[FEATURE] Simplify customizing the TextGeneration task with custom …

…prompts (#974)

* Simplify customization of TextGeneration

* Update tests loading the task

* Extra tests for the new functionality

* Added examples and extra checks

* Include missing attributes and info in docstrings

* Fix model_post_init call to super

* Force a template for the task

* Trying to fix the pickling error

* It's unused, but the argument of generate was wrongly spelled

* Checking if works without an instance of Template

* Remove template in unload to fix error on offline batch generation

src/distilabel/steps/tasks/text_generation.py

@@ -12,9 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import TYPE_CHECKING, Any, Dict, List, Union
+import re
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
-from pydantic import Field
+from jinja2 import Template
+from pydantic import Field, PrivateAttr
 
 from distilabel.errors import DistilabelUserError
 from distilabel.steps.tasks.base import Task
@@ -26,23 +28,31 @@
 
 
 class TextGeneration(Task):
-    """Simple text generation with an `LLM` given an instruction.
+    """Text generation with an `LLM` given a prompt.
 
-    `TextGeneration` is a pre-defined task that defines the `instruction` as the input
-    and `generation` as the output. This task is used to generate text based on the input
-    instruction. The model_name is also returned as part of the output in order to enhance it.
+    `TextGeneration` is a pre-defined task that allows passing a custom prompt using the
+    Jinja2 syntax. By default, a `instruction` is expected in the inputs, but the using
+    `template` and `columns` attributes one can define a custom prompt and columns expected
+    from the text. This task should be good enough for tasks that don't need post-processing
+    of the responses generated by the LLM.
 
     Attributes:
         system_prompt: The system prompt to use in the generation. If not provided, then
             it will check if the input row has a column named `system_prompt` and use it.
             If not, then no system prompt will be used. Defaults to `None`.
+        template: The template to use for the generation. It must follow the Jinja2 template
+            syntax. If not provided, it will assume the text passed is an instruction and
+            construct the appropriate template.
+        columns: A string with the column, or a list with columns expected in the template.
+            Take a look at the examples for more information. Defaults to `instruction`.
         use_system_prompt: DEPRECATED. To be removed in 1.5.0. Whether to use the system
             prompt in the generation. Defaults to `True`, which means that if the column
             `system_prompt` is defined within the input batch, then the `system_prompt`
             will be used, otherwise, it will be ignored.
 
     Input columns:
-        - instruction (`str`): The instruction to generate text from.
+        - dynamic (determined by `columns` attribute): By default will be set to `instruction`.
+            The columns can point both to a `str` or a `List[str]` to be used in the template.
 
     Output columns:
         - generation (`str`): The generated text.
@@ -51,6 +61,9 @@ class TextGeneration(Task):
     Categories:
         - text-generation
 
+    References:
+        - [Jinja2 Template Designer Documentation](https://jinja.palletsprojects.com/en/3.1.x/templates/)
+
     Examples:
         Generate text from an instruction:
 
@@ -61,7 +74,7 @@ class TextGeneration(Task):
         # Consider this as a placeholder for your actual LLM.
         text_gen = TextGeneration(
             llm=InferenceEndpointsLLM(
-                model_id="mistralai/Mistral-7B-Instruct-v0.2",
+                model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
             )
         )
 
@@ -76,41 +89,193 @@ class TextGeneration(Task):
         # [
         #     {
         #         'instruction': 'your instruction',
-        #         'model_name': 'mistralai/Mistral-7B-Instruct-v0.2',
+        #         'model_name': 'meta-llama/Meta-Llama-3.1-70B-Instruct',
         #         'generation': 'generation',
         #     }
         # ]
         ```
+
+        Use a custom template to generate text:
+
+        ```python
+        from distilabel.steps.tasks import TextGeneration
+        from distilabel.llms.huggingface import InferenceEndpointsLLM
+
+        CUSTOM_TEMPLATE = '''Document:
+        {{ document }}
+
+        Question: {{ question }}
+
+        Please provide a clear and concise answer to the question based on the information in the document and your general knowledge:
+        '''.rstrip()
+
+        text_gen = TextGeneration(
+            llm=InferenceEndpointsLLM(
+                model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
+            ),
+            system_prompt="You are a helpful AI assistant. Your task is to answer the following question based on the provided document. If the answer is not explicitly stated in the document, use your knowledge to provide the most relevant and accurate answer possible. If you cannot answer the question based on the given information, state that clearly.",
+            template=CUSTOM_TEMPLATE,
+            columns=["document", "question"],
+        )
+
+        text_gen.load()
+
+        result = next(
+            text_gen.process(
+                [
+                    {
+                        "document": "The Great Barrier Reef, located off the coast of Australia, is the world's largest coral reef system. It stretches over 2,300 kilometers and is home to a diverse array of marine life, including over 1,500 species of fish. However, in recent years, the reef has faced significant challenges due to climate change, with rising sea temperatures causing coral bleaching events.",
+                        "question": "What is the main threat to the Great Barrier Reef mentioned in the document?"
+                    }
+                ]
+            )
+        )
+        # result
+        # [
+        #     {
+        #         'document': 'The Great Barrier Reef, located off the coast of Australia, is the world's largest coral reef system. It stretches over 2,300 kilometers and is home to a diverse array of marine life, including over 1,500 species of fish. However, in recent years, the reef has faced significant challenges due to climate change, with rising sea temperatures causing coral bleaching events.',
+        #         'question': 'What is the main threat to the Great Barrier Reef mentioned in the document?',
+        #         'model_name': 'meta-llama/Meta-Llama-3.1-70B-Instruct',
+        #         'generation': 'According to the document, the main threat to the Great Barrier Reef is climate change, specifically rising sea temperatures causing coral bleaching events.',
+        #     }
+        # ]
+        ```
+
+        Few shot learning with different system prompts:
+
+        ```python
+        from distilabel.steps.tasks import TextGeneration
+        from distilabel.llms.huggingface import InferenceEndpointsLLM
+
+        CUSTOM_TEMPLATE = '''Generate a clear, single-sentence instruction based on the following examples:
+
+        {% for example in examples %}
+        Example {{ loop.index }}:
+        Instruction: {{ example }}
+
+        {% endfor %}
+        Now, generate a new instruction in a similar style:
+        '''.rstrip()
+
+        text_gen = TextGeneration(
+            llm=InferenceEndpointsLLM(
+                model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
+            ),
+            template=CUSTOM_TEMPLATE,
+            columns="examples",
+        )
+
+        text_gen.load()
+
+        result = next(
+            text_gen.process(
+                [
+                    {
+                        "examples": ["This is an example", "Another relevant example"],
+                        "system_prompt": "You are an AI assistant specialised in cybersecurity and computing in general, you make your point clear without any explanations."
+                    }
+                ]
+            )
+        )
+        # result
+        # [
+        #     {
+        #         'examples': ['This is an example', 'Another relevant example'],
+        #         'system_prompt': 'You are an AI assistant specialised in cybersecurity and computing in general, you make your point clear without any explanations.',
+        #         'model_name': 'meta-llama/Meta-Llama-3.1-70B-Instruct',
+        #         'generation': 'Disable the firewall on the router',
+        #     }
+        # ]
+        ```
     """
 
     system_prompt: Union[str, None] = None
     use_system_prompt: bool = Field(default=True, deprecated=True)
+    template: str = Field(
+        default="{{ instruction }}",
+        description=(
+            "This is a template or prompt to use for the generation. "
+            "If not provided, it is assumed a `instruction` is placed in the inputs, "
+            "to be used as is."
+        ),
+    )
+    columns: Union[str, List[str]] = Field(
+        default="instruction",
+        description=(
+            "Custom column or list of columns to include in the input. "
+            "If a `template` is provided which needs custom column names, "
+            "then they should be provided here. By default it will use `instruction`."
+        ),
+    )
 
     _can_be_used_with_offline_batch_generation = True
+    _template: Optional["Template"] = PrivateAttr(default=...)
+
+    def model_post_init(self, __context: Any) -> None:
+        self.columns = [self.columns] if isinstance(self.columns, str) else self.columns
+        super().model_post_init(__context)
+
+    def load(self) -> None:
+        super().load()
+
+        def check_column_in_template(column, template):
+            pattern = (
+                r"(?:{%.*?\b"
+                + re.escape(column)
+                + r"\b.*?%}|{{\s*"
+                + re.escape(column)
+                + r"\s*}})"
+            )
+            if not re.search(pattern, template):
+                raise DistilabelUserError(
+                    (
+                        f"You required column name '{column}', but is not present in the template, "
+                        "ensure the 'columns' match with the 'template' to avoid errors."
+                    ),
+                    page="components-gallery/tasks/textgeneration/",
+                )
+
+        for column in self.columns:
+            check_column_in_template(column, self.template)
+
+        self._template = Template(self.template)
+
+    def unload(self) -> None:
+        super().unload()
+        self._template = None
 
     @property
     def inputs(self) -> "StepColumns":
-        """The input for the task is the `instruction`."""
-        return {"instruction": True, "system_prompt": False}
+        """The input for the task is the `instruction` by default, or the `columns` given as input."""
+        columns = {column: True for column in self.columns}
+        columns["system_prompt"] = False
+        return columns
+
+    def _prepare_message_content(self, input: Dict[str, Any]) -> "ChatType":
+        """Prepares the content for the template and returns the formatted messages."""
+        fields = {column: input[column] for column in self.columns}
+        return [{"role": "user", "content": self._template.render(**fields)}]
 
     def format_input(self, input: Dict[str, Any]) -> "ChatType":
         """The input is formatted as a `ChatType` assuming that the instruction
         is the first interaction from the user within a conversation."""
-
-        if is_openai_format(input["instruction"]):
-            raise DistilabelUserError(
-                "Providing `instruction` formatted as an OpenAI chat / conversation is"
-                " deprecated, you should use `ChatGeneration` with `messages` as input instead.",
-                page="components-gallery/tasks/textgeneration/",
-            )
-
-        if not isinstance(input["instruction"], str):
-            raise DistilabelUserError(
-                f"Input `instruction` must be a string. Got: {input['instruction']}.",
-                page="components-gallery/tasks/textgeneration/",
-            )
-
-        messages = [{"role": "user", "content": input["instruction"]}]
+        # Handle the previous expected errors, in case of custom columns there's more freedom
+        # and we cannot check it so easily.
+        if self.columns == ["instruction"]:
+            if is_openai_format(input["instruction"]):
+                raise DistilabelUserError(
+                    "Providing `instruction` formatted as an OpenAI chat / conversation is"
+                    " deprecated, you should use `ChatGeneration` with `messages` as input instead.",
+                    page="components-gallery/tasks/textgeneration/",
+                )
+
+            if not isinstance(input["instruction"], str):
+                raise DistilabelUserError(
+                    f"Input `instruction` must be a string. Got: {input['instruction']}.",
+                    page="components-gallery/tasks/textgeneration/",
+                )
+
+        messages = self._prepare_message_content(input)
 
         row_system_prompt = input.get("system_prompt")
         if row_system_prompt:

tests/integration/test_offline_batch_generation.py

@@ -35,7 +35,7 @@ def model_name(self) -> str:
         return "test"
 
     def generate(  # type: ignore
-        self, input: "FormattedInput", num_generations: int = 1
+        self, inputs: "FormattedInput", num_generations: int = 1
     ) -> "GenerateOutput":
         return ["output" for _ in range(num_generations)]