feat: added tutoral

autointent/generation/intents/_description_generation.py

@@ -139,6 +139,8 @@ def generate_descriptions(
         prompt: Template for model prompt with placeholders for intent_name,
                user_utterances, and regex_patterns.
         model_name: OpenAI model identifier for generating descriptions.
+
+    See :ref:`intent_description_generation` tutorial.
     """
     samples = []
     for split in dataset.values():

autointent/generation/utterances/_evolution/incremental_evolver.py

@@ -43,7 +43,7 @@ class IncrementalUtteranceEvolver(UtteranceEvolver):
     """Incremental evolutionary strategy to augmenting utterances.
 
     This method adds LLM-generated training samples until the quality
-    of linear classification on resulting dataset is rising.
+    of linear classification on resulting dataset stops rising.
 
     Args:
         generator: Generator instance for generating utterances.

docs/source/augmentation_tutorials/index.rst

@@ -8,3 +8,4 @@ Data augmentation tutorials
 
    balancer
    dspy_augmentation
+   intent_description

docs/source/augmentation_tutorials/intent_description.rst

@@ -0,0 +1,100 @@
+.. _intent_description_generation:
+
+Intent Description Generation
+#############################
+
+This documentation covers the implementation and usage of the Intent Description Generation module. It explains the function of the module, the underlying mechanisms, and provides examples of usage.
+
+The approach used in this module is based on the paper `Exploring Description-Augmented Dataless Intent Classification <https://arxiv.org/pdf/2407.17862>`_.
+
+.. contents:: Table of Contents
+    :depth: 2
+
+Overview
+--------
+
+The Intent Description Generation module is designed to automatically generate detailed and coherent descriptions of intents using large language models (LLMs). It enhances datasets by creating human-readable explanations for intents, supplemented by examples (utterances) and regex patterns.
+
+How the Module Works
+--------------------
+
+The module leverages prompt engineering to interact with LLMs, creating structured intent descriptions that are suitable for documentation, user interaction, and training purposes. Each generated description includes:
+
+- **Intent Name**: Clearly identifies the intent.
+- **Examples (User Utterances)**: Demonstrates real-world user inputs.
+- **Regex Patterns**: Highlights relevant regex patterns associated with the intent.
+
+The module uses a templated approach, defined through `PromptDescription`, to maintain consistency and clarity across descriptions.
+
+Installation
+------------
+
+Ensure you have the necessary dependencies installed:
+
+.. code-block:: bash
+
+    pip install autointent openai
+
+Usage
+-----
+
+Here's an example demonstrating how to generate intent descriptions:
+
+.. code-block:: python
+
+    import openai
+    from autointent import Dataset
+    from autointent.generation.intents import generate_descriptions
+    from autointent.generation.chat_templates import PromptDescription
+
+    client = openai.AsyncOpenAI(
+        api_key="your-api-key"
+    )
+
+    dataset = Dataset.from_hub("AutoIntent/clinc150_subset")
+
+    prompt = PromptDescription(
+        text="Describe intent {intent_name} with examples: {user_utterances} and patterns: {regex_patterns}",
+    )
+
+    enhanced_dataset = generate_descriptions(
+        dataset=dataset,
+        client=client,
+        prompt=prompt,
+        model_name="gpt4o-mini",
+    )
+
+    enhanced_dataset.to_csv("enhanced_clinc150.csv")
+
+Prompt Customization
+--------------------
+
+The `PromptDescription` can be customized to better fit specific requirements. It uses the following placeholders:
+
+- ``{intent_name}``: The name of the intent being described.
+- ``{user_utterances}``: Example utterances related to the intent.
+- ``{regex_patterns}``: Associated regular expression patterns.
+
+Adjusting the prompt allows tailoring descriptions to different contexts or detail levels.
+
+Model Selection
+---------------
+
+This module supports various LLMs available through OpenAI-compatible APIs. Configure your preferred model via the `model_name` parameter. Refer to your LLM provider’s documentation for available models.
+
+Recommended models include:
+
+- ``gpt4o-mini`` (for balanced performance and efficiency)
+- ``gpt-4`` (for maximum descriptive quality)
+
+API Integration
+---------------
+
+Ensure your OpenAI-compatible client is properly configured with an API endpoint and key:
+
+.. code-block:: python
+
+    client = openai.AsyncOpenAI(
+        base_url="your-api-base-url",
+        api_key="your-api-key"
+    )

user_guides/basic_usage/03_automl.py

@@ -103,6 +103,7 @@
 
 # %%
 from autointent.configs import DataConfig
+
 custom_pipeline.set_config(DataConfig(scheme="cv", n_folds=3))
 
 # %% [markdown]