Add MixtureOfAgentsLLM (#735)

* Update `RuntimeParametersMixin` to handle `list`s

* Check if `generation_kwargs` is present

* Add `get_generation_kwargs` method

* Add `_num_generation_param_supported` attribute to avoid code
duplication

* Refactor `OllamaLLM` and `VertexAILLM`

* Add `MixtureOfAgents` llm

* Add docstrings

* Fix unit tests

* Update docstrings

* Fix missing #

* Update Arena Hard tasks docstrings

* Fix cross-reference

* Update unit tests

* Rename to `MixtureOfAgentsLLM`

* Update `_extra_serializable_fields` to work with `List[_Serializable]`
attributes

* Remove `from_dict` method for `Step`

* Update to render list

* Update handling `List[RuntimeParametersMixin]` attributes

* Fix unit tests

* Remove test code

* Add `MixtureOfAgentsLLM` docstring example

* Add alias for runtime parameters names

docs/sections/how_to_guides/advanced/structured_generation.md

@@ -139,7 +139,7 @@ When working with model providers behind an API, there's no direct way of access
     ```
 
 !!! Note
-    Take a look at [`InstructorStructuredOutputType`][distilabel.steps.tasks.structured_outputs.instructor.InstructorStructuredOutputType] to see the expected format
+    Take a look at [`InstructorStructuredOutputType`][distilabel.steps.tasks.typing.InstructorStructuredOutputType] to see the expected format
     of the `structured_output` dict variable.
 
 The following is the same example you can see with `outlines`'s `JSON` section for comparison purposes.

src/distilabel/cli/pipeline/utils.py

@@ -120,7 +120,8 @@ def get_pipeline(config: str) -> "BasePipeline":
         FileNotFoundError: If the configuration file does not exist.
     """
     if valid_http_url(config):
-        return Pipeline.from_dict(get_config_from_url(config))
+        data = get_config_from_url(config)
+        return Pipeline.from_dict(data)
 
     if Path(config).is_file():
         return Pipeline.from_file(config)
@@ -200,32 +201,38 @@ def _build_steps_panel(pipeline: "BasePipeline") -> "Panel":
     from rich.table import Table
 
     def _add_rows(
-        table: Table, runtime_params: List[Dict[str, Any]], prefix: str = ""
+        table: Table,
+        runtime_params: List[Dict[str, Any]],
+        prefix: str = "",
     ) -> None:
         for param in runtime_params:
+            if isinstance(param, str):
+                _add_rows(table, runtime_params[param], f"{prefix}{param}.")
+                continue
+
             # nested (for example `LLM` in `Task`)
             if "runtime_parameters_info" in param:
                 _add_rows(
                     table=table,
                     runtime_params=param["runtime_parameters_info"],
                     prefix=f"{prefix}{param['name']}.",
                 )
-                continue
-
             # `LLM` special case
-            if "keys" in param:
+            elif "keys" in param:
                 _add_rows(
                     table=table,
                     runtime_params=param["keys"],
                     prefix=f"{prefix}{param['name']}.",
                 )
-                continue
-
-            optional = param.get("optional", "")
-            if optional != "":
-                optional = "Yes" if optional else "No"
+                return
+            else:
+                optional = param.get("optional", "")
+                if optional != "":
+                    optional = "Yes" if optional else "No"
 
-            table.add_row(prefix + param["name"], param.get("description"), optional)
+                table.add_row(
+                    prefix + param["name"], param.get("description"), optional
+                )
 
     steps = []
     for step_name, runtime_params in pipeline.get_runtime_parameters_info().items():
@@ -239,7 +246,7 @@ def _add_rows(
             expand=True,
         )
 
-        table.add_column("Runtime parameter", style="dim", width=50)
+        table.add_column("Runtime parameter", style="dim", width=60)
         table.add_column("Description", width=100)
         table.add_column("Optional", justify="right")
         _add_rows(table, runtime_params)

src/distilabel/llms/__init__.py

@@ -23,6 +23,7 @@
 from distilabel.llms.llamacpp import LlamaCppLLM
 from distilabel.llms.mistral import MistralLLM
 from distilabel.llms.mixins import CudaDevicePlacementMixin
+from distilabel.llms.moa import MixtureOfAgentsLLM
 from distilabel.llms.ollama import OllamaLLM
 from distilabel.llms.openai import OpenAILLM
 from distilabel.llms.together import TogetherLLM
@@ -43,6 +44,7 @@
     "LlamaCppLLM",
     "MistralLLM",
     "CudaDevicePlacementMixin",
+    "MixtureOfAgentsLLM",
     "OllamaLLM",
     "OpenAILLM",
     "TogetherLLM",

src/distilabel/llms/anthropic.py

@@ -12,11 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import asyncio
 import os
 from typing import (
     TYPE_CHECKING,
-    Any,
     List,
     Literal,
     Optional,
@@ -28,13 +26,14 @@
 
 from httpx import AsyncClient
 from pydantic import Field, PrivateAttr, SecretStr, validate_call
-from typing_extensions import override
 
 from distilabel.llms.base import AsyncLLM
 from distilabel.llms.typing import GenerateOutput
 from distilabel.mixins.runtime_parameters import RuntimeParameter
-from distilabel.steps.tasks.typing import FormattedInput, InstructorStructuredOutputType
-from distilabel.utils.itertools import grouper
+from distilabel.steps.tasks.typing import (
+    FormattedInput,
+    InstructorStructuredOutputType,
+)
 
 if TYPE_CHECKING:
     from anthropic import AsyncAnthropic
@@ -112,11 +111,7 @@ class User(BaseModel):
 
         llm.load()
 
-        # Synchronous request
         output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
-
-        # Asynchronous request
-        output = await llm.agenerate(input=[{"role": "user", "content": "Create a user profile for the following marathon"}])
         ```
     """
 
@@ -148,6 +143,8 @@ class User(BaseModel):
         )
     )
 
+    _num_generations_param_supported = False
+
     _api_key_env_var: str = PrivateAttr(default=_ANTHROPIC_API_KEY_ENV_VAR_NAME)
     _aclient: Optional["AsyncAnthropic"] = PrivateAttr(...)
 
@@ -282,29 +279,3 @@ async def agenerate(  # type: ignore
             )
         generations.append(content)
         return generations
-
-    # TODO: remove this function once Anthropic client allows `n` parameter
-    @override
-    def generate(
-        self,
-        inputs: List["FormattedInput"],
-        num_generations: int = 1,
-        **kwargs: Any,
-    ) -> List["GenerateOutput"]:
-        """Method to generate a list of responses asynchronously, returning the output
-        synchronously awaiting for the response of each input sent to `agenerate`.
-        """
-
-        async def agenerate(
-            inputs: List["FormattedInput"], **kwargs: Any
-        ) -> "GenerateOutput":
-            """Internal function to parallelize the asynchronous generation of responses."""
-            tasks = [
-                asyncio.create_task(self.agenerate(input=input, **kwargs))
-                for input in inputs
-                for _ in range(num_generations)
-            ]
-            return [outputs[0] for outputs in await asyncio.gather(*tasks)]
-
-        outputs = self.event_loop.run_until_complete(agenerate(inputs, **kwargs))
-        return list(grouper(outputs, n=num_generations, incomplete="ignore"))

src/distilabel/llms/anyscale.py

@@ -50,11 +50,7 @@ class AnyscaleLLM(OpenAILLM):
 
         llm.load()
 
-        # Synchronous request
         output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
-
-        # Asynchronous request
-        output = await llm.agenerate(input=[{"role": "user", "content": "Hello world!"}])
         ```
     """
 

src/distilabel/llms/azure.py

@@ -103,11 +103,7 @@ class User(BaseModel):
 
         llm.load()
 
-        # Synchronous request
         output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
-
-        # Asynchronous request
-        output = await llm.agenerate(input=[{"role": "user", "content": "Create a user profile for the following marathon"}])
         ```
     """