2.0 component refactor

cookbook/flows/github_digest.py

@@ -3,7 +3,7 @@
 
 import httpx
 import marvin
-from marvin import ai_fn
+from marvin import fn
 from marvin.utilities.strings import jinja_env
 from prefect import flow, task
 from prefect.artifacts import create_markdown_artifact
@@ -47,7 +47,7 @@
 )  # noqa: E501
 
 
-@ai_fn
+@fn
 async def summarize_digest(markdown_digest: str) -> str:
     """Produce a short story based on the GitHub digest.
 

cookbook/maze.py

@@ -14,12 +14,12 @@
 import random
 from enum import Enum
 from io import StringIO
+from typing import Literal
 
 from marvin.beta.applications import AIApplication
 from pydantic import BaseModel
 from rich.console import Console
 from rich.table import Table
-from typing_extensions import Literal
 
 GAME_INSTRUCTIONS = """
 This is a TERROR game. You are the disembodied narrator of a maze. You've hidden a key somewhere in the
@@ -205,7 +205,8 @@ def move(self, direction: CardinalDirection) -> str:
         if move_monster := random.random() < 0.4:
             self.shuffle_monster()
         return (
-            f"User moved {direction} and is now at {self.user_location}.\n{self.render()}"
+            f"User moved {direction} and is now at"
+            f" {self.user_location}.\n{self.render()}"
             f"\nThe user may move in any of the following {self.movable_directions()!r}"
             f"\n{'The monster moved somewhere.' if move_monster else ''}"
         )

cookbook/slackbot/keywords.py

@@ -1,4 +1,4 @@
-from marvin import ai_fn
+from marvin import fn
 from marvin.utilities.slack import post_slack_message
 from prefect import task
 from prefect.blocks.system import JSON, Secret, String
@@ -36,7 +36,7 @@ async def get_reduced_kw_relationship_map() -> dict:
     }
 
 
-@ai_fn
+@fn
 def activation_score(message: str, keyword: str, target_relationship: str) -> float:
     """Return a score between 0 and 1 indicating whether the target relationship exists
     between the message and the keyword"""

cookbook/slackbot/parent_app.py

@@ -3,7 +3,7 @@
 from contextlib import asynccontextmanager
 
 from fastapi import FastAPI
-from marvin import ai_fn
+from marvin import fn
 from marvin.beta.applications import AIApplication
 from marvin.beta.applications.state.json_block import JSONBlockState
 from marvin.beta.assistants import Assistant
@@ -28,7 +28,7 @@ class Lesson(TypedDict):
     heuristic: str | None
 
 
-@ai_fn(model="gpt-3.5-turbo-1106")
+@fn(model="gpt-3.5-turbo-1106")
 def take_lesson_from_interaction(
     transcript: str,
     assistant_instructions: str,

docs/api_reference/components/ai_classifier.md

@@ -1 +1 @@
-::: marvin.components.ai_classifier
+::: marvin.components.classifier

docs/api_reference/components/functions.md

@@ -0,0 +1 @@
+::: marvin.components.function

docs/api_reference/components/models.md

@@ -0,0 +1 @@
+::: marvin.components.model

docs/api_reference/index.md

@@ -1,7 +1,7 @@
 # Sections
 
 ## Components
-- [AI Classifiers](/api_reference/components/ai_classifier/)
+- [AI Classifiers](/api_reference/components/classifier/)
 - [AI Functions](/api_reference/components/ai_function/)
 - [AI Models](/api_reference/components/ai_model/)
 

docs/components/ai_classifier.md → docs/components/classifier.md

@@ -5,13 +5,13 @@ AI Classifiers are a high-level component, or building block, of Marvin. Like al
 <div class="admonition abstract">
   <p class="admonition-title">What it does</p>
   <p>
-    <code>@ai_classifier</code> is a decorator that lets you use LLMs to choose options, tools, or classify input. 
+    <code>@classifier</code> is a decorator that lets you use LLMs to choose options, tools, or classify input. 
   </p>
 </div>
 
 !!! example 
     ```python
-    from marvin import ai_classifier
+    from marvin import classifier
     from enum import Enum
 
     class CustomerIntent(Enum):
@@ -26,7 +26,7 @@ AI Classifiers are a high-level component, or building block, of Marvin. Like al
         ACCOUNT_CANCELLATION = 'ACCOUNT_CANCELLATION'
         OPERATOR_CUSTOMER_SERVICE = 'OPERATOR_CUSTOMER_SERVICE'
 
-    @ai_classifier
+    @classifier
     def classify_intent(text: str) -> CustomerIntent:
         '''Classifies the most likely intent from user input'''
 
@@ -67,15 +67,15 @@ AI Classifiers are a high-level component, or building block, of Marvin. Like al
 ## Features
 #### 🚅 Bulletproof
 
-`ai_classifier` will always output one of the options you've given it
+`classifier` will always output one of the options you've given it
 
 
 ```python
-from marvin import ai_classifier
+from marvin import classifier
 from enum import Enum
 
 
-@ai_classifier
+@classifier
 class AppRoute(Enum):
     """Represents distinct routes command bar for a different application"""
 
@@ -102,8 +102,8 @@ AppRoute("update my name")
 
 #### 🏃 Fast
 
-`ai_classifier` only asks your LLM to output one token, so it's blazing fast - on the order of ~200ms in testing.
+`classifier` only asks your LLM to output one token, so it's blazing fast - on the order of ~200ms in testing.
 
 #### 🫡 Deterministic
 
-`ai_classifier` will be deterministic so long as the underlying model and options does not change.
+`classifier` will be deterministic so long as the underlying model and options does not change.

docs/components/ai_model.md → docs/components/models.md

@@ -393,7 +393,7 @@ CapTable("""\
 import datetime
 from typing import List
 from pydantic import BaseModel
-from typing_extensions import Literal
+from typing import Literal
 from marvin import ai_model
 
 

docs/components/overview.md

@@ -264,10 +264,10 @@ AI Classifiers let you build multi-label classifiers with no code and no trainin
 
 !!! example "Example"
     === "As a decorator"
-        `ai_classifier` can decorate python functions whose return annotation is an `Enum` or `Literal`. The prompt is tuned for classification tasks, 
+        `classifier` can decorate python functions whose return annotation is an `Enum` or `Literal`. The prompt is tuned for classification tasks, 
         and uses a form of `constrained sampling` to make guarantee a fast valid choice.
         ```python
-        from marvin import ai_classifier
+        from marvin import classifier
         from enum import Enum
 
         class AppRoute(Enum):
@@ -283,7 +283,7 @@ AI Classifiers let you build multi-label classifiers with no code and no trainin
             PROJECTS = "/projects"
             WORKSPACES = "/workspaces"
 
-        @ai_classifier(client = client)
+        @classifier(client = client)
         def classify_intent(text: str) -> AppRoute:
             '''Classifies user's intent into most useful route'''
 
@@ -329,7 +329,7 @@ AI Classifiers let you build multi-label classifiers with no code and no trainin
 
     === "As a function"
         ```python
-        from marvin import ai_classifier
+        from marvin import classifier
         from enum import Enum
 
         class AppRoute(Enum):
@@ -348,12 +348,12 @@ AI Classifiers let you build multi-label classifiers with no code and no trainin
         def classify_intent(text: str) -> AppRoute:
             '''Classifies user's intent into most useful route'''
 
-        ai_classifier(classify_intent, client = client)("update my name")
+        classifier(classify_intent, client = client)("update my name")
         ```
         ??? info "Generated Prompt"
             You can view and/or eject the generated prompt by simply calling 
             ```python
-            ai_classifier(classify_intent, client = client).as_prompt("update my name").serialize()
+            classifier(classify_intent, client = client).as_prompt("update my name").serialize()
             ```
             When you do you'll see the raw payload that's sent to the LLM. The prompt you send is fully customizable. 
             ```json

docs/welcome/overview.md

@@ -31,10 +31,10 @@ or fully use its engine to work with OpenAI and other providers.
         Marvin exposes a number of high level components to simplify working with AI. 
 
         ```python
-        from marvin import ai_classifier
+        from marvin import classifier
         from typing import Literal
 
-        @ai_classifier
+        @classifier
         def customer_intent(text: str) -> Literal['Store Hours', 'Pharmacy', 'Returns']:
             """Classifies incoming customer intent"""
 

docs/welcome/quickstart.md

@@ -15,7 +15,7 @@ After [installing Marvin](../installation), the fastest way to get started is by
     MARVIN_OPENAI_ORGANIZATION=org-xxx
     ```
 
-    - Pass your API Key to Marvin's `OpenAI` client constructor and pass it to Marvin's `ai_fn`, `ai_classifier`, or `ai_model` decorators.
+    - Pass your API Key to Marvin's `OpenAI` client constructor and pass it to Marvin's `ai_fn`, `classifier`, or `ai_model` decorators.
 
     ```python
     from marvin import ai_fn
@@ -233,10 +233,10 @@ AI Classifiers let you build multi-label classifiers with no code and no trainin
 
 !!! example "Example"
     === "As a decorator"
-        `ai_classifier` can decorate python functions whose return annotation is an `Enum` or `Literal`. The prompt is tuned for classification tasks, 
+        `classifier` can decorate python functions whose return annotation is an `Enum` or `Literal`. The prompt is tuned for classification tasks, 
         and uses a form of `constrained sampling` to make guarantee a fast valid choice.
         ```python
-        from marvin import ai_classifier
+        from marvin import classifier
         from enum import Enum
 
         class AppRoute(Enum):
@@ -252,7 +252,7 @@ AI Classifiers let you build multi-label classifiers with no code and no trainin
             PROJECTS = "/projects"
             WORKSPACES = "/workspaces"
 
-        @ai_classifier(client = client)
+        @classifier(client = client)
         def classify_intent(text: str) -> AppRoute:
             '''Classifies user's intent into most useful route'''
 
@@ -298,7 +298,7 @@ AI Classifiers let you build multi-label classifiers with no code and no trainin
 
     === "As a function"
         ```python
-        from marvin import ai_classifier
+        from marvin import classifier
         from enum import Enum
 
         class AppRoute(Enum):
@@ -317,12 +317,12 @@ AI Classifiers let you build multi-label classifiers with no code and no trainin
         def classify_intent(text: str) -> AppRoute:
             '''Classifies user's intent into most useful route'''
 
-        ai_classifier(classify_intent, client = client)("update my name")
+        classifier(classify_intent, client = client)("update my name")
         ```
         ??? info "Generated Prompt"
             You can view and/or eject the generated prompt by simply calling 
             ```python
-            ai_classifier(classify_intent, client = client).as_prompt("update my name").serialize()
+            classifier(classify_intent, client = client).as_prompt("update my name").serialize()
             ```
             When you do you'll see the raw payload that's sent to the LLM. The prompt you send is fully customizable. 
             ```json

docs/welcome/what_is_marvin.md

@@ -62,10 +62,10 @@ or fully use its engine to work with OpenAI and other providers.
         Marvin exposes a number of high level components to simplify working with AI. 
 
         ```python
-        from marvin import ai_classifier
+        from marvin import classifier
         from typing import Literal
 
-        @ai_classifier
+        @classifier
         def customer_intent(text: str) -> Literal['Store Hours', 'Pharmacy', 'Returns']:
             """Classifies incoming customer intent"""
 

mkdocs.yml

@@ -23,9 +23,9 @@ nav:
 
     - Components: 
       - Overview: components/overview.md
-      - AI Function: components/ai_function.md
+      - Function: components/functions.md
       - AI Model: components/ai_model.md
-      - AI Classifier: components/ai_classifier.md
+      - Classifier: components/ai_classifier.md
       - AI Application: components/ai_application.md
 
     - Examples:
@@ -35,7 +35,7 @@ nav:
     - api_reference/index.md
     - AI Components:
       - ai_classifier: api_reference/components/ai_classifier.md
-      - ai_function: api_reference/components/ai_function.md
+      - function: api_reference/components/functions.md
       - ai_model: api_reference/components/ai_model.md
     - Settings:
       - settings: api_reference/settings.md

src/marvin/__init__.py

@@ -1,7 +1,6 @@
 from .settings import settings
 
-# legacy
-from .components import ai_fn, ai_model, ai_classifier
+from .components import fn, image, speech, model, cast, extract, classify
 from .components.prompt.fn import prompt_fn
 
 try:
@@ -10,9 +9,18 @@
     __version__ = "unknown"
 
 __all__ = [
-    "ai_fn",
-    "ai_model",
-    "ai_classifier",
+    "fn",
+    "image",
+    "model",
+    "cast",
+    "extract",
+    "classify",
+    "speech",
     "prompt_fn",
     "settings",
 ]
+
+
+# compatibility with Marvin v1
+from .components import fn as ai_fn, model as ai_model
+from .components.classifier import classifier as ai_classifier

src/marvin/_mappings/types.py

@@ -1,10 +1,9 @@
 from enum import Enum
 from types import GenericAlias
-from typing import Any, Callable, Optional, Union, get_args, get_origin
+from typing import Any, Callable, Literal, Optional, Union, get_args, get_origin
 
 from pydantic import BaseModel, create_model
 from pydantic.fields import FieldInfo
-from typing_extensions import Literal
 
 from marvin.requests import Grammar, Tool, ToolSet
 from marvin.settings import settings

src/marvin/ai.py

@@ -1,6 +1,6 @@
 from marvin.client.openai import paint, speak
-from marvin.components.ai_classifier import ai_classifier as classifier
-from marvin.components.ai_function import ai_fn as fn
-from marvin.components.ai_model import ai_model as model
+from marvin.components.classifier import classifier
+from marvin.components.function import fn
+from marvin.components.model import model
 
 __all__ = ["speak", "fn", "model", "speak", "paint", "classifier"]

src/marvin/beta/applications/__init__.py

@@ -1 +1 @@
-from .applications import AIApplication
+from .applications import Application