Adding Mirascope Third Party Integration Docs Page

docs/integrations/third_party/mirascope.md

@@ -0,0 +1,83 @@
+# Mirascope
+
+[Mirascope](https://github.com/Mirascope/mirascope) is an intuitive approach to building AI-powered applications using LLMs. Their library is the first third party integration with Logfire to make observability and monitoring for LLMs easy and seamless.
+
+You can enable it using their [`@with_logire`](https://docs.mirascope.io/latest/integrations/logfire/#how-to-use-logfire-with-mirascope) decorator, which will work with all of the [model providers that they support](https://docs.mirascope.io/latest/concepts/supported_llm_providers/) (e.g. OpenAI, Anthropic, Groq, and more).
+
+```py hl_lines="1 2 5 8"
+import logfire
+from mirascope.logfire import with_logfire
+from mirascope.anthropic import AnthropicCall
+
+logfire.configure()
+
+
+@with_logfire
+class BookRecommender(AnthropicCall):
+    prompt_template = "Please recommend some {genre} books"
+
+    genre: str
+
+
+recommender = BookRecommender(genre="fantasy")
+response = recommender.call()  # this will automatically get logged with logfire
+print(response.content)
+#> Here are some recommendations for great fantasy book 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.
+* 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>
+</figure>
+
+Since Mirascope is build 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](https://docs.mirascope.io/latest/concepts/extracting_structured_information_using_llms/) using LLMs:
+
+```py hl_lines="3 4 8 17"
+from typing import Literal, Type
+
+import logfire
+from mirascope.logfire import with_logfire
+from mirascope.openai import OpenAIExtractor
+from pydantic import BaseModel
+
+logfire.configure(pydantic_plugin=logfire.PydanticPlugin(record="all"))
+
+
+class TaskDetails(BaseModel):
+    description: str
+    due_date: str
+    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
+
+
+task = "Submit quarterly report by next Friday. Task is high priority."
+task_details = TaskExtractor(
+    task=task
+).extract()  # 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'
+```
+
+For more information on Mirascope and what you can do with it, check out their [documentation](https://docs.mirascope.io)
+
+[pydantic]: https://docs.pydantic.dev/latest/
+[pydantic-plugin]: https://docs.pydantic.dev/latest/concepts/plugins/

mkdocs.yml

@@ -117,6 +117,8 @@ nav:
           - Logging: integrations/logging.md
           - Structlog: integrations/structlog.md
           - Loguru: integrations/loguru.md
+      - Third Party:
+          - Mirascope: integrations/third_party/mirascope.md
       - Use Cases:
           - Web Frameworks: integrations/use_cases/web_frameworks.md
   - Reference: