Made embeddings and LLMs dependent on metric in evaluate function

src/ragas/evaluation.py

@@ -10,7 +10,8 @@
 
 from ragas._analytics import EvaluationEvent, track
 from ragas.callbacks import new_group
-from ragas.embeddings.base import BaseRagasEmbeddings, LangchainEmbeddingsWrapper
+from ragas.embeddings.base import BaseRagasEmbeddings, LangchainEmbeddingsWrapper, embedding_factory
+from ragas.llms import llm_factory
 from ragas.exceptions import ExceptionInRunner
 from ragas.executor import Executor
 from ragas.llms.base import BaseRagasLLM, LangchainLLMWrapper
@@ -57,11 +58,11 @@ def evaluate(
         evaluation on the best set of metrics to give a complete view.
     llm: BaseRagasLLM, optional
         The language model to use for the metrics. If not provided then ragas will use
-        the default language model. This can we overridden by the llm specified in
+        the default language model for metrics which require an LLM. This can we overridden by the llm specified in
         the metric level with `metric.llm`.
     embeddings: BaseRagasEmbeddings, optional
         The embeddings to use for the metrics. If not provided then ragas will use
-        the default embeddings. This can we overridden by the embeddings specified in
+        the default embeddings for metrics which require embeddings. This can we overridden by the embeddings specified in
         the metric level with `metric.embeddings`.
     callbacks: Callbacks, optional
         Lifecycle Langchain Callbacks to run during evaluation. Check the
@@ -144,34 +145,30 @@ def evaluate(
     validate_column_dtypes(dataset)
 
     # set the llm and embeddings
-    if llm is None:
-        from ragas.llms import llm_factory
-
-        llm = llm_factory()
-    elif isinstance(llm, LangchainLLM):
+    if isinstance(llm, LangchainLLM):
         llm = LangchainLLMWrapper(llm, run_config=run_config)
-    if embeddings is None:
-        from ragas.embeddings.base import embedding_factory
-
-        embeddings = embedding_factory()
-    elif isinstance(embeddings, LangchainEmbeddings):
+    if isinstance(embeddings, LangchainEmbeddings):
         embeddings = LangchainEmbeddingsWrapper(embeddings)
+
     # init llms and embeddings
     binary_metrics = []
     llm_changed: t.List[int] = []
     embeddings_changed: t.List[int] = []
     answer_correctness_is_set = -1
+
     for i, metric in enumerate(metrics):
         if isinstance(metric, AspectCritique):
             binary_metrics.append(metric.name)
-        if isinstance(metric, MetricWithLLM):
-            if metric.llm is None:
-                metric.llm = llm
-                llm_changed.append(i)
-        if isinstance(metric, MetricWithEmbeddings):
-            if metric.embeddings is None:
-                metric.embeddings = embeddings
-                embeddings_changed.append(i)
+        if isinstance(metric, MetricWithLLM) and metric.llm is None:
+            if llm is None:
+                llm = llm_factory()
+            metric.llm = llm
+            llm_changed.append(i)
+        if isinstance(metric, MetricWithEmbeddings) and metric.embeddings is None:
+            if embeddings is None:
+                embeddings = embedding_factory()
+            metric.embeddings = embeddings
+            embeddings_changed.append(i)
         if isinstance(metric, AnswerCorrectness):
             if metric.answer_similarity is None:
                 answer_correctness_is_set = i