-18T17:54:59.171Z + + diff --git a/styles.css b/styles.css new file mode 100644 index 0000000..66ccc49 --- /dev/null +++ b/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + font-style: italic; + font-size: 135%; + opacity: 70%; +}
+ + +