docs: update mirascope third party integration docs with new v1 release + custom panels (#394)

Author: willbakst

docs/images/logfire-screenshot-mirascope-anthropic-call.png

@@ -1,49 +1,47 @@
-[Mirascope](https://github.com/Mirascope/mirascope) is an intuitive approach to building AI-powered applications using LLMs. Their library integrates with Logfire to make observability and monitoring for LLMs easy and seamless.
+[Mirascope][mirascope-repo] is a developer tool for building with LLMs. Their library focuses on abstractions that aren't obstructions and integrates with Logfire to make observability and monitoring for LLMs easy and seamless.
 
-You can enable it using their [`@with_logire`][mirascope-logfire] decorator, which will work with all of the [model providers that they support][mirascope-supported-providers] (e.g. OpenAI, Anthropic, Groq, and more).
+You can enable it using their [`@with_logire`][mirascope-logfire] decorator, which will work with all of the [model providers that they support][mirascope-supported-providers] (e.g. OpenAI, Anthropic, Gemini, Mistral, Groq, and more).
 
-```py hl_lines="1 2 5 8"
+```py hl_lines="1 3 5 8"
 import logfire
-from mirascope.logfire import with_logfire
-from mirascope.anthropic import AnthropicCall
+from mirascope.core import anthropic, prompt_template
+from mirascope.integrations.logfire import with_logfire
 
 logfire.configure()
 
 
-@with_logfire
-class BookRecommender(AnthropicCall):
-    prompt_template = "Please recommend some {genre} books"
+@with_logfire()
+@anthropic.call("claude-3-5-sonnet-20240620")
+@prompt_template("Please recommend some {genre} books")
+def recommend_books(genre: str): ...
 
-    genre: str
 
-
-recommender = BookRecommender(genre="fantasy")
-response = recommender.call()  # this will automatically get logged with logfire
+response = recommend_books("fantasy")  # this will automatically get logged with logfire
 print(response.content)
-#> Here are some recommendations for great fantasy book series: ...
+# > Certainly! Here are some popular and well-regarded fantasy books and series: ...
 ```
 
 This will give you:
 
-* A span around the `AnthropicCall.call()` that captures items like the prompt template, templating properties and fields, and input/output attributes
+* A span around the `recommend_books` that captures items like the prompt template, templating properties and fields, and input/output attributes
 * Human-readable display of the conversation with the agent
 * Details of the response, including the number of tokens used
 
 <figure markdown="span">
   ![Logfire Mirascope Anthropic call](../../images/logfire-screenshot-mirascope-anthropic-call.png){ width="500" }
-  <figcaption>Mirascope Anthropic Call span and Anthropic span and conversation</figcaption>
+  <figcaption>Mirascope Anthropic call span and Anthropic span and conversation</figcaption>
 </figure>
 
 Since Mirascope is built on top of [Pydantic][pydantic], you can use the [Pydantic plugin][pydantic-plugin] to track additional logs and metrics about model validation, which you can enable using the [`pydantic_plugin`][logfire.configure(pydantic_plugin)] configuration.
 
 This can be particularly useful when [extracting structured information][mirascope-extracting-structured-information] using LLMs:
 
-```py hl_lines="3 4 8 17"
+```py hl_lines="3 5 8 17"
 from typing import Literal, Type
 
 import logfire
-from mirascope.logfire import with_logfire
-from mirascope.openai import OpenAIExtractor
+from mirascope.core import openai, prompt_template
+from mirascope.integrations.logfire import with_logfire
 from pydantic import BaseModel
 
 logfire.configure(pydantic_plugin=logfire.PydanticPlugin(record="all"))
@@ -55,30 +53,23 @@ class TaskDetails(BaseModel):
     priority: Literal["low", "normal", "high"]
 
 
-@with_logfire
-class TaskExtractor(OpenAIExtractor[TaskDetails]):
-    extract_schema: Type[TaskDetails] = TaskDetails
-    prompt_template = """
-    Extract the task details from the following task:
-    {task}
-    """
-
-    task: str
+@with_logfire()
+@openai.call("gpt-4o-mini", response_model=TaskDetails)
+@prompt_template("Extract the details from the following task: {task}")
+def extract_task_details(task: str): ...
 
 
 task = "Submit quarterly report by next Friday. Task is high priority."
-task_details = TaskExtractor(
-    task=task
-).extract()  # this will be logged automatically with logfire
+task_details = extract_task_details(task)  # this will be logged automatically with logfire
 assert isinstance(task_details, TaskDetails)
 print(task_details)
-#> description='Submit quarterly report' due_date='next Friday' priority='high'
+# > description='Submit quarterly report' due_date='next Friday' priority='high'
 ```
 
 This will give you:
 
 * Tracking for validation of Pydantic models
-* A span around the `OpenAIExtractor.extract()` that captures items like the prompt template, templating properties and fields, and input/output attributes
+* A span around the `extract_task_details` that captures items like the prompt template, templating properties and fields, and input/output attributes
 * Human-readable display of the conversation with the agent including the function call
 * Details of the response, including the number of tokens used
 
@@ -87,10 +78,12 @@ This will give you:
   <figcaption>Mirascope OpenAI Extractor span and OpenAI span and function call</figcaption>
 </figure>
 
-For more information on Mirascope and what you can do with it, check out their [documentation](https://docs.mirascope.io).
+For more information on Mirascope and what you can do with it, check out their [documentation][mirascope-documentation].
 
-[mirascope-logfire]: https://docs.mirascope.io/latest/integrations/logfire/#how-to-use-logfire-with-mirascope
-[mirascope-supported-providers]: https://docs.mirascope.io/latest/concepts/supported_llm_providers/
-[mirascope-extracting-structured-information]: https://docs.mirascope.io/latest/concepts/extracting_structured_information_using_llms/
+[mirascope-repo]: https://github.com/Mirascope/mirascope
+[mirascope-documentation]: https://mirascope.io/docs
+[mirascope-logfire]: https://mirascope.io/docs/latest/integrations/logfire/
+[mirascope-supported-providers]: https://mirascope.io/docs/latest/learn/calls/#supported-providers
+[mirascope-extracting-structured-information]: https://mirascope.io/docs/latest/learn/response_models/
 [pydantic]: https://docs.pydantic.dev/latest/
 [pydantic-plugin]: https://docs.pydantic.dev/latest/concepts/plugins/