Refactor the sequence generation

README.md

@@ -83,34 +83,21 @@ is to ensure that there is a well-defined interface between their output and
 user-defined code. **Outlines** provides ways to control the generation of
 language models to make their output more predictable.
 
-### Early stopping
-
-You can stop the generation after a given sequence has been found:
-
-``` python
-import outlines.text.generate as generate
-import outlines.models as models
-
-model = models.transformers("gpt2")
-answer = generate.continuation(model, stop=["."])("Tell me a one-sentence joke.")
-```
-
 ### Multiple choices
 
 You can reduce the completion to a choice between multiple possibilities:
 
 ``` python
-import outlines.text.generate as generate
-import outlines.models as models
+import outlines
 
-model = models.transformers("gpt2")
+model = outlines.models.transformers("gpt2")
 
 prompt = """You are a sentiment-labelling assistant.
 Is the following review positive or negative?
 
 Review: This restaurant is just awesome!
 """
-answer = generate.choice(model, ["Positive", "Negative"])(prompt)
+answer = outlines.generate.choice(model, ["Positive", "Negative"])(prompt)
 ```
 
 ### Type constraint
@@ -119,16 +106,15 @@ You can instruct the model to only return integers or floats:
 
 
 ``` python
-import outlines.text.generate as generate
-import outlines.models as models
+import outlines
 
-model = models.transformers("gpt2")
+model = outlines.models.transformers("gpt2")
 
 prompt = "1+1="
-answer = generate.integer(model)(prompt)
+answer = outlines.generate.format(model, int)(prompt)
 
 prompt = "sqrt(2)="
-answer = generate.float(model)(prompt)
+answer = outlines.generate.format(model, float)(prompt)
 ```
 
 ### Efficient regex-guided generation
@@ -138,15 +124,13 @@ Outlines also comes with fast regex-guided generation. In fact, the `choice`,
 hood:
 
 ``` python
-import outlines.models as models
-import outlines.text.generate as generate
+import outlines
 
-
-model = models.transformers("gpt2-medium")
+model = outlines.models.transformers("gpt2-medium")
 
 prompt = "Is 1+1=2? "
-unguided = generate.continuation(model, max_tokens=30)(prompt)
-guided = generate.regex(model, r"\s*([Yy]es|[Nn]o|[Nn]ever|[Aa]lways)", max_tokens=30)(
+unguided = outlines.generate.continuation(model, max_tokens=30)(prompt)
+guided = outlines.generate.regex(model, r"\s*([Yy]es|[Nn]o|[Nn]ever|[Aa]lways)", max_tokens=30)(
     prompt
 )
 
@@ -162,15 +146,13 @@ print(guided)
 ```
 
 ``` python
-import outlines.models as models
-import outlines.text.generate as generate
-
+import outlines
 
-model = models.transformers("gpt2-medium")
+model = outlines.models.transformers("gpt2-medium")
 
 prompt = "What is the IP address of the Google DNS servers? "
 unguided = generate.continuation(model, max_tokens=30)(prompt)
-guided = generate.regex(
+guided = outlines.generate.regex(
     model,
     r"((25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)",
     max_tokens=30,
@@ -199,9 +181,7 @@ Outlines 〰 allows to guide the generation process so the output is *guaranteed
 from enum import Enum
 from pydantic import BaseModel, constr
 
-import outlines.models as models
-import outlines.text.generate as generate
-
+import outlines
 import torch
 
 
@@ -228,10 +208,10 @@ class Character(BaseModel):
     strength: int
 
 
-model = models.transformers("gpt2", device="cuda")
+model = outlines.models.transformers("gpt2", device="cuda")
 
 # Construct guided sequence generator
-generator = generate.json(model, Character, max_tokens=100)
+generator = outlines.generate.json(model, Character, max_tokens=100)
 
 # Draw a sample
 rng = torch.Generator(device="cuda")
@@ -269,14 +249,14 @@ The method works with union types, optional types, arrays, nested schemas, etc.
 Outlines can infer the structure of the output from the signature of a function. The result is a dictionary, and can be passed directly to the function using the usual dictionary expansion syntax `**`:
 
 ```python
-from outlines import models
-from outlines import text
+import outlines
+
 
 def add(a: int, b: int):
     return a + b
 
-model = models.transformers("mistralai/Mistral-7B")
-generator = text.generate.json(model, add)
+model = outlines.models.transformers("mistralai/Mistral-7B")
+generator = outlines.generate.json(model, add)
 result = generator("Return two integers named a and b respectively. a is odd and b even.")
 
 print(add(**result))
@@ -300,9 +280,7 @@ Template functions require no superfluous abstraction, they use the Jinja2
 templating engine to help build complex prompts in a concise manner:
 
 ``` python
-import outlines.text as text
-import outlines.models as models
-
+import outlines
 
 examples = [
     ("The food was digusting", "Negative"),
@@ -311,7 +289,7 @@ examples = [
     ("The waiter was rude", "Negative")
 ]
 
-@text.prompt
+@outlines.prompt
 def labelling(to_label, examples):
     """You are a sentiment-labelling assistant.
 
@@ -321,9 +299,9 @@ def labelling(to_label, examples):
     {{ to_label }} //
     """
 
-model = models.transformers("gpt2")
+model = outlines.models.transformers("gpt2")
 prompt = labelling("Just awesome", examples)
-answer = text.generate.continuation(model, max_tokens=100)(prompt)
+answer = outlines.generate.continuation(model, max_tokens=100)(prompt)
 ```
 
 ### Tools
@@ -337,7 +315,7 @@ extract the function's name, description, signature and source:
 
 ``` python
 from typing import Callable, List
-import outlines.text as text
+import outlines
 
 
 def google_search(query: str):
@@ -350,7 +328,7 @@ def wikipedia_search(query: str):
     pass
 
 
-@text.prompt
+@outlines.prompt
 def my_commands(tools: List[Callable]):
     """AVAILABLE COMMANDS:
 
@@ -374,7 +352,7 @@ extract the expected response's schema:
 
 ``` python
 from pydantic import BaseModel, Field
-import outlines.text as text
+import outlines
 
 
 class Joke(BaseModel):
@@ -384,7 +362,7 @@ class Joke(BaseModel):
     )
 
 
-@text.prompt
+@outlines.prompt
 def joke_ppt(response_model):
     """Tell a joke and explain why the joke is funny.
 

docs/api/fsm.md

@@ -1 +1 @@
-::: outlines.text.fsm
+::: outlines.fsm.fsm

docs/api/json_schema.md

@@ -1 +1 @@
-::: outlines.text.json_schema
+::: outlines.fsm.json_schema

docs/api/parsing.md

@@ -1 +1 @@
-::: outlines.text.parsing
+::: outlines.fsm.parsing

docs/api/prompts.md

@@ -1 +1 @@
-::: outlines.text.prompts
+::: outlines.prompts

docs/api/regex.md

@@ -1 +1 @@
-::: outlines.text.generate.regex
+::: outlines.generate.regex

docs/api/samplers.md

@@ -0,0 +1 @@
+::: outlines.generate.samplers

docs/examples/chain_of_density.md

@@ -29,9 +29,9 @@ 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
-from outlines import text
+import outlines
 
-@text.prompt
+@outlines.prompt
 def chain_of_density(article):
     """Article: {{ article }}
 
@@ -86,12 +86,10 @@ class Summaries(BaseModel):
 We now generate the prompt by passing the article we want to summarize to the template. We load a quantized version of Mistral-7B using the AutoAWQ library, and then use JSON-guided generation to generate the summaries:
 
 ```python
-from outlines import models
-
-model = models.awq("TheBloke/Mistral-7B-OpenOrca-AWQ")
+model = outlines.models.awq("TheBloke/Mistral-7B-OpenOrca-AWQ")
 
 prompt = chain_of_density(article)
-result = text.generate.json(model, Summaries)(prompt)
+result = outlines.generate.json(model, Summaries)(prompt)
 ```
 
 We can now check the results:

docs/examples/dating_profiles.md

@@ -10,8 +10,7 @@ import torch
 import transformers
 from pydantic import BaseModel, conlist, constr
 
-import outlines.models as models
-import outlines.text as text
+import outlines
 ```
 
 ## Defining the profile with Pydantic
@@ -59,7 +58,7 @@ We will use Outlines' prompt templating abilities to generate the prompt for us.
 
 ```python
 
-@text.prompt
+@outlines.prompt
 def dating_profile_prompt(description: str, examples: list[Example]):
     """
     You are a world-renowned matchmaker who understands the modern dating
@@ -136,7 +135,7 @@ config = transformers.AutoConfig.from_pretrained(
     "mosaicml/mpt-7b-8k-instruct", trust_remote_code=True
 )
 config.init_device = "meta"
-model = models.transformers(
+model = outlines.models.transformers(
     model_name="mosaicml/mpt-7b-8k-instruct",
     device="cuda",
     model_kwargs={
@@ -163,7 +162,7 @@ it's  a good excuse for a date. I watch the latest series because I'm paying,
 with my hard-earned money, for every streaming service."""
 
 prompt = dating_profile_prompt(new_description, samples)
-profile = text.generate.json(model, DatingProfile)(prompt)
+profile = outlines.generate.json(model, DatingProfile)(prompt)
 parsed_profile = DatingProfile.model_validate_json(profile)
 ```
 

examples/babyagi.py

@@ -7,8 +7,8 @@
 from collections import deque
 from typing import Deque, List
 
+import outlines
 import outlines.models as models
-import outlines.text as text
 
 model = models.openai("gpt-3.5-turbo")
 
@@ -18,7 +18,7 @@
 #################
 
 
-@text.prompt
+@outlines.prompt
 def perform_task_ppt(objective: str, task: str):
     """You are an AI who performs one task based on the following objective: {{objective}}.
 
@@ -33,7 +33,7 @@ def perform_task_ppt(objective: str, task: str):
 #####################
 
 
-@text.prompt
+@outlines.prompt
 def create_tasks_ppt(
     objective: str, previous_task: str, result: str, task_list: List[str]
 ):
@@ -69,7 +69,7 @@ def create_tasks_fmt(result: str) -> List[str]:
 ########################
 
 
-@text.prompt
+@outlines.prompt
 def prioritize_tasks_ppt(objective: str, task_names: List[str], next_task_id: int):
     """You are a task prioritization AI tasked with cleaning the formatting of \
     and reprioritizing the following tasks: {{task_names}}.

examples/dating_profile.py

@@ -5,8 +5,8 @@
 import transformers
 from pydantic import BaseModel, conlist
 
-import outlines.models as models
-import outlines.text as text
+import outlines
+from outlines import models
 
 
 class QuestionChoice(str, Enum):
@@ -41,7 +41,7 @@ class Example:
     profile: DatingProfile
 
 
-@text.prompt
+@outlines.prompt
 def dating_profile_prompt(description: str, examples: list[Example]):
     """
     You are a world-renowned matchmaker who understands the modern dating market. Your job is to generate dating app profiles for male clients interested in women based on a provided description. The profiles should be authentic, show off their strengths, and maximize their likelihood of getting matches on dating apps.
@@ -121,7 +121,7 @@ def dating_profile_prompt(description: str, examples: list[Example]):
 new_description = "I'm a laid-back lawyer who spends a lot of his free-time gaming. I work in a corporate office, but ended up here after the start-up I cofounded got acquired, so still play ping pong with my cool coworkers every day. I have a bar at home where I make cocktails, which is great for entertaining friends. I secretly like to wear suits and get a new one tailored every few months. I also like weddings because I get to wear those suits, and it's a good excuse for a date. I watch the latest series because I'm paying, with my hard-earned money, for every streaming service."
 
 prompt = dating_profile_prompt(description=new_description, examples=samples)
-profile = text.generate.json(model, DatingProfile)(prompt)  # type: ignore
+profile = outlines.generate.json(model, DatingProfile)(prompt)  # type: ignore
 print(profile)
 
 # Sample generated profiles

examples/math_generate_code.py

@@ -1,6 +1,6 @@
 """Example from https://dust.tt/spolu/a/d12ac33169"""
+import outlines
 import outlines.models as models
-import outlines.text as text
 
 examples = [
     {"question": "What is 37593 * 67?", "code": "37593 * 67"},
@@ -17,7 +17,7 @@
 question = "Carla is downloading a 200 GB file. She can download 2 GB/minute, but 40% of the way through the download, the download fails. Then Carla has to restart the download from the beginning. How load did it take her to download the file in minutes?"
 
 
-@text.prompt
+@outlines.prompt
 def answer_with_code_prompt(question, examples):
     """
     {% for example in examples %}