feat: Add support for llamaIndex in evaluation

pyproject.toml

@@ -25,6 +25,7 @@ all = [
     "rapidfuzz",
     "pandas",
     "datacompy",
+    "llama_index",
 ]
 docs = [
     "mkdocs>=1.6.1",

src/ragas/evaluation.py

@@ -8,6 +8,9 @@
 from langchain_core.embeddings import Embeddings as LangchainEmbeddings
 from langchain_core.language_models import BaseLanguageModel as LangchainLLM
 
+from llama_index.core.base.llms.base import BaseLLM as LlamaIndexLLM
+from llama_index.core.base.embeddings.base import BaseEmbedding as LlamaIndexEmbedding
+
 from ragas._analytics import EvaluationEvent, track, track_was_completed
 from ragas.callbacks import ChainType, RagasTracer, new_group
 from ragas.dataset_schema import (
@@ -19,13 +22,14 @@
 from ragas.embeddings.base import (
     BaseRagasEmbeddings,
     LangchainEmbeddingsWrapper,
+    LlamaIndexEmbeddingsWrapper,
     embedding_factory,
 )
 from ragas.exceptions import ExceptionInRunner
 from ragas.executor import Executor
 from ragas.integrations.helicone import helicone_config
 from ragas.llms import llm_factory
-from ragas.llms.base import BaseRagasLLM, LangchainLLMWrapper
+from ragas.llms.base import BaseRagasLLM, LangchainLLMWrapper, LlamaIndexLLMWrapper
 from ragas.metrics import AspectCritic
 from ragas.metrics._answer_correctness import AnswerCorrectness
 from ragas.metrics.base import (
@@ -56,8 +60,8 @@
 def evaluate(
     dataset: t.Union[Dataset, EvaluationDataset],
     metrics: t.Optional[t.Sequence[Metric]] = None,
-    llm: t.Optional[BaseRagasLLM | LangchainLLM] = None,
-    embeddings: t.Optional[BaseRagasEmbeddings | LangchainEmbeddings] = None,
+    llm: t.Optional[BaseRagasLLM | LangchainLLM | LlamaIndexLLM] = None,
+    embeddings: t.Optional[BaseRagasEmbeddings | LangchainEmbeddings | LlamaIndexEmbedding] = None,
     callbacks: Callbacks = None,
     in_ci: bool = False,
     run_config: RunConfig = RunConfig(),
@@ -182,8 +186,12 @@ def evaluate(
     # set the llm and embeddings
     if isinstance(llm, LangchainLLM):
         llm = LangchainLLMWrapper(llm, run_config=run_config)
+    elif isinstance(llm, LlamaIndexLLM):
+        llm = LlamaIndexLLMWrapper(llm, run_config=run_config)
     if isinstance(embeddings, LangchainEmbeddings):
         embeddings = LangchainEmbeddingsWrapper(embeddings)
+    elif isinstance(embeddings, LlamaIndexEmbedding):
+        embeddings = LlamaIndexEmbeddingsWrapper(embeddings)
 
     # init llms and embeddings
     binary_metrics = []