Merge remote-tracking branch 'origin/dev' into dev

Author: NotBioWaste905

dialogue2graph/pipelines/d2g_light/three_stages_light.py

@@ -69,6 +69,7 @@ def __init__(
         filling_llm: str = "three_stages_light_filling_llm:v1",
         formatting_llm: str = "three_stages_light_formatting_llm:v1",
         sim_model: str = "three_stages_light_sim_model:v1",
+
         step2_evals: list[Callable] | None = [],
         end_evals: list[Callable] | None = [],
     ):

dialogue2graph/pipelines/d2g_llm/three_stages_llm.py

@@ -97,6 +97,7 @@ def __init__(
         filling_llm: str = "three_stages_filling_llm:v1",
         formatting_llm: str = "three_stages_formatting_llm:v1",
         sim_model: str = "three_stages_sim_model:v1",
+
         step2_evals: list[Callable] | None = None,
         end_evals: list[Callable] | None = None,
     ):

docs/source/userguides/generate_graphs.rst

@@ -12,6 +12,7 @@ First of all we need to import the :py:class:`~dialogue2graph.pipelines.model_st
     from dialogue2graph.pipelines.d2g_llm import LLMGraphGenerator
     from dialogue2graph.pipelines.helpers.parse_data import PipelineRawDataType
 
+
 Now, we need to read the dialogues we want to generate a graph for. In this example we will read the dialogues from a JSON file. The dialogues should be in the following format:
 
 .. code-block:: json
@@ -90,4 +91,5 @@ Now we can generate the graph. We will pass the dialogues ``.invoke()`` method o
     graph, report = graph_generator.invoke(data, enable_evals=True)
     graph.visualise()
 
+
     print(report)