Update documentation and examples following outlines.prompt depreca…

…tion

docs/cookbook/chain_of_density.md

@@ -29,10 +29,10 @@ The prompt also asks the model to return a list of JSON objects that contain the
 We can now implement the prompt provided in the paper:
 
 ```python
-import outlines
+from outlines import Template
 
-@outlines.prompt
-def chain_of_density(article):
+
+chain_of_density = Template.from_string(
     """Article: {{ article }}
 
     You will generate increasingly concise, entity-dense summaries of the above Article.
@@ -61,6 +61,7 @@ def chain_of_density(article):
 
     Answer in JSON. The JSON should be a a dictionary with key "summaries" that contains a list (length 5) of dictionaries whose keys are "Missing_Entities" and "Denser_Summary".
     """
+)
 ```
 
 ??? Note

docs/cookbook/classification.md

@@ -16,8 +16,10 @@ model = outlines.models.transformers("TheBloke/Mistral-7B-OpenOrca-AWQ", device=
 We will use the following prompt template:
 
 ```python
-@outlines.prompt
-def customer_support(request):
+from outlines import Template
+
+
+customer_support = Template.from_string(
     """You are an experienced customer success manager.
 
     Given a request from a client, you need to determine when the
@@ -36,6 +38,7 @@ def customer_support(request):
 
     Request: {{ request }}
     Label: """
+)
 ```
 
 ## Choosing between multiple choices

docs/cookbook/dating_profiles.md

@@ -57,9 +57,9 @@ class Example:
 We will use Outlines' prompt templating abilities to generate the prompt for us. This help clearly separate the general prompting logic from what is specific to an example.
 
 ```python
+from outlines import Template
 
-@outlines.prompt
-def dating_profile_prompt(description: str, examples: list[Example]):
+dating_profile_prompt = Template.from_string(
     """
     You are a world-renowned matchmaker who understands the modern dating
     market. Your job is to generate dating app profiles for male clients
@@ -79,6 +79,7 @@ def dating_profile_prompt(description: str, examples: list[Example]):
     Description: {{ description }}
     Profile:
     """
+)
 ```
 
 We will provide the model with several few-shot examples:

docs/cookbook/extraction.md

@@ -15,8 +15,10 @@ model = outlines.models.transformers("TheBloke/Mistral-7B-OpenOrca-AWQ", device=
 And we will be using the following prompt template:
 
 ```python
-@outlines.prompt
-def take_order(order):
+from outlines import Template
+
+
+take_order = Template.from_string(
     """You are the owner of a pizza parlor. Customers \
     send you orders from which you need to extract:
 
@@ -42,6 +44,7 @@ def take_order(order):
 
     ORDER: {{ order }}
     RESULT: """
+)
 ```
 
 We now define our data model using Pydantic:

docs/cookbook/simtom.md

@@ -23,18 +23,15 @@ To implement SimToM with Outlines, we will need to:
 
 Let's dive into it!
 
-### Using Prompt Functions
-
-With Outlines, you can write your prompts as Python functions by adding the `@outlines.prompt` decorator. The prompt template is contained in their docstring, and their arguments correspond to variables used in the prompt.
+### Using Prompt Templates
 
 The authors have shared their code, prompts and data in [this GitHub repository](https://github.com/shawnsihyunlee/simulatedtom). Below, we define in Outlines the prompts they used for the ToMI dataset:
 
 ```python
-import outlines
+from outlines import Template
 
 
-@outlines.prompt
-def perspective_taking(story: str, character: str) -> None:
+perspective_taking = Template.from_string(
     """<s>[INST] The following is a sequence of events about some characters, that takes place in multiple locations.
     Your job is to output only the events that the specified character, {{character}}, knows about.
 
@@ -45,16 +42,17 @@ def perspective_taking(story: str, character: str) -> None:
 
     Story: {{story}}
     What events does {{character}} know about? Only output the events according to the above rules, do not provide an explanation. [/INST]""" # noqa
+)
 
-@outlines.prompt
-def simulation(events: list, name: str, question: str) -> None:
+simulation = Template.from_string(
     """<s>[INST] {% for event in events %}
     {{event}}
     {% endfor %}
     You are {{name}}.
     Based on the above information, answer the following question:
     {{question}}
     You must choose one of the above choices, do not say there is not enough information. Answer with a single word, do not output anything else. [/INST]""" # noqa
+)
 ```
 
 ### JSON Structured Generation

docs/quickstart.md

@@ -115,10 +115,9 @@ Or use the [requests][requests]{:target="_blank"} library from another python pr
 Prompting can lead to messy code. Outlines' prompt functions are python functions that contain a template for the prompt in their docstring. We use a powerful templating language to allow you to loop over lists, dictionaries, add conditionals, etc. directly from the prompt. When called, a prompt function returns the rendered template:
 
 ```python
-import outlines
+from outlines import Template
 
-@outlines.prompt
-def few_shots(instructions, examples, question):
+few_shots = Template.from_string(
     """{{ instructions }}
 
     Examples
@@ -135,6 +134,7 @@ def few_shots(instructions, examples, question):
     Q: {{ question }}
     A:
     """
+)
 
 instructions = "Please answer the following question following the examples"
 examples = [
@@ -175,9 +175,9 @@ Once you are done experimenting with a prompt and an output structure, it is use
     import outlines
 
 
-    @outlines.prompt
-    def tell_a_joke(topic):
+    tell_a_joke = outlines.Template.from_string(
         """Tell me a joke about {{ topic }}."""
+    )
 
     class Joke(BaseModel):
         setup: str